Multivariate analysis of CO2, H2S and CH4 diffuse degassing and correlation with fault systems in Agua Caliente - Tzitzio, Michoacán, México

Journal Pre-proof Multivariate analysis of CO2, H2S and CH4 diffuse degassing and correlation with fault systems in Agua Caliente - Tzitzio, Michoacán, México

M.P. Jácome-Paz, I.A. González-Romo, R.M. Prol-Ledesma, M.A. Torres Vera, D. Pérez-Zárate, A.A. Rodríguez-Díaz, A.M. Estrada-Murillo PII:

S0377-0273(19)30351-8

DOI:

https://doi.org/10.1016/j.jvolgeores.2020.106808

Reference:

VOLGEO 106808

To appear in:

Journal of Volcanology and Geothermal Research

Received date:

28 June 2019

Revised date:

3 February 2020

Accepted date:

3 February 2020

Please cite this article as: M.P. Jácome-Paz, I.A. González-Romo, R.M. Prol-Ledesma, et al., Multivariate analysis of CO2, H2S and CH4 diffuse degassing and correlation with fault systems in Agua Caliente - Tzitzio, Michoacán, México, Journal of Volcanology and Geothermal Research(2020), https://doi.org/10.1016/j.jvolgeores.2020.106808

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© 2020 Published by Elsevier.

Journal Pre-proof Multivariate analysis of CO2, H2S and CH4 diffuse degassing and correlation with fault systems in Agua Caliente - Tzitzio, Michoacán, México. M.P. Jácome-Paz a*, I. A. González – Romoa, R.M. Prol-Ledesma

a, c

, M. A. Torres Vera b, D.

Pérez-Zárate c, A.A. Rodríguez-Díaz a, A.M. Estrada-Murillo a. a

Departamento de Recursos Naturales, Instituto de Geofísica, Universidad Nacional Autónoma

de México; Circuito interior s/n, Coyoacán, 04510 Ciudad de México, México. b

CONACYT- Instituto de Geofísica, Universidad Nacional Autónoma de México; Circuito

interior s/n, Coyoacán, 04510 Ciudad de México, México. d

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c

Posgrado en Ciencias de la Tierra, Universidad Nacional Autónoma de México, UNAM.

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Centro Mexicano de Innovación en Energía Geotérmica (CeMIE-Geo); Carretera Tijuana-

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Ensenada 3919, Zona Playitas, 22860, Ensenada, B.C., México. *

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Corresponding author: [email protected]

Abstract

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The quantitative relationships between soil gas flux anomalies and faults systems in Agua

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Caliente-Tzitzio geothermal system were analyzed statistically. The main degassing features are related with thermal springs. The data used in the multivariate analysis are geothermal gas

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fluxes and spatial structural data (i.e., local fault strike). Exploratory Data Analysis (EDA) and Multivariate Analysis of Variance (MANOVA) were implemented, for the first time, to statistically describe the relation between the location of degassing features and the presence of some local fault systems, discriminating the direction of the main discharge channels and pointing to higher permeability faults. The results show that there is a statistically significant correlation between diffuse degassing of CO2 and CH4 and the N-S and E-W fault systems. Conversely, H2S degassing does not show any correlation with the fault systems, which may be related with the higher H2S reactivity compared with that of CO2 and CH4. It is highly recommended to use EDA and MANOVA techniques in order to establish the main degassing directions in geothermal and volcanic areas.

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Journal Pre-proof Keywords: geothermal springs, CO2–H2S –CH4 degassing, fluid geochemistry, Agua Caliente – Tzitzio, exploratory data analysis. 1. Introduction Studies of diffuse degassing are fundamental to understand and to study volcanic and geothermal systems (e.g.,; Allard et al.,1991; Farrar et al., 1995; Giammanco et al., 1998; Gerlach et al., 1998; Hernandez et al., 2001a; 2001b; Notsu et al., 2006; Mazot and Taran, 2009; Granieri et al., 2010; Bloomberg et al., 2012; Inguaggiato et al., 2011; 2012a; 2012b; 2013;

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Italiano et al., 2014; 2017; Ring et al., 2016; Jácome Paz et al., 2016a; 2016b; Inguaggiato et al., 2017). CO2 is the main component of diffuse degassing in volcanic and geothermal areas due to

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its low solubility in silicate melts (Baubron et al., 1991).

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It has been concluded in several studies, that diffuse degassing structures (DDS; Chiodini et al.,

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2001) are related to local and regional faults, because faults act as barriers or conduits for fluids (e.g. Klusman, 1993; Caine et al., 1996; Giammanco et al., 1998; Baubron et al., 2002; Yuce

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and Ugurluoglu, 2003; Yaltirak et al, 2005; Fu et al., 2005; Yuce et al., 2010; 2014; 2017a;

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Granieri et al., 2010; Delgado – Granados et al., 2011; Italiano et al., 2013; Bond et al., 2017; Tamburello et al., 2018; Pfanz et al., 2019 and references therein).

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High fault density areas are lithologically heterogeneous and define structural anisotropic discontinuities in the upper crust (Caine et al., 1996). Faults are characterized by (i) displacement zone known as fault core, (ii) damaged zone as the fault growth and (iii) removed adjacent protolith rocks. According to Caine et al. (1996), the dynamics among these three elements control fluid flow within and near the fault zone. Several research studies have been done on the structures and mechanics of faults and their relationship with fluid flow (e.g. Doglioni et al., 2011; 2013; 2014). Kurz et al. (2008) reviewed studies on the „damage zone‟ with faults and fractures of Riedel structures cutting sedimentary rocks, and Sibson (2000) noted that interconnecting fault network planes act as conduits for fluid circulation. They concluded that the highest concentration of deformation bands occurs close to the main faults, favoring fluid flow within the damage zone in a direction parallel to the principal fault plane. Roberts et al. (2015) investigated surface controls on the distribution of 2

Journal Pre-proof CO2 degassing characteristics using a large geographical and historical data set.

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concluded that some CO2 seeps are strongly governed by the flow properties of the local lithology and topography. Nevertheless, permeability and local faulting (Yuce et al., 2017a; D‟Alessandro et al., 2018) govern some CO2 seeps. CO2 vents typically occur along faults in rocks that are located above the water table or have low permeability. Diffuse seeps develop where CO2 (laterally supplied by these faults) emerges from the vadose zone and where CO2 degassing from groundwater follows a different path due to flow differences for water and CO 2

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gas. Bubbling water seeps arise where CO2 supply enters the phreatic zone or an aquifer (Yuce and Taskiran, 2013; Yuce et al., 2017b).

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The relation between faults and gas discharge has always been evaluated qualitatively; recently,

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Lamberti et al. (2019) used CO2 flux field data and structural information to define the relation

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between gas flow and faults by overlying degassing and fault trace maps. Nonetheless, quantitative relations can be calculated by applying multivariate analysis to a set of continuous

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variables that include the CO2, H2S and CH4 fluxes and incorporating in this analysis the

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presence/absence and the specific strike of the local fault systems as categorical variables. Therefore, based on implemented techniques to investigate the correlation between fluids and

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DDS in geothermal and volcanic areas, this work proposes the use of exploratory data analysis as a new tool for diffuse degassing data analysis. We want to highlight that the analysis will be done for the three studied gases and not only for CO2 flux, as it has been usually presented in the literature. In addition, it is important to note that this kind of analysis can be implemented in all zones with presence of diffuse degassing and not only in active geothermal/volcanic zones. In areas without active volcanic or hydrothermal manifestations, gas discharge may be present as normal respiration in soils (mainly represented by CO2 degassing due to biogenic processes and degassing of cortical rocks) and this statistical analysis can be implemented to reveal the relation with some fault systems. The objectives of this study are (i) to make distribution maps of CO2, H2S and CH4 fluxes in this area, (ii) to calculate the total contribution of diffuse degassing in the area and (iii) to investigate

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Journal Pre-proof the correlation between local faults and the spatial structure of CO2, H2S and CH4 diffuse degassing. 2. Overview of Study Area The study area is located 30 km southeast of the city of Morelia in Michoacán State, eastern México, and 2 km from the nearest town called Tzitzio (Figure 1). It is in the limits of the physiographic province of the Mexican Volcanic Belt (MVB) and sub-province of Sierra Mil Cumbres composed of volcanic and pyroclastic rocks of Miocene age (Gómez-Vasconcelos et

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al., 2015).

Figure 1. Regional map of the Mexican volcanic belt (MVB), Michoacán state and Tzitzio Gap. Location of Agua Caliente Tzitzio hot springs. The study area is divided in Agua Caliente Northern (ACN) and Agua Caliente Southern (ACS). Main structural features are distinguished between faults with and without detected movement (Modified of Jácome Paz et al. 2019). Main geological units are indicated following Gómez–Vasconcelos et al., 2015;

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Journal Pre-proof faults without detected motion and normal faults are indicated following Garduño–Monroy et al., 2009 and Morelia-E14, mineral geological chart (1998)).

The MVB is a continental arc that shows variation in its composition and tectonic regime. This arc evolved from the Neogen to the Quaternary, because of the subduction of the Cocos Plate and the Rivera microplate under the North American plate (Ferrari et al., 2012). Two main fracture zones are located to the southwest of the MVB: the Rivera fracture zone (RFZ) and the

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Orozco fracture zone (OFZ), the latter delimiting segments with different ages and densities of the Cocos Plate (Blatter and Hammersly, 2010).

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The OFZ is a major feature of the subduction Cocos slab that has been continuously subducting

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beneath the overriding North American Plate under the zone of the Tzitzio Gap (TG) (Blatter

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and Hammersly, 2010). The denomination TG is due to the lack of active volcanism in the area, although there are several volcanic features from Early Oligocene to Middle Miocene in the

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2010).

