Geomatics, Natural Hazards and Risk, 2014 http://dx.doi.org/10.1080/19475705.2014.960011
Validation of strong-motion stochastic model using observed ground motion records in north-east India DIPOK K. BORA*y, VLADIMIR YU SOKOLOVz and FRIEDEMANN WENZELz yDepartment of Physics, Diphu Government College, Diphu, Karbi Anglong 782 462, India zGeophysical Institute, Karlsruhe University, Hertzstrasse 16, Karlsruhe 76187, Germany (Received 7 April 2014; accepted 27 August 2014) We focused on validation of applicability of semi-empirical technique (spectral models and stochastic simulation) for the estimation of ground-motion characteristics in the northeastern region (NER) of India. In the present study, it is assumed that the point source approximation in far field is valid. The one-dimensional stochastic point source seismological model of Boore (1983) (Boore, DM. 1983. Stochastic simulation of high frequency ground motions based on seismological models of the radiated spectra. Bulletin of Seismological Society of America, 73, 1865–1894.) is used for modelling the acceleration time histories. Total ground-motion records of 30 earthquakes of magnitudes lying between MW 4.2 and 6.2 in NER India from March 2008 to April 2013 are used for this study. We considered peak ground acceleration (PGA) and pseudospectral acceleration (response spectrum amplitudes) with 5% damping ratio at three fundamental natural periods, namely: 0.3, 1.0, and 3.0 s. The spectral models, which work well for PGA, overestimate the pseudospectral acceleration. It seems that there is a strong influence of local site amplification and crustal attenuation (kappa), which control spectral amplitudes at different frequencies. The results would allow analysing regional peculiarities of ground-motion excitation and propagation and updating seismic hazard assessment, both the probabilistic and deterministic approaches.
1. Introduction Northeastern region (NER) India (between 89 E and 98 E longitude and 22 N and 29 N latitude) is in the zone of the most severe seismic hazard (i.e. zone V) as per the Indian standard code of practice for earthquake-resistant design of structures, IS 1893 (2002) in the country, and along with its adjoining regions is considered as one of the most intense seismic zones in the world. Comprising the seven states, i.e. Assam, Arunachal Pradesh, Manipur, Meghalaya, Mizoram, Nagaland, and Tripura, spreading over an area of 254,979 km2, NER India houses 11 important cities with a population more than 300,000 and another 20 cities with a population more than 100,000 (Raghukanth & Somala 2009). With the Himalayan mountain belt in the north, Mishmi hills in the west, Naga Patkoi mountain range in the south, and the Brahmaputra plain at the middle, along with the Shillong plateau, the Burmese arc, the Tripura folded belt, and the Surma Valley, NER India and its adjoining region have an extremely complex tectonic and geological set-up. The most *Corresponding author. Email:
[email protected] Ó 2014 Taylor & Francis
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Figure 1. Tectonic settings of NER India and surrounding regions. The major tectonic features in the region are indicated: SP: Shillong Plateau, MH: Mikir Hills, MCT: Main Central Thrust, MBT: Main Boundary Thrust, DF : Dauki Fault, DT : Dapsi Thrust, Dh F :Dudhnoi Fault, OF: Oldham Fault, CF : Chedrang Fault, BS: Barapani Shear Zone, KF: Kopili Fault, NT : Naga Thrust, DsT :Disang Thrust and EBT: Eastern Boundary Thrust, BL: Bomdila Lineament. MT: Mishmi Thrust, LT: Lohit Thrust, SF: Sagain Fault. The red stars represent the great earthquakes of 1897 and 1950. Yellow circle represents the earthquake with magnitude more than seven. Inset: Map of India indicating the study region. To view this figure in colour, please see the online version of the journal.
