Spatial analysis and modelling of glaciogenic geochemical dispersion – Implication for mineral exploration in Finland

Journal of African Earth Sciences xxx (2016) 1e11

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Spatial analysis and modelling of glaciogenic geochemical dispersion e Implication for mineral exploration in Finland €nen Pertti Sarala*, Vesa Nyka Geological Survey of Finland, P.O. Box 77, FIN-96101, Rovaniemi, Finland

a r t i c l e i n f o

a b s t r a c t

Article history: Received 23 June 2016 Received in revised form 28 November 2016 Accepted 1 December 2016 Available online xxx

Spatial modelling for prospectivity mapping involves the integration of various geoscientific digital map data. It is essential that the quality of data is high class and that the geological processes involved are well understood. Effects of glacial dynamics and glaciogenic geochemical dispersion need to be taken into consideration when using till geochemistry as one of such input dataset for prospectivity modelling. This paper investigates this issue by developing a fuzzy logic prospectivity model that integrates airborne geophysical data with two different till geochemical datasets. First we use the original geochemical sample set and secondly a spatially corrected dataset that is based on the knowledge of glacial dynamics on the regional scale within the study area. The effect of this correction is tested by comparing the modelling results of both cases using the receiver operating characteristics (ROC) technique to validate the models by using the location of known gold deposits represented by exploration drilling. This study confirms that by taking into consideration the transport caused by the glaciation we can significantly improve the performance of a prospectivity model using till geochemical data. © 2016 Published by Elsevier Ltd.

Keywords: Glacial dispersion Geochemistry Till Spatial modelling Prospectivity Exploration Gold Finland

1. Introduction Geochemical exploration in glaciated terrains is based on the tracing possible sources or source areas of indicator elements using glacial dispersion models. There are numerous factors that influence the processes and transport distances of indicator elements in the glacial environment. However, glaciomorphology and certain glaciogenic morphology areas (like drumlin fields and ribbed moraine areas) are usable as an indicator of the glaciogenic processes during the erosion, transport and deposition. This knowledge is applicable in tracing the source(s) of mineralized material (i.e. boulders, heavy minerals and till fines) using the glacial till (Aario and Peuraniemi, 1992; Sarala et al., 2007b; Sarala and Ojala, 2008; Sarala, 2015). Glacial till is an effective sample media in mineral exploration because glacially eroded and transported material can be dispersed significant distances from the source(s) in the down-ice direction. Till can also be a mixture of glacially drifted sediments, pre-glacial weathered bedrock and/or fresh bedrock reflecting the

* Corresponding author. E-mail addresses: pertti.sarala@gtk.fi €nen). (V. Nyka

(P.

Sarala),

vesa.nykanen@gtk.fi

composition of source rocks in bedrock hosting mineralization. Till sampling is typically carried out using percussion drilling with variable sampling depths (i.e. till samples from different stratigraphical units) or having constant depth range like in the case of Finnish regional till geochemical data (Salminen, 1995). It is essential to understand the stratigraphic control and the deposition history of any sample material to get as homogeneous as possible dataset for geochemical analyses (Salminen and Hartikainen, 1985; Kauranne et al., 1992; Sarala and Peuraniemi, 2007). By using this information as a tool for interpreting glacial dispersion and transport distances it is possible to trace back to the original sources or source areas of any mineralized till material. The aim of this paper is to demonstrate how the use of till geochemical data can be improved as an input to mineral prospectivity mapping. Glacial till deposits were formed during the evolution of glaciers that existed and moved in Northern Hemisphere during the Quaternary period. Therefore, the present till cover is a mixture of till material eroded and deposited during several phases, but having the most influence from the latest glacial movement and transportation. To use such datasets in mineral exploration, it is crucial to understand the glaciogenic history and have some understanding of the glacial dynamics within the exploration area in question.

http://dx.doi.org/10.1016/j.jafrearsci.2016.12.002 1464-343X/© 2016 Published by Elsevier Ltd.

