ground Motion synthesis and seismic scenario in guwahati city a stochastic approach

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1 ground Motion synthesis and seismic scenario in guwahati city a stochastic approach S. K. Nath, A. Raj, K. K. S. Thingbaijam, and A. Kumar S. K. Nath, A. Raj, K. K. S. Thingbaijam, and A. Kumar Indian Institute of Technology Kharagpur INTRODUCTION Guwahati is fast emerging as a multi-ethnic cosmopolitan city with a burgeoning population and rapid unplanned urbanization. It is a commercial hub, catering to eight states in northeast India. The city is located in the lower Brahmaputra Valley and underlain by thick alluvial deposits ranging from 5 to 600 m with granitic exposure at places. The regional seismotectonics of northeast India exhibit a rather large potential for high-magnitude earthquakes, as reported by Thingbaijam et al. (008). Large earthquakes in the past caused widespread damage to life and property in this terrain. Presently, the urban agglomeration in this area is quite vulnerable to possible future great earthquakes. Given the devastation caused recently by the Sichuan earthquake of 1 May 008 (M W 7.9), with a death toll of nearly 70,000 and loss of property worth billions of dollars, it is imperative that we have a realistic hazard scenario for this extremely vulnerable seismic province. Seismic hazard quantification at a site is based, essentially, on understanding the site response and the propagation path of seismic waves, as well as the source characterization of a damaging earthquake. A typical deterministic seismic hazard model takes these physical attributes into account while estimating peak ground acceleration (PGA). The results of the simulations of four scenario earthquakes based on four large historical earthquakes in the region have been reported here. An extended finite-fault stochastic modeling approach has been adapted for strong ground-motion synthesis for the predicted maximum earthquakes in the terrain as reported by Thingbaijam and Nath (008). An attempt also has been made to establish regional as well as site-specific attenuation relations for Guwahati city through successive ground-motion synthesis and subsequent regressions on the model parameters for a range of earthquakes in the territory with magnitude M W > 5 to a great earthquake (e.g., predicted maximum earthquake of magnitude M W > 8) considered to be nucleated from the hypocenters of historical earthquakes. REGIONAL SEISMOTECTONICS The major tectonic background in northeast India consists of the eastern Himalayan structures, the Mishmi massif (Nandy 001), the Indo-Myanmar arc, the Brahmaputra Valley, and the Shillong plateau. The Shillong plateau has been the main contributor to the seismicity of the Guwahati region. Because of the proximity of Guwahati city to the great 1897 earthquake of M W 8.1 as shown in Figure 1, it is expected that the city s seismicity will be dominated by the prevailing seismotectonics of Figure 1. Seismotectonic map of northeast India adapted from Thingbaijam et al. (008) wherein the fault rupture zones of four scenario earthquakes have been depicted with rectangular polygons. doi: /gssrl Seismological Research Letters Volume 80, Number March/April

2 Figure. Geological and geomorphological map of Guwahati depicting road networks, strong-motion monitoring stations, and borehole sites. the plateau. Based on their seismicity analysis of northeastern India, Thingbaijam et al. (008) identified four seismic source zones the Eastern Himalayan zone (EHZ), the Mishmi block zone (MBZ), the Eastern boundary zone (EBZ), and the Shillong zone (SHZ). Subsequently, Thingbaijam and Nath (008) estimated the maximum earthquake in the corresponding zones to be M W 8.35 (±0.59) in EHZ, M W 8.79 (±0.31) in MBZ, M W 8.0 (±0.50) in EBZ, and M W 8.73 (±0.70) in SHZ. The 1934 Nepal Bihar earthquake of magnitude M W 8.1 adheres to EHZ source tectonics. The earthquake was felt over an area of 4.9 million sq km in India, Nepal, and Tibet. In the meizoseismal region, the earthquake caused major destruction and induced numerous fractures, landslides, and even a slump belt (Singh and Gupta 1980). The great 1950 Assam earthquake of M W 8.7 adhering to the MBZ source regime is believed to have originated due to right lateral shear movement in the Po Chu fault (Ben-Menahem et al. 1974), while the 1988 Manipur earthquake of M W 7. is associated with the Indo- Myanmar