Deep and Convolutional Neural Networks for identifying vertically-propagating incoming seismic wave motion into a heterogeneous, damped soil column

Identification of the incoming seismic wave motion at a geotechnical site plays an integral role in the seismic analysis and design of the critical components of the civil infrastructure. Current practice relies on using one-dimensional models for the soil medium at the site, and a process called deconvolution, which identifies seismic input due to measurements made at the ground surface. Using multi-dimensional models for the underlying soil medium becomes important when a site exhibits considerable heterogeneity or change in topography. These situations necessitate using a gradient-based optimization method for inverting the multi-dimensional incoming seismic-wave motion. However, such methods are computationally expensive and time consuming. We explore the effectiveness and robustness of a data-informed framework for the inverse-source problem, due to its potential in reducing the computational cost, compared to a gradient-based approach. We design deep and convolutional neural network architectures to predict the incoming wave motion based on measurements made at the ground surface. We demonstrate their effectiveness and robustness on blind test examples, where measured data are contaminated with noise, and when the incident signals in the training data set may or may not resemble a realistic seismic signal. Lastly, the presented artificial neural networks are shown to be effective in predicting incoming wave motion when the subsurface material properties lack accuracy, or are uncertain, which is likely the case in realistic situations. While only one-dimensional problems are considered here, generalization of our data-informed approach to handle multi-dimensional problems appears to be straightforward. Overall, our data-informed approach seems to be robust, fast, and promising for identifying the incoming seismic wave motion.