€nen, V., Spatial analysis and modelling of glaciogenic geochemical dispersion e Implication for Please cite this article in press as: Sarala, P., Nyka mineral exploration in Finland, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.12.002

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For this research, we estimate the transport distance and direction of till material in a core part of the Kuusamo Ice-lobe, in eastern Finland based on a glacial morphological interpretation (cf. Sarala, 2015). The estimations of movement of till material is assumed to be in the down-ice directions and all till geochemical data coordinates are corrected constrained by the transfer directions and distances. The corrected regional till geochemical data is then used together with high resolution airborne geophysical and mineral deposit data to create fuzzy logic mineral prospectivity models (e.g. Bonham-Carter, 1994; Carranza, 2008) for gold deposits within Kuusamo Belt located in Northern Fennoscandian Shield (Fig. 1). Finally, we tested the performance of the predictive models by comparing the resulting prospectivity maps with the known gold occurrences within the study area and then used exploration drilling data together with the ore deposit database for validation. The Kuusamo area is located in the centre of a large ice-lobe area (Fig. 2), where the glacial morphology indicates a surging type glacial environment formed mainly during the Late Weichselian deglaciation (Sarala, 2007). The large Kuusamo drumlin field in the eastern part of the Kuusamo ice-lobe reflects relatively warm glacial conditions at the marginal part of the glacier in the Koillismaa area. Around Kuusamo, thousands of drumlins and flutings occur, forming the Kuusamo drumlin field (Glückert, 1973; Aario € m, 1979). This drumlin field is one of the largest and Forsstro fields in Finland with an extent over 10,000 km2. The main ice flow direction has been from the northwest to the southeast, but during the formation of the main field ice flow was spread in variable directions (west to east in the north and northwest to southeast in the southern parts). There also is an older drumlin field below the younger one with a west-east orientation indicating an early ice €rvi) from the last deglaciation (Aario and flow phase (Tuoppaja €m, 1979). Forsstro Drumlin formation transport distances of till material and surficial boulders can achieve distances up to 30e50 km, for example in Canada (e.g. McClenaghan and Kjarsgaard, 2007; Paulen et al., 2013) and distances up to 10e15 km have also been documented in Finland (Aario and Peuraniemi, 1992; Hirvas and Nenonen, 1990). However, shorter distances are also common, and based on, for example, the surficial boulder fan research from Finland (Salonen, 1986) the transport distances are 1e5 km in many drumlin fields. The pattern of boulder fans is typically comparable with the form of till fines dispersion fans. This phenomenon is replicated in the regional geochemistry as several kilometres long dispersion fans and a transit of element (like Au, Co, Fe, Mo, Ni) anomalies variable distances to the down-ice direction. 2. Study area and data The study area, Kuusamo Belt, is located in the Northern Fennoscandian shield (Fig. 1) on the boundary between Archaean and Proterozoic map domains. The volcano-sedimentary sequence of the Kuusamo Belt was deposited unconformably on an Archaean granite gneiss complex in a Paleoproterozoic rift environment roughly 2.44e2.05 Ga ago (Vanhanen, 2001). The Kuusamo Belt is composed of mafic volcanic rocks and metasedimentary rocks intruded by several stages of mafic dykes, sills and gabbros (Fig. 3). Kuusamo Belt hosts known gold mineralizations, which have been explored intermittently over the past 30 years or so. This exploration has resulted in the discovery of 20 gold occurrences that have been drilled within the study area (Fig. 3). These include gold-only and gold-cobalt-copper ± uranium occurrences that can be classified as either orogenic gold deposit types with an atypical metal association, iron oxide-copper-gold types, or syngenetic