subduction tectonics in the EBZ seismic province. DATA SOURCE For the analysis, we used strong-motion waveforms recorded by the Indian Institute of Technology Guwahati (IITG) strongmotion network and geotechnical data acquired by Assam Engineering College at 00 borehole locations in the territory. The waveform data were used for source, path, and site characterization in Guwahati city (Nath et al., 008a) while site-amplification values estimated from geotechnical data corroborated by those computed from strong ground-motion recordings are utilized for hazard analysis. A map of the study region depicting roads, strong-motion monitoring stations, and borehole sites is presented in Figure. METHODOLOGY The prediction of strong ground motion is based both on empirical and theoretical approaches. Over the past two decades, as we have gained a better understanding of how seismic wave fields shake manmade structures, theoretical methods have gained momentum. The stochastic approach (Housner and Jennings 1964; Hanks and McGuire 1981; Boore 1983; Boore and Atkinson 1987; McGuire et al. 1984) is one of the most expedient methods of synthesizing strong ground motion and is modeled with Gaussian noise using a spectrum that is either empirical or based on a physical model of the earthquake source (Halldorsson et al. 00). This method has been used to match the observed waveform even up to very high magnitude or seismic moment in different tectonic environments. Another approach, the frequency-wavenumber (F-K) integration method, has proved to be a very powerful simulator of wave progression in layered media as well as in complex geological domains (Bouchon and Aki 1977). This method employs an elastodynamic representation theorem to compute the motion 34 Seismological Research Letters Volume 80, Number March/April 009

3 (e.g., Aki and Richards 1980) wherein the rupture process is modeled by postulating a slip function on a fault plane. Most often, the F-K integration approach accurately reflects the wave propagation phenomena. However, due to ease in the formulation of strong-motion simulation parameters, the stochastic algorithm has been found to be effective in the generation of high-frequency ( f > 0.1Hz) ground motion and is widely used, especially where earthquake recordings are scanty. The stochastic algorithm uses standard convolution theorem to model spectral acceleration. The amplitude spectrum A(w) can be written, in the frequency domain, as the product of source function SO(w, w c ), a propagation path term P(w), and a site function SI(w) (Boore 1983; Nath et al. 005) as given below: A( ω) = SO( ω, ω). SI( ω). P( ω), (1) c where w c ( = pf c ) refers to corner frequency. Acceleration spectra often show a sharp decrease with increasing frequency so a high-cut filter F(ω, ω m ) is incorporated in the above equation such that A( ω) = SO( ω, ω). SI( ω). P( ω). F( ω, ω ). () c Papageorgiou and Aki (1983) related ω m to source processes while Hanks (198) associated ω m to attenuation near the recording site. However, the high-cut filter F(ω, ω m ) has been taken to be F( ωω, m) = ω 1 + ω m s 1 / m, (3) where s controls the decay rate at higher frequencies. The highcut filter F(ω) given by Anderson and Hough (1984) is presented as F( ) e k ω/ ω =, (4) where k is a spectral decay parameter and controls the decay rate at higher frequencies. The conventional point source approximation is unable to characterize key features of ground motions from large earthquakes, such as their long duration and the dependence of amplitudes and duration on the azimuth to the observation point (source directivity).a finite source model is, thus, used to simulate ground motion that contributes not only to the duration and directivity but also affects the shape of the spectra of seismic waves. In this model, the fault plane is subdivided into elements (subfaults), and the radiation from a large earthquake is obtained from the summation of contributions from all elements, each of which acts as an independent source. The stochastic method for strong ground motion simulation of Beresnev and Atkinson (1997) employs a finite-fault modeling in which sub-source moment depends on the subfault dimension that can be calculated using a relation given by Beresnev and Atkinson (001). However, a large uncertainty is associated with the suggested relation due to paucity of large-earthquake recordings. To overcome this limitation, Motazedian