striking feature, in this region, is that the Himalaya takes a sharp bend along the Assam syntaxis and continues in a broadly north–south (NS) arcuate direction to the east of Burma and joins the Andaman arc giving rise to a complex plate boundary. The past seismicity data from 1897 shows that the NER India has experienced two great earthquakes with magnitudes above 8.0 (figure 1) and about 20 large earthquakes with magnitudes varying between 8.0 and 7.0 (Kayal et al. 2006). These earthquakes have caused immense damage to life and property. The great 1897 earthquake (MW 8.1; Bilham & England 2001), which took place in the Shillong plateau, caused tremendous damage in the NER India. Several ancient monumental structures such as Kamakhya temple in the city of Guwahati and several other small stone bridges in the northeastern region were severely damaged during this earthquake (Oldham 1899). The 1950 Great Assam earthquake, which is regarded as one of the biggest earthquakes of the twentieth century with epicentre at the Assam syntaxes (Tandon 1954), caused widespread damage over upper Assam, Abor, and Mishmi hill of Arunachal Pradesh spreading over a 99,840 km2 area. This great
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earthquake changed the topography of northeastern India and the course of the river Brahmaputra. The economic loss to the oil industry in Assam was estimated to be five million Indian rupees. Apart from these two great earthquakes, several strong events have also caused widespread damage in the NER of India (Nandy 2001). With the fast increasing density of population and large infrastructure developments in recent times, the vulnerability of NER India to damaging earthquakes is increasingly alarming day by day. Two great earthquakes occurred at a time when the population density and infrastructural development were several times lower than the present scenario. The occurrence of another great earthquake in the present scenario in this region might be highly devastating. Therefore, it is highly essential to have a reliable estimation of seismic hazard in the northeastern region. Figure 2 shows the seismic zonation scheme and the results of probabilistic seismic hazard assessment (PSHA) for a particular area in NER India with particular
Figure 2. Probabilistic seismic hazard assessment (PSHA) for northwestern India. Location of Guwahati city is shown by black circle. (a) Scheme of seismic source zonation (Nath & Thingbaijam 2012). The boundaries of the seismic zones are shown by dashed lines, while the numbers denote maximum possible magnitude within the zone. Solid line shows the area, for which PSHA has been performed. (b) Distribution of peak ground acceleration (cm/s2) for return period 475 years. (c) Seismic hazard curve for Guwahati city. (d) Contribution of earthquakes of different magnitudes and distances to PGA hazard for Guwahati city.
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emphasis on Guwahati city. The PSHA is based on the input data described by Nath and Thingbaijam (2012) and calculations were performed using Monte Carlo technique (V. Sokolov, personal communication). The resulting peak ground acceleration (PGA) value for rock site conditions for the city of Guwahati is about 300350 cm/s2 for return period 475 years and 450500 cm/s2 for return period 2475 years. Actually, the return period of 475 years is used for characterization of moderate earthquake the structure shall continue to be usable with minor damage. To prevent a collapse, much larger return period (or smaller probability of exceedence), e.g. 2475 years, should be used. Seismic hazard deaggregation procedure, i.e. analysis of influence of particular earthquakes of different magnitudes and distances, showed that for the city of Guwahati the largest contribution to hazard for the return period 475 and 2475 years is produced by events of magnitude 6.57.5, which may occur at hypocentral distances up to 30 km (figure 2(d)). However, the larger events (magnitudes up to 8.5), which may occur at hypocentral distances up to 100120 km, may also be dangerous for smaller return periods. Khattri et al. (1984) delivered a hazard map depicting PGA with 10% probability of exceedance in 50 years. It may be appropriate to mention here the Global Seismic Hazard Assessment Programme (GSHAP) which involved about 500 scientists from all over the world and completed it in a period of seven years (19921999) was one such endeavour, which has generated data that form the basis of seismic hazard assessment in several regions under International Decade of Natural Disaster Reduction (Verma & Bansal 2013). For the purpose of seismic hazard assessment, the most sought information is a ground-motion model for predicting the PGA and response spectra. These parameters are normally obtained from strong-motion accelerogram data from several past earthquakes. However, in many seismically active regions, the lack of strong-motion data does not allow the development of regional ground-motion models. In the most recent PSH studies for India, Nath and Thingbaijam (2012) used a set of groundmotion equations obtained for other regions using a logic-tree framework. For the case of sparse strong-motion data, which are collected at present by the northeastern India network, the quantification of the regional ground motions may be obtained using seismological models in which the source, path, and site effects are specified analytically (Boore 1983, 2003). The input source, wave propagation, and site parameters in the seismological model should be region specific and should be obtained from the available strong-motion data in the region. A lot of literature regarding the stress-drop and attenuation characteristics is available for Himalaya region and Peninsular India (Aman et al. 1995; Singh et al. 1996). Recently, Parvez et al. (2003) and Nath and Thingbaijam (2012) attempted the deterministic and probabilistic seismic hazard assessment of India, respectively. A very few literature related to the analysis of the earthquake source parameters and attenuation characteristics is available for NER India (Raghukanth & Somala 2009; Nath & Thingbaijam 2012; Nath et al. 2012; Bora et al. 2013a). At present, some ground-motion records from several earthquakes were accumulated for NER India. The work has been focused on validation of applicability of semi-empirical technique (spectral models and stochastic simulation) for the estimation of ground-motion characteristics in the region. The recently developed spectral models (Raghukanth & Somala 2009) and the available ground-motion records were used. The results would allow analysing regional peculiarities of ground-motion excitation and propagation and updating seismic hazard assessment, both the probabilistic and deterministic approaches.