deposit types (Eilu, 2007). According to Eilu (2007), the timing of the ore forming events has more in common with an orogenic deposit type, while alteration, metal association and the composition of the mineralizing fluids are similar to the IOCG deposit type. Therefore, the classification of the gold deposits in the Kuusamo Belt remains slightly uncertain and therefore we propose to use the term Kuusamo type gold deposit in this paper. High-resolution airborne geophysical datasets that include simultaneous magnetic, radiometric and electromagnetic measurements cover the study area. The geophysical surveys that generated these data were flown at an average 40 m altitude and 200 m line spacing across the entire country of Finland from 1970's to early 2000 (Airo, 2005). In the current study area, the airborne surveys were carried out between 1982 and 1998, with flight altitudes varying from 33 to 44 m. The magnetic data were collected using either proton (1977e1991) or caesium (1992e2004) magnetometers installed either on the rear boom or in the wingtip pods of aircraft. The flight direction of the surveys within the study area was EW or NS. A regional till geochemical survey, covering the entire country of Finland, was conducted in the 1980s (Salminen, 1995). The average sampling density was one sample per 4 km2 and average sampling depth was 1.5 m. The sampling instrument was a portable percussion drill using a through-flow bit. The majority of the samples were collected as a composite of 3e5 subsamples from about 100 m2 area. The samples taken from chemically unaltered parent till were dried and the <0.06 mm fraction sieved for analysis using aqua regia digestion and inductively coupled plasma atomic emission spectrometry (Kontas, 1981; Kontas et al., 1990). The airborne magnetic line data was interpolated by using a minimum curvature interpolation technique with a 50
50 m grid cell size. The geochemical data were interpolated into a 200
200 m grid cell size using an inverse distance weighting (IDW) interpolation technique with a variable search radius and fixed amount of samples (N ¼ 12) to calculate the cell values within the study area. In addition to the original sample data set we also created a corrected sample data set by moving the location of each till sample within the study area roughly 3 km up along the latest ice sheet transportation direction as discussed below. Then we interpolated a new set of grids using the same IDW methodology as for the original till samples. The elements selected for this study were Fe, Co, Ni and Te. First three elements reflect presence of sulphides and Te can be used as a pathfinder element for Au. From the multielement survey we selected those elements whose assays can be considered as statistically reliable (Salminen, 1995) Fig. 4 illustrates the effect of correcting the location of the sample sites according to the assumed glacial dispersion model for Co anomalies in the till. After correcting the anomalies coincide well with the known gold mineralization. 3. Methodology 3.1. Mineral prospectivity mapping Mineral prospectivity mapping involves the mapping of land areas according to mineral favourability (Bonham-Carter, 1994). There are many techniques implemented in geographical information systems (GIS) that can be used for this purpose. The main idea is to integrate spatial data-sets that support the hypothesis of being indicative for a specific mineral deposit type. Bonham-Carter (1994) divided these techniques into two different approaches 1) data-driven and 2) knowledge-driven. A data-driven approach takes into account the location of known mineral deposits or mineral occurrences that can be used to train the models. A knowledge-driven approach is based on expert opinions and can be

€nen, V., Spatial analysis and modelling of glaciogenic geochemical dispersion e Implication for Please cite this article in press as: Sarala, P., Nyka mineral exploration in Finland, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.12.002

€nen / Journal of African Earth Sciences xxx (2016) 1e11 P. Sarala, V. Nyka

Fig. 1. Location of study area. Bedrock map of Fennoscandian Shield modified from Koistinen et al. (2001).

€nen, V., Spatial analysis and modelling of glaciogenic geochemical dispersion e Implication for Please cite this article in press as: Sarala, P., Nyka mineral exploration in Finland, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.12.002

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€nen / Journal of African Earth Sciences xxx (2016) 1e11 P. Sarala, V. Nyka

Fig. 2. a) Location of the study area in middle of the Kuusamo ice lobe together with the b) topographic map of the study area based on the LiDAR data. Known gold occurrences/ deposits are shown as yellow symbols. The ice lobe map was modified after Johansson et al. (2011). The LiDAR and topographic map data are based on the National Land Survey of Finland Topographic Database 03/2015 (National Land Survey open data CC 4.0 licence). (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