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margins of the TG (Pasquare et al., 1991; Morán-Zenteno et al., 2007; Blatter and Hammersly,

The study area is located at the end of the Tzitzio anticline trace (Figure 1), which is the result

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of Laramide folds and thrusts that occurred during late Pliocene with an NNW-SSE trend (Gómez-Vasconcelos et al., 2015). Four regional fault systems affect the area: the old NW-SE and N-S; NE-SW; and the recent E-W (Garduño-Monroy et al., 2009). Jácome Paz et al. (2019) concluded that the hydrothermal system of Agua Caliente - Tzitzio is controlled by the interaction of the Tzitzio Anticline with the intersections of NE-SW and E-W normal faults related to the intra-arc extension of the MVB. There are two Eocene units of the Tzitzio Formation that outline the study area (Pasquare et al., 1991): a) conglomerate and b) sandstone. The conglomerate is polymictic, matrix-supported and poorly sorted consisting of milky subangular quartz clasts with a variable size of 0.5-5 cm, subrounded andesite clasts of 1-7 cm, clasts of metamorphic rocks with size between 1-4 cm, and occasionally limestone clasts <3 cm within a sandstone matrix (30 %). A fine-grained sandstone with moderate oxidation presenting reddish colors overlies the conglomerate. Thermal discharge 5

Journal Pre-proof occurs through the conglomerate that has a porosity of approximately 8%, estimated from petrographic observations (Pérez-Zárate et al., 2017). 2.1 Previous Degassing studies In 2017, Jácome Paz et al. (2019) reported CO2 fluxes with a minimum value of 3 g m-2 d-1, a maximum value of 29,484 g m-2 d-1 and a mean value of 1,427 g m-2 d-1 in the Agua Caliente north area (ACN). According with the GSA method, the total data set was divided in two degassing groups: the low flux group was represented by 70% of the values (group A) and the

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high flux group was represented by 30% of the data (group B). The total CO2 diffuse flux obtained was 0.15 ton d-1. There were some locations closer to the main thermal spring and on

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bubbling zones (~ 20% of data set) that show CH4 degassing. The range of CH4 flux values was

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within 2.2 to 5.3 g m-2 d-1.

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On Agua Caliente South area (ACS), the obtained CO2 flux had a minimum value of less than 1 g m-2 d-1, a maximum value of 1,925 g m-2 d-1 and a mean value of 227 g m-2 d-1. The GSA

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method shows two degassing groups: the low flux group represents 80% of the data (group A) and the high flux group represents 20% of the data (group B). The total CO2 diffuse flux was

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0.17 ton d-1. Some locations close to the main warm spring and on intensely bubbling areas

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(~30% of total measured data) present CH4 degassing with values within 4.8 to 20.6 g m-2 d-1. In both zones, there was no diffuse emission of H2S within the detection range of the sensor. 3. Materials and Methods

3.1 Field geology and structural measurements The studied area is divided in Agua Caliente North (ACN) and Agua Caliente South (ACS) (Figure 1). Basic field exploration, including geology and diffuse emission measurements, was carried out at local level in ACN and ACS. The geological exploration consisted in the identification of main fault characteristics. Structural measurements were collected at two sites located along the river where thermal springs occur. Fieldwork included both qualitative and quantitative analyses of fault structural data (orientation, dimension, spatial distributions, fault type and spacing) in the outcrops of the sedimentary sequence. Mapping of lithology, structural

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Journal Pre-proof data and hydrothermal alteration was integrated in a Geographic Information System (QGIS®); additionally, the structural data were plotted in Stereonet® to identify the main fault systems. 3. 2 Soil gas flux determinations A new soil CO2 flux survey at Agua Caliente-Tzitzio thermal springs was performed on February 2018, during dry season and under stable atmospheric pressure. Soil flux measurements were performed following the accumulation chamber method using a West Systems fluxmeter (Chiodini et al., 1998). Gas flux on the water surface (thermal springs,

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bubbling pools and water bubbling pools) was measured following the method proposed by Bernard and Mazot (2004).

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In both cases, soil and water, the fluxmeter had the following sensors: (1) LICOR LI-800, a

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nondispersive infrared CO2 sensor with detection range of 0.1 to 20,000 ppm, accuracy of <3%

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and reproducibility below 10% for the range 0.2 to 10,000 g m-2 d-1; (2) CH4-WS infrared, which has a flux detection range from 0.02 to 1,444 g m-2 d-1, 5% accuracy, 2% of repeatability,

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22 ppm resolution and ± 25% precision for the range 0.1 - 5 moles m-2 d-1 and ± 10% precision for the range 5 – 150 moles m-2 d-1; and (3) WEST H2S-BH chemical detector, which has a flux

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detection range from 0.2 x 10-3 to 0.6 moles m-2 d-1, accuracy of 3%, factory calibration of 20

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ppm and zero offset ≤ 0.2 ppm. Due to the rate of consumption of the detector, the H2S flux is systematically 10% underestimated in the accumulation chamber A and 5% underestimated in the accumulation chamber B. In addition, the H2S detector is affected by interference sensitivity for several species (SO2, NO, NO2, Cl2, H2, C2H4, CO, NH3); therefore, low fluxes can be interpreted to be the result of the interference sensitivity effect (West Systems, 2012). Flux Revision software of the West system, Wingslib®, Surfer® and Origin® software were used for flux data post-processing. The graphical statistical approach method (GSA) and sequential Gaussian simulation (SGSIM) were performed to compute the total CO2 flux (Sichel, 1966; Sinclair, 1974; Chiodini et al., 1998; Deutsch and Journel, 1998; Cardellini et al., 2003). The GSA method permits differentiation among different CO2 degassing groups or families (e.g. Jácome-Paz et al., 2016b). Usually these groups can be related with degassing mechanisms: (i) low flux values may be associated with background or soil respiration and (ii) anomalously high 7

Journal Pre-proof values of fluxes with hydrothermal input. The SGSIM technique allows to calculate the total flux and to describe main degassing alignments showing the spatial distribution of fluxes. All distribution maps were done with Gaussian simulation, using a grid of 5 x 5 m and with 100 simulations for each area. The total covered areas of each zone were computed using both, GPS coordinates and Wingslib® software. 3.3 Exploratory data analysis The hypothesis of this paper is that gas anomalies generally occur as linear, fault linked-

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features, as well as in irregularly shaped diffuse or halo anomalies and irregularly spaced plumes or spot anomalies. Therefore, spatial gas flux patterns in faulted areas appear to be

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suitable tools for identifying these features. Geostatistical techniques were applied to the study

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of spatially correlated data, as described below.

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Exploratory data analysis (EDA) performs an examination of the data to suggest partial descriptions and hidden relationships, regardless of the statistical criteria used in confirmatory

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settings. EDA is an approach to analyze data sets and summarize their main characteristics,

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often with graphic methods that include descriptive statistics. One of the most important objectives of geospatial data EDA is to characterize the range of spatial autocorrelation that is

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exhibited by the data. The graphic results of EDA are presented as box and variance plots that provide a qualitative evaluation of the correlation among variables (Tukey, 1977; Davis, 2002). Data processing was performed with MANOVA for the 3 continuous variables that represent the gas flux of CO2, H2S and CH4, and two categorical variables: fault system strike and the degassing groups (A and B). MANOVA analyzes the relationship between the variables (gas flux) and co-variables represented by degassing groups (A and B) and fault system strikes by comparing the mean value and the covariance structure of the data among the dependent and independent variables. The result of this comparison yields the correlation coefficient and the p-value, which represents the statistical significance of the results: the smaller the p-value, the greater the statistical significance. In Earth Sciences, the convention is that a p-value lower than 0.05 is considered as evidence that the mean values differences are statistically significant. The evaluation of the 8

Journal Pre-proof results was done using Wilks criterion. In MANOVA tests there are differences between group means for a combination of dependent variables. Wilks Criterion is a measure of the percent variance in dependent variables not explained by differences in levels of the independent variable. A p-value of zero means that there is no variance that is not explained by the independent variables (Wilks, 1932); therefore, a p-value equal to zero is expected if the variance of the dependent variable is totally explained by the variance of the independent variables. The p-value measures the probability that the studied hypothesis is true, or the

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probability that the data were produced by random chance, in this case the correlation between degassing and the presence of faults. A small p-value means that there is strong evidence that a

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statistical test is valid. The maximum value to consider a hypothesis to be true is 0.05 (Gibbons

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and Pratt, 1975).

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Data processing included the collected data of the diffuse emissions of CH4, CO2 and H2S in [g m-2 d-1] units. Information on the presence/absence and direction of the faults was included

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numerically as categorical variables with the same spatial coordinates as the diffuse emission

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variables. The fault variables were classified in four types: (0) no faults, (1) ORT fault system, which includes N-S and W-E fault systems, (2) NW-SE fault system and (3) NE-SW fault

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system. An additional variable with information about the degassing groups A and B was also included in the analysis (see Appendix A). The location of both, flow data and faults, was expressed in UTM coordinates (14 north) and spatial reference system WGS 1984. Collected data were processed with the software: Statistica 10.0 (StatSoft Inc.).

