Efficient incorporation of rