Regional variation of coda wave attenuation in Northeast India: An understanding of the physical state of the medium

Journal Pre-proof Regional variation of coda wave attenuation in Northeast India: An understanding of the physical state of the medium

Rabin Das, Sagarika Mukhopadhyay PII:

S0031-9201(19)30043-3

DOI:

https://doi.org/10.1016/j.pepi.2019.106404

Reference:

PEPI 106404

To appear in:

Physics of the Earth and Planetary Interiors

Received date:

15 February 2019

Revised date:

22 November 2019

Accepted date:

22 November 2019

Please cite this article as: R. Das and S. Mukhopadhyay, Regional variation of coda wave attenuation in Northeast India: An understanding of the physical state of the medium, Physics of the Earth and Planetary Interiors(2018), https://doi.org/10.1016/ j.pepi.2019.106404

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© 2018 Published by Elsevier.

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Regional Variation of Coda Wave Attenuation in Northeast India: An Understanding of the Physical State of the Medium

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Rabin Das*, Sagarika Mukhopadhyay Department of Earth Sciences, IIT Roorkee, Roorkee-247667, India

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*Corresponding author e-mail: [email protected]

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Journal Pre-proof Abstract In this study an attempt has been made to estimate spatial variation in attenuation characterisitics in Northeast India using coda Q. The entire study region is divided into three sub-regions for this purpose. Estimated average frequency dependency of coda wave attenuation for 30s window length are

,

and

for Shillong Plateau, Mikir hills and surrounding River valley, and Indo-Burma Ranges respectively. It is observed that Q0 is greater for the Shillong Plateau than

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the other sub-regions. This indicates lower attenuation due to more rigid high-density material

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present in this area than the other sub-regions. The depth variations of the Qc, Q0 and n values

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were also examined. It is observed that the rate of increase of Q0 with depth is not uniform for all the sub-regions. Region 3 has the smallest Q0 and the largest n values at all depth levels

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amongst the three sub-regions. These results indicate that central part of Indo-Burma Ranges is

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the most attenuative, seismically active and heterogeneous in nature. However, this region has smaller Qc values than the other two sub-regions for all window lengths up to the 6 Hz. This

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means at lower frequencies the subsurface beneath this area is more attenuative compared to the

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other two sub-regions. Similar trends are observed at 8, 10 and 12 Hz, up to 45 s window

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lengths. For window lengths ≥ 55 s, central part of Indo-Burma Ranges has higher Qc values at 10 and 12 Hz compared to Shillong plateau. Qc values are lower for Shillong plateau compared to the other two regions for window length  55s at 10 and 12 Hz, which corresponds to depth levels  90 km. Such a complicated variation in Qc values is a manifestation of complex nature of tectonic regime in Northeast India.

Keywords: Northeast India, Quality factor, Coda wave, Attenuation.

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Journal Pre-proof 1. Introduction The attenuation property of seismic wave energy is quantitatively described by an inverse of the quality factor (Q-1) (Knopoff, 1964), which is the ratio between the dissipated energy and the stored energy during one cycle of the wave oscillation (Johnston nd o s , 1981). Coda quality factor Qc is used to investigate attenuation characteristics of the earth. It tells us about the material and physical conditions of the E rth’s interior. Attenuation could be

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termed as the reduction in amplitude of a seismic wave, with distance which is caused by

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anelasticity and heterogeneity of the Earth’s medium. Variation in attenuation of seismic waves

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from place to place depends on the spatial variation in the thermo-mechanical properties of the earth (Aki, 1969; Mukhopadhyay and Sharma, 2010; Akyol, 2015; Bora et al., 2018b). For a

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quantitative study of the seismic hazard assessments and a better understanding of source

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processes, tectonics and seismicity in a particular area, it is essential to have the knowledge of Q distribution (Pulli, 1984; Mukhopadhyay and Tyagi, 2007; Mukhopadhyay and Sharma, 2010;

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Das et al., 2018). Our study region, Northeast India is a seismically active region which falls in

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zone V of seismic hazard zonation map of India (IS 1893-Part 1, 2002). A study of the

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attenuation property of the medium beneath Northeast Indian regions (Fig. 1) is a very important issue in the sense of scaling the seismic hazard for the region. The populations of millions are at the risk of death and damage caused by a possible future large earthquake in the region (Bilham et al., 2001). Various assumptions are made while estimating Qc. Aki and Chouet (1975) proposed that the coda wave, i.e. the waves scattered by numerous small scale heterogeneities are too weak to undergo multiple scattering. Hence they used single scattering hypothesis for estimation of Q c. However, many researchers argue that multiple scattering is possible (e.g. Gao et al., 1983). Other possible mechanism for coda generation is diffusion (Aki and Chouet, 1975). Although the assumption of non-isotropic and/or multiple scattering is realistic, numerous studies have 3

Journal Pre-proof been carried out for Qc estimations utilizing the single backscattering model (Jin and Aki, 2005; Mukhopadhyay and Tyagi, 2007; Hazarika et al., 2009; Mukhopadhyay and Sharma, 2010; Mukhopadhyay et al., 2016). This is because it has been observed that Qc estimated using this assumption also shows correlation with seismicity and tectonics of a given region In this work, lapse time and frequency dependent coda wave attenuation characteristics of Northeast Indian regions are studied, using the local earthquakes recorded by Northeast Telemetry Network operated by the India Meteorological Department (IMD), Delhi. The method

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used for this study is known as single backscattering model (Aki and Chouet, 1975). All over the

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world, a substantial number of researchers have used this method. They have shown that coda Q

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(Qc) is strongly frequency-dependent (Rautian and Khalturin, 1978; Roecker et al., 1982; Ibáñez et al., 1990; Mukhopadhyay et al., 2008; Das et al., 2018). In this region, very few studies were

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carried out to estimate the attenuation characteristics (Hazarika et al., 2009; Padhy and

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Subhadra, 2010, 2013; Bora and Biswas, 2017; Banerjee and Kumar, 2018; Bora et al., 2018b). To develop an attenuation relationship of the study region, frequency dependency of Q c values

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were estimated for different lapse times. Lapse time (correspondingly depth) dependency of the

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Qc, Q0 and n values were examined. Depth-dependent attenuation properties can be used as a

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measure of variation in tectonic activity and level of heterogeneity with increasing depth. The results were compared with the results of previous Qc studies in the region for interpretation. In this study we have tried to find if there is any spatial variation in attenuation characteristics in the entire Northeast India, which comprises of geologically and tectonically very diverse units. According to these units, the entire study region is divided into three subregions (Regions 1, 2, and 3), as shown in Fig. 1, Region 1 covers Shillong Plateau, whereas, Region 2 covers Mikir Hills and Brahmaputra River valley and Region 3 covers northern parts of Indo-Burma Ranges. Only the events and stations within the borders of sub-regions are used for region-wise analysis.