4 Results 4.1 Geological Description 4.1.1 Agua Caliente North (ACN) Surface hydrothermal manifestations are represented by several hot springs with Na-HCO3 water type (< 50 °C, Jácome Paz et al. 2019) and diffuse emanations of gas (Jácome Paz et al. 2019 and this work) at the crossing of faults with trend NW-SE and E-W. In the surroundings of the springs there is argillic hydrothermal alteration with clay minerals like kaolinite and 9

Journal Pre-proof halloysite in < 50 cm halos, deposits of red crusts of schwertmannite [Fe3+ 16O16 (OH)12(SO4)2)] and stockwork veins of opal, quartz and pyrite (Jácome-Paz et al., 2019). The sedimentary rock outcrops along the NNE-SSW river section show faults, tensile fractures, joints and veinlets. This zone presents intense faulting, the four regional fault systems are reflected locally (NW-SE, N-S, NE-SW and E-W, Figure 2) with the presence of the rightlateral strike-slip normal faults, normal with right and left lateral components, and inverse faults. Fault structures are still well preserved and recognizable even with a low fracture density.

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Window sampling method was used for assessing fracture variability, density and intensity on conglomerate unit outcrops (e.g. Priest, 1993; Zeeb et al., 2013). Rectangular window sampling

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(4 x 2 m) records a total of 110 measurements, fractures with an interval of length from 0.05 to

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3.5 m, an average density of 5 (m-2) with a variation between 4 and 10 (m-2), and an average

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intensity of 6.5 (m x m-2) in a range of 4 to 11 (m x m-2).

These structures are characterized by a main slip plane that is striated and polished, and

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occasionally has associated abrasive slickensides. The striations and orientations of the

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slickensides, where present, are consistent with a predominantly normal slip, with right- or leftlateral slip components depending on local surfaces. In these structures, the fault core is very

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thin and poorly developed, a few millimeters thick or single fault planes, and the damage zone consists of <8 cm wide fracture zone. In general, there is no evidence of circulation of thermal fluids through the main faults (e.g. halos of alteration), but associated faults show laminated veinlets (< 1cm) of opal and quartz filling normal, predominantly, and strike-slip faults, also tensile fractures present hydrothermal alteration. The structures form an association of faults and fractures that intersect and reach each other in a network-like arrangement. The fault network has ~2 m wide and occasionally replicates every ~20 m along the west edge of the river. Inside each fault network, the crosscutting and contact relationships among the fractures, faults and striated slip surfaces suggest that these structures belong to the same deformation event. Interconnecting fault planes provide a conduit for fluid circulation. The argillic alteration develops in halos (< 50 cm), mainly crosscutting or in the contact between faults. Occasionally, opal and quartz veinlets (< 1cm) can be observed. 10

Journal Pre-proof The structural analysis shows that the fault distribution and type agree with a Riedel-type model (Riedel, 1929), where the master fault is of lateral type that generates and/or intersects normal faults (N) and normal faults with lateral component (R‟ and P‟ faults), in addition to inverse faults. The identified Riedel systems consist of N70-85°E master right lateral faults (Y), related to: (a) N65-75°E right lateral faults P, (b) N06-12°E normal faults with left lateral component P´, (c) N10-20°W normal faults with lateral component R´, and (d) N20-34°E fractures and normal faults T (Figure 2).

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The hydrothermal discharge is related to the systems of faults and fractures P, P´, R´, and T

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types.

Figure 2. Field photos of the hot spring sites and associated faults. Detailed map of the hot springs of ACN. (A) Photo of a fault network, with the structural assemblage composed of normal faults and thermal discharges. The white lines indicate the fault trace of Riedel network. Arrows indicate direction of discharge. (B) Network of Riedel structures within structurally influenced hydrothermal flow. Yellow dotted lines indicate halo of alteration and arrows indicate direction of flow (C) Structural settings for ACN and location of measuring points of diffuse degassing. Main hot springs observed on field are reported.

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4.1.2 Agua Caliente South (ACS) In the southern area there are two main thermal springs (< 52 °C, Jácome-Paz et al., 2019) located at the intersection of NW and E-W faults, and a cold spring discharge on the trace of the N64°E fault (Figure 3). The hydrothermal alteration present is analogous to that of the northern portion: kaolinite, halloysite, schwertmannite and pyrite, having wider distribution and higher intensity than in the northern area.

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Structurally, it presents faulting similar to the north zone, with the presence of normal faults with right and left lateral components. The results evaluated for the second network of natural

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fractures, with rectangular window sampling of 4 x 2 m show fracture lengths between 0.05 and

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3.8 m, a lower average density of fractures 4.4 (m-2), with a variation between 4 and 9 (m-2) and

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an average intensity of 5.8 (m x m-2) in a range between 4 and 10 (m x m-2), with respect to ACN. The Riedel system in this section is composed of the N70-84°W master faults, associated

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with: (a) N50-60°E left lateral faults P´, (b) N55-70°W normal faults with right side component

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R, (c) N4-10°W normal fault with left side component R´, (d) N10-20°W normal faults N, and (e) N56-60°E fractures and normal faults T with orientation (Figure 3).

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The intersection of P´, R, R´, N, and T faults and fractures types control the occurrence of hydrothermal discharges.

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Figure 3. Field photos of the (A) hot spring at the intersection of fault and stratification of conglomerate. (B) Photo of a fault network, with the structural assemblage composed of

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faults type P, N, T and R‟. (C) Structural settings for ACS and location of measuring points

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4. 2 Diffuse degassing

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of diffuse degassing.

4.2.1 Agua Caliente North (ACN)

Some 57 points of fluxes with a CO2 flux minimum value of ~ 0 g m-2 d-1, maximum value of 181,764 g m-2 d-1 and mean value of 6,675 g m-2 d-1 were measured in ACN area. The GSA method, through cumulative probability plot for the total data set of CO2 flux, shows two degassing groups: the low values group (A) represented by 80% of the data set with a maximum flux value of 1,614 g m-2 d-1 and the high values group (B) represented by 20% of the data corresponding to the values measured at bubbling zones and with maximum value of ~ 181,764 g m-2 d-1 (Figure 4). After Gaussian simulation, the mean value (corresponding with bubbling zones) was 2,829 g m-2 d-1. The model was a spherical variogram with sill 1 and nugget of 0.2. The grid was done with 5 x 5 meters cell covering an area of 25,200 m2. The total

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Journal Pre-proof CO2

diffuse

flux

obtained

by

SGSIM

was

71.29

ton

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(Table

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Figure 4. Cumulative probability plot for CO2, CH4 and H2S fluxes on ACN. Two degassing

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groups were inferred by GSA method in each measured gas.

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Table 1. CO2 fluxes values obtained with GSA and SGSIM techniques for ACN area. Group

Mean value GSA

Mean value GSIM

Total SGSIM CO2 flux (ton d-1)

(%)

technique

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Area : 25,200 m2

( g m-2 d-1)

( g m-2 d-1) 2,829

Group A

80

276

Group B

20

33,512

f o

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e

Total flux

71

l a

r P

n r u

o J

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Journal Pre-proof There were some locations closer to the main thermal spring and on bubbling zones that show CH4 degassing. The range of measured CH4 flux is within zero - 78 g m-2 d-1. The mean flux was 10.7 g m-2 d-1. The GSA method shows two degassing families: the low values group (A) represented by 85 % of the data set with a maximum flux value of 2.04 g m-2 d-1 and the high values group (B) represented by 15 % of the data with maximum value of ~ 78 g m-2 d-1. After Gaussian simulation, the mean value, without outliers, was 1.8 g m-2 d-1. The model was a spherical variogram with sill 1.1 and nugget of 0.6. The grid was done with 5 x 5 meters cell

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covering an area of 25,200 m2. The total CH4 flux obtained by SGSIM was 0.04 ton d-1. There were some locations closer to the main thermal spring with visible alteration of pyrite and

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significant values of H2S flux. The range of measured H2S flux values is within zero - 2.7 g m-2

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d-1. The GSA method shows two degassing families: the low values group (A) represented by

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70 % of the data set with a maximum flux value of 0.310 g m-2 d-1 and the high values group (B) represented by 30 % of the data with maximum value of 2.7 g m-2 d-1. After Gaussian

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simulation, the mean value of H2S was 0.3 g m-2 d-1. The model was a spherical variogram with sill 1 and nugget of 0.4. The grid was done with 5 x 5 meters cell covering an area of 25,200 m2.

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The total H2S flux obtained by SGSIM was 0.007 ton d-1.

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The obtained distribution maps (Figure 5) of the three gases show some spatial patterns. Three different maps were made for the three gases, by changing the scale to identify some spatial patterns in the degassing features. In the case of CO2 flux, the ranges are 0-1,000 g m-2 d-1, 1000 – 10,000 g m-2 d-1 and all values 0 – 190,000 g m-2 d-1. High degassing values were observed in the intersection of faults (northern of ACN area, corresponding with the hot springs location verified in the field) and low CO2 fluxes were observed in NE-SW tending faults. The presence of active faults controls the CO2 diffusion, which is influenced by both the pH value and the hydrolysis of silicates such as plagioclase generating the Na-HCO3 water type as reported by Jácome-Paz et al. (2019). Thermal springs have between 6.2 – 6.6 pH, while river water downstream of thermal springs and upstream of thermal springs has 7 – 8.6 pH respectively, (Jácome Paz et al. 2019))

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Journal Pre-proof In the case of H2S the ranges where 0 – 0.5 g m-2 d-1, 0.5–2.750 g m-2 d-1 and all values 0–2.750 g m-2 d-1. High values seem to be divided by the W-E alignment in the central part of the zone allowing major degassing of H2S in the northern part of ACN area across hot springs and their surroundings. Main H2S degassing was detected along NW-SE and NE-SW directions that correlate with pyrite deposits and main hot springs. The ranges of 0-10 g m-2 d-1, 10–80 g m-2 d-1 are characteristic of CH4 distribution maps. The behavior of this gas is like H2S degassing with high values in the northern part of the area

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around main hot springs location and, additionally, a small zone in the southern part which corresponds with a minor bubbling zone. CH4 flux seems to be dominated by NW-SE and W-E

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directions.