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2. Seismicity of northeastern India The NER India is characterized by high seismic activity. Several studies are available on geology and tectonics of the Himalayan region, but the seismotectonics of the northeastern part of India is considered to be highly complicated (Nandy 2001). The region comprises distinct geological units, like the Himalayan frontal arc to the north, the highly folded Indo-Burma mountain ranges or Burmese arc to the east, and the Brahmaputra-river alluvium in the Assam valley and the ShillongMikir plateau in between these two arcs, and thick sediments of the Bengal basin to the south. Seismotectonics of the region has been a subject of several studies (e.g. Nandy 2001; Kayal et al. 2006; Angelier & Baruah 2009; Bora & Baruah 2012; Bora et al. 2013a). Bilham and England (2001), on the basis of Geodetic and GPS data, interpreted that the Shillong plateau is a popup structure bounded by two reverse faults and argued that the 1897 great earthquake was produced by a south dipping hidden fault at the northern boundary of the Shillong plateau; they named ‘Oldham fault’ that extends from a depth of about 9 km down to 45 km. They further suggested that the Shillong plateau earthquakes are caused by the deformation of this ‘pop-up’ structure between the Dauki fault and the Oldham fault (figure 1). The northwestsoutheast trending Kopili fault separates the Shillong plateau from its fragment part Mikir Hills. The Assam valley is an east-northeastwest-southwest trending narrow valley, which lies between Shillong plateau and the eastern Himalayan tectonic domains. Many researchers suggest that a large earthquake is due in the northeastern part of India. Almost no earthquake activity is observed in the area to the east of Shillong and south of the Tura areas. This precisely falls between the zones of the two major earthquakes, that is 1897 and 1950 earthquakes (figure 1). Khattri and Wyss (1987) have shown that this area called the Assam Gap is a potential source for an impending large earthquake. Guha and Bhattacharya (1984) have reported the possibility of the occurrence of a major earthquake (MW > 8) in the near future. There are several potential source zones, which can cause earthquakes leading to severe damage in northeastern India. Prominent geological units of the NER, India, however, remain geophysically less studied due to inaccessibility of the terrain.
3. Strong-motion data The strong-motion instrumentation programme of the Earthquake Engineering Department at Indian Institute of Technology (IIT), Roorkee, funded by Ministry of Earth Sciences (MoES), New-Delhi, has led to the installation of a strong groundmotion network which covers the Indian Himalayan range from Jammu and Kashmir to Meghalaya (Kumar et al. 2012; Mittal et al. 2012). In total, 298 strong-motion stations have been installed in the states of Himachal Pradesh, Punjab, Haryana, Delhi, Rajasthan, Uttarakhand, Uttar Pradesh, Bihar, Sikkim, West Bengal, Andaman and Nicobar, Meghalaya, Arunachal Pradesh, and Assam. These networks have recorded a number of earthquakes, and ensembles of records are available for several events. Because seismic hazard of northeastern India is of interest in the present study, the location of strong-motion stations, their site geology and earthquakes recorded by each station are given in table 1. These strong-motion networks have
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Figure 3. Epicentral plot with station location in northeastern India.
produced 30 ground-motion records for events of magnitudes lying between MW 4.2 and 6.2 in northeastern India from 2008 to April 2013. Thus, a total of 83 three-component strong-motion records are used for northeastern India. The first step of the data processing was the selection of time windows for which S-wave spectra has to be computed. The shear-wave portion in each acceleration time history is windowed such that it includes the main S-wave arrival and at least 85% of the total energy (Garcıa et al. 2004). The selected spectra were low-pass filtered, cut-off at 35 Hz after correcting them for their individual baseline effect. The epicentral plot with sourceto-station seismic ray path for each earthquake is shown in figure 3. The location, focal depth, and magnitudes of these events recorded in northeastern India are reported in table 2. Figure 4 presents the available data in northeastern India as a function of magnitude and epicentral distance. The strong-motion data for all these events are available through the website http://www.pesmos.in. The epicentral locations of the earthquakes used in this study were collected from the U.S. Geological Survey (USGS) website (http://neic.usgs.gov).