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Journal Pre-proof 2. Geology and Seismotectonic Settings The study region comprises the major Himalayan Crustal discontinuities as shown in Fig.1: from north to south Indus–Tsangpo Suture (ITS), the Main Central Thrust (MCT), the Main Boundary Thrust (MBT) and the Main Frontal Thrust (MFT) (Le Fort, 1975; Yin, 2006). MFT, MBT and MCT are the major lithological discontinuities along the Himalayan trend where the rocks are over thrusted (Le Fort, 1975). The Eastern Himalayas comprises of the Sikkim

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Himalayas, Bhutan Himalayas, Arunanchal Himalayas and the Eastern Himalayan Syntaxis. The

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great Himalayan arc meets the Indo–Burmese Arc at the Eastern Himalayan Syntaxis, consisting

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features like the Mishmi Thrust (MMT), Tiding Suture (TDS), Lohit Thrust (LHT) and a part of

structure of Northern part of Himalaya.

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Disang Thrust (DST) and Naga Thrust (NGT) (Kayal, 2008) which come through the elongated

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The Indian plate subducts within the western part of the Indo–Burma Ranges (IBR) and

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creates an arcuate fold-thrust belt. The IBR is surrounded by several tectonic features such as,

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the dextral transform Sagaing fault at the eastern boundary; the MMT, DST and NGT to its north and to the south Andaman Spreading Ridge. This region is considered as an active

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accretionary wedge. This is a result of tectonic deformation caused by active eastward subduction of the Indian Plate beneath the Burmese plate. The Brahmaputra Basin is in between two tectonic arcs, viz. the Eastern Himalayas and IBR. This basin is covered by ~ 3–5 km thick sediments resting over hard basement. The Shillong Plateau–Mikir Hills is a fragmented part of the Indian shield having an elevation of ~ 1 km that comprises of Archean Gneissic complex (Evans, 1964). Shillong Plateau is well demarcated by Dhubri Fault (DBF) to its west and Dauki fault (DKF) to its south (Evans, 1964). The complex tectonic forces at the Shillong Plateau–Mikir Hills are due to the Himalayan collision zone and the Indo–Burma subduction zone (Rajasekhar and Mishra, 2008; Raoof et al., 2017). There are several hypothesis about the genesis of Shillong Plateau and Mikir Hills. Some 5

Journal Pre-proof researchers opined that they are overthrusted southward above part of the Bengal Basin (Najman et al., 2010). Bilham and England (2001) opined that Shillong Plateau is a pop–up structure bounded by two reverse faults. They are the Dauki fault (DKF) to its south and Oldham fault (OF) to its north. The fault bounding pop-up structures can penetrate the whole crust as postulated by Bilham and England (2001). Rao and Kumar (1997) initially suggested that popping up is made possible by the DKF to the south, Brahmaputra river fault to the north, DBF to the west and Disang thrust (DST) to the east. Raoof et al. (2017) on the other hand postulate

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that Shillong Plateau-Mikir Hills sit atop buckled up part of the Indian plate. Buckling up is

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caused because it is in a vice-like grip between the Eastern Himalayas towards north below

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which it underthrusts and the IBR towards east below which it subducts. To the south of the plateau lies the Bengal Basin (Bangladesh) which is covered by sediments with maximum

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thickness of about ~ 20 km (Kumar et al., 2018). DKF separates the Shillong Plateau to the north

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and the Bengal Basin to the south and extends over ~ 200 km along E–W direction. A high rate of seismic activity was observed in the study region due to northward and

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eastward movement of the Indian plate with respect to the Eurasian and Burmese plates,

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respectively. This region has experienced many large/great damaging earthquakes in the past,

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such as the 1897 Mw 8.1 (Bilham and England, 2001) in the Shillong Plateau, 1950 M 8.6 (Richter, 1958), in Assam syntaxis and the 1946 Mw 8.0 (USGS) near the Indo–Burmese arc (Fig. 1). Two damaging earthquakes occurred in the region; the 2009 Mw 6.3 earthquake in Bhutan (Kayal et al., 2010), and the 2016 Mw 6.7 earthquake in Manipur in the IBR (Borgohain et al., 2018; Bora et al. 2018a). 3. Dataset Local earthquake data recorded by 8 stations that fall within the three regions shown by circles in Fig. 1, out of 20 Broadband stations of Northeast Telemetry Network operated by the India Meteorological Department (IMD) was used. We have taken digital data of the earthquakes that occurred between 2011 and 2013 in Northeast India region. Each station 6

Journal Pre-proof contains external GPS system and deploys VSAT-based communication. The broadband stations are equipped with a RT151-120 sensor with natural periods of 120 s. Three-component data are acquired in continuous mode at 40 samples per second at each station. The details of these stations are mentioned in Table 1. The earthquake locations are estimated by using SEISAN software (Ottemöller et al., 2016) based on velocity model Bhattacharya et al. (2008). However, only 27 , 36 and 33 earthquakes data could be used for Region 1, 2, and 3 respectively, because of the constraints put on them that they should lie within the specified regions and also should

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satisfy the signal to noise (S/N) ratio, focal depth and correlation coefficient criteria specified in

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data analysis. The distribution of the earthquake epicenters and the seismic station locations are

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shown in Fig. 1. Out of available 275 earthquakes, records of only 77 earthquakes having signalto-noise (S/N) ratio ≥ 2 nd foc l depth ≤ 40 m are processed. The local Magnitude range of

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earthquakes used in this study is from 2.8 to 4.5. The focal depth for these events is less than 40

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km with a mean value of 20.83 (±3.5) km. The average epicentral distance of these events is around 110 km with minimum and maximum values of 15 km and 217 km respectively. Ray

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path plot of epicenter-station pairs from which data is used for each tectonic area is shown in

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Fig. 2.