Figure 5. Distribution maps were overlaid on local structural features reported on this study. All color scales are presented in g m-2 d-1 unit. Different ranges of fluxes were used for each figure to highlight possible alignments and spatial patterns. (A) CO2 fluxes. Left to right: 01,000; 1,000 – 10,000 and 0 – 190,000 g m-2 d-1. (B) CH4 fluxes. Left to right: 0-10, 10 – 80 and 0-80 g m-2 d-1. (C) H2S fluxes. Left to right: 0 – 0.5, 0.5 – 2.750 and 0 – 2.750 g m-2 d-1. 17

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4.2.2 Agua Caliente South (ACS) Some 49 points were measured in ACS area. CO2 flux minimum value of 2.5 g m-2 d-1, a maximum value of 27,000 g m-2 d-1 and a mean value of 3,217 g m-2 d-1were obtained. The cumulative probability plot for the total data set shows two degassing groups: the low flux group (A) represented by 90 % of the data set with maximum value of 36.45 g m-2 d-1 and the high values (group B) was represented by 10% of the data with a maximum value of 27,000 g

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m-2 d-1 (Figure 6). After Gaussian simulation, the mean value without the outliers was 105 g m-2 d-1. The model was a spherical variogram with sill 1 and nugget of 0.2. The grid was done with

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5 x 5 meters cells covering an area of 23,200 m2. The total CO2 diffuse flux obtained by SGSIM

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was 2.43 ton d-1 (Table 2).

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Figure 6. Cumulative probability plot for CO2, CH4 and H2S fluxes on ACS. Two degassing groups were inferred by GSA method in each measured gas.

There were some locations close to the main thermal spring and on bubbling zones that show CH4 degassing. The range of measured CH4 flux values is within zero - 30 g m-2 d-1. The GSA method shows two degassing families: the low values group (A) represented by 60 % of the data set with a maximum flux value of 0.2608 g m-2 d-1 and the high values group (B) represented by 40 % of the data with maximum value of 30 g m-2 d-1. After Gaussian simulation, the mean value, without outliers, was 0.04 g m-2 d-1. The model was a spherical variogram with sill 1.2 and nugget of 0.15. The grid was done with 5 x 5 meters cell covering an area of 23,200 m2. The total CH4 diffuse flux obtained by SGSIM was 0.0009 ton d-1.

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Journal Pre-proof There were some locations with significant values of H2S fluxes. The range of measured H2S flux values is within 0-0.2 g m-2 d-1. The GSA method shows two degassing families of fluxes: the low values group (A) represented by 90 % of the data set with a maximum flux value of 0.0999 g m-2 d-1 and the high values group (B) represented by 10 % of the data with maximum value of 0.2 g m-2 d-1. After Gaussian simulation, the mean value of H2S was 0.04 g m-2 d-1. The model was a spherical variogram with sill 1 and nugget of 0.3. The grid was done with 5 x 5 meters cell covering an area of 23,200 m2. The total H2S diffuse flux obtained by SGSIM was

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0.0001 ton d-1.

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Table 2. CO2 fluxes values obtained with GSA and SGSIM techniques for ACS area. Group (%)

Mean value GSA

Mean value

Total SGSIM CO2 flux (ton d-1)

technique

SGSIM technique

Area : 23,200 m2

( g m-2 d-1)

( g m-2 d-1) 105

Group A

90

20.9

Group B

10

5,294

f o

e

Total flux

2.5

l a

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r P

n r u

o J

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Journal Pre-proof The obtained distribution maps of the three gases (Figure 7) show some spatial structures. Three different maps were made, one for each gas, changing the scale of discharge values to visualize the possible spatial structure in the degassing values. In the case of CO2, the ranges are 0-400, zero – 1,000 and all values 0 – 28,000 g m-2 d-1. Main degassing can be observed in the trace of NE-SW direction (western part of ACS area, corresponding with the location of the main hot spring as reported in Jácome Paz et al. 2019 and verified in the field). In the case of H2S the ranges where 0–0.05, 0–0.2 and all values 0–4 g m-2 d-1. High H2S fluxes are reported at the

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same locations, where pyrite deposits can be observed and across the same direction as CO2 discharge. For the CH4 distribution maps, the observed ranges are 0–0.15 g m-2 d-1; 0–2 g m-2 d-1

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and all values are within the interval 0–24 g m-2 d-1. The highest flux values are located directly

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on the main hot springs area.

Figure 7. Distribution maps were overlaid with local structural features reported in this study. All color scales are presented in g m-2 d-1 unit. For each figure, different ranges of fluxes were used to highlight possible alignments and spatial patterns. (A) CO2 fluxes. Left

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Journal Pre-proof to right: 0-400; 0 – 1,000 and 0 – 28,000 g m-2 d-1. (B). CH4 fluxes. Left to right: 0 – 0.15; 0 – 2 and 0 – 24 g m-2 d-1. (C) H2S fluxes. Left to right: 0 – 0.05; 0 – 0.2 and 0 – 4 g m-2 d-1 4.3 Exploration Data Analysis EDA results show that CH4 and H2S flux values have low dispersion, as highlighted by the low value of the variation coefficient (Std. Dev.). Otherwise, the wide ranges, as well as the high skewness values for CO2, indicate the presence of statistical outliers. (Table 3). Table 3. Descriptive statistics of CO2, CH4 and H2S degassing data used for EDA analysis.

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All flux values are in g m-2 d-1 unit. Valid N

Mean

Median

Minimum

Maximum

Std. Dev.

CO2

2,082

2,797.43

525

34.68

181,764.0

7,978.75

CH4

1,991

1.09

0.03

0.0

78.0

4.52

H2S

949

0.004

0.06

0.0

0.3

0.01

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Variable

Box plots are used to display and compare qualitatively the distribution of the gas data set with

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those related with faults with different orientation (Figures 8, 9, 10). The plots indicate that the mean CO2 flux value is similar for all fault system directions, but there are some high values

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that may be considered as statistical outliers; however, they are reliable data that provide valuable information on intense CO2 degassing (Figure 8). In the case of CH4 and H2S, the mean values are similar in all directions as well as the standard deviation; therefore, there are no statistical outliers (Figures 9, 10). The MANOVA results (Table 4) show that there is a statistically significant correlation (p<0.05) between diffuse degassing of CO2 and CH4 and the presence of faults.

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Journal Pre-proof Table 4. MANOVA results: Wilks‟ Criterion correlation analysis of gas flux and faults presence.

Wilks‟ Criterion Correlation coef.

p-value

CO2

0.985

0.000

CH4

0.947

0.000

H2S

0.996

0.341

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Gas Flux

groups (A and B for all gases).

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Wilks‟ Criterion

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Table 5. MANOVA results: Wilks‟ Criterion correlation analysis of gas flux and degassing

Correlation coef.

CO2

0.979

0.000

CH4

0.792

0.000

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H2S

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Gas Flux

0.873

p-value

0.000

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Figure 8. Boxplot of the CO2 diffuse flux data, where 0 - no faults, 1- ORT fault system,

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which includes N - S and W - E faults, 2 - NW - SE faults and 3 – NE - SW faults.

Figure 9. Boxplot of the CH4 diffuse flux data, where 0 - no faults, 1- ORT fault system, which includes N - S and W - E faults, 2 - NW - SE faults and 3 – NE - SW faults.

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Figure 10. Boxplot of the H2S diffuse flux data, where 0 - no faults, 1- ORT fault system,

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which includes N - S and W - E faults, 2 - NW - SE faults and 3 – NE - SW faults.

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Figures 11 and 12 show that degassing is mostly correlated with the ORT system (N-S and E-W

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fault systems). Conversely, H2S degassing does not show a statistically significant correlation

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(p=0.341) with any of the fault systems (Table 4).

Figure 11. Variance plot of the CO2 diffuse flux data, where 0 - no faults, 1- ORT fault system, which includes N - S and W - E faults, 2 - NW - SE faults and 3 – NE - SW faults.

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Figure 12. Variance plot of the CH4 diffuse flux data, where 0 - no faults, 1- ORT fault

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system, which includes N - S and W - E faults, 2 - NW - SE faults and 3 – NE - SW faults.

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Figures 13, 14 and 15 are variance plots that show that degassing group A (background soil degassing) consistently has the lowest values for all fault systems, independently of the fault

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direction. The highest gas flux of the B degassing group, which is linked to the anomalous geothermal gas flux, are associated with the ORT fault system and the lowest values correspond

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to the no fault group. MANOVA results (Table 5) show that there is a statistically significant correlation for CO2, CH4 and H2S (p < 0.05) with diffuse degassing groups (A and B) according to GSA method.

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Figure 13. Variance plot of the CO2 diffuse flux data assembled by degassing group. Group A represents the background soil degassing and group B represents the anomalous geothermal

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degassing. F = 6.3327, p = 0.00029. Vertical bars denote 0.95 confidence intervals.

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Figure 14. Variance plot of the CH4 diffuse flux data assembled by degassing group. Group A represents the background soil degassing and group B represents the anomalous geothermal

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degassing. F = 12.063, p = 0.00000. Vertical bars denote 0.95 confidence intervals.

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Figure 15. Variance plot of the H2S diffuse flux data assembled by degassing group. Group A represents the background soil degassing and group B represents the anomalous geothermal

5. Discussion

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degassing. F = 12.063, p = 0.000. Vertical bars denote 0.95 confidence intervals.

