Ground motion models for regions with limited data: Data‐driven approach

Abstract

The ground motion model (GMM) for response spectra is crucial to hazard studies and structural design. GMM developed for regions with abundant data can capture appropriate spectral attenuation characteristics. However, several seismically active regions, such as the Himalayas, have limited recorded data, and thus, traditionally, simulated, or other regional data are supplemented with the available data for developing a GMM. Despite data augmentation, GMMs developed for such regions cannot appropriately depict attenuation characteristics. Therefore, the paper presents a comprehensive study encompassing the development of data‐driven approaches to obtain GMM for response spectra using the Himalayan crustal data with inputs: magnitude, distance, site class, depth, and a flag corresponding to the horizontal and vertical components. In this regard, GMM using transfer learning is initially developed from a pre‐trained model using global crustal data. Additionally, GMM using the XGBoost approach with monotonic constraints enforced on inputs is developed. These approaches could not capture trends appropriately. Finally, a monotonic constrained transfer learning model is developed to appropriately depict magnitude saturation and scaling, magnitude‐dependent geometric spreading, anelastic attenuation, depth scaling, site amplification, and other desired characteristics.