used especially in exploration areas lacking prior information on the deposit type in question. Data-driven techniques include weights of evidence, logistic regression, random forests and artificial neural networks, which have been applied to a range of mineral deposits and a variety of scales for prospectivity mapping (e.g., Brown et al., 2000; Carranza and Hale, 2001a; Harris et al., 2001; 2006; Brown et al., 2003a,b; €nen and Salmirinne, 2007; Sarala Partington et al., 2006; Nyka €nen et al., 2008b; et al., 2007a; Carranza et al., 2008; Nyka Debba et al., 2009; Fallon et al., 2010; He et al., 2010; Partington, 2010; Porwal et al., 2010; Arias et al., 2011; Ziaii et al., 2011; Zuo, 2011; Abedi and Norouzi, 2012; Wang et al., 2012; Hayward et al., 2013; Mejía-Herrera et al., 2015; Carranza and Laborte, 2015). As summarized by Carranza (2008), knowledge-driven prospectivity modelling techniques include fuzzy logic, evidential belief functions, Dempster-Shafer models and decision tree techniques. These all require parameters that are estimated by an expert. For this study, we used the fuzzy logic technique, which has also been widely used for prospectivity analysis for various commodities at a variety of scales and resolution (e.g., Brown et al., 2000; D'Ercole et al., 2000; Knox-Robinson, 2000; Carranza and Hale, 2001b; Brown et al., 2003b; Porwal et al., 2003; €nen et al., 2007; 2008a; 2008b; Nyka €nen and Salmirinne, Nyka 2007; Carranza et al., 2009; Lusty et al., 2009; Zuo et al., 2009; Gonzalez-Alvarez et al., 2010; Costa e Silva et al., 2012; Joly et al., 2012; Lisitsin et al., 2013; Lusty et al., 2012; Yousefi et al., 2013; €nen et al., 2015). Nyka The fuzzy logic technique is based on fuzzy-set theory, which was first introduced by Zadeh (1965). It is a flexible method for imitating the decision making process during mineral exploration. In fuzzy-set theory the membership of a set is defined on a continuous scale from full membership to full non-membership (e.g. from prospective to non-prospective or favourable to non-

favourable) allowing partial membership of a set. A fuzzy set of A is a set of ordered pairs:

A ¼ f½x; mAðxÞjx2Xg

(1)

where X is a collection of objects and mA(x) is the membership function of x in A. This means that mA(x) defines the degree of membership of x in A. This membership function can be linear or non-linear. The first phase of a prospectivity analysis workflow starts with the definition of an exploration model, which can be based on mineral systems models (e.g. McCuaig et al., 2010). It forms the basis for the selection of the evidential (supporting) datasets. On the other hand, a simple exploration model can also be based on €nen et al., 2015). Via these practical exploration expertise (e.g. Nyka models we are constraining the predictive data to map proxies to the physiochemical conditions that were present during ore formation. By this way, we can find other areas where mineralisation may have formed. The selected data are pre-processed into meaningful map patterns, i.e. evidential maps. In this study, map data were first selected based on previous studies on orogenic gold €nen and Salmirinne, 2007; prospectivity in Central Lapland (Nyka Nyk€ anen et al., 2008b) and then each map was rescaled from zero to one (from non-prospective to prospective) based on subjective expert opinions. The exploration criteria and derived data sets are summarized in Table 1. The fuzzy membership function (2) adopted from Tsoukalas and Uhrig (1997) was used for re-scaling both the geochemical and geophysical data sets listed in Table 2

mðxÞ ¼ 1=ð1 þ ðx=f2Þ∧ðf1ÞÞ

(2)

where f1 ¼ spread (range from 1 to 10) and f2 ¼ midpoint (range from min to max of input data). For the geophysical datasets we

€nen, V., Spatial analysis and modelling of glaciogenic geochemical dispersion e Implication for Please cite this article in press as: Sarala, P., Nyka mineral exploration in Finland, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.12.002

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Fig. 3. Regional scale bedrock map of the study area. Map is modified from Bedrock of FinlandeDigiKP.

€nen, V., Spatial analysis and modelling of glaciogenic geochemical dispersion e Implication for Please cite this article in press as: Sarala, P., Nyka mineral exploration in Finland, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.12.002

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€nen / Journal of African Earth Sciences xxx (2016) 1e11 P. Sarala, V. Nyka

Fig. 4. Till geochemistry. A) Original B) Shifted.

used a negative spread value (ascending) and for the geochemical data positive (descending), respectively. The spread parameter defines the shape of the function and midpoint defines the fuzzy membership value of 0.5 within the input data range. For a

midpoint value we selected the mean value of each data set. Spread was set at 3e8 so the functions had moderate steepness. By changing these values it is possible to create several input maps to be tested in the data integration process.