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4. Methodology and data analysis Coda Q (Qc) was estimated using single back-scattering hypothesis proposed by Aki and Chouet (1975). In this method, it is assumed that coda waves are single back-scattered waves. Coda amplitude at lapse time t (measured from the origin time of an earthquake) and circular frequency  is given by:

A(, t )  A0   t 1 exp( t/ 2Qc )...................................(1) A0   is source factor. Taking natural logarithm of Eq. 1, we get ln[A(, t) t]  ln[A0  ]   t/ (2Qc )................................(2) ln[A(, t) t] versus time t is plotted for a given  and a least square line is fitted through

it. Qc can be obtained from the slope of this line using Eq. 2. We have marked beginning of coda 7

Journal Pre-proof window (tstart, Havskov et al., 1989) at twice the S-wave travel time (ts) to avoid contamination by direct S wave arrivals (Rautian and Khalturin, 1978). The average lapse time is tc= tstart+W/2, where W is coda window length selected for analysis (Havskov et al., 1989). We used verticalcomponent (Z-component) records for estimation of Qc. Qc values are estimated for seven central frequencies ranging from 1.5 and 12.0 Hz (Table 2) and fifteen lapse times ranging from 20 to 90 s, in steps of 5 s. Criteria of signal-tonoise ratios (S/N≥2, Havskov et al., 1989; Das et al., 2018) and correlation coefficients (C≥0.5,

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Havskov et al., 1989; Das et al., 2018) are used for accepting Qc values. The S/N ratios are

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computed for every central frequency for each record separately. The selected codas are

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bandpass filtered (Butterworth filter) at central frequencies together with low and high cut-off frequencies shown in Table 2. For increasing central frequency bandwidth is increased to avoid

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ringing and to keep relative bandwidths constant (Ottemöller et al., 2016). SEISAN software

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package is used for this analysis (Ottemöller et al., 2016). Qc estimation process is illustrated in Fig. 3. Using moving window averaging method on filtered data coda envelope for different

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frequency component is created. Coda window lengths used are mentioned in Table 3. To

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obtain coda envelope moving window averaging is carried out over a sub-window of 5 s length

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and this is shifted by an amount of 2.5 s to get the next value. The average value is plotted at the center of such sub-window. Qc values were estimated from the slope of the coda envelope and the linear regression correlation coefficients (C  0.5) are used for accepting Qc values. Accepted number of records for estimation of Qc values at different lapse times and frequencies are shown in Fig. 4. Total number of observation increases from 20s to 60s window length and then it decreases. Sato (1988) opined that single backscattering model of coda generation can be used for lapse time window length less than 100s. At higher lapse times multiple scattering becomes dominant and hence assumption of a single scattering becomes invalid. Here, 15 different window lengths from 20s to 90s in steps of 5s have been used. Minimum window length for which stable results could be obtained is 20s (Havskov et al., 8

Journal Pre-proof 1989). From Fig. 4 it is also observed that number of data used for estimation of Q c is highest at 10 and 12 Hz for Region 1, at 8 Hz for Region 2 and at 2 Hz for Region 3. These numbers are total number of data which was evaluated for each time window and frequency for a given subregion. 5. Result and Discussion Qc values have been estimated for 3 encircled parts of Northeast India region (Figs. 1 and 2). Table 3 shows estimated Qc values with standard deviations for different frequencies

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and time window lengths for the three regions. Average Q c value for the three regions for each

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frequency and lapse time is also reported in this table. It is observed that standard deviations are

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very low. Average Qc values are plotted versus the frequencies for different window lengths for three sub-regions in Fig. 5. This figure shows that Qc always increases with increasing

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frequency, which has been observed by all previous studies referred to so far (Aki, 1980). It is

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observed that for all three Regions there is a large difference between Qc for 20s and 30s window lengths. The difference between Q c for 30s and 40s for Region 1 is small, it increases a

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little bit for Region 2 and it is significantly large for Region 3. Whereas such differences

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between 40s and rest of the window lengths are very small for all the three regions. As discussed

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below, with increasing window length, average lapse time increases. This in turn entails that the volume of material from where coda arrives at a station also increases with increasing window length. The depth from where coda arrives also increases with increase in volume. It is normally accepted that lapse time variation of Q c is more strongly associated with its variation with depth (Aki, 1980; Roecker et al., 1982; Akinci et al., 1994; Mukhopadhyay and Sharma, 2010). Therefore the tectonic implications for such observation will be discussed in terms of its variation with average lapse time/ depth later. The differences in the rate of increase in Qc values with frequency for the three sub-regions indicate that attenuation characteristics vary from Region to Region.

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Journal Pre-proof Qc varies with lapse time and generally it increases with increasing lapse time as has been observed by various researcher for other study areas around the world (Aki and Chouet, 1975; Sato, 1978; Pulli, 1984; Havskov et al., 1989; Ibáñez et al., 1990; Del Pezzo et al., 1995; Hellweg et al., 1995; Mukhopadhyay and Tyagi, 2007; Mukhopadhyay et al., 2008; Mukhopadhyay and Sharma, 2010). They opine that it is due to the variation of Qc with depth. Fig. 6 and Table 3 show that for our study area at larger lapse times and frequencies Qc values either remain more or less constant or even decrease slightly. The average volume sampled by

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coda can be assumed to be represented by the average lapse time tc  tstart  W / 2 (Havskov et al., 1989), where tstart is the coda window start time and W is the coda window length. The

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plot of the Qc values versus the average lapse time gives an idea about an average attenuation

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characteristic of expanding volume of materials with depth from which coda reaches a station at

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an average tc of W. Qc values, together with standard deviations, versus the average lapse times for different frequencies are shown in Fig. 6. From this figure we see that Qc increases with

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increasing average lapse times at small lapse times (t c  85s). Similar results have been

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interpreted by researchers as a manifestation of decrease in the attenuation with depth (Aki, 1980; Akinci et al., 1994; Mukhopadhyay and Sharma, 2010). However, the rate of increase in

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Qc values at larger lapse times is lower than the ones at lower lapse times. By comparing the Qc values for the three regions (Table 3), it is observed that Region 3 has lower Qc values than the other two sub-regions at all lapse times, up to the frequency of 6 Hz. This means that Region 3 is more attenuative at lower frequencies then the other two sub-regions. Similar trends are observed at 8, 10 and 12 Hz, up to 45 s window length. At higher frequencies (10 and 12 Hz) and window lengths ≥ 55 s, Regions 3 has higher Qc values compared to Region 1. Such a complicated variation in Qc values is a manifestation of complex nature of tectonic regime in Northeast India. The ellipsoidal volume from which back-scattered waves generating coda is given by (Sato, 1978; Pulli, 1984): 10

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2

 vtc     2 

2



y

2

2  vtc   R        2   4  2

 1................................(3)

where x and y are the surface coordinates of ellipsoid volume having earthquake source and station as foci, R is the hypocentral distance, v is the velocity of S wave and tc is the average lapse time. We assumed that the S-wave velocity is 3.5 km/s and the average lapse time duration is tc= tstart +W/2. The average depth of volume of medium from which the coda wave generation

(4)

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 vtc 2  R 2  h         hav  2   2  

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would occur for different lapse time is given by the formula:

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where hav is the average hypocentral depth (Pulli, 1984; Havskov et al., 1989; Mukhopadhyay et

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al., 2008; Mukhopadhyay and Sharma, 2010). The average depths (h) of investigation

