Ground motion estimation during the Kashmir earthquake of 8th October 2005

In this article, analytical methods have been used to estimate ground motion during the 8 October 2005, Kashmir earthquake. Peak ground acceleration (PGA) values at several stations in the epicentral region have been estimated by empirical analytical source mechanism models. As an alternate analysis, PGA estimates have also been obtained using the stochastic finite fault seismological model. The estimated PGAs are compared with that obtained from damage values. A PGA contour map in the near-source region is provided. It is found that very near to the epicenter, PGA would have reached more than 1 g. It is demonstrated that empirical analytical models can be effectively used to estimate ground motion due to rupture of active faults.     

Stochastic earthquake source model for ground motion simulation

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.     

Seismicity parameters for important urban agglomerations in India

India’s urban population has increased in the recent times. An earthquake near an urban agglomeration has the potential to cause severe damage. In this article, seismicity parameters for region surrounding important urban agglomerations in India are estimated. A comprehensive earthquake catalogue for the region (6°E–42°E latitude and 60°N–100°N longitude) including historic and pre-historic events has been compiled from various sources. To estimate the parameters, past earthquake data in a control region of radius 300 km has been assembled to quantify the seismicity around each urban agglomeration. The collected earthquake data is first evaluated for its completeness. From combined (historical and instrumental) data, the seismicity parameters b-value, seismic activity rate, λ and maximum expected magnitude (m _max ) have been obtained from the methodology proposed by Kijko and Graham (1998). The obtained activity rates indicate that region surrounding Guwahati urban agglomeration is the most seismically active region followed by Srinagar, Patna, Amritsar and Chandigarh.     

Finite element models to represent seismic activity of the Indian plate

Quantification of seismic activity is one of the most challenging problems faced by earthquake engineers in probabilistic seismic hazard analysis. Currently, this problem has been attempted using empirical approaches which are based on the regional earthquake recurrence relations from the available earthquake catalogue. However, at a specified site of engineering interest, these empirical models are associated with large number of uncertainties due to lack of sufficient data. Due to these uncertainties, engineers need to develop mechanistic models to quantify seismic activity. A wide range of techniques for modeling continental plates provides useful insights on the mechanics of plates and their seismic activity. Among the different continental plates, the Indian plate experiences diffused seismicity. In India, although Himalaya is regarded as a plate boundary and active region, the seismicity database indicates that there are other regions in the Indian shield reporting sporadic seismic activity. It is expected that mechanistic models of Indian plate, based on finite element method, simulate stress fields that quantify the seismic potential of active regions in India. This article explores the development of a finite element model for Indian plate by observing the simulated stress field for various boundary conditions, geological and rheological conditions. The study observes that the magnitude and direction of stresses in the plate is sensitive to these conditions. The numerical analysis of the models shows that the simulated stress field represents the active seismic zones in India.     

Estimation of Seismic Site Coefficient and Seismic Microzonation of Imphal City,India, Using the Probabilistic Approach

Seismic site coefficients (F _s ) for Imphal city have been estimated based on 700 synthetically generated earthquake time histories through stochastic finite fault method, considering various combinations of magnitudes and fault distances that may affect Imphal city. Seismic hazard curves and Uniform Hazard Response Spectra (UHRS) are presented for Imphal city. F _s have been estimated based on site response analyses through SHAKE-91 for a period range of engineering interest (PGA to 3.0 s), for 5% damping. F _s were multiplied by UHRS values to obtain surface level spectral acceleration with 2 and 10% probability of exceedance in 50 year (～2500 and ～500 year) return period. Comparison between predicted mean surface level response spectra and IS-1893 code shows that spectral acceleration value is higher for longer periods (i.e., >1.0 s), for ～500 year return period, and lower for periods shorter than 0.2 s for ～2500 year return period.     

Stochastic ground motion models to NGA-West2 and NGA-Sub databases using Bayesian neural network

Ground motion models (GMMs) are traditionally developed from a frequentist approach. The Bayesian framework has received recent attention in developing nonergodic models, measuring uncertainty, or updating the model with additional data. However, no neural networks are developed to date in this framework to predict ground motion parameters or spectra. Hence, the present work develops a probabilistic Bayesian neural network (PBNN) to next-generation attenuation – West2 and Subduction databases using variational inference with mean-field assumption. Network inputs are magnitude, rupture distance, hypocentral depth, shear wave velocity, style of faulting, and region flags; outputs are peak ground values and response spectra. Both models have two hidden layers with seven neurons in each hidden layer. The models are verified for potential overfit, and their performance is validated through the parametric study by varying inputs. The output of a deterministic model is a point estimate. Considering probabilistic layers in hidden and output layers enables the model to capture within-model epistemic uncertainty and aleatory variability. Obtained aleatory standard deviations are consistent with other models. Mean epistemic uncertainty and aleatory variability are in the range 0.07–0.10 and 0.62–0.78 (ln units) for NGA-West2 and 0.09–0.16 and 0.67–0.95 for NGA-Sub models, respectively. The correlation coefficients between recorded and overall mean predictions ranged from 0.94 to 0.97 for NGA-the West2 model and from 0.91 to 0.95 for the NGA-Sub models. Network performance for out-of-training inputs showed increased epistemic deviations with no effect on aleatory deviations.     

Seismic recurrence parameters for India and adjoined regions

Journal of Seismology - This article focuses on estimating the seismic recurrence parameters of India and adjoining regions based on a comprehensive catalogue assimilated from various sources. The...     

Rating of Indian ground motion records

Natural Hazards - A nonlinear dynamic analysis method in earthquake-resistant design requires real-time ground motion records. However, a definite criterion is required for ground motion selection....     

Ground Motion Relations for Active Regions in India

In this article, a study on development of ground motion prediction equations (GMPEs) is undertaken for seismically active regions in India. To derive the equations, the seismically active regions are divided into four units based on seismotectonic setting and geology. Due to lack of strong motion data, a stochastic finite-fault simulation method is used for generating a complete synthetic database with respect to magnitude and distance. The input parameters in the stochastic seismological model, such as site amplification and stress drop, are first derived from the past strong-motion data. A total of 236 three-component records from 62 earthquakes with magnitudes ranging from M _w 3.4 to 7.8 are used to calibrate the seismological model. The obtained stress drops of these 62 events lie in between 60 and 165 bars. With the help of a large synthetic database generated from the calibrated seismological model, ground motion relations for 5 % damped spectral acceleration are obtained by regression analysis. The developed ground motion relations are compared with the existing GMPEs of the other active regions in the world. Although the proposed equations have trends similar to those of the existing relations, there are some differences attributed to stress drop and the quality factor of active regions in India. These relations will be useful to prepare spectral acceleration hazard maps of India for a given annual probability of exceedance.