€nen, V., Spatial analysis and modelling of glaciogenic geochemical dispersion e Implication for Please cite this article in press as: Sarala, P., Nyka mineral exploration in Finland, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.12.002

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Table 1 Exploration criteria and input data. Exploration criteria

Data set

Derived data set

Sulphide mineralization present Gold mineralization present Alteration zones

Regional till geochemistry (1 sample per 4 square km) Regional till geochemistry (1 sample per 4 square km) High resolution airborne geophysics (200 m line spacing, 40 m flight altitude) High resolution airborne geophysics (200 m line spacing, 40 m flight altitude)

Interpolated grids (IDW, 200
200 m cell size) Fe, Co and Ni Interpolated grids (IDW, 200
200 m cell size Te Interpolated grid (Spline, 50
50 m cell size) Electromagnetic Interpolated grid (Spline, 50
50 m cell size) Magnetic field total intensity

Structural control (pathways, traps etc.)

Table 2 Input data-sets and key parameters. Spread and midpoint are referring to parameter f1 and f2 in Eq. (2), respectively. Dataset

Data range

Midpoint

Spread

Co in till (ppm) Fe in till (ppm) Ni in till (ppm) Te in till (ppm) Apparent resistivity Airborne magnetic residual (nT)

1e80 4800e124000 5e304 0.001e0.889 712e2123 5307e7463

8.1 16934 19 0.009 888 24

5 5 5 3 5 8

3.2. Model validation Validation of a predictive spatial model can be done in many different ways. There are several well-established procedures used

for validating a mineral prospectivity map. These include e.g. crossvalidation (Agterberg and Bonham-Carter, 2005; Chung and Fabbri, 2008; Fabbri and Chung, 2008) and jack-knifing (Bonham-Carter, €nen and Salmirinne, 2007). The mutual condition for 1994; Nyka all these techniques is that a set of known mineral occurrences that were not used as input to a model is used as an independent variable for testing the performance of that model. When dealing with a knowledge-driven technique, like fuzzy logic, we can use all the known mineral occurrences within our study area for validation. The exploration drilling sites provided an accurate location for gold occurrences within our study area and assured the reliability of the validation procedure. The receiver operating characteristics (ROC) technique (Obuchowski, 2003) was used both to statistically validate the predictive abilities of the prospectivity models and to quantify the

Fig. 5. Flow chart of fuzzy logic prospectivity model.

€nen, V., Spatial analysis and modelling of glaciogenic geochemical dispersion e Implication for Please cite this article in press as: Sarala, P., Nyka mineral exploration in Finland, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.12.002

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effect of correcting the location of till geochemical anomalies on the €nen modelling outcomes. As Robinson and Larkins (2007), Nyka (2008) and Nyk€ anen et al. (2015) demonstrated the ROC technique can be used to statistically test the validity of a spatial predictive model. Especially machine learning and data mining research have been using this technique increasingly (Fawcett, 2006). This validation technique requires the known locations of the modelled phenomena. These locations represent “true positive” sites (i.e. the locations of known mineral occurrences or deposits). In addition, a set of “true negative” sites representing areas where no mineral occurrences are found is required to generate the €nen et al. receiver operating characteristics (ROC) curves. Nyka (2015) suggested using the locations of other deposit types or randomly selected locations within the study area. In the latter case, these points do not represent real “true negative” sites but €nen et al., 2015). rather a set of random points (Nyka 4. Results The rescaled evidential maps listed in Tables 1 and 2 were integrated in a simple combination using fuzzy operators (BonhamCarter, 1994) to produce a single prospectivity map. This map defines the most favourable areas for Kuusamo type gold deposits taking into account the selected input maps. The model is documented in a flow-chart describing the data sets and the operators in