In this work, geostatistical analysis was performed to investigate the presence of phenomena acting along specific directions (e.g., fault-related anisotropy effect). Recent works observed a relation of anomalous gas concentrations with active faults and confirmed that the gas discharge is a deep fault indicator (Ciotoli et al., 1998: 1999; 2005; Lombardi et al., 1996; Klusman, 1993; Zhiguan, 1991; Baubron et al., 2002). Active faults favor gas flow because they generally increase rock and soil permeability, thus the presence of linear soil gas anomalies longer than several meters is often taken as strong evidence of tectonic features (Ciotoli et al., 1998; Fridman, 1990). It is important to note that faults have typically wide fracture zones that can

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Journal Pre-proof also be crosscut by other structures, thus resulting in diffuse or „„halo‟‟ anomalies (Matthews,

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1985; Sokolov, 1971).

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Figure 16. Conceptual model of degassing in Agua Caliente (North and South) zone. The

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deepest failures allude to the master failures of the area. The ACS NW-SE fault whose surface feature is characterized by the exit of the cold springs on the surface is sufficiently deep to let

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the groundwater in the area rise.

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The structural survey performed throughout the river with thermal discharges has defined a Riedel system along two principle trends, one almost E-W and the other with a NW direction (Figures 2 and 3), which agree with the regional trends observed throughout Tzitzio anticlinal and Mil Cumbres (Gómez-Vasconcelos et al., 2015; Jácome-Paz et al., 2019). At the surface, the intersection of major faults, with their associated Riedel type fractures and faults, forms openings (< 2 cm opening) in the compact conglomerate unit that hosts them, providing a highly permeable pathway for the hydrothermal discharges to escape towards the surface. Degassing related to faults can be either „direct leak anomalies‟ where the gas measured corresponds to the deep gas phase or „secondary anomalies‟ linked to the geological characteristics of the fault (Baubron et al., 2002); according to Jácome Paz et al., (2019) the isotope value of carbon (CO2) in gas samples (bubbling zones) is -5.7 ‰ δC13 PDB for ACN and -5.9 ‰ δC13 PDB for ACS,

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Journal Pre-proof indicating a magmatic origin in both zones. Because of that, we propose a model (Figure 16) for ACN and ACS, where faults going deep correspond to those defining bubbling zones (and thermal spring manifestations) in surface, and coincidentally, all these are the master faults of the area. As this model is presented as a complement and visual tool for the statistical correlation obtained between faults strikes and degassing, two different and unique depths are assumed for the master faults and for the remaining faults. The illustrative depth that allows contact with deeper layers and a possible thermal reservoir was chosen for the master faults that

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allow the degassing to rise. Determining the depth of the failures goes beyond the scope of this article.

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Similarly to what has been observed in other areas, where gas diffusion is closely related

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specifically with active faults (e. g. Ciotoli et al., 1998; Fridman, 1990; Lamberti et al., 2019),

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the field evidence shows greater fluid flow at the intersections of the P´, R´, and T faults (Figures 2 and 3), and it does not show substantial flow along major faults, consistently with the

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results of the statistical analysis. According to Tamburello et al., (2018) extensional tectonics

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and, in particular, normal and strike slip faults are the most suitable scenario for CO2 degassing, our results are in agreement with this.

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In addition, in Agua Caliente zone, the hydrothermal discharges does not occur along the entire length of any given fault but instead it is focused in small areas (Figures 5 and 7). This implies a complex permeability distribution related to the fault system, where hydrothermal flow occurs via preferential pathways consisting of well-interconnected fault networks. According to Faulkner et al. (2010) flow can be dominated by a small number of fractures within the surrounding damage zone in the fault systems and the complex structure in a fault, fundamental role in CO2 propagation by creating pathways extending from the deep crust to the earth surface. Regarding the preferential degassing direction (named as DDS, diffuse degassing structures by Chiodini et al., 2001), in Agua Caliente it is observed that the ORT fault system is correlated with the highest anomalous values (Family B) of gas flux for CO2 and CH4; therefore, it is very likely that the ORT fault system is more active in comparison with the NW-SE and NE-SW fault systems. In the case of H2S, the lack of correlation with any fault system is possibly 31

Journal Pre-proof associated with higher H2S reactivity compared with that of CO2 and CH4; that produces abundant pyrite deposition observed in the surface manifestations and affected the total H2S flow measured.

6. Concluding Remarks Gas release at the surface is not continuous along strike-slip faults; instead, it occurs at specific NW and NE trends due to channeled flow along more permeable pathways along the P´, R´, and

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T faults. Work at Agua Caliente-Tzitzio thermal zone fault shows a clear link between deformation style, secondary permeability characteristics, and the spatial distribution and flux

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rates of deep gas to the surface.

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EDA and MANOVA were implemented, for the first time, to statistically describe the relation

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between the location of degassing features and the presence of some local fault systems,

faults.

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discriminating the direction of the main discharge channels and pointing to higher permeability

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EDA and MANOVA are well-established methods that would be valuable in basic geothermal exploration to define correlations between local structural settings and diffuse degassing on

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soils that may be useful to infer permeable structures to be considered for exploitation planning. Local structural geology, which usually is well described as part of the basic exploration of a geothermal area, must be known for this kind of study. If possible, a more complete data set of structural parameters should be used after reconnaissance exploration. It is highly recommended to use EDA and MANOVA techniques in order to establish the main degassing directions in geothermal and volcanic areas. Spatial correlation methods allow, as shown in the present work, to validate the statistical significance of the correlation between fault systems and gas discharge in the studied area. There is an obvious relation between faults and permeability that allows gas discharge; however, the specific strike that favors gas flow toward the surface can be objectively determined with the multivariate statistical analysis.

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Journal Pre-proof A statistically significant correlation was found between ORT fault systems and CO2 flux. The other fault systems do not present any significant correlation. According to these results, and taking Roberts et al. (2015) classification, the CO2 seeps at Agua Caliente – Tzitzio occur along faults in rocks that are located above the water table or have low permeability. This configuration allows CO2 diffuse seeps to be laterally supplied by the local faults and, in this case, bubbling CO2 occurs due to the presence of the river and the hot springs. Thermal springs

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discharge at the surface and CO2 diffuse degassing follow a similar flow path.

Data Availability

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Datasets related to this article are available and can be requested to the corresponding author.

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ACKNOWLEDGMENTS

This work was financially supported by the Mexican Center for Innovation in Geothermal

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Energy (grant number 207032 CONACyT/SENER) through Project P01. The authors wish to

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thank Juan Luis Carrillo de la Cruz for his help in processing data. The authors thank the comments by two anonymous reviewers that greatly improved the original manuscript and

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editor Prof. Alessandro Aiuppa.

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Journal Pre-proof Appendix A Example of data and format file used for MANOVA and EDA methods. Agua Caliente Norte (CAN) CO2 for all fault systems.

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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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2167931 2167941 2167946 2167936 2168156 2167956 2167956 2167951 2167951 2167961 2167966 2167946 2168041 2168061 2168006 2168101 2168151 2168146 2167961 2167971 2167931 2168136 2168041 2167951 2168021 2168086 2167986 2168091 2168016 2168126 2167941 2167946 2168131 2168106 2167956 2168036 2167976 2168111

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299989 299989 299989 299989 300029 300019 299989 299989 300019 300019 300019 300019 300024 300019 300034 300024 300029 300029 299989 300019 300019 300029 300029 300024 300034 300029 300014 300029 300034 300029 299994 299994 300029 300029 300024 300029 300019 300024

Without CO2 faults 34.68011 56.66412 67.68835 72.60001 91.21203 109.6921 170.4403 183.0751 188.3476 209.744 218.9699 226.1434 315.5662 338.7496 390.412 391.401 392.0548 396.4644 429.922 495.2152 500.1033 520.1593 523.4522 545.8918 560.2153 565.1402 566.4672 566.4956 567.2105 569.4689 576.5685 579.6982 582.4548 593.7277 596.8278 603.0911 613.4253 618.2233

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Group A ACN CO2 Without faults (0)

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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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622.7193 629.9614 642.1877 642.8066 644.6273 692.9899 694.5719 700.5005 701.6691 703.1625 714.2386 752.694 757.5518 758.0287 760.7935 761.9862 779.8254 803.0764 816.4586 829.5568 843.5129 873.5274 888.6371 888.8605 911.0417 916.739 919.0677 935.4426 937.7693 952.2748 959.1161 988.6716 999.868 1000.289 1007.739 1015.041 1045.942 1052.891 1054.94 1061.085 1063.001 1070.564 1075.696 1076.656 1080.513 1115.572

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2168156 2167951 2167991 2168096 2168031 2167971 2167966 2167961 2168141 2167931 2167991 2168101 2168026 2168091 2168071 2167931 2167976 2168036 2167981 2167971 2167961 2168136 2167981 2168031 2168206 2168111 2168126 2168146 2168031 2168016 2168061 2167966 2167941 2167981 2168096 2167976 2167981 2168026 2168016 2167951 2168076 2167976 2167986 2167951 2167991 2167956

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300034 299994 300034 300029 300029 300024 299989 300024 300034 299994 300059 300054 300019 300074 300029 300024 300054 299994 300019 299989 299994 300034 300034 299989 299989 300029 300034 299989 299994 299989 300049 300024 300024 300014 300074 300014 300029 300034 299994 300014 300029 300059 300004 300034 300054 300014

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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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1133.282 1148.491 1176.308 1187.249 1200.835 1204.237 1216.634 1222.035 1228.268 1230.76 1233.84 1233.9 1235.144 1274.128 1288.231 1301.681 1304.571 1314.635 1349.063 1350.331 1350.588 1367.899 1382.732 1382.843 1413.348 1448.789 1455.118 1471.379 1472.467 1488.395 1492.031 1499.318 1517.775 1519.876 1520.974 1537.174 1548.235 1555.52 1578.373 1587.41 1592.679 1604.632 1614.406