and Atkinson (005) enhanced the algorithm by introducing dynamic corner frequencies. The improved approach conserves the radiated energy at higher frequency at any sampling of the subfault size thereby controlling the relative amplitude of higher versus lower frequencies. Accordingly, the Fourier amplitude spectrum due to the nth subfault is given as A ( f ) = CM H n n n ( π f ) f + e 1 f n() t 0 frn π ωq G, (5) where C is a scaling factor, M n is the nth subfault moment, H n is the spreading factor responsible for conserving the energy at the high-frequency spectral level of the subfault, and f 0n (t) is the dynamic corner frequency. G, R n, and Q refer to geometric spreading factor, hypocentral distances corresponding to nth subfault, and quality factor, respectively. The scaling factor is given as C = R φ j 3 4πρβ, (6) where R φj, ρ, and β refer to radiation pattern, average crustal density, and shear wave velocity, respectively. The moment M n of the nth subfault is calculated using the slip distribution as follows: 0 n MD M n = ( D ), (7) n where D n is the corresponding average slip and Σ(D n ) denotes the total slip of the fault during the entire rupturing process. The dynamic corner frequency is expressed as follows: f 0n 6[ R ()] ( t)= / 13 / N t N βσ β M o 13 /, (8) where N R (t) is the number of rupture subfaults at a time, t. N refers to total number of subfaults totaling to N R (t) at the end of the rupture and Δσ is the stress drop. The spreading factor in Equation 5 is computed as Seismological Research Letters Volume 80, Number March/April

4 Figure 3. (A) Two representative site-amplification plots observed at boreholes located in the high site-amplification zone. (B) The site-amplification distribution exhibits dominance of site-amplification factor in the range of times. H n = N f f f + f 1 0, (9) f f 1+ f0n () t f where f 0 is the corner frequency at the end of the rupture. The seismic scenario in the Guwahati region has been generated by characterizing the maximum earthquake, source parameters, path attenuation, and site effects accordingly. SOURCE, PATH, AND SITE CHARACTERIZATION Seismic sources characterized by physical parameters like corner frequency f c, seismic moment M o, and stress drop σ have been estimated from the waveform data using Brune s source approximation. The detailed study of source parameters made by Nath et al. (008a) estimates a stress drop range of to 44.4 bars for the earthquakes recorded in the Guwahati region. The very high stress drop observed in the region indicates significant strong motion amplitude during an earthquake. The actual stress drop during any scenario event cannot be predicted from the available strong motion data, but an average stress drop can be estimated that can be used in the scenario earthquake simulations, as has been done by Nath et al. (008b). The derived stress drop is especially useful when there are no records of a strong earthquake in the study region. However, in the case of a simulation of a historical earthquake, observed parameters are preferable. In the present investigation, we computed stress drops for the scenario/maximum earthquakes using either the observed slip value or the reported ones. On the basis of the recorded strong-motion data in the region, the shear wave quality factor Q s has been found to vary from 180f 0.86 to 733f 0.35 (Nath et al. 008a). Accordingly, a power-law frequency-dependent relationship has been established for the region as given by Q s = 34f (10) The Q s obtained in the present study is derived from the overall attenuation of the seismic wave energy, which includes the direct S wave, early coda, and possibly L g phase of the recorded data. Equation 10 represents an average attenuation specific to the Guwahati region for the earthquakes originating from different surrounding sources. On the basis of geotechnical investigations in the region, amplification of ground motion varies from 1 to 15 times (Nath et al. 008a). The geotechnical analysis involves a combination of wave propagation theory with the material properties and the expected ground motion computed at the site of interest. In the horizontally layered soil deposits, the recurrent and circular soil behavior can be simulated using a linear equivalent model of a nonlinear phenomenon through an iterative process to compute shear modulus and damping compatible with the equivalent uniform strain induced in each sub-layer