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

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.