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corresponding to the coda lapse times are given in Table 4. As the velocity in the real earth varies with depth so these depth estimations are approximate. Fig. 6 also shows the Qc values

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versus the average coda-generating depths for different frequencies. It is observed that the rate

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of increase of Qc is larger for average lapse times smaller than 90s, 80s and 70s for Regions 1, 2

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and 3 respectively, (Fig. 6), especially at higher frequencies. This corresponds to approximate depth of volume of material involved in coda wave generation as 160km, 155km and 130km for Regions 1, 2 and 3 respectively. This indicates that rate of decrease of attenuation with depth is faster in the upper level, compared to that at larger depth. The results also indicate that medium becomes less attenuative with increasing depth for these lapse time/depth ranges. For lapse times/depths greater than above mentioned values, the Qc values are almost constant or decrease slightly at higher frequencies. It is also observed that Region 3 is more attenuative compared to the other two sub-regions at lower frequencies for all depth levels. However, Region 1 is more attenuative compared to the other two regions at higher frequencies and larger depths. Travel time tomography by Raoof et al. (2017) showed that Shillong Plateau sits atop the buckled up part of the Indian plate that underthrusts northward below the Eastern Himalayas and subducts 11

Journal Pre-proof eastward below the Arakan Yoma Ranges. Similar phenomenon was observed by Kumar (2017) too. As a consequence of this buckling, as being in a vice like grip between two tectonically active region, this part of the Indian plate is fractured and small scale heterogeneities are present even at deeper regions. This could explain obtained attenuation characteristics. Besides Kumar (2017) also observed low velocity anomaly at deeper region below Shillong Plateau. They opined that this was caused by this part of Northeast India passing over Kerguelen hotspot during Jurassic time as suggested by Talukdar and Murthy (1970) and Kailasam (1979). This

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could also cause higher amount of attenuation at greater depth below Region 1 compared to the

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other two regions. Rai et al. (1999) assumed an ultramafic composition of uppermost mantle and

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temperature gradient is 12ºC/km for sub-Moho mantle in this region. Biswas et al. (2007) reported surface temperature of 15ºC and geothermal gradient of 20º-30ºC/km at southern part

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of Shillong Plateau. High geothermal gradient (>110ºC at 3km depth) has been observed along

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NW of Eocene hinge zone (Guha, 2010). Shillong Plateau lies in the NW side of this trend. Also high temperature gradient south of Dauki fault (Biswas et al., 2007) may be considered as

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supportive evidence of low velocity anomaly at deeper depth below the Plateau, which may be a

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remnant of partially molten material caused by passing of the plateau above Kerguelen hotspot

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(Talukdar and Murthy, 1970; Kailasam, 1979). Hence available heat flow data also support our observation more attenuation at greater depth for Region 1 compared to the other two regions. Region 1 comprises of Shillong Plateau and its surroundings, Region 2 comprises of Mikir Hills, Kopili Graben and part of Brahmaputra River Basin and Region 3 comprises of the central part of the Indo-Burma Ranges. All the three regions are seismically very active. However, earthquakes occur up to deeper depth below Region 3 compared to the other two regions. Here the subducted Indian lithosphere, which is colder compared to its surrounding mantle material is present up to greater depth. Which means that attenuation should be less, especially at higher frequencies and greater depths for this region compared to the other two regions. Our results

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Journal Pre-proof support this observation (Table 3, Fig. 5). Hence, the results show that attenuation/tectonic characteristics the three sub-regions are different from each other at larger depth. Tectonically active regions are more attenuative compared to stable regions (Pulli, 1984; Mukhopadhyay and Tyagi, 2007; Mukhopadhyay and Sharma, 2010; Akyol, 2015; Das et al., 2018). For lower lapse times the Q0 and n values (Fig. 7) are similar for tectonically active regions as reported by numerous studies (Aki, 1980; Roecker et al., 1982; Van-Eck, 1988; Akinci et al., 1994). Fig. 7 and Table 5 show that at depths less than 140 km all the three

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regions have almost same Q0 values, indicating that they have similar level of attenuation

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characteristics and seismic activity. These values are similar to those observed for tectonically

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active regions. It is normally observed that older and more tectonically stable regions show less attenuation compared to tectonically active regions (Rhea, 1984; Kvamme and Havskov, 1989;

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Hellweg et al., 1995; Mandal and Rastogi, 1998; Del Pezzo et al., 2006). At depths greater than

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140 km, Q0 values for Region 1 is higher than the ones for the other sub-regions and Q0 values for Region 2 is higher than the ones for Region 3. That indicates decrease in Q 0 values from

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westward to eastward. This matches with the fact that in the eastern part of the study area

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seismic activity at deeper depth is mostly confined below the IBR. This matches with the

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observation worldwide that seismically active parts have lower Q 0 as mentioned before. Also n values at all depth levels are higher for Region 3 compared to the other two sub-regions and the difference among the three regions increases with increasing depth, reiterating the fact that Region 3 is tectonically more active compared to the other two regions, especially at greater depth. It is observed that IBR is seismically more active compared to the rest of the study area (Raoof et al., 2017). It also shows lesser value for both P and S wave velocities compared to Shillong Plateau and Mikir Hills (Raoof et al., 2017). Kumar (2017) also observed lower value of S wave velocity for IBR compared to Shillong Plateau and Mikir Hills based on tomographic inversion of Rayleigh wave data. Our results (Tables 3 and 5) show that both Qc at all 13

Journal Pre-proof frequencies as well as Q0 values are lowest for IBR (Region 3) and they increase systematically as we go from Region 3 to Region 2 to Region 1. This shows that seismically more active regions having lower P and S wave velocities are more attenuative compared to less active regions having higher velocities. 6. Comparison of Qc with other observation reported worldwide The amount of heterogeneity and level of tectonic activity of an area influences the coda wave attenuation characteristics (Aki, 1980; Roecker et al., 1982). Comparison of results of Qc

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studies obtained by various authors show that different regions worldwide can be classified

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broadly in to two groups, stable and active, on the basis of the parameter Q0 (Rhea, 1984;

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Kvamme and Havskov, 1989; Hellweg et al., 1995; Mandal and Rastogi, 1998; Del Pezzo et al., 2006). Q0 of seismically active and stable regions around the world are shown in Table 6 and

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compared with our results. The Q0 value obtained for Northeast Indian regions for tstart = 2ts and

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W = 30s is also shown in the table. These are similar to the t start and W values chosen by most of the researchers reported in the table. Also, we observed that Qc at 1 Hz (Q0) values obtained for

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Northeast Indian regions are similar to that obtained for tectonically active regions arround the

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world (Table 6). However, it is to be noted that in tectonically stable areas the values of Q 0 and

(Table 6).