accounting for the nonlinear behavior of soil (Kramer 1996). Each geological unit, e.g., soil profile, is defined by its shear wave velocity, damping, total unit weight, and thickness. An initial estimate of damping has been taken to be 5% for soil. Two representative plots for site amplification factor against frequency are given in Figure 3A, while the histogram of the site response obtained at all the observation points (00 boreholes) is depicted in Figure 3B. The site amplification computed from geotechnical data has been corroborated with the horizontal to vertical spectral ratio (HVSR) and is found to exhibit a satisfactory match in the required frequency range (Nath et al. 008a). Site-amplification factors derived from the geotechnical data have been taken to account for site-specific seismic hazard representation due to wider data coverage in the terrain. The rock sites (hills where borehole data are not available) are assumed to have negligible site amplifications. However, geological information such as type of soil, topography, etc., has been considered to control the extrapolation of the estimated PGA. SEISMIC SCENARIOS We used finite source approximation based on dynamic corner frequency (Motazedian and Atkinson 005) for the generation of seismic hazard scenario in the greater Guwahati region. As already discussed, the algorithm considers not only finite 36 Seismological Research Letters Volume 80, Number March/April 009

5 Figure 4. Both time history and spectral acceleration as simulated using extended finite fault stochastic modeling with the source parameters derived by Nath et al. (008a) for M W 4.84 at (A) AMTRON, and (B) AEC strong-motion monitoring stations respectively. rupture but also provides increased resolution in the lower-frequency band. The robustness of the algorithm has been proved by simulating time history and spectral acceleration using observed source, path, and site parameters. The representative simulated accelerograms at the strong-motion monitoring stations AMTRON and AEC for an earthquake that occurred on February 006 of magnitude M W 4.84 are depicted in Figure 4 in both time history and spectral form. This earthquake is located at 7.0 N 9.00 E with a focal depth of 33 km. The source, path, and site parameters for this earthquake groundmotion simulation are adapted from Nath et al. (008a). The stress drop and seismic moment have been observed to be 180 bars and dyne-cm respectively, while the path parameter represented by the quality factor is estimated to be 180f The earthquake nucleated northeast of the study region at a distance of about 119 km with an azimuth of 191 N from AMTRON, and 17 km with an azimuth 196 N from AEC. In the present study, the simulations have been performed using the EXSIM code of Motazedian and Atkinson (005). Seismic scenario in the territory has been generated based on the stochastic simulation of strong ground motion for a predicted maximum earthquake assumed to be nucleating from the hypocenter of the four largest reported earthquakes in the region, namely, the 1934 Nepal Bihar earthquake, 1950 Assam, 1988 Manipur, and 1897 Shillong earthquakes. M w 8.4 Scenario Earthquake Simulation at 1934 Nepal Bihar Earthquake Source The historical 1934 earthquake was caused by strike-slip faulting striking 100 N and dipping 30 S, with a focal depth of 0 km and stress drop of 75 bars as reported by Singh and Gupta (1980). The other simulation parameters, such as shear-wave velocity, density, and quality factor have been taken from Nath et al. (008a). The kappa value is assumed to be 0.05 for the northeastern Indian region and a geotechnically derived site response as the representative site amplification with 5% damping is considered for the simulation. Table 1 lists the parameters that we used in modeling the simulations. The estimated PGA distribution for this scenario earthquake of M W 8.4 is presented in Figure 5A in gray scale, with the maximum being 0.4 g in the study region. M w 8.8 Scenario Earthquake Simulation at 1950 Assam Earthquake Source This is the deadliest earthquake known to have occurred in the region; Richter (1958) assigned it an instrumentally determined magnitude of 8.7. The affected area encompassed more than 3 million square kilometers across India, Myanmar, Bangladesh, Tibet, and China, and the event resulted in approximately 1,500 deaths. The spread of aftershock activities extended from 91 to 97 E and 