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<p>Ground motion models (GMMs) to the recorded ground motion time histories are essential input to the hazard analysis. With recent vast array of strong motion instruments to seismically active regions such as Japan, California, and Mexico, large amounts resulted in abundant recorded data huge metadata. Several global and regional GMMs are developed with these strong motion datasets. However, many active regions (e.g., The Himalayas) are in dearth of recorded strong motion data and metadata to develop predictive models. Despite recent instrumentations by different networks to the Himalayan region, the problem of near-field strong-motion records resulting from sparse instrumentation is the key concern. Traditionally, stochastic models are used in developing GMMs, as developing empirical models with limited data is challenging. Additionally, GMMs developed to other data-rich regions with similar tectonics are used in the hazard estimations. Thus, developing predictive models to these data-poor regions is a key concern which needs to be addressed. In the current work, we address this problem from the data-driven approach such as neural network. Neural networks learn the functional form from the data during training making it suitable for our present problem. Magnitude, epicentre distance, hypocentre depth, and shear wave velocity flag are used as inputs to estimate both the horizontal and vertical response spectra. In this regard, we attempt several approaches in developing the GMM using shallow neural network. Initially we develop model with seven neurons in the hidden layer using the available regional Western Himalayan crustal data and as one expects the model scaled poorly at the near-field. The obtained mean squared error (MSE) mean absolute error (MAE), and coefficient of determination (R2) are 0.6858, 0.6504, and 0.7592, respectively. To address this lack of near-field data, we supplement our regional data with records from global near-field strong motion and in developing GMM. This model has seven neurons in the hidden layer and performed better than the previous model but still had scaling issues at the large magnitude near-field. Further, supplementing data from other regions would influence the predictions. The obtained MSE, MAE, and R2 of the combined database are 0.5690, 0.5830, and 0.8659, respectively. However, the MSE, MAE, and R2 of the Western Himalaya data are 0.8006, 0.7057, and 0.7216, respectively. Finally, we use transfer learning technique: we develop GMM to the global crustal data and global near-field data and use it as a base model to develop GMM with six neurons in the hidden layer using the Western Himalayan data. The obtained MSE, MAE, and R2 of the Western Himalayan database are 0.8688, 0.7282, and 0.6970, respectively. Despite large error compared to previous two models, this model could capture large magnitude near-field effects and distance scaling effects and performed better than the previous two models. We conclude that transfer learning could be used to regions with limited strong motion data in developing GMM.</p>

<p>The 1934 Bihar-Nepal earthquake, one of the most catastrophic events ever to occur in the Himalayas, inflicted extensive devastation with reported MMI of IX-VI in the Kathmandu valley and the Indo-Gangetic (IG) basin. The earthquake triggered significant ground liquefaction and landslides as it occurred in the proximity of densely populated river basins causing a huge economic loss and over 15700 fatalities. However, it is unfortunate that there are no ground motion data available for the event, as it remained unrecorded due to a lack of instrumentation. Therefore, simulating ground motions for the 1934 Bihar-Nepal earthquake would provide new insights into the influence of regional characteristics on Himalayan earthquakes. However, incorporating the Himalayan topography and the IG basin in the ground motion simulation is very challenging. In contrast, proper validation of modeling of ground motions is difficult due to the unavailability of recorded data. To circumvent these challenges, we simulated broadband ground motions for the 2015 Nepal earthquake, another significant catastrophe that occurred in the same seismo-tectonic region in the Himalayas which provides a well-recorded database. For the 2015 Nepal earthquake, a thorough comparison of the recorded and simulated ground motion spectra reveals that the simulated ground motions are consistent with the recorded data in terms of amplitude, strong motion duration, and spectral ordinates. Therefore, we considered the same medium characteristics to simulate broadband seismograms for the 1934 Bihar-Nepal earthquake by combining deterministically generated low-frequency (LF) and stochastically simulated high-frequency (HF) ground motions. The HF accelerograms are generated by considering incident and azimuthal angles obtained from rays of P and S waves traced from the finite fault slip model to the station, passing through the regional layered stratified velocity model, free surface factors and energy partition factors (Otarola and Ruiz, 2016). For deterministic simulation, a 3D computational model (Sreejaya et al., 2022) for the study region of approximately 9°×7° (between 80°–89°E longitude and 23°-30°N latitude), incorporated with basin geometry, material properties, and topography of the region is embedded with the finite fault rupture model of the event to generate LF ground motions. For the finite fault source model, five samples with various spatial variability of the slip on the rupture plane are simulated as a random field (Mai and Beroza, 2000; 2002) using the seismic moment and fault dimensions provided by Pettanati et al. (2017). Ultimately, the broadband (0.01–25 Hz) ground motions are obtained at 6461 hypothetically gridded stations with a 0.1°×0.1° spacing by combining the suitably filtered LF and HF ground motions in the frequency domain with the target frequency of 0.3 Hz with a bandwidth up to 0.05 Hz. A systematic comparison of estimated MMI values (Iyengar and Raghukanth, 2003) and the observed MMI values at 459 sites revealed that the PGA between 0.25-0.6g is significant within 200 km of the epicentral distance. Thus, the results can be used for addressing the ground failure and liquefaction caused due to the earthquake and also find applications in seismic hazard assessment of the cities in the basin.</p>

The 25th April 2015 Nepal earthquake is the first major event in the Himalayan orogeny that provides a relatively well recorded dataset. This paper presents a comprehensive analysis of the mainshock and its five major aftershocks through 21 well established ground motion parameters. The analysis is presented for near field stations of the Kathmandu basin and far field stations of the Indo-Ganga basin, including the site response behavior with varying sediment thickness. In addition, a new ground motion model is derived for all the 21 parameters using moment magnitude, rupture distance, site class and sediment depth as predictor variables.