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n may vary substantially with some values being similar to those for tectonically active regions

Qc versus frequency for Northeast India is plotted for tstart=2ts and W = 30s for comparison of other region of the world in Fig. 8. It is observed that the Qc values of Northeast India follow similar trend of Qc decay with frequency (f) like tectonically active regions around the world. Fig. 8 shows that the Qc trend for the study area is similar to Garwhal–Kumaun Himalayas, Turkey, Italy, Delhi and Koyna (India). Q c variation with frequency for these areas is higher than that for Parkfield, South Iberia, and Mexico and less than South India, South Carolina and North Iberia. The latter group belongs to the stable continental regions, where Qc is expected to be higher than tectonically active areas. It is to be noted that although Norway is tectonically 14

Journal Pre-proof stable, its Qc trend is similar to that for tectonically active regions. Among tectonically active areas, there seem to be two groups. The group having lower Qc values, viz. Parkfield, southern Iberia and Mexico are more attenuative compared to the rest of the tectonically active regions, that include our study region. The Qc values for window length 30s for Kopili fault zone (Bora et al., 2018b) are similar to those for Regions 1 and 2 that encompasses this fault zone. These values are similar to Qc values for Delhi region obtained by Das et al. (2018). Region 3 is more attenuative compared to the other two regions. Thus it can be said that even within a tectonically

f

active region like Northeast India lateral variation in attenuation is observed. The Qc of

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Northeast India is similar to the group showing higher Qc. The values for Kopili fault zone

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estimated by Bora et al. (2018b) also fall within this region.

Hazarika et al. (2009) estimated coda Q for three regions which are similar to ours. They have

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used records from earthquakes which are located within each region. However, recording

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stations lie both within and outside those regions. We have used records only when both earthquakes and stations lie within a given region. Hence, the results give better representation

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of spatial variation of coda Q in this region. Padhy and Subhadra (2010) estimated Q c for the

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entire Northeast India for 40, 50 and 60s window lengths. Their values are similar to the average

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values which we obtained for the entire Northeast India (Table 3). Bora et al. (2018b) estimated Qc for Kopili fault zone of Northeast India using the data from six stations, that are located near this fault and earthquakes that occurred around this fault. Hence they have estimated Qc values for only Kopili fault zone. Their Qc values are slightly lower than the average Q c values, which were estimated in this study for entire Northeast India. This is expected as our results are affected by regions like Shillong Plateau, where rocks are more compact compared to those available along the Kopili fault zone. Banerjee and Kumar (2018) used the data from 4 stations, all located in the Brahmaputra River Valley, just north of Shillong Plateau and Mikir Hills and earthquake sources in and around this valley. Their estimated Q 0 and n values for different window lengths are similar to the average values, which were estimated in this study. 15

Journal Pre-proof 7. Conclusions Estimated average frequency relations for 30s window length are ,

and

for Shillong Plateau,

Mikir hills and surrounding River valley, and central parts of Indo-Burma Ranges respectively. The Q0 is greater for the Shillong Plateau area compared to other sub-regions. This indicates lower attenuation due to a high-density material present in this area compared to other regions.

f

The depth variation of the Qc values were also examined. The results indicate that the rate of

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increase of Qc and Q0 with depth is not uniform for all over the sub-regions. Beneath Shillong

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Plateau and Mikir Hills area, the rate of increase of Q0 is more beyond the depth of 160 km in comparison to Indo-Burma Ranges. This is because in the Indo-Burma Ranges seismic activity

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occurs up to greater depth, hence at greater depth it is tectonically more active and attenuative

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compared to the other two regions. However, at higher frequency deeper regions below Shillong Plateau shows higher level of attenuation, probably caused by development of small scale

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heterogeneity there caused by buckling up of the Indian plate below the Plateau caused by it

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being in a vice like grip between two tectonically active zones of convergence. It could also be

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due to thermal disturbance at greater depth below the Plateau caused by it passing over the Kerguelen hotspot during Jurassic period. The comparison of obtained Q c values with the ones for different regions from all over the world show that the results are similar to the ones for tectonically active regions. Acknowledgment The Authors are thankful to India Meteorological Department (IMD), Delhi for providing the required waveforms data for this study. First author RD is grateful to the Ministry of Human Resources and Development (MHRD), India for providing fellowship. We express our gratitude to the editor and all anonymous reviewers for their constructive comments that significantly improved the manuscript. 16

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4. Akinci, A., Taktak, A.G., Ergintav, S., 1994. Attenuation of coda waves in Western Anatolia. Phys. Earth Planet. Inter. 87, 155–165.

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5. Akyol, N., 2015. Lapse time dependence of coda wave attenuation in Central West Turkey. Tectonophysics. 659, 53–62. doi:10.1016/j.tecto.2015.07.027

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7. Banerjee, S., Kumar, A., 2018. Determination of S and Coda Wave Attenuation in Selected Regions of Lower and Northern Assam within North Eastern India. Indian Geotech. J. 48, 442–458. https://doi.org/10.1007/s40098-017-0259-1

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65. Scherbaum, F., Kisslinger, C., 1985. Coda Q in the Adak seismic zone. Bull. Seismol. Soc. Am., 75, 615-620.

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67. Van-Eck, T., 1988. Attenuation of coda wave in the Dead Sea Region. Bull. Seismol. Soc. Am. 78, 770-779. 68. Yin, A., 2006. Cenozoic tectonic evolution of the Himalayan orogen as constrained by along-strike variation of structural geometry, exhumation history, and foreland sedimentation, Earth Sci. Rev., 76(1–2), 1–131, doi:10.1016/j.Earscirev.2005.05.004.

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Table Caption Table-1 Details of the broadband stations operated by IMD. *represent the stations whose data are used in this study.

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Table 2: Table showing central frequencies of bandpass filter with low and high cut-off frequencies.

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Table 3: The average value of Q c together with standard deviations for different frequencies and window lengths (W) for the three sub-regions together with their averages.

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Table 4: Average lapse times (t c) and corresponding coda-generating depths (h) (km) calculated for three sub-regions.

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Table 5: Estimated Q0 and n values together with standard deviations for different window lengths and three sub-regions.

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Table 6: Worldwide comparative study of observed Q0 and n value for various active and stable regions.