Fig. 5. We used two different operators: 1) The ‘fuzzy and’ operator is equivalent to logical intersection in GIS terminology. It returns the minimum value of the inputs in each location and is also called ‘minimum-operator’ (Bonham-Carter, 1994). The ‘fuzzy and’ operator restricts the most favourable areas into narrow zones where all the selected element concentrations are considered as favourable. This is useful with the sparse geochemical data but can also lead into false negative results due to spatially incomplete data. 2) The ‘fuzzy gamma’ operator is combination of ‘fuzzy algebraic sum’ and ‘fuzzy algebraic product’ (Bonham-Carter, 1994). The gamma value used in this paper varied from 0.5 to 0.7. We combined the geochemical data reflecting sulphides (Co, Fe, Ni) using Fuzzy Gamma operator (gamma value 0.5) and presence of gold (Te as proxy) using Fuzzy AND operator to produce the geochemical evidence map. The same combination was done for both the original anomaly maps and for the shifted anomaly maps. The geophysical data (magnetic and electromagnetic) were combined together using Fuzzy Gamma operator with gamma value of 0.7. The final combination of these two evidence maps, geochemical and geophysical, was achieved using Fuzzy Gamma operator with gamma value of 0.65.(see Fig. 6) The locations of the known Kuusamo type gold deposits were used to validate the model results. We selected drilling data from databases including both drilling conducted by GTK and Outokumpu company from early 1960's to present. We used first the

Fig. 6. Fuzzy logic prospectivity map.

€nen, V., Spatial analysis and modelling of glaciogenic geochemical dispersion e Implication for Please cite this article in press as: Sarala, P., Nyka mineral exploration in Finland, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.12.002

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Fig. 7. ROC validation. Black line (chance diagonal) has an AUC value of 0.5.

location of 465 gold exploration drilling sites for validation as true positive sites. In addition we calculated ROC curves and AUC values using gold cut off value of 0.1 ppm (135 sites) and 1 ppm (24 sites), respectively. Also an equal number of random points generated for each run within the study area were used as true negative sites. To avoid bias due to number of either true positive or true negative sites we used the same number of both. A ROC curve is a plot of the sensitivity (true positive rate: TP/(TP þ FN)) on the y-axis compared with 1 - specificity (false positive rate: FP/(FP þ TN)) on the x-axis. The area under a ROC curve (AUC) can be used as a measure of the accuracy of a diagnostic test and can also be used to measure the performance of a spatial predictive model, as in this paper. The AUC values may vary from 0 to 1, with an AUC value of 1 indicating the result is perfectly accurate having a sensitivity value of 1 and a 1Table 3 AUC values for original vs. shifted geochemical data and the resulting fuzzy gamma combination maps.

Original Geochemistry Shifted Geochemistry Fuzzy Gamma 1 Fuzzy Gamma 1 shifted

All drilling N ¼ 465

Au cut off 0.1 ppm N ¼ 135

Au cut off 1 ppm N ¼ 24

0.54 0.70 0.60 0.75

0.58 0.71 0.68 0.89

0.71 0.73 0.63 0.83

specificity value of 0. A totally random model would result in an AUC value of 0.5 and the curve would follow the chance diagonal. The ROC curve and AUC calculations were made by using an inhouse built Python code. The AUC values of the shifted or corrected geochemistry (AUC ¼ 0.7) and resulting fuzzy gamma model (AUC ¼ 0.75) are indicating significantly better performance than the original geochemistry (AUC ¼ 0.54) and original fuzzy model (AUC ¼ 0.60), indicating that shifting the geochemistry improved the modelling results (Fig. 7). In addition to using all the gold exploration drilling sites we also used fewer drilling sites by selecting only those with gold assay results indicating presence of gold. As described above, we used two different cut off thresholds, 1 ppm and 0.1 ppm. When the validation sites were selected using these gold cut-off values we achieved higher AUC values than when using all the exploration drilling sites as can be seen in Table 3. This is confirming the relatively good performance of the model, although the uncertainty due to small number of validation sites may be higher. Effect of using random true negative sites may cause more uncertainty, but this was not tested in this research. 5. Discussion and conclusions The quality of spatial mineral prospectivity model is heavily dependent on the quality of the data used for modelling and the