4 to 33 N. It is believed that the earthquake was caused by a motion of the Asian plate relative to the eastern flank of the Indian plate where the northeast Assam block is imparted a tendency of rotation with fracture lines being developed along its periphery. According to Ben-Menahem et al. (1974), the earthquake had a strike-slip rupture with a velocity of 3 km/s, a strike of northeast, and a dip of ENE. They estimated the stress drop corresponding to this event to be 66 bars and we generated a seismic scenario in Guwahati city for a scenario earthquake magnitude of M W 8.8 that nucleated from the hypocenter of the historic event. The simulation parameters are listed in Table 1 and a spatial PGA distribution map is presented in Figure 5B. It can be seen that the spatial pattern of PGA is quite similar to the one for the M W 8.4 Nepal Bihar scenario (Figure 5A), but the PGA value ranges between and g, unlike that for the 1934 Nepal Bihar earthquake scenario. Seismological Research Letters Volume 80, Number March/April

6 TABLE 1 Parameters Used for Strong Ground-motion Simulation in Guwahati Scenario Sources Parameter 1897 Shillong 1950 Assam 1934 Nepal Bihar 1988 Manipur Strike 9 N N 100 N 84 N Dip 40 ESE 57.5 SW 30 S 45 E Focal depth (km) Source/Hypocenter* (Lat, Lon) 6.00 N, E 8.38 N, E 6.60 N, E 6.19 N, E Observed Historical Earthquake Magnitude (M W ) Scenario/Maximum Earthquake Magnitude (M W ) Fault length (km) Fault width (km) No. of subfaults along Strike No. of subfaults along Dip Stress (bar) Shear wave velocity (km/s) 3.5 Crustal density (g/cm 3 ).7 Pulsating area 5% K 0.05 Q s 34f 0.7 Geometrical spreading 1/R (R<100 km) 1/R 0.5 (R>100 km) Windowing function Saragoni and Hart Site amplification Geotechnical Damping 5 * Hypocenter has been positioned at the center of the fault length. M w 8. Scenario Earthquake Simulation at 1988 Manipur Earthquake The Manipur-Burma border earthquake of M W 7. rocked the entire northeastern region of India early in the morning of 6th August The tremor was felt throughout northeast India, Bangladesh, and parts of Burma. It lasted for approximately two minutes and caused a loss of four human lives along with considerable damage to buildings, railway tracks, roads, etc. Field surveys showed that the maximum intensity reached VIII on the Modified Mercalli Intensity scale near the epicentral region (Kayal and De 1991). For the scenario earthquake simulation of M W 8. in this source region, we considered a rupture dimension equal to km with 100-km focal depth, since most of the earthquakes in this region have an intermediate focal depth ranging from 90 to 110 km. The stress drop for this event has been determined empirically to be equal to 83 bars, while the other parameters are the same as depicted in Table 1. The simulated PGA distribution in Figure 5C shows a variation from g to g. M w 8.7 Scenario Earthquake Simulation at 1897 Shillong Earthquake This event, the largest intraplate earthquake on the Indian subcontinent, raised the northern edge of the Shillong plateau by more than 10 m, resulting in the destruction of structures over much of the plateau and surrounding areas. The earthquake also caused widespread liquefaction and flooding in the Brahmaputra and Sylhet floodplains. Many researchers have analyzed the source parameters for this great earthquake (Ambraseys and Bilham 003; Hough et al. 005; Bilham and England 001). The strike and dip of the fault have been taken as 9 N and 40 ESE, respectively, as reported by Bilham and England (001), while the fault dimension of km has been modeled for the simulation. The earthquake is considered to have originated due to a pop-up mechanism bounded by at least two major faults, the Oldham and Dauki faults in this case. The scenario earthquake of M W 8.7 has been projected in the rupture plane of the Oldham fault. The 1897 earthquake produced a gigantic slip of 16 m (maximum up to 1 m), which implied a stress drop equal to 159 bars computed through the relation Dσ = μu( )/L, (11) where μ is the shear modulus of the crust, u( ) is the final slip, and L is the subfault length. The other simulation parameters applicable to this region are taken from Nath et al. (008b) and are listed in Table 1. The final PGA maps produced both at the rock level and the surface are presented in Figures 6A and 6B, respectively. It is apparent from Figure 6B that the PGA value 38 Seismological Research Letters Volume 80, Number March/April 009