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Figure Caption Fig. 1: Map showing main tectonic features of the study region (modified after Bora et al., 2014; Raoof et al., 2017), station locations (Red inverted triangles) and the distribution of earthquakes (yellow and navy blue circles). Navy blue circles represent earthquakes whose data were used. The three big circles show the sub-regions (Regions 1, 2 and 3) for which Q c values were estimated separately. MCT: Main Central Thrust, MBT: Main Boundary Thrust, MFT: Main Frontal Thrust, MT: Mishmi Thrust, DT: Dapsi Thrust, OF: Oldham Fault. Fig. 2: Ray path plot of epicenter-station for each tectonic area. Greenish-blue, Black and Martian Green ray paths are for Region 1, Region 2 and Region 3 respectively. MCT: Main Central Thrust, MBT: Main Boundary Thrust, MFT: Main Frontal Thrust, EBT: Eastern Boundary Thrust, IBR: Indi-Burma Ranges.

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Fig. 3a and b: Top panel shows vertical component seismograms for an earthquake that occurred on 10th of February 2012 (26.112°N, 94.167°E, Depth - 10 km and ML- 4.3) recorded by a) TEZP and b) ITAN stations. Left vertical arrow of top panel shows origin time (O.T.) and the next two vertical arrows demarcate the coda window used for estimation of Q c. Rest of the panels show filtered seismograms for the coda window portion. Central frequencies of filters are 1.5, 2, 4, 6, 8, 10, and 12 Hz. Red lines in these panels show the coda envelope based on which Qc values were estimated. C = correlation coefficient, SN = signal to noise ratio.

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Fig. 4: Total numbers of observations (NT) versus different window lengths and frequencies at tstart = 2ts for (a, d) Region 1, (b, e) Region 2, and (c, f) Region 3.

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Fig 5: Variation of average Qc values with frequency for window lengths of 20 s, 30 s, 40s, 50 s, 60 s, 70 s, 80 s and 90 s for a) Region 1 b) Region 2 c) Region 3.

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Fig. 6: Obtained Qc values versus the average lapse times and depths, with standard deviations for the central frequencies for a) Region 1 b) Region 2 and c) Region 3. Fig. 7: Obtained Q0 and n values versus the depths for three sub-regions. Fig. 8: Comparison of worldwide Qc versus frequency for 30 sec window length. The references for different region are given in Table 6.

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Table-1: Details of the broadband stations operated by IMD. * represents the stations whose data are used in this study.

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Code

Elevations

GUWA* IMP* ITAN* JORH* KOHI* MOKO* TEZP* TURA* ZIRO LKP TAWA PASG SAIH SILR GTK AGT AZL BELO DHUB DIBR

88 792 214 79 1353 1353 83 406 160 139 297 167 729 18 1348 18 969 20 33 90

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Longitude (Deg min) 91 41.48’ 93 56.79’ 93 43.32’ 94 15.08’ 94 6.48’ 94 30.94’ 92 47.93’ 90 13.44’ 93 50.99’ 95 50.76’ 91 52.02’ 95 19.56’ 92 59.24’ 92 41.48’ 88 36.11’ 91 14.77’ 92 41.38’ 91 26.83’ 89 59.73’ 94 54.67’

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Guwahati Imphal Itanagar Jorhat Kohima Mokokchung Tezpur Tura Ziro Lekhapani Tawang Pasighat Saiha Silchar Gangtok(Tadong) Agartala Aizwal Belonia Dhubri Dibrugarh

Latitude (Deg min) 26 11.60’ 24 49.80’ 27 08.68’ 26 44.58’ 25 43.22’ 26 19.26’ 26 37.01’ 25 31.02’ 27 31.59’ 27 19.98’ 27 35.64’ 28 03.66’ 22 29.36’ 24 46.88’ 27 19.15’ 23 53.33’ 23 44.30’ 23 14.91’ 26 01.21’ 27 28.06’

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Table 2: Table showing central frequencies of bandpass filter with low and high cut-off frequencies. Central frequency (Hz) 1.5 2.0 4.0 6.0 8.0 10.0 12.0

High cut-off (Hz) 1.75 2.5 5.0 7.5 10.0 12.5 15.0

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Low cut-off (Hz) 1.25 1.5 3.0 4.5 6.0 7.5 9.0

25

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Table 3: The average value of Q c together with standard deviations for different frequencies and window lengths (W) for the three sub-regions together with their averages. . 1.5 Hz 2.0 Hz 4.0 Hz 6.0 Hz 8.0 Hz 10.0 Hz 12.0 Hz W = 20 s Qc   Qc   Qc   Qc   Qc   Qc   Qc   Region 1 145 ± 15 193 ± 20 385 ± 41 575 ± 61 765 ± 82 954 ± 102 1143 ± 122 Region 2 131 ± 8 178 ± 11 372 ± 23 573 ± 36 779 ± 49 988 ± 62 1199 ± 75 Region 3 103 ± 3 134 ± 5 258 ± 9 377 ± 14 494 ± 18 609 ± 23 723 ± 27 Average value 126 ± 9 168 ± 12 338 ± 24 508 ± 37 679 ± 50 850 ± 62 1022 ± 75 W = 25 s Region 1 163 ± 10 218 ± 14 442 ± 29 668 ± 44 895 ± 59 1123 ± 75 1352 ± 90 Region 2 151 ± 8 206 ± 11 435 ± 23 673 ± 36 917 ± 49 1166 ± 62 1418 ± 76 Region 3 120 ± 5 159 ± 7 315 ± 14 470 ± 22 624 ± 29 778 ± 37 932 ± 44 Average value 145 ± 8 194 ± 11 397 ± 22 604 ± 34 812 ± 46 1022 ± 58 1234 ± 70 W = 30 s Region 1 203 ± 10 269 ± 14 536 ± 28 802 ± 43 1067 ± 57 1332 ± 72 1596 ± 86 Region 2 171 ± 10 234 ± 15 503 ± 32 787 ± 50 1081 ± 69 1383 ± 89 1691 ± 108 Region 3 139 ± 3 187 ± 4 385 ± 9 586 ± 15 789 ± 20 995 ± 25 1202 ± 31 Average value 171 ± 8 230 ± 11 475 ± 23 725 ± 36 979 ± 49 1237 ± 62 1496 ± 75 W = 35 s Region 1 238 ± 15 312 ± 20 601 ± 39 883 ± 57 1159 ± 75 1431 ± 93 1701 ± 111 Region 2 200 ± 10 271 ± 14 568 ± 30 874 ± 47 1188 ± 63 1506 ± 81 1829 ± 98 Region 3 145 ± 8 201 ± 11 441 ± 24 700 ± 38 971 ± 53 1251 ± 69 1539 ± 85 Average value 194 ± 11 261 ± 15 537 ± 31 819 ± 47 1106 ± 64 1396 ± 81 1690 ± 98 W = 40 s Region 1 270 ± 35 350 ± 45 652 ± 85 938 ± 122 1215 ± 158 1484 ± 194 1748 ± 228 Region 2 221 ± 6 298 ± 9 616 ± 19 940 ± 29 1270 ± 40 1604 ± 50 1940 ± 61 Region 3 167 ± 7 229 ± 10 494 ± 22 775 ± 35 1066 ± 49 1366 ± 63 1672 ± 77 Average value 219 ± 16 292 ± 21 587 ± 42 884 ± 62 1184 ± 82 1485 ± 102 1787 ± 122 W = 45 s Region 1 287 ± 38 369 ± 49 677 ± 90 965 ± 129 1242 ± 166 1510 ± 202 1772 ± 237 Region 2 240 ± 11 322 ± 15 654 ± 30 990 ± 46 1328 ± 62 1668 ± 78 2009 ± 94 Region 3 179 ± 6 245 ± 9 523 ± 19 813 ± 30 1113 ± 42 1420 ± 54 1732 ± 65 Average value 235 ± 18 312 ± 24 618 ± 46 923 ± 68 1228 ± 90 1533 ± 111 1838 ± 132 W = 50 s Region 1 349 ± 40 436 ± 50 746 ± 86 1020 ± 118 1274 ± 147 1515 ± 175 1744 ± 202 Region 2 280 ± 9 368 ± 11 708 ± 22 1040 ± 33 1365 ± 44 1686 ± 54 2003 ± 64 Region 3 204 ± 11 276 ± 15 565 ± 32 861 ± 49 1160 ± 66 1462 ± 84 1766 ± 101 Average value 278 ± 20 360 ± 25 673 ± 47 974 ± 67 1266 ± 86 1554 ± 104 1838 ± 122 W = 55 s Region 1 364 ± 34 452 ± 42 760 ± 71 1031 ± 97 1280 ± 120 1514 ± 142 1736 ± 163 Region 2 302 ± 9 393 ± 11 739 ± 22 1069 ± 32 1389 ± 41 1702 ± 51 2009 ± 60 Region 3 219 ± 10 294 ± 14 597 ± 28 903 ± 43 1212 ± 58 1522 ± 73 1833 ± 88 Average value 295 ± 18 380 ± 22 699 ± 40 1001 ± 57 1294 ± 73 1579 ± 89 1859 ± 104 W = 60 s Region 1 383 ± 36 471 ± 45 777 ± 74 1042 ± 99 1282 ± 123 1506 ± 144 1718 ± 164 Region 2 318 ± 14 410 ± 19 757 ± 35 1084 ± 50 1399 ± 65 1704 ± 79 2003 ± 93 Region 3 243 ± 10 322 ± 13 630 ± 26 934 ± 38 1234 ± 51 1532 ± 63 1829 ± 75 Average value 315 ± 20 401 ± 26 721 ± 45 1020 ± 62 1305 ± 80 1581 ± 95 1850 ± 111 W = 65 s 26