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2167956 2168121 2168131 2167956 2167946 2168021 2168086 2168011 2168126 2168041 2167986 2167961 2167991 2168136 2168131 2167986 2168191 2168051 2168141 2167996 2168096 2168201 2168036 2167991 2167941 2168036 2168141 2167986 2168071 2168091 2167946 2168021 2168106 2168031 2168056 2168191 2167956 2168131 2168021 2168086 2168006 2168126 2168061

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299994 300024 300034 300034 300034 300019 300054 300024 300059 299989 300034 300014 300019 299989 300044 300059 300069 299994 300039 300074 300019 300044 299989 300029 300014 300034 299989 300019 300049 300059 300014 299989 300069 300049 299994 300014 299999 300069 300049 300074 299999 300044 299999

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Group B ACN CO2 Without faults (0)

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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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2167991 2168076 2168011 2168056 2167976 2168191 2168041 2168081 2167961 2168106 2168006 2167991 2167941 2168081 2167996 2168001 2168136 2167996 2168101 2168011 2167951 2167966 2168061 2168001 2168101 2167981 2167946 2168136 2168156 2168121 2168176 2167976 2168131 2167971 2168141 2167996 2167986 2168141 2168131 2168196 2168036 2168011 2168001 2167951

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300009 299989 299989 300049 300029 299994 300014 299989 300029 300059 300029 300014 300034 300074 300019 300054 300044 300009 300059 300029 300064 300014 299994 300059 300074 300004 300059 300039 300059 300034 299994 300004 300039 300014 299994 300014 300029 300059 299989 300044 300049 300074 300009 300004

Without CO2 faults 1616.586 1632.788 1633.489 1634.297 1642.405 1646.87 1656.32 1667.878 1675.84 1677.159 1677.451 1679.436 1680.75 1681.206 1681.479 1683.234 1687.946 1694.033 1694.872 1696.589 1698.39 1707.908 1709.477 1711.784 1712.965 1717.85 1722.605 1728.207 1730.275 1734.381 1735.364 1746.085 1759.494 1762.573 1768.176 1779.006 1783.795 1787.029 1790.124 1790.765 1794.648 1797.503 1798.544 1798.801

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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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1800.271 1801.554 1802.613 1802.889 1805.689 1805.804 1807.442 1807.742 1808.683 1809.499 1811.018 1811.093 1813.224 1813.377 1816.585 1823.113 1826.082 1826.857 1827.45 1830.696 1831.059 1831.808 1835.523 1837.677 1838.547 1838.65 1843.633 1844.525 1845.244 1847.712 1851.005 1851.261 1856.441 1863.274 1864.342 1864.697 1866.558 1869.952 1870.206 1870.756 1871.844 1871.914 1874.126 1877.399 1881.146 1886.096

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2167966 2168026 2168111 2167941 2167976 2168126 2167966 2168071 2167941 2167971 2168186 2167966 2168066 2168071 2168181 2167961 2168096 2168016 2167971 2168196 2168106 2168026 2167986 2168116 2168051 2167946 2168081 2168011 2167971 2167976 2168131 2168146 2168111 2168026 2168166 2167941 2167981 2168091 2168056 2168106 2168176 2168016 2167986 2168111 2167986 2167976

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299994 300049 300044 300029 300024 300039 300029 300034 299999 300064 299989 300064 300059 300009 300069 299999 299994 300049 300034 300049 300039 300074 300024 300059 300064 300029 300059 300054 300004 300034 300059 300039 300054 300044 299994 300049 300054 300034 300029 300074 300069 300074 300009 300049 300064 299989

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0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

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1888.521 1889.702 1889.763 1892.62 1892.827 1899.381 1900.344 1900.713 1901.797 1903.314 1906.839 1909.805 1911.671 1911.818 1912.753 1912.99 1913.273 1915.364 1917.639 1917.992 1921.319 1926.927 1927.02 1928.485 1930.366 1930.697 1931.612 1931.896 1931.977 1932.823 1935.219 1935.229 1936.848 1941.807 1949.088 1953.152 1953.392 1954.233 1955.73 1957.491 1957.53 1959.289 1960.431 1961.357 1961.536 1962.153

na

2168181 2168106 2167931 2168181 2168141 2168011 2168086 2168161 2167981 2168121 2167931 2167966 2168171 2168171 2168116 2168011 2168181 2168061 2167951 2168011 2168201 2168101 2168101 2168191 2168151 2168206 2168011 2168161 2168041 2168201 2168056 2168206 2168081 2167946 2168131 2168001 2168096 2168021 2167931 2168041 2167966 2168106 2167961 2168046 2168006 2168146

Jo ur

300009 300054 300064 299999 300044 300009 299989 300069 300009 300029 300054 300034 299994 299989 300074 300064 299989 300009 299999 299994 300059 300069 300019 300009 299989 300069 299999 300064 300044 299994 300044 300044 300004 300054 300064 299999 300059 299994 300059 300034 300059 300049 300064 300049 300064 300064

of

Journal Pre-proof

39

ro

-p

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

re

lP

1963.286 1964.013 1969.11 1970.119 1970.449 1970.466 1977.404 1977.48 1980.301 1980.318 1980.641 1981.996 1982.451 1986.316 1986.415 1988.517 1990.31 1996.532 1996.768 2001.871 2004.349 2005.734 2007.273 2009.833 2010.083 2011.536 2015.358 2022.161 2030.501 2030.626 2034.896 2039.021 2042.025 2042.479 2047.696 2062.218 2068.177 2070.539 2071.21 2077.317 2081.921 2084.272 2089.939 2090.6 2093.085 2094.458

na

2168201 2167966 2168151 2167951 2168066 2168146 2168196 2167946 2168201 2168121 2167991 2168031 2168016 2167971 2168116 2168006 2168151 2168056 2168186 2168156 2167961 2168136 2168166 2168026 2168066 2168191 2168206 2168086 2168081 2168151 2167971 2168006 2168006 2168091 2168001 2168096 2168141 2168101 2168196 2168076 2168176 2167976 2167956 2168101 2168111 2167966

Jo ur

300049 299999 300059 300044 300064 300059 299989 299999 299989 299989 300049 300044 299999 300009 300054 300074 300064 300054 300014 300064 300054 300069 299989 299999 299989 299989 300014 300009 300064 300004 299999 299989 300009 300069 300014 300069 300069 300064 300014 300044 300064 300009 300054 299989 300039 300074

of

Journal Pre-proof

40

ro

-p

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

re

lP

2108.616 2113.021 2120.094 2135.896 2138.698 2140.387 2156.088 2170.469 2172.113 2172.538 2183.854 2189.186 2194.047 2196.789 2198.213 2203.717 2205.64 2208.713 2214.345 2222.776 2224.31 2237.995 2239.335 2251.917 2260.001 2266.54 2271.833 2279.283 2289.24 2289.405 2290.535 2294.219 2297.261 2300.307 2308.338 2310.969 2312.438 2329.375 2330.428 2343.641 2344.445 2345.953 2346.86 2348.819 2349.167 2353.799

na

2167986 2168206 2168186 2168086 2167946 2167981 2168111 2168061 2168071 2168206 2168096 2167991 2168036 2168071 2168111 2168171 2168096 2168046 2168161 2168001 2168206 2167931 2168066 2168051 2168081 2167931 2168136 2168141 2168206 2167931 2168046 2168146 2168181 2168186 2168151 2168041 2168041 2167966 2168011 2167956 2168071 2168091 2167971 2167991 2168201 2167971

Jo ur

300069 300054 300069 300064 300064 300024 300059 300054 300069 300074 300054 299999 299999 300004 300069 300034 299989 300064 300004 299989 299994 300004 300069 300054 300054 300029 300059 300064 300049 300049 300069 300044 299994 300039 300069 300039 299999 300004 300004 300059 300059 300039 299994 300004 300014 300029

of

Journal Pre-proof

41

ro

-p

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

re

lP

2354.153 2356.226 2362.664 2364.039 2366.423 2366.635 2382.239 2384.375 2386.019 2402.67 2418.618 2425.732 2441.007 2446.52 2447.504 2455.285 2460.533 2461.649 2467.667 2472.582 2476.722 2479.496 2486.911 2488.813 2498.488 2508.634 2509.08 2517.158 2523.144 2536.036 2537.501 2537.588 2540.194 2540.805 2546.78 2557.099 2566.157 2575.189 2580.659 2581.541 2581.849 2582.846 2601.226 2608.102 2609.779 2615.862

na

2168061 2168146 2168186 2168001 2168041 2168006 2167996 2167961 2168156 2168101 2168106 2168006 2168051 2168036 2167991 2167991 2168176 2168136 2168061 2167986 2168181 2168171 2168036 2168161 2168131 2168121 2167951 2168016 2167996 2167951 2168071 2168181 2167976 2167941 2168126 2168131 2167946 2167961 2168176 2167986 2168076 2168036 2168151 2168001 2168071 2168136

Jo ur

300069 300069 300064 299994 299994 299994 300004 300004 299989 299999 300064 300019 300074 300009 300024 299994 300009 299994 300044 299999 300014 300064 300014 300054 299999 300074 300059 300044 300064 300054 300054 300034 300064 300054 300064 299994 300004 300074 299989 300044 300049 300004 300039 300004 300074 300064

of

Journal Pre-proof

42

ro

-p

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

re

lP

2616.743 2622.054 2626.986 2628.895 2637.781 2638.603 2642.803 2646.327 2646.663 2647.879 2649.604 2654.879 2655.196 2660.052 2662.494 2663.395 2677.056 2678.47 2678.863 2688.982 2701.935 2715.185 2716.359 2731.394 2732.877 2734.834 2749.223 2751.245 2756.62 2757.898 2762.927 2763.606 2763.615 2764.319 2766.971 2769.244 2772.841 2791.193 2795.445 2796.171 2797.675 2802.197 2806.181 2812.716 2827.73 2841.333

na

2167986 2167941 2167996 2168021 2167951 2168136 2168016 2168111 2168196 2168026 2168061 2168006 2168156 2168116 2167996 2168086 2168056 2168011 2168026 2167976 2168121 2167941 2168191 2168141 2168021 2168056 2168191 2167951 2168006 2168136 2167956 2168086 2168056 2167956 2168191 2168056 2168186 2168031 2168106 2168081 2168131 2168116 2167976 2167981 2168021 2168201