7 (A) (B) Figure 5. Spatial distribution of peak ground acceleration obtained through extended finite source stochastic simulation across the study region for scenario source regime of (A) 1934 Nepal Bihar, (B) 1950 Assam, and (C) 1988 Manipur earthquakes respectively with the corresponding maximum earthquake magnitudes of M W 8.4, 8.8, and 8.. at the surface, ranging from 0. to 1.7 g, has been enhanced due to site amplification, while a maximum PGA of only 0.4 g has been estimated at rock level. The time and spectral domain presentation of a representative accelerogram is shown in Figure 7. Time history of the acceleration computed at bedrock level at IITG station (Figure ) is depicted in Figure 7A while Figure 7B shows the effect of geotechnical amplification. Figure 7C depicts the site-specific spectral acceleration simulated using geotechnical as well as HVSR site response. We note that the geotechnical amplifications, which have been calibrated with those of strong motion data, exhibit an average effect, so far as the spectral amplitudes are concerned (Nath et al. 008a). Apparently, the Shillong source region represents the major contributor to the seismic hazard in Guwahati city, compared to the other three seismic sources. REGIONAL AND SITE-SPECIFIC ATTENUATION RELATIONS The most common means of estimating ground motion at a site of interest is the use of an attenuation relationship, which Figure 6. Spatial distribution of peak ground acceleration computed at (A) the rock level and (B) at the surface for the scenario earthquake M W 8.7 assumed to be nucleated from the 1897 Shillong earthquake source region. relates a specific strong motion parameter of ground shaking to one or more seismological parameters of an earthquake like the source, the wave propagation path between the source and the site, the soil, and the geologic profile beneath the site. In the present analysis, a first-order attenuation relationship has been derived by regression analysis, considering the similar relation given by Campbell and Bozorgnia (003): ln(pga) = C 1 + C M + C 3 (10 M) 3 + C 4 ln[r rup + C 5 exp(c 6 M)], (1) where C i (i = 1 to 6) are the regression coefficients, PGA is in g, M is the earthquake moment magnitude, and r rup is the rupture distance (km). However, for estimating the site-specific attenuation relationship, local site parameters need to be incorporated in the fundamental equation. Therefore, site response, shear wave velocity, and spectral response have been incorporated as follows: ln(pga) = C 1 + C M + C 3 (10 M) 3 + C 4 ln[r rup + C 5 exp(c 6 M)] + C 7 S v + C 8 ln(sr) + C 9 ln(sa), (13) Seismological Research Letters Volume 80, Number March/April

8 Figure 7. Accelerograms generated through stochastic extended finite-fault modeling (A) at the bedrock level and (B) at the surface using site amplification derived through geotechnical analysis. (C) shows spectral acceleration computed at the surface using site amplification derived from geotechnical analysis (bold line) with the acceleration spectra simulated using HVSR site amplification (lighter shade). Figure 8. (A) Comparison of the present attenuation relation (bold line) with other relations established around the globe. Log residual of PGA with respect to (B) rupture distance and (C) moment magnitude (M W ) to validate to model parameters. where S v represents the effective shear wave velocity averaged over the top 30 meters overburden, SR is the site response, and SA is the spectral acceleration. The rupture distance, r rup, refers to the distance closest to the fault rupture from the observation point. For computation of regional attenuation relationships, we estimated PGA by simulating all the observed earthquakes in the region from magnitude M W 4.8 to M W 8.1. We used a grid of for near-field effects for the purpose. Thereafter, we performed multiple regression analysis to estimate the model. We have used a semi-empirical approach to minimize the difference between the observed and the predicted values of ground motion using the least-square-error energy minimization. The estimated regional attenuation relationship, thus, is derived as follows, ln(pga) = M 0.014(10 M) 3.697ln(r rup exp (0.0663M)). (14) The PGA attenuation with rupture distance using this equation for magnitude M W 7 is shown in Figure 8A along with some already available attenuation relationships around the globe. Our relation for Guwahati is quite comparable to those of Singh et al. (1996) for the Himalaya and Nath et al. (005) for the Sikkim region. Although the relation given by Atkinson and Boore (1995) for eastern North America matches our relation for hypocentral distance greater than 30 km, the considerable divergence observed under 30 km could be due to lack of near-source data required for calibration of the simulation technique. However we cannot discount differ- 40 Seismological Research Letters Volume 80, Number March/April 009

