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 fags. 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.
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This work aims at developing a hybrid ground motion prediction equation (GMPE) for spectral acceleration in Western Himalayas and North-Eastern India. The GMPE is derived using an efcient nonparametric modelling based on neural network algorithm. In this study, owing to sparsity in the recorded ground motions (498 recordings) for the region, the available information is combined with 13,294 records from the well-tested NGA-West 2 database. For the methodology adopted in the study, regional fags are assigned to the records. Thus, given a magnitude, distance, shear wave velocity, fault type and region, the model is able to predict the possible spectral acceleration. The developed GMPE is observed to be unbiased with respect to region. Further, the inter- and intra-event standard deviations are also in acceptable ranges. It is observed that developed GMPE for Western Himalayas and North-Eastern India is able to capture all the known ground motion characteristics. Additionally, the GMPE is compared with the existing GMPE for rock-type soil condition available for the Western Himalayas and North-Eastern India. Furthermore, applicability of the developed GMPE model in estimating hazard is analysed by obtaining the uniform hazard response spectra for Delhi and Guwahati.
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This article presents four regional site-specific ground motion relations developed for the state of Himachal Pradesh in northwest Himalaya, situated in a seismically active region. These relations are developed from synthetic free surface ground motion databases obtained from a calibrated stochastic seismological model considering the characteristic properties of this specific region. The adopted methodology incorporates the site effects characterised through active MASW tests conducted in 22 important cities. The estimated ground motion levels from the developed relations are found to be in reasonable agreement with the recorded data.
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This article presents site-specific probable seismic hazard of the Himachal Pradesh province, situated in a seismically active region of northwest Himalaya, using the ground motion relations presented in a companion article. Seismic recurrence parameters for all the documented probable sources are established from an updated earthquake catalogue. The contour maps of probable spectral acceleration at 0, 0.2, and 1 s (5% damping) are presented for 475 and 2475 years return periods. Also, the hazard curves and uniform hazard response spectrums are presented for all the important cities in this province. Results indicate that the present codal provision underestimates the seismic hazard at cities of Bilaspur, Shimla, Hamirpur, Chamba, Mandi, and Solan. In addition, regions near Bilaspur and Chamba exhibit higher hazard levels than what is reported in literature
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One of the most challenging problems in the estimation of seismic hazard is the ability to quantify seismic activity. Empirical models based on the available earthquake catalogue are often used to obtain activity of source regions. The major limitation with this approach is the lack of sufficient data near a specified source. The non-availability of data poses difficulties in obtaining distribution of earthquakes with large return periods. Such events recur over geological time scales during which tectonic processes, including mantle convection, formation of faults and new plate boundaries, are likely to take place. The availability of geometries of plate boundaries, plate driving forces, lithospheric stress field and GPS measurements has provided numerous insights on the mechanics of tectonic plates. In this article, a 2D finite element model of Indo-Australian plate is developed with the focus of representing seismic activity in India. The effect of large scale geological features including sedimentary basins, fold belts and cratons on the stress field in India is explored in this study. In order to address long term behaviour, the orientation of stress field and tectonic faults of the present IndoAustralian plate are compared with a reconstructed stress field from the early Miocene (20 Ma).
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Seismic site coefficients (Fs) 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. Fs 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. Fs 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.
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