Journal Pre-proof 535 ± 34 439 ± 18 343 ± 16 439 ± 23

833 ± 53 784 ± 33 667 ± 32 761 ± 39

1080 ± 69 1099 ± 46 985 ± 47 1055 ± 54

1298 ± 83 1397 ± 59 1298 ± 62 1331 ± 68

1498 ± 96 1684 ± 71 1608 ± 77 1597 ± 81

1683 ± 108 1960 ± 83 1916 ± 92 1853 ± 94

488 ± 29 376 ± 11 271 ± 12 378 ± 17

579 ± 35 471 ± 14 356 ± 16 469 ± 22

874 ± 53 812 ± 24 684 ± 32 790 ± 36

1113 ± 67 1116 ± 33 1001 ± 47 1077 ± 49

1320 ± 80 1399 ± 42 1313 ± 62 1344 ± 61

1508 ± 91 1667 ± 50 1620 ± 77 1598 ± 73

1680 ± 102 1923 ± 58 1923 ± 91 1842 ± 84

513 ± 44 374 ± 8 280 ± 12 389 ± 21

604 ± 52 469 ± 11 365 ± 16 479 ± 26

892 ± 76 809 ± 19 696 ± 31 799 ± 42

1120 ± 96 1114 ± 26 1014 ± 45 1083 ± 56

1317 ± 113 1397 ± 33 1325 ± 59 1346 ± 68

1493 ± 128 1666 ± 40 1630 ± 72 1596 ± 80

1654 ± 142 1923 ± 46 1931 ± 86 1836 ± 91

526 ± 41 380 ± 10 292 ± 11 399 ± 21

616 ± 48 475 ± 12 378 ± 15 490 ± 25

901 ± 71 813 ± 21 706 ± 28 807 ± 40

1126 ± 89 1114 ± 30 1018 ± 41 1086 ± 53

1318 ± 104 1392 ± 37 1320 ± 53 1343 ± 65

1490 ± 118 1655 ± 44 1615 ± 65 1587 ± 76

1647 ± 130 1906 ± 51 1903 ± 77 1819 ± 86

513 ± 42 399 ± 8 304 ± 9 405 ± 20

602 ± 49 495 ± 11 393 ± 12 497 ± 24

885 ± 72 831 ± 18 725 ± 22 814 ± 37

1109 ± 90 1126 ± 25 1038 ± 32 1091 ± 49

1301 ± 106 1397 ± 31 1340 ± 41 1346 ± 59

1473 ± 120 1651 ± 36 1632 ± 50 1585 ± 69

1630 ± 133 1893 ± 42 1918 ± 59 1814 ± 78

515 ± 47 406 ± 12 324 ± 10 415 ± 23

603 ± 55 502 ± 15 414 ± 13 506 ± 28

882 ± 80 1102 ± 100 1290 ± 118 840 ± 25 1134 ± 34 1404 ± 42 747 ± 24 1055 ± 34 1347 ± 43 823 ± 43 1097 ± 56 1347 ± 68

1458 ± 133 1657 ± 50 1628 ± 52 1581 ± 78

1612 ± 147 1897 ± 58 1901 ± 61 1803 ± 89

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445 ± 28 345 ± 14 260 ± 12 350 ± 18

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Region 1 Region 2 Region 3 Average value W = 70 s Region 1 Region 2 Region 3 Average value W = 75 s Region 1 Region 2 Region 3 Average value W = 80 s Region 1 Region 2 Region 3 Average value W = 85 s Region 1 Region 2 Region 3 Average value W = 90 s Region 1 Region 2 Region 3 Average value

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Table 4: Average lapse times (t c) and corresponding coda-generating depths (h) (km) calculated for three sub-regions.

