S T G Raghukanth

This paper deals with the estimation of spectral acceleration for Manipur based on probabilistic seismic hazard analysis (PSHA). The 500km region surrounding Manipur is divided into seven tectonic zones and major faults located in these zones are used to estimate seismic hazard. The earthquake recurrence relations for the seven zones have been estimated from past seismicity data. Ground motion prediction equations proposed by Boore and Atkinson (2008 Earthq. Spectra 24 99-138) for shallow active regions and Atkinson and Boore (2003 Bull. Seismol. Soc. Am. 93 1703-29) for the Indo-Burma subduction zone are used for estimating ground motion. The uniform hazard response spectra for all the nine constituent districts of Manipur (Senapati, Tamenglong, Churachandpur, Chandel, Imphal east, Imphal west, Ukhrul, Thoubal and Bishnupur) at 100-, 500- and 2500-year return periods have been computed from PSHA. A contour map of peak ground acceleration over Manipur is also presented for 100-, 500-, and 2500-year return periods with variations of 0.075-0.225, 0.18-0.63 and 0.3-0.1.15g, respectively, throughout the state. These results may be of use to planners and engineers for site selection, designing earthquake resistant structures and, further, may help the state administration in seismic hazard mitigation. © 2012 Sinopec Geophysical Research Institute.

Developing a ground motion model (GMM) for Fourier amplitude spectrum (FAS) is essential in seismology and engineering for generating response spectrum and synthetic time histories. Despite data-driven techniques being efficient in modeling complex relations, very few GMMs are developed for FAS using them. An efficient hybrid data-driven algorithm combining genetic algorithm and artificial neural network is implemented using the GeoNet database with 905 records from 77 events in the current work. The input parameters of the model are moment magnitude, Joyner–Boore distance, shear wave velocity, depth to the top of the rupture plane, fault, and tectonic flags. The developed FAS model is statistically tested to be robust and has good agreement with the recorded data and other available GMMs. The developed GMM to FAS has an overall correlation coefficient in the range of 0.8108–0.9298 and sigma in the range of 0.26–0.4 (in log10 units). Further, synthetic time histories are generated from the predicted FAS values and are consistent with various ground motion parameters and the response spectra.

The present work aims at exploring the application of nonlinear principal component analysis in dimensionality reduction and prediction of response spectra. The evaluation is performed based on log10 scaled response spectra at 91 spectral periods corresponding to 13552 records available in the NGA-West2 database. The non-linear principal component analysis performed on the data showed that 91 spectral periods can be addressed with just 3 principal components. Further, an artificial neural network (ANN) model is developed to predict these three principal components with magnitude, distance, shear wave velocity and focal mechanism as input. The inter- and intra-event residuals obtained for the response spectra predicted using the developed model are comparable with the existing ground motion prediction equations (GMPEs) from the same database. The developed model is also observed to capture all the prominent attenuation features of ground motions. Hence, the study indicates that the response spectra can be described with just three uncorrelated variables.

A novel methodology for obtaining a seismic zone map of India is demonstrated in this study, wherein a concrete theoretical framework is provided for deriving the zones and the respective zonal response spectra. The method involves time series clustering of uniform hazard response spectra (UHRS) that were obtained for the entire country on a 0.1° × 0.1° grid by performing probabilistic analysis corresponding to a 2475-year return period. The Euclidean distance between the UHRS values at all periods (27 data points between 0.01 s and 5 s) was taken as the similarity measure in an evolutionary particle swarm optimization algorithm. The analysis was conducted with a swarm population of 100 over 3000 iterations, and the mean UHRS of the resulting clusters was assumed as the cluster centre. Various quality/validity indices including the compactness measure, similarity measure, combined measure and Dunn Index were used to verify the results of the clustering. Based on these clusters, the entire country can be divided into seven zones, with a unique zonal spectrum for each zone.

In this article, analytic expressions for the frequency wave number spectra are derived for the displacement and rotational components at the ground surface of a micropolar elastic half-space. The ground motion is caused by buried sources described in terms of unit impulsive force and micro-moments. Closed-form analytic expressions for both plane strain and anti-plane strain conditions are established. In the sequel, Green's functions for reduced micro polar half-space where the couple stresses are zero and the stress tensor is nonsymmetric are also presented. Numerical results are presented for anti-plane strain case. The derived expressions can be used to simulate rotational and displacement components of ground motion during earthquakes.