4. Seismological model of Fourier amplitude spectrum (FAS) and stochastic simulation In the present study, it is assumed that the point source approximation in far field is valid. The one-dimensional stochastic point source seismological model of Boore
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Table 1. Strong-motion stations and their site conditions in NER India (Mittal et al. 2012). Station code
Station name
Latitude ( N)
Longitude ( E)
Site class
Site geology
Earthquake recorded
Alluvium Alluvium Soils (slope washed) Alluvium Alluvium Shale/sandstones Alluvium sandstones Alluvium Alluvium Alluvium Alluvium Soils (slope washed) Alluvium Alluvium Alluvium Alluvium Granite Alluvium Alluvium Alluvium Sandstones
3 3 4
BAR BOK BON
Barpeta Boko Bongaigaon
26.33 25.98 26.47
91.01 91.23 90.56
DS DS DS
COB DIB DIP GLP GOL GUA HKD JHR KRM KOK
Kooch Vihar Dibrugarh Diphu Goalpara Golaghat Guwhati Hailakandi Jorhat Karimganj Khokrajhar
26.32 27.47 25.84 26.16 26.51 26.19 24.68 26.76 24.87 26.4
89.44 94.91 93.44 90.63 93.97 91.75 92.56 94.21 92.35 90.26
DS DS HA DS HA DS DS DS DS DS
LKH MON MOR NAU NON SIL TEJ TIN TUR
North Lakhimpur Mangaldai Morigaon Naogaon Nongstoin Silchar Tejpur Tinsukia Tura
27.24 26.44 26.25 26.35 25.52 24.83 26.62 27.5 25.51
94.11 92.03 92.34 92.69 91.26 92.8 92.8 95.33 90.22
DS DS DS DS HA DS DS DS HA
1 1 4 3 6 7 4 4 2 6 4 2 2 7 4 2 6 2 4
(1983) is used for modelling the acceleration time histories. The theory and application of stochastic seismological models for estimating ground motion has been discussed by Boore (1983, 2003). The essential component for the stochastic method is the spectrum of the ground motion, where the physics of the earthquake process and wave propagation are contained, usually encapsulated and put into the form of simple equations. Briefly, the FAS of ground acceleration Y(r, f ) at a site is expressed as follows (Boore 1983): Y ðr; f Þ ¼ CSðf ÞDðr; f ÞFðf Þ
(1)
where C is a constant, S(f) is the source spectral function, D(r, f ) characterizes the attenuation of seismic waves propagating within the earth’s crust, the term F( f ) accounts for site amplification, and r is the hypocentral distance. In the present study for the source, the single corner frequency model Sðf Þ ¼ ð2pf Þ2
M0 ½1 þ ðf =fc Þ2
(2)
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D.K. Bora et al. Table 2. Epicentral parameters of earthquakes.
Date 2008-03-13 2008-05-29 2008-07-07 2008-07-26 2009-02-15 2009-02-24 2009-04-25 2009-08-11 2009-08-19 2009-08-30 2009-09-03 2009-09-21 2009-10-29 2009-10-29 2009-12-29 2009-12-31 2010-03-12 2010-07-26 2010-09-11 2010-12-12 2011-02-04 2011-11-21 2012-05-11 2012-07-01 2012-07-14 2012-08-19 2012-10-02 2013-01-09 2013-03-02 2013-04-16
Latitude ( N)
Longitude ( E)
Depth
Mw
26.784 26.308 25.972 24.788 26.000 25.836 26.112 24.323 26.556 25.280 24.332 27.332 27.262 26.548 24.357 27.319 23.062 26.500 25.720 24.792 24.618 24.955 26.175 25.592 25.424 26.599 26.879 25.332 24.671 26.047
91.937 91.791 95.33 90.536 90.200 94.670 91.446 94.793 92.470 95.101 94.669 91.437 91.417 90.018 94.807 91.510 94.623 91.300 90.205 93.304 94.680 95.236 92.889 94.696 94.480 92.570 92.818 95.050 92.229 91.943
38.0 45.5 72.0 17.5 30.0 33.0 37.8 99.0 10.0 82.2 95.0 14.0 26.0 30.4 124.8 10.0 102.1 31.0 22.3 47.4 85.0 113.7 43.3 58.0 52.1 48.9 36.8 93.0 45.8 49.2
4.3 4.4 4.9 4.7 4.3 4.7 4.3 5.5 4.9 5.3 5.9 6.1 5.1 4.7 5.6 5.4 5.5 4.2 4.6 4.7 6.2 5.7 5.4 5.6 5.2 4.7 5.1 5.8 5.1 4.5
of Brune (1970) is used, where the corner frequency fC (in Hz), the seismic moment M0 (in dyne cm), and the stress drop Ds (in bars) are related through 1=3 Ds 6 fc ¼ 4:9£10 VS (3) M0 where VS is the shear-wave velocity in the source region corresponding to bedrock conditions. The function D(r, f ) is defined as
¡ pfr Dðr; f Þ ¼ G exp VS Qðf Þ
(4)
where G refers to the geometric attenuation, and the remaining term denotes anelastic attenuation. Q( f ) is the quality factor of the region to be determined from the
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Figure 4. Available instrumental database in northeastern India.
