Convolutional neural network for identifying effective seismic force at a DRM layer for rapid reconstruction of SH ground motions

Abstract

We introduce a novel data‐informed convolutional neural network (CNN) approach that utilizes sparse ground motion measurements to accurately identify effective seismic forces in a truncated two‐dimensional (2D) domain. Namely, this paper presents the first prototype of a CNN capable of inferring domain reduction method (DRM) forces, equivalent to incident waves, across all nodes in the DRM layer. It achieves this from sparse measurement data in a multidimensional setting, even in the presence of incoherent incident waves. The method is applied to shear (SH) waves propagating into a domain truncated by a wave‐absorbing boundary condition (WABC). By effectively training the CNN using input‐layer features (surface sensor measurements) and output‐layer features (effective forces at a DRM layer), we achieve significant reductions in processing time compared to PDE‐constrained optimization methods. The numerical experiments demonstrate the method's effectiveness and robustness in identifying effective forces, equivalent to incoherent incident waves, at a DRM layer.