9 TABLE Regression Coefficient of Spectral Attenuation in Guwahati City Frequency (Hz) c 1 c c 3 c 4 c 5 c 6 c 7 c 8 c Composite ences in the tectonic environments. Other relations estimated by Parvez et al. (00), and Chandrasekaran (1994) for different tectonic environments depict higher values than the present estimate, while the attenuation relationships of Joyner and Boore (1981), Ambraseys (1995), and Sharma (1998) underestimate the same. The log residuals computed with respect to rupture distance and magnitude has been depicted in Figures 8B and 8C, respectively. The log residual in the present estimate shows a ±0.6, variation that reflects the uncertainty bound with respect to the rupture distance as well as magnitude. Furthermore, site-specific attenuation coefficients that consider the local site attributes have been derived at various frequencies as listed in Table. The stochastic ground-motion simulation employed in the present study generally underestimates the near-source peak amplitudes as compared to deterministic/analytical assessments. CONCLUSION We estimated PGA in the greater Guwahati region through the simulation of four scenario earthquakes. The maximum earthquake predicted for the region has been considered for deterministic seismic hazard estimation that has been assumed to be nucleating from the hypocenter of historical earthquakes, but with projected maximum magnitude. It is seen that the areas of high acceleration correspond to those of high site amplification, which suggests that site amplification is the predominant hazard-deciding factor. Furthermore, directivity effects could not be ascertained due to overwhelming influence of the site amplification on the estimated peak ground acceleration. Estimated site-specific spatial PGA distributions depict a maximum of 1.7g for the predicted M W 8.7 at the 1897 Shillong earthquake source region. The intensity for this scenario earthquake following the relation of Murphy and O Brien (1977) predicts a maximum intensity of MMI XI in the Guwahati region, which is significantly higher than the observed intensity value of MMI VIII for the earthquake of M W 8.1 as reported by Ambraseys and Bilham (003). Furthermore, areas of high PGA (>1 g) is observed to be associated to the Bordang surface that corresponds to lower predominant frequency (< 1.0 Hz) and lower shear wave velocity for 30-m depth profiles (V s 30 ) in the range of m/s. However, PGA distribution due to the Nepal Bihar earthquake source of magnitude M W 8.4 is no less hazardous in seismic terms with maximum PGA of 0.5 g and MMI VIII in the region. The high intensity estimated for these earthquakes may be attributed to the consideration of scenario/maximum magnitude as well as local site effects. The higher PGA distribution has also been observed along the Brahmaputra River floodplain. Finally, a site-specific attenuation relationship has been worked out that can be utilized for the prediction of PGA in the region at a denser mesh. The results achieved in this study can be considered a benchmark for similar investigation elsewhere toward updating universal building code provisions for areas with rapid urban growth that wish to plan for sustainable development. ACKNOWLEDGMENTS We are grateful to Dr. Luciana Astiz and an anonymous reviewer for their comments and critical suggestions. This research was supported by the Department of Science and Technology, Seismology Division, Government of India via sanction order no. DST/Exp Group/ Guwahati-microzonation/00. REFERENCES Aki, K., and P. Richards (1980). Quantitative Seismology: Theory and Methods. Vols. 1 and. San Francisco: W. H. Freeman and Company, 948 pps. Ambraseys, N. N. (1995). The prediction of earthquake peak ground acceleration in Europe. Earthquake Engineering and Structural Dynamics 4, Seismological Research Letters Volume 80, Number March/April

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