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Region 3 tc (s) h (km) 60.22 110.67 63.89 117.45 67.93 124.76 70.06 129.79 76.04 139.26 80.43 147.49 84.30 154.96 89.20 163.15 92.35 168.99 95.99 175.6 98.01 179.14 102.30 188.26 104.14 191.05 107.62 197.03 108.24 197.23

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20 25 30 35 40 45 50 55 60 65 70 75 80 85 90

Region 1 Region 2 tc (s) h tc (s) h (km) (km) 68.50 125.02 64.63 126.25 74.85 136.58 67.11 131.69 79.96 145.78 70.98 137.78 83.24 153.35 75.44 145.71 85.64 158.07 78.21 152.47 87.42 161.39 80.26 156.84 88.83 164.39 84.29 164.07 90.87 169.15 86.64 168.42 92.16 171.53 88.61 172.19 94.65 176.14 90.90 175.68 97.43 181.28 93.10 180.76 99.88 185.79 95.40 185.13 102.27 188.91 97.26 187.48 104.01 193.23 99.76 193.33 107.14 198.33 101.82 195.74

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Window length(s)

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Journal Pre-proof Table 5: Estimated Q0 and n values together with standard deviations for different window lengths and three sub-regions. W = 25 s n  n Q0  Q0

W = 30 s n  n Q0  Q0

W = 35 s n  n Q0  Q0

Region 1 Region 2 Region 3 Average value

97 ± 10 0.99 ± 0.05 85 ± 5 1.07 ± 0.04 70 ± 3 0.94 ± 0.03 84 ± 6 1 ± 0.04 W = 40 s n  n Q0  Q0

108 ± 7 1.02 ± 0.03 98 ± 5 1.08 ± 0.03 80 ± 4 0.99 ± 0.03 95 ± 5 1.03 ± 0.03 W = 45 s n  n Q0  Q0

135 ± 7 0.99 ± 0.03 109 ± 7 1.1 ± 0.03 91 ± 2 1.04 ± 0.02 112 ± 5 1.04 ± 0.03 W = 50 s n  n Q0  Q0

162 ± 11 0.95 ± 0.03 130 ± 7 1.07 ± 0.03 91 ± 5 1.14 ± 0.03 128 ± 8 1.05 ± 0.03 W = 55 s n  n Q0  Q0

Region 1 Region 2 Region 3 Average value

188 ± 25 0.9 ± 0.06 145 ± 5 1.05 ± 0.02 106 ± 5 1.11 ± 0.03 146 ± 12 1.02 ± 0.04 W = 60 s n  n Q0  Q0

201 ± 27 0.88 ± 0.06 159 ± 7 1.02 ± 0.02 115 ± 4 1.09 ± 0.02 158 ± 13 1 ± 0.03 W = 65 s n  n Q0  Q0

255 ± 30 0.77 ± 0.05 191 ± 6 0.95 ± 0.02 134 ± 8 1.04 ± 0.03 193 ± 15 0.92 ± 0.03 W = 70 s n  n Q0  Q0

268 ± 25 0.75 ± 0.04 209 ± 6 0.91 ± 0.02 145 ± 7 1.02 ± 0.03 207 ± 13 0.89 ± 0.03 W = 75 s n  n Q0  Q0

Region 1 Region 2 Region 3 Average value

286 ± 27 0.72 ± 0.04 222 ± 10 0.89 ± 0.02 164 ± 7 0.97 ± 0.02 224 ± 15 0.86 ± 0.03 W = 80 s n  n Q0  Q0

343 ± 22 0.64 ± 0.03 246 ± 10 0.83 ± 0.02 176 ± 8 0.96 ± 0.03 255 ± 13 0.81 ± 0.03 W = 85 s n  n Q0  Q0

383 ± 23 0.59 ± 0.03 273 ± 8 0.79 ± 0.02 185 ± 9 0.94 ± 0.03 280 ± 13 0.77 ± 0.03 W = 90 s n  n Q0  Q0

409 ± 35 272 ± 7 192 ± 9 291 ± 17

Region 1 Region 2 Region 3 Average value

421 ± 33 278 ± 7 202 ± 8 300 ± 16

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al 409 ± 34 294 ± 7 212 ± 7 305 ± 16

0.56 ± 0.04 0.75 ± 0.01 0.89 ± 0.02 0.73 ± 0.02

413 ± 38 300 ± 9 230 ± 7 314 ± 18

0.56 ± 0.04 0.79 ± 0.01 0.93 ± 0.03 0.76 ± 0.03

0.55 ± 0.05 0.74 ± 0.02 0.85 ± 0.02 0.71 ± 0.03

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0.55 ± 0.04 0.77 ± 0.01 0.9 ± 0.03 0.74 ± 0.03

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W = 20 s n  n Q0  Q0

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Journal Pre-proof Table 6: Worldwide comparative study of observed Q0 and n value for various active and stable regions. n

Source

135

0.99

This study

109

1.10

This study

90 63 135 65 126 200 155 50 119 79 63 65 80 183 47 169 113 100 60

1.04 1.33 0.96 1 0.9 1.05 0.89 1 0.99 0.74 0.97 1.05 1.1 0.76 0.87 0.77 1.01 0.7 1

This study Bora et al. (2018b) Das et al. (2018) Ambieh and Fairhead (1989) Gupta et al. (1995) Scherbaum and Kisslinger (1985) Ibañez et al. (1990) Rovelli (1984) Mukhopadhyay and Sharma (2010) Hellweg et al. (1995) Havskov et al. (1989) Van-Eck (1988) Rovelli (1982) Akinci et al. (1994) Rodriguez et al. (1983) Mandal and Rastogi (1998) Mukhopadhyay and Tyagi (2007) Pujades et al. (1991) Roecker et al. (1982)

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Pr 600 120 460 460 190

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Q0

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Places Active regions North-East India (Shillong plateau) (Region 1) North-East India (Mikir hills and its surrounding) (Region 2) North-East India (Indo-Burma Ranges) (Region 3) Kopili Fault Zone Delhi Region Mt. Cameroon, West Africa Garhwal, Himalayas Aleutian South Spain Yugoslavia Garhwal–Kumaun Himalayas Parkfield Washington State Dead Sea Friuli, Italy West Anatolia,Turkey Guerero, Mexico Koyna NW Himalayas South Iberia Hindukush Stable region North Iberia Norway South India New England South Carolina

0.45 1.09 0.83 0.4 0.94

Pujades et al. (1991) Kvamme and Havskov (1989) Rao et al. (1997) Pulli (1984) Rhea (1984)

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Highlights  Coda Q of Northeast India has been estimated using

relationship.

 Estimated Qc values are strongly dependent on the frequency and lapse time.  Estimated average frequency relations are and

, for Shillong plateau, Mikir Hills and

surrounding River valley, and Indo-Burma Ranges respectively for 30 s window length.  The Q0 and n values show that the medium is highly heterogeneous.

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 Obtained estimates are compatible with other tectonically active regions of the world.

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Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8