In this article, a predictive model for ground motion characteristics is developed using the artificial neural network (ANN) technique. This model is developed to predict peak ground acceleration (PGA), peak ground velocity (PGV), peak ground displacement (PGD), spectral acceleration at 0.2 and 1 s. The input parameters of the model are moment magnitude (Mw), closest distance to rupture plane (Rcd), shear wave velocity in the region (Vs30), and focal mechanism (F). The updated NGA-West2 database released by Pacific Engineering Research Center (PEER) is employed to develop the model. A total of 13,678 ground motion records are used to develop the model. The ANN architecture considered in the study has four input nodes in the input layer, three neurons in the hidden layer, and three output nodes in the output layer. The ANN is trained by a hybrid technique combining genetic algorithm and Levenberg–Marquardt technique. The results of the study are found to be comparable with the existing relation in the global database. The model developed can be further used to estimate seismic hazard.

In the analysis and design of important structures with relatively long life spans, there is a need to generate strong motion data for possible large events. The source of an earthquake is characterized by the spatial distribution of slip on the fault plane. For future events, this is unknown. In this paper, a stochastic earthquake source model is developed to address this issue. Here, 1D and 2D stochastic models for slip distribution developed by Lavallée et al. (2006) are used. The random field associated with the slip distribution is heavy-tailed stable distribution which can be used for large events. Using 236 past rupture models, the spectral scaling parameter and the four stable or Levy’s parameters against empirical relationship for known quantities like magnitude or fault length are developed. The model is validated with data from 411 stations of 1999 Chi-Chi earthquake. The simulated response spectrum showed good agreement to actual data. Further the proposed model is used to generate ground motion for the 1993 Killari Earthquake where strong motion data is not available. The simulated mean peak ground velocity was in turn related to the intensity (MSK) and compared against values in the literature.

The present study aims at developing a model for simulating ground motion for earthquakes in the Sumatran region where one of the most devastating earthquakes took place in 2004 with a moment magnitude (Mw) of 9.1. With advancements in instrumentation, the three-dimensional material properties, topography and bathymetry of the region are available in the global database. These parameters are used as inputs in Spectral Finite Element Method to simulate ground motions. The model is first validated with the IGCAR broadband velocity data for 2012 Mw 8.6 Sumatra Earthquake. Due to favourable comparison, our model is also used to generate ground displacement characteristics of Mw 9.1 event. The source uncertainties are accounted by using three finite fault slip models available in the global database. The simulated time histories showed that the ground motion is sensitive to input slip models. The peak ground displacement (PGD) and ground residual displacement (GRD) in both horizontal and vertical directions are presented as contour plots. PGD obtained from various slip models in the epicentral region is of the order of 14-22 m in horizontal direction and 7-16 m in vertical direction. GRD in the epicentral region is of the order of 6-17 m in East-West (E-W) 4-17 m in the North-South (N-S) directions. The vertical uplift obtained from various slip models is around 2-8 m. The developed model can be used to simulate ground motion time histories, which can be further used in hazard analysis, tsunami simulations, etc.

Great earthquakes in the past (e.g. 1869 Cachar earthquake, 1897 great Assam earthquake) have caused large scale damage and ground liquefaction in the Guwahati city. Moreover, seismologists are of opinion that a great earthquake might occur in the unruptured segment of the North-East Himalaya that is near to Guwahati city. In this paper, the liquefaction hazard due to these events have been simulated. The obtained results are in general agreement with the reported damages due to the past earthquakes. The central part of the city (i.e. Dispur, GS road), that has large thickness of soft soil deposit and shallow ground water table, is highly vulnerable to liquefaction. © 2009 Springer-Verlag.

Standard penetration test (SPT) reveals the spatial complexity of standard penetration resistance (N-value) with depth. In this paper, a 1D stochastic characterization of spatial complexity of N-values is developed by considering data obtained from sixty two boreholes in Guwahati City. The N-value profile is modeled as the sum of deterministic part and a stochastic component. The deterministic part which characterizes the non-stationary mean of the data is determined by linear regression analysis. The remaining error is modeled as a spatial random field. The characterization of error heterogeneity as a homogeneous Gaussian random field successfully captures the observed auto-correlation function. The proposed stochastic model is used to compute the probability of factor of safety against liquefaction by Monte-Carlo simulation. The results obtained are presented in form of fragility surfaces, expressing the probability of liquefaction as a function of magnitude of the earthquake and epicentral distance. It is observed that the probability of liquefaction at Guwahati city due to strong earthquakes occurring even at large distances is very high. © 2008 World Scientific Publishing Company.