Jo ur

300074 300074 299989 299999 300074 300074 300009 300074 300069 300059 300074 300044 300004 300069 300069 300034 300034 300019 300009 299994 299999 300009 300049 300074 300009 300009 300029 300009 300069 300004 300004 300049 300064 300074 300004 300004 299999 300059 299999 300069 300074 300044 299999 300074 300044 300074

of

Journal Pre-proof

43

ro

-p

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

re

lP

2851.35 2871.173 2878.982 2885.934 2899.561 2904.193 2909.096 2922.137 2930.844 2935.648 2935.804 2937.188 2937.607 2938.333 2941.928 2950.415 2951.68 2951.853 2954.64 2959.642 2973.227 2973.462 2982.092 2982.171 2982.869 2985.384 2986.556 2988.722 2988.775 2989.182 2993.69 2994.507 2997.752 2999.526 3001.711 3005.31 3008.428 3011.599 3014.719 3014.966 3020.631 3023.333 3029.156 3041.538 3043.222 3043.433

na

2168051 2168066 2168196 2168021 2168001 2167941 2168056 2167996 2168171 2168201 2168196 2168101 2168176 2168151 2168131 2168111 2168146 2168126 2168166 2168001 2168076 2168156 2168026 2168116 2168196 2168061 2168061 2168056 2167931 2168161 2168176 2168181 2167981 2168031 2167946 2168011 2168206 2168026 2168206 2168136 2168016 2168071 2168116 2167931 2168121 2168071

Jo ur

299989 300074 300009 300074 300019 300004 299999 299994 300054 300024 300074 300039 300014 300044 300024 300064 300004 300049 300069 300024 299999 299999 300054 299994 300059 299989 300064 300074 300044 299994 300034 300004 299999 300039 300049 300049 300029 300069 300009 300049 300004 299999 300049 299999 299994 300064

of

Journal Pre-proof

44

ro

-p

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

re

lP

3046.667 3047.398 3051.341 3055.575 3058.691 3062.258 3066.023 3067.139 3072.271 3084.874 3086.081 3086.627 3088.031 3092.515 3095.319 3098.566 3099.234 3099.823 3102.353 3109.179 3111.414 3111.736 3115.698 3118.131 3120.532 3121.005 3123.673 3127.872 3128.233 3129.026 3134.394 3136.835 3140.376 3142.799 3149.811 3152.957 3154.038 3154.663 3162.739 3164.369 3169.922 3172.224 3174.856 3178.305 3181.208 3181.221

na

2168181 2168026 2168141 2167976 2168096 2167996 2168041 2168076 2168151 2168106 2168081 2168026 2168011 2168191 2168116 2168186 2168096 2167931 2167981 2168156 2167941 2168171 2168111 2168181 2167946 2168171 2168156 2168016 2167961 2168111 2168146 2167941 2168131 2168201 2168146 2168101 2168131 2168061 2167996 2168196 2168021 2168186 2167986 2168051 2167941 2168176

Jo ur

300039 300039 300054 300044 300034 300024 300009 299994 299999 300044 299994 300014 300069 299999 300064 300049 300009 300074 300049 300069 300059 300039 299989 300064 300044 300009 300044 300064 300009 300009 299999 300044 300049 300029 300074 299994 300054 300059 299999 299999 300004 300024 299989 300069 300064 300019

of

Journal Pre-proof

45

ro

-p

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

re

lP

3182.32 3187.547 3190.042 3191.157 3191.184 3192.501 3193.917 3194.161 3197.57 3198.91 3199.437 3210.049 3213.432 3213.502 3214.845 3215.356 3221.036 3221.17 3221.234 3227.608 3231.702 3232.743 3234.791 3241.106 3241.498 3244.219 3245.179 3247.477 3249.318 3253.42 3256.027 3260.621 3261.283 3261.441 3263.388 3263.686 3269.542 3270.151 3277.333 3279.397 3280.255 3285.396 3291.149 3292.024 3292.823 3293.269

na

2168191 2168021 2167981 2168206 2168196 2168191 2168021 2167971 2167981 2168031 2168056 2168061 2168186 2168191 2168021 2168071 2167991 2168061 2167981 2168196 2168086 2168026 2168201 2168121 2167931 2168201 2168186 2168046 2168191 2168016 2167971 2168036 2168071 2168201 2167956 2168096 2168151 2168006 2168206 2168201 2167966 2168021 2167946 2168171 2168186 2167961

Jo ur

300039 300054 300069 299999 299994 300064 300069 300059 299989 300054 299989 300004 299994 300044 300059 299994 299989 300039 299994 300029 300039 300064 300064 300069 300039 299999 300009 300074 300074 300054 300074 300054 299989 300034 300049 299999 300054 300004 300024 300009 300049 300039 300009 300014 300029 300049

of

Journal Pre-proof

46

ro

-p

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

re

lP

3295.608 3299.934 3301.987 3302.675 3306.669 3307.291 3328.084 3342.87 3343.28 3346.93 3348.338 3353.568 3360.232 3361.933 3369.802 3370.07 3376.762 3383.867 3394.131 3399.112 3408.588 3410.875 3416.138 3418.299 3422.829 3424.603 3428.031 3428.105 3428.188 3428.225 3428.262 3429.691 3430.162 3430.662 3431.98 3433.006 3433.193 3433.352 3433.473 3433.811 3434.039 3435.183 3436.455 3436.808 3438.005 3438.455

na

2168111 2167961 2168031 2168086 2168181 2168086 2168021 2168026 2167951 2168101 2168016 2168171 2168151 2168126 2168161 2168086 2168056 2168171 2167956 2168006 2168036 2168171 2168056 2168111 2168146 2168196 2168156 2168206 2168096 2167956 2167971 2168116 2168156 2168051 2168176 2168196 2167966 2168141 2167951 2167941 2167931 2168121 2167931 2168036 2168206 2168206

Jo ur

300019 300069 300069 300069 300059 300044 300014 300004 300049 300049 300069 300004 300074 300054 300049 300014 300069 300019 300039 300024 300069 300044 300059 299999 300054 300039 300054 300039 300064 300044 300069 300009 300049 300059 300004 300024 300009 300004 300039 300069 300034 300009 300069 300064 300019 300004

of

Journal Pre-proof

47

ro

-p

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

re

lP

3439.808 3441.485 3442.923 3443.34 3443.444 3445.945 3447.073 3447.831 3447.842 3448.548 3449.365 3451.261 3451.466 3451.893 3453.737 3453.974 3454.681 3455.373 3457.638 3457.823 3458.611 3460.01 3460.852 3461.173 3461.302 3462.915 3463.59 3464.657 3465.005 3465.781 3466.227 3466.835 3466.945 3467.069 3468.433 3468.628 3470.172 3470.307 3471.456 3471.787 3473.088 3474.014 3476.334 3477.442 3478.961 3479.353

na

2168101 2168206 2168001 2167961 2168051 2167946 2167971 2168041 2168036 2168181 2168031 2168136 2167946 2168141 2167956 2168106 2168176 2167946 2168191 2167966 2168041 2168161 2168161 2168056 2167986 2168186 2168146 2167966 2168176 2167961 2168036 2168196 2168181 2168186 2167971 2168041 2167931 2168091 2168061 2168011 2168031 2168091 2168041 2168171 2168151 2168181

Jo ur

300044 300064 300044 300039 299999 300074 300044 300064 300074 300074 300074 300054 300039 300049 300009 299994 300059 300069 300034 300044 300004 300059 300074 300039 299994 300004 300049 300069 300074 300044 300059 300004 300049 300044 300049 300069 300009 300009 300034 300014 300064 300064 300074 300059 300009 300024

of

Journal Pre-proof

48

ro

-p

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

re

lP

3486.001 3486.65 3488.039 3488.833 3491.777 3494.142 3495.021 3495.833 3501.042 3501.679 3506.241 3507.039 3507.129 3507.682 3508.32 3513.651 3514.491 3515.116 3515.458 3515.6 3515.744 3520.157 3522.259 3524.74 3565.029 3582.825 3617.048 3640.374 3781.621 3868.06 3950.879 4048.07 4223.946 4225.264 4252.624 4270.2 4286.616 4340.478 4561.69 4776.292 5544.003 5588.354 6845.645 11286.93

na

2168186 2168071 2168201 2168006 2168016 2167956 2168116 2168181 2168096 2168091 2168166 2168176 2168176 2168171 2168176 2168186 2168201 2168201 2168151 2168176 2167941 2168161 2168181 2168196 2168206 2168141 2168191 2168041 2168156 2168091 2168046 2168041 2168136 2168141 2168146 2168146 2168161 2168121 2168156 2168151 2168131 2168101 2168106 2168141