€nen, V., Spatial analysis and modelling of glaciogenic geochemical dispersion e Implication for Please cite this article in press as: Sarala, P., Nyka mineral exploration in Finland, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.12.002

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knowledge of the mineral deposit type in question (Carranza, 2008). Although using all available evidential data is in most cases desirable for best results, also a simple prospectivity model €nen et al., 2015). The aim of can lead into meaningful results (Nyka this paper was to demonstrate the importance of the quality of the data rather that create an ultimate prospectivity model and therefore the amount of different evidential data was kept in minimum. With such encouraging results, however, there is definitely a need to improve the modelling by adding geological input, €nen et al., particularly lithological and structural information (Nyka 2008b). Despite the enhancement at success rate after correcting the till geochemical anomalies we expect that the results can be improved if the glacial dynamics are even more precisely taken into account when till geochemistry is used as a spatial evidence data on mineral prospectivity mapping. This experimental research highlighted the importance of understanding glacial processes behind the transportation and deposition of till material. Glaciodynamic variations produce different glaciogenic deposits and morphologies that can be used in an estimation of the transport distances of mineralized till material. Furthermore, separate glaciation phases would produce complex glacial dispersion patterns that should be considered as a possible diluting factor for the element concentrations and a cohesion at transport distance and direction calculations. However, our results show that particularly in active streaming, warm-based subglacial conditions, like in the case of the Kuusamo drumlin field, the latest glacial advance phase is the most dominant factor in the transportation of till material. This is proved by the shift of the AUC values from original 0.54 to 0.74 in the corrected till geochemistry using rough transport distance estimation (3 km in up-ice direction(s)). The feedback of the glaciogenic geochemical dispersions will be easily improved by considering glaciogenic factors more detailed in different parts of glaciated terrain. As a summary, this research proved that by using the ‘glaciodynamic’-calibrated till geochemistry as a part of spatial modelling it is possible to increase the accuracy of widely used till geochemical datasets in mineral exploration. Interestingly, the prospectivity model produced in this paper not only classifies the known gold occurrences within Kuusamo belt within favourable areas, but also defines target areas that have not been tested so far. Acknowledgments We are thankful to the support provided by the Geological Survey of Finland and the valuable comments given by the Guest Editor Mahyar Yousefi and two anonymous reviewers. References €m, L., 1979. Glacial stratigraphy of Koillismaa and north Kainuu, Aario, R., Forsstro Finland. Fennia 157 (2), 1e49. Aario, R., Peuraniemi, V., 1992. Glacial dispersal of till constituents in morainic landforms of different types. Geomorphology 6, 9e25. Agterberg, F.P., Bonham-Carter, G.F., 2005. Measuring performance of mineralpotential maps. Nat. Resour. Res. 14, 1e17. Abedi, M., Norouzi, G.H., 2012. Integration of various geophysical data with geological and geochemical data to determine additional drilling for copper exploration. J. Appl. Geophys. 83, 35e45. Aerogeophysics in Finland 1972-2004: methods, system characteristics and applications. In: Airo, M.-L. (Ed.), 2005. Geological Survey of Finland, Special Paper, vol. 39, p. 197. Arias, M., Gumiel, P., Sanderson, D.J., Martin-Izard, A., 2011. A multifractal simulation model for the distribution of VMS deposits in the Spanish segment of the Iberian Pyrite Belt. Comput. Geosci. 37, 1917e1927. Bedrock of FinlandeDigiKP, Digital map database [Electronic resource], Espoo Geological Survey of Finland [referred 19.03.2016]. Version 1.0. Bonham-Carter, G.F., 1994. Geographic information systems for geoscientistsemodelling with GIS. Comput. Methods Geosci. 13, 398. Pergamon,

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€nen, V., Spatial analysis and modelling of glaciogenic geochemical dispersion e Implication for Please cite this article in press as: Sarala, P., Nyka mineral exploration in Finland, Journal of African Earth Sciences (2016), http://dx.doi.org/10.1016/j.jafrearsci.2016.12.002