past seismic recordings. The scaling factor C is pffiffiffi h Ruf i 2 C¼ 4prVS3
(5)
where h Ruf i is the radiation coefficient averaged over an appropriate range of azimuths and take-off angles, for shear waves. The radiation pattern coefficient Ruf D 0.55 is used for all events in accordance with Raghukanth and Somala p 2009, ffiffiffi and r is the density (in g cm¡3) of the crust at the focal depth. The coefficient 2 in equation (5) arises as the product of the free surface amplification and partitioning of energy in orthogonal directions. In the present study, the geometrical attenuation term G in equation (4) in the pffiffinortheastern region is taken to be equal to 1/r for r < 100 km and equal to 1=ð10 rÞ for r > 100 km (Singh et al. 1999). The shear-wave velocity and density in the near source region are taken as 3.6 km/sec and 2900 kg/ m3, respectively (Mitra et al. 2005). It can be observed from equation (1) that once the stress drop, site amplification, and spectral attenuation are known, one can simulate acceleration spectrum for any combination of magnitude and hypocentral distance. Thus, the problem of finding a region-specific seismological source model reduces to the determination of spectral attenuation, stress drop, and site amplification such that the modelled Fourier spectra are compatible with the recorded data. When applying the stochastic technique, the sets of several tens of synthetic acceleration time functions are generated using the magnitude, distance, and site-dependent spectra (Boore 2003). The duration model, in which it is assumed that most (90%) of the spectral energy is spread over a duration t0:9 of the accelerogram, consisted of two terms as follows: t0:9 ¼ t S C t P , where tS ¼ 1=fC is the source duration (fC is the corner frequency) and t P is the path duration (e.g. tP ¼ 0:05R, Boore 2003). The peak amplitudes of ground acceleration and velocity, as well as the pseudospectral acceleration (PSA), are calculated from the synthetic accelerograms, as the averaged values from a given set.
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Figure 5. The generalized site-dependent ground-motion models (Sokolov & Wenzel 2009).
5. Results and discussion We analysed the ability of seismological model of ground-motion spectra to predict the ground-motion parameters in northeastern India. We considered PGA and pseudospectral acceleration (response spectrum amplitudes, RSA) with 5% damping ratio at three fundamental natural periods, namely 0.3, 1.0, and 3.0 s. The fundamental natural periods for ordinary construction, for storied building, and for bridges are considered as 0.3, 1.0, and 3.0 s, respectively. The modelled ground-motion
Table 3. Stress drop values for different moment magnitude ranges. Stress drop (bars) 30.0 40.0 100.0 150.0 220.0
Moment magnitude (MW) 4.04.5 4.65.0 5.15.5 5.66.0 6.16.5
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Figure 6. Stress drop as a function of moment magnitude (MW).