Jo ur

300034 300044 300019 300014 300014 300069 299989 300044 300049 300044 300074 300029 300044 300029 300039 300074 300004 300039 300049 300054 300039 299999 300029 300034 300034 300024 300024 300059 300074 300014 300059 300054 300009 300009 300024 300009 300014 300019 300014 300024 300019 300014 300014 300014

of

Journal Pre-proof

49

Journal Pre-proof

ro

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

-p

CO2 ORT 234.362 391.8999 391.9795 392.964 405.0967 425.45 438.8697 520.5204 526.2678 538.1046 539.8513 560.9192 572.8805 597.8368 802.1051 861.3291 1026.689 1044.697 1199.143 1364.767

re

2168076 2168046 2168051 2168066 2168056 2168071 2168046 2168151 2167936 2168061 2168041 2167936 2168126 2168036 2168046 2167936 2168031 2168166 2167936 2168126

lP

Y 300024 300024 300024 300024 300024 300024 300019 300019 300019 300024 300019 299994 300024 300019 300029 300024 300019 300034 300014 299989

na

X

of

Group A ACN CO2 ORT faults

Group B ACN CO2 ORT faults Y 300019 300019 300019 300034 300059 300014 300044 300009 300049 300034 300054 300019 300009 300029 300004 300004 300064 300074

CO2 1676.406 1754.609 1830.683 1867.445 1872.673 1932.705 1986.368 2012.555 2050.795 2121.835 2253.309 2264.5 2274.831 2302.116 2411.504 2432.499 2506.739 2532.296

Jo ur

X

2168136 2168156 2168161 2167936 2167936 2168046 2168046 2168126 2167936 2168046 2167936 2168146 2168046 2167936 2168126 2167936 2167936 2167936

ORT 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 50

Group A ACN CO2 NE faults

300024 300019 300019 300019 300029 300019 300019 300024 300024 300024 300034 300019 300019 300024 300024 300019 300029 300024 300029 300024 300034

CO2 2168091 2168051 2168066 2168056 2168141 2168076 2167941 2168066 2168056 2168096 2168146 2168081 2167936 2168061 2168021 2168086 2168101 2167946 2168066 2168116 2168116

292.0329 319.5718 353.726 364.6775 391.654 391.7811 392.573 392.964 405.0967 409.0628 482.3307 485.6227 526.2678 538.1046 569.6289 576.9324 592.885 618.223 634.2493 656.2502 815.3839

na

Y

Jo ur

X

ro

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1

-p

2543.429 2584.272 2605.725 2615.223 2624.799 2700.804 2950.353 3082.818 3083.248 3151.879 3158.25 3223.952 3249.223 3282.532 3370.467 3451.439 3475.462 3478.269 3692.195 4502.162 181764

re

2167936 2168126 2168166 2168126 2168166 2168046 2167936 2168046 2168141 2168166 2167936 2168166 2168166 2167936 2167936 2168166 2168046 2168166 2168166 2168126 2168126

lP

299999 299999 300004 299994 300019 300004 300009 300039 300019 299999 300069 300029 300039 300044 300039 300009 299999 300024 300014 300019 300014

of

Journal Pre-proof

NE 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 51

Journal Pre-proof 2168116 2168066 2168111 2168206 2168161 2168071 2168026 2168031 2168151 2168061 2168121 2167951 2168026

849.5044 855.92 968.6455 996.5646 999.3557 1020.633 1042.131 1157.691 1218.697 1468.039 1566.426 1578.099 1612.487

2 2 2 2 2 2 2 2 2 2 2 2 2

of

300029 300014 300034 300059 300034 300014 300029 300034 300034 300014 300059 300029 299989

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

-p

1622.749 1694.83 1708.837 1744.189 1790.737 1827.457 1857.978 1864.709 1868.739 1871.225 1875.881 1902.896 1909.238 1915.243 1919.833 1932.705 1935.336 1961.103 1991.595 1991.64 2004.573 2012.555 2028.486 2058.087 2121.928 2123.053 2146.53 2167.361 2198.51

re

2168121 2168066 2167961 2168116 2168066 2168066 2168001 2167956 2168026 2168016 2167996 2168171 2167931 2168106 2168166 2168046 2168056 2168121 2168106 2168066 2168066 2168126 2168171 2168046 2168111 2168201 2168121 2168031 2168061

NE

lP

300039 300049 300034 300039 300009 299994 300049 300029 299994 300019 300049 300049 300014 300034 300064 300014 300014 300054 300004 300054 300044 300009 300069 299989 300004 300069 300044 300009 300029

CO2

na

Y

Jo ur

X

ro

Group B ACN CO2 NE faults

52

Journal Pre-proof

ro

of

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

-p

lP

re

2248.723 2268.051 2307.574 2330.039 2345.977 2470.856 2484.436 2563.751 2611.228 2662.429 2665.33 2727.958 2728.087 2730.968 2743.844 2895.179 2957.822 3020.079 3035.124 3036.205 3062.123 3071.784 3074.755 3086.365 3088.601 3131.928 3132.528 3134.131 3141.683 3167.978 3186.472 3201.373 3203.61 3253.866 3254.82 3277.473 3283.117 3315.642 3326.652 3330.015 3427.914 3430.978 3435.061 3437.822 3440.342 3445.895

na

2168121 2168066 2168106 2168091 2168046 2168006 2168116 2168156 2168041 2168066 2168191 2168021 2168066 2168186 2168011 2168086 2168031 2167991 2168196 2167986 2168031 2167981 2168126 2168176 2168121 2168051 2168031 2168051 2168201 2168166 2168036 2168046 2168101 2168166 2167966 2168106 2168096 2168166 2168121 2168016 2168196 2168171 2168191 2168036 2168161 2168116

Jo ur

300004 300004 299989 300004 299994 300054 300004 300039 300049 300034 300054 300064 299999 300054 300059 299999 299999 300044 300054 300039 300004 300039 300069 300049 300049 300004 300014 300009 300054 300054 300039 300054 300009 300044 300039 300009 300004 300049 300064 300059 300064 300074 300059 300044 300044 299999

53

Journal Pre-proof

Group A ACN CO2 NW faults

300024 300019 300024 300019 300024 300019 300024 300024 300024 300034 300019 300034 300019 300019 300019 300024 300029 300024 300029 300029 300034

2168086 2168071 2168036 2168051 2168081 2168066 2168051 2168031 2168066 2168001 2168046 2168011 2168081 2168151 2168041 2168026 2168021 2168106 2168101 2168081 2167996

CO2 162.3159 187.9241 239.4922 319.5718 341.3139 353.726 391.9795 392.7883 392.964 424.657 438.8697 456.2895 485.6227 520.5204 539.8513 546.9658 581.0135 585.6545 592.885 608.7298 626.619

na

Y

Jo ur

X

ro

of

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

-p

3454.494 3455.108 3460.012 3475.462 3479.18 3485.781 3489.834 3509.765 3513.209 3672.46 3751.843 4059.025 4137.764 4259.857 5878.411 7561.094 15893.71 17054.57 30121.1 127819.5 129396.6

re

2168066 2168161 2168101 2168046 2168076 2168166 2168181 2168071 2168111 2168126 2168186 2168116 2168136 2168131 2168161 2168111 2168136 2168156 2168116 2168131 2168121

lP

300039 300039 300004 299999 300039 300059 300054 300039 299994 300074 300059 300019 300024 300009 300029 300014 300014 300024 300014 300014 300014

NW 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 54

Journal Pre-proof 2168051 2167996 2168016 2168001 2168091 2168071 2167996 2168051 2168051 2168076 2167986 2167981 2168091

635.0508 867.7784 926.4806 955.5576 1001.434 1020.633 1126.184 1379.796 1442.489 1519.895 1585.509 1590.271 1591.381

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CO2 1638.643 1638.785 1664.113 1676.406 1690.597 1692.994 1697.118 1700.78 1719.962 1754.609 1766.657 1769.539 1775.607 1797.836 1818.583 1830.683 1839.81 1875.881 1880.519 1883.587 1931.933 1936.539 1937.677 1947.671 1958.971 1982.443 2000.813 2004.573 2081.245

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Group B ACN CO2 NW faults

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2149.475 2175.346 2236.922 2264.5 2304.575 2304.602 2363.105 2385.193 2403.866 2442.045 2470.856 2476.669 2493.181 2525.68 2553.667 2570.751 2600.263 2605.725 2624.799 2627.285 2780.256 2788.466 2789.325 2807.954 2825.112 2843.965 2922.085 2923.705 2929.709 2941.321 2941.531 2958.253 3020.079 3020.479 3032.669 3034.822 3036.205 3043.734 3053.101 3059.893 3068.811 3071.784 3083.248 3086.84 3243.945 3252.964

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Journal Pre-proof Author Statement Mariana Patricia Jácome Paz, fieldwork,conceptualization, methodology, writing original draft. Irving Antonio Gonzalez Romo, fieldwork, conceptualization and methodogy. Rosa

María

Prol

Ledesma,

project

administration,

resources,

validation,

conceptualization

Daniel Pérez Zárate, fieldwork, methodology, review.

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Aurora Estrada Murillo, fieldwork, methodology.

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Augusto Rodríguez, fieldwork, methodology, review.

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Marco Antonio Torres Vera, conceptualization, methodology, review

66

Journal Pre-proof Highlights 

Quantitative relationships between soil gas flux and faults systems in Agua CalienteTzitzio geothermal system were analyzed statistically.



Exploratory Data Analysis (EDA) and Multivariate Analysis of Variance (MANOVA) were implemented, for the first time, to statistically describe the relation between the location of degassing features and the presence of some local fault systems. The main results indicate that the hydrothermal discharge in Agua Caliente-Tzitzio does

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not occur along the entire length of any given fault but instead it is focused in small

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areas. This implies a complex permeability distribution related to the fault system, where hydrothermal flow occurs via preferential pathways consisting of well-

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interconnected fault networks. Conceptual model was done to illustrate this.

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

67