S T G Raghukanth

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.

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 efficient 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 flags 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.

This article focuses on developing a ground motion prediction equation based on artificial neural network (ANN) technique for shallow crustal earthquakes. A hybrid technique combining genetic algorithm and Levenberg–Marquardt technique is used for training the model. The present model is developed to predict peak ground velocity, and 5% damped spectral acceleration. The input parameters for the prediction are moment magnitude (Mw), closest distance to rupture plane (Rrup), shear wave velocity in the region (Vs30) and focal mechanism (F). A total of 13,552 ground motion records from 288 earthquakes provided by the updated NGA-West2 database released by Pacific Engineering Research Center are utilized to develop the model. The ANN architecture considered for the model consists of 192 unknowns including weights and biases of all the interconnected nodes. The performance of the model is observed to be within the prescribed error limits. In addition, the results from the study are found to be comparable with the existing relations in the global database. The developed model is further demonstrated by estimating site-specific response spectra for Shimla city located in Himalayan region.

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.

The present work aims at developing the first probabilistic fling hazard map of India and adjoined regions. First, we developed a new ANN-based ground motion prediction equation (GMPE) for fling corresponding to horizontal and vertical directions. The developed GMPE is based on permanent ground residual displacement form 556 scenario events considered consistent with the regional characteristics. The corresponding simulations are performed by suitably combining the Okada’s solutions. Developed GMPE is comparable with the existing relations and the few available data which contained fling characteristics. Further, the developed GMPE, along with the other two available prediction equations for the fling, is incorporated to represent the ground motion characteristics in the estimation of hazard using a suitable logic tree. In addition to the fling prediction equations, the evaluation of regional fling hazards requires identifying the location of all the probable seismic sources and their seismicity characteristics. In this study, we used the linear-fault model, as the fling is a near field phenomenon. We report the resultant probabilistic fling hazard map for 10%,2% 1%, and 0.5% probability in 50 Years for the region. The maps showed that the active regions in Himalayas, North-Eastern India, and Andaman experience higher values for fling than stable Peninsular India. Thus, this study develops the fling hazard map for the first time, and the results are essential in the design and rehabilitation of important structures in the region.

A deep learning model is developed for the Next Generation Attenuation–Subduction database for predicting spectral accelerations and peak amplitude measures. The developed model satisfies the statistical criteria necessary for prediction. Standard deviations lie in 0.2864–0.3809, 0–0.2696, and 0.4514–0.7892, range for inter-event, -region, and intra-events, respectively. Transfer learning is applied to the New Zealand region. Probabilistic seismic hazard analysis is performed for the Andaman-Nicobar region and obtained a peak ground acceleration of 0.6–0.7 g and 0.4–0.5 g at the Andaman and the Nicobar Islands, respectively, for a 2475-year return period.