parameters were compared with the observed data to study the influence of the model characteristics on the results of the modelling and to determine specific values of the characteristics that fit the observations. In this study, some characteristics of the spectral model were fixed (e.g. function describing the source spectra; geometrical and anelastic attenuation, and crustal attenuation), and we analysed the influence of stress drop and site amplification. The geometrical attenuation has been taken as that reported by Boore (2003). It is well known that the quality factor, which characterizes the anelastic attenuation of the medium, varies from place to place depending on the seismotectonics features (Aki 1980; Jin & Aki 1988). The quality factor increases with frequency as Q D Q0f n, where the scaling constant Q0 characterizes the heterogeneities in the medium, and n is related to the seismic activity in the region. Generally, a low Q0 and high n value is observed in seismically active regions such as Himalaya, Kutch, and Koyna regions in India (Mandal & Rastogi 1998; Kumar et al. 2005). On the other hand, stable regions like Peninsular India show a high Q0 and low n-value (Singh et al. 2004). Apart from characterizing tectonic activity, the quality factor also plays an important role in simulating strong-motion accelerograms (Boore 1983). In the present study, the quality factor Q0 D 225 and n D 0.93 is used in simulating the ground motion (Raghukanth & Somala 2009). It is well known that the soil layers at a given site influence the final surface ground motion. The amplitude and frequency content of the bedrock motion gets modified due to the effect of local soil conditions. Although this modification is a part of the path effect, local site conditions are considered independent of the distance between the source and the site. For this reason, it becomes convenient to separate the amplification term F( f ) in equation (1) into further site and path effects as (Boore 2003) Fðf Þ ¼ Aðf ÞPðf Þ;
(6)
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Figure 7. PGA as a function of distance for different stress drop and site conditions. HA and DS site conditions are represented by black and red colour, respectively. To view this figure in colour, please see the online version of the journal.
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Figure 8. Distribution of residuals for PGA with distance (a) site condition HA and (b) site condition DS.
where A(f) represents site amplification due to propagation of earthquake waves from the bedrock toward the surface. P( f ) is the diminution function for characterizing the path-independent loss of energy and is taken as Pðf Þ ¼ e ¡ pk0 f ;
(7)
Table 4. Average residuals for PGA and RSA. PGA Magnitude range
Site condition
4.06.5
HA site DS site
Average residuals ¡0.0337119 0.0644815 RSA Site condition
Average residuals
Average residuals (MW > 6.0)
Magnitude range
Period
5.16.5
0.3
HA site DS site
¡0.5368 ¡0.4230
¡0.452 ¡0.327
5.16.5
1.0
HA site DS site
¡0.6256 ¡0.9142
¡0.329 ¡0.608
5.16.5
3.0
HA site DS site
¡0.7652 ¡1.1445
¡1.333 ¡1.721
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Figure 9. RSA for period 0.3 s as a function of distance for different stress drop and site conditions. HA and DS site conditions are represented by black and red colour, respectively. To view this figure in colour, please see the online version of the journal.
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Figure 10. Distribution of residuals for RSA for 0.3 s with distance (a) site condition HA and (b) site condition DS.
where k0 is the kappa factor to reduce the high-frequency amplitudes above some threshold frequency and characterizes the near-surface attenuation (Anderson & Hough 1984). The k0 D 0.041 (Raghukanth & Somala 2009) is considered for this study. In this study, we used two generalized site-amplification functions estimated by Sokolov and Wenzel (2009) for hilly area (mostly rock covered by firm coarsegrained soil, site condition HA) and for deep sedimentary basin covered by soft finegrained soil (site condition DS). The amplification functions are shown in figure 5. As can be seen, the characteristics of amplification (amplitude and frequency range of highest amplification) are different for the models; the model HA represents intermediate-frequency amplification and the model DS represents low-frequency amplification. Site geology for each of the stations is given in table 1. Based on this site geology, the stations are classified as HA or DS. Based on this theoretical consideration, we started the modelling considering various stress drop values ranging between 30 and 220 bars and also using the two site-amplification functions. Table 3 shows the stress drop values for different moment magnitude ranges. A trend of increasing stress drop with increasing seismic moment is evident. The apparent dependency of stress drop on moment magnitude is shown in figure 6. From the figure, it can be interpreted that with the increase of moment magnitude the stress drop used in the model also increases and fit the observations. The stress drop values estimated for different moment magnitudes are in good agreement with the earlier studies by Nath et al. (2012) and Bora et al. (2013a, 2013b). Comparison between the observed PGA and the modelled PGA values using different stress drop and site amplification is shown in figure 7. The PGA residuals, i.e. difference between the observed PGAOBS (geometric mean of two horizontal components) and the modelled PGAMOD groundmotion parameters, estimated RES ¼ lnðPGAOBS =PGAMOD Þ, where indices OBS and MOD denote the observed data and modelled values, respectively. The residual
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Figure 11. RSA for period 1.0 s as a function of distance for different stress drop and site conditions. HA and DS site conditions are represented by black and red colour, respectively. To view this figure in colour, please see the online version of the journal.
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Figure 12. Distribution of residuals for RSA for 1.0 s with distance (a) site condition HA and (b) site condition DS.
plots are shown in figure 8. The average values of the residuals are given in table 4. The residual values show good approximation for PGA, but show overestimates for pseudospectral acceleration. Based on the results of modelling, it is possible to conclude that the stochastic model with magnitude-dependent stress drop and based on the described attenuation parameters (distance and near-surface attenuation) provides a good agreement with the observed PGA (mean residuals are close to zero). The HA model of local site amplification resulted in a slightly higher PGAs than the DS model (figure 7). However, the spectral models, which work well for PGA, overestimate the pseudospectral acceleration, and the degree of overestimation depends on the natural period of vibration, moment-magnitude, and models of local site amplification (figures 914 and table 4). It seems that the model parameters, which control the spectral amplitudes at different frequencies, e.g. shape of source spectra, local site amplification, and crustal attenuation (kappa), should be adjusted in detail which is a scope of further studies.
6. Conclusion Based on the results of the analysis, it is possible to conclude that, in general, the stochastic approach together with the regional spectral model may be successfully used for the modelling of strong ground motion in north-east India for the purposes of seismic hazard and risk estimations. However, a comprehensive study is necessary to specify the characteristics of the model, which affect the spectral content of the modelled ground motion. It seems to be reasonable to consider the more sophisticated source models instead of single corner frequency model, which was used in this study. Specification of site amplification models, both in regional and local scales, is of great importance.
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Figure 13. RSA for period 3.0 s as a function of distance for different stress drop and site conditions. HA and DS site conditions are represented by black and red colour, respectively. To view this figure in colour, please see the online version of the journal.
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Figure 14. Distribution of residuals for RSA for 3.0 s with distance (a) site condition HA and (b) site condition DS.
Acknowledgements Two anonymous reviewers and editor-in-chief Prof. Ramesh Singh are gratefully acknowledged for their valuable comments and suggestions which upgrade the manuscript significantly.
Funding The first author (DB) thanks University Grant Commission, Government of India and DAAD (German Academic Exchange Service), Germany for providing the financial support under UGC-DAAD Scientist Exchange Programme, 2013 [UGC letter No. F. 1-6/2011 (IC) and DAAD letter No. A/13/07731] to carry out the research and expedite the work.
References Aki K. 1980. Attenuation of shear waves in the lithosphere for frequencies from 0.05 to 25 Hz. Phys Earth Planet Inter. 21:5060. Aman A, Singh UK, Singh RP. 1995. A new empirical relation for strong seismic ground motion for the Himalayan region. Curr Sci. 69:772777. Anderson J, Hough S. 1984. A model for the shape of the Fourier amplitude spectrum of acceleration at high frequencies. Bull Seismol Soc Am. 74:19691993. Angelier J, Baruah S. 2009. Seismotectonics in Northeast India: a stress analysis of focal mechanism solutions of earthquakes and its kinematic implications. Geophys J Int. 178:303326. Bilham R, England P. 2001. Plateau “pop-up” in the Great 1897 Assam earthquake. Nature. 410:806809. Boore DM. 1983. Stochastic simulation of high-frequency ground motions based on seismological models of the radiated spectra. Bull Seismol Soc Am. 73:18651894. Boore DM. 2003. Simulation of ground motion using the stochastic method. Pure Appl Geophys. 160:635675. Bora D, Baruah S. 2012. Mapping the crustal thickness in ShillongMikir Hills Plateau and its adjoining region of northeastern India using Moho reflected waves. J Asian Earth Sci. 48:8392.
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D.K. Bora et al.
Bora D, Baruah S, Biswas R, Gogoi NK. 2013a. Estimation of source parameters of local earthquakes originated in ShillongMikir plateau and its adjoining region of northeastern India. Bull Seismol Soci Am. 103:437446. Bora D, Baruah S, Biswas R, Gogoi NK. 2013b. Estimation of source parameters of 21 September 2009 Bhutan earthquake in northeast Himalaya region. Proceeding of two day workshops on Holistic Scientific Approach using Integrated Geophysical Studies for the management of Natural Hazards. Organized by North Eastern Space Application Centre (NESAC) and Indian Geophysical Union (IGU); Meghalaya, India, pp. 38–39. Brune JM. 1970. Tectonic stress and spectra of seismic shear waves from earthquakes. J Geophys Res. 75:49975009. Garcıa D, Singh SK, Herraiz M, Pacheco JF, Ordaz M. 2004. Inslab earthquakes of central Mexico: Q, source spectra, and stress drop. Bull Seismol Soc Am. 94:789802. Guha SK, Bhattacharya U. 1984. Studies on prediction of seismicity in northeastern India. Presented at the Eighth World Conference on Earthquake Engineering; July; San Francisco (CA). p. 2127. [IS] Indian Standard 1893. 2002. Criteria for earthquake resistant design of structures: part I (fifth revision). New Delhi: Bureau of Indian Standard. Jin A, Aki K. 1988. Spatial and temporal correlation between Coda Q and seismicity in China. Bull Seismol Soc Am. 78:741769. Kayal JR, Arefiev SS, Baruah S, Hazarika D, Gogoi N, Kumar A, Chowdhury SN, Kalita S. 2006. Shillong plateau earthquakes in northeast India region: complex tectonic model. Curr Sci. 19:109114. Khattri K, Wyss M. 1987. Precursory variation of seismicity rate in Assam area, India. Geology. 6:685688. Khattri KN, Rogers AM, Perkins DM, Algermissen ST. 1984. A seismic hazard map of India and adjacent areas. Tectonophysics. 108:93134. Kumar A, Mittal H, Sachdeva R, Kumar A. 2012. Indian strong motion instrumentation network. Seismol Res Lett. 83:5966. Kumar N, Parvez IA, Virk HS. 2005. Estimation of coda waves attenuation for NW Himalaya region using local earthquakes. Phys Earth Planet Inter. 151:243258. Mandal P, Rastogi BK. 1998. A frequency-dependent relation of coda Qc for Koyna Warna region, India. Pure Appl Geophys. 153:163177. Mitra S, Priestley K, Bhattacharyya AK, Gaur VK. 2005. Crustal structure and earthquake focal depths beneath northeastern India and southern Tibet. Geophys J Int. 160:227248. Mittal H, Kumar A, Ramhmachhuani R. 2012. Indian national strong motion instrumentation network and site characterization of its station. Int J Geosci. 3:11511167. Nandy DR. 2001. Geodynamics of northeastern India and the adjoining region. Calcutta: ACB Publications. Nath SK, Thingbaijam KKS. 2012. Probabilistic seismic hazard assessment of India. Seismol Res Lett. 83:135149. Nath SK, Thingbaijam KKS, Maiti SK, Nayak A. 2012. Ground-motion predictions in Shillong region, northeast India. J Seismol. 16:475488. Oldham RD. 1899. Report on the Great earthquake of 12 June 1897. Memoir of Geological Society of India, 29, p. 1379, Geological Survey of India, Calcutta. Parvez IA, Vaccari F, Panza GF. 2003. A deterministic seismic hazard map of India and adjacent areas. Geophys J Int. 155:489508. Raghukanth STG, Somala SN. 2009. Modeling of strong-motion data in northeastern India: Q, stress drop, and site amplification. Bull Seismol Soc Am. 99:705725. Singh RP, Aman A, Prasad YJJ. 1996. Attenuation relations for strong seismic ground motion in the Himalayan region. Pure Appl Geophys. 147:161180.
Geomatics, Natural Hazards and Risk
21
Singh SK, Garcia D, Pacheco JF, Valenzuela R, Bansal BK, Dattatrayam RS. 2004. Q of the Indian shield. Bull Seismol Soc Am. 94:15641570. Singh SK, Ordaz M, Dattatrayam RS, Gupta HK. 1999. A spectral analysis of the 21 May 1997, Jabalpur, India, earthquake (Mw 5:8) and estimation of ground motion from future earthquakes in the Indian shield region. Bull Seismol Soc Am. 89:16201630. Sokolov V, Wenzel F. 2009. First step toward realistic ground-motion prediction for SWGermany. In A Oth, editor. Proceedings of workshop “Seismicity patterns in the Euro-Mediterranean Region”, European Center for Geodynamics and Seismology; November 1719; Luxemburg, p. 5368. Tandon AN. 1954. Study of the great Assam earthquake of August 1950 and its aftershocks. Indian J Meteorol Geophys. 5:95137. Verma M, Bansal B. 2013. Seismic hazard assessment and mitigation in India: an overview. Int J Earth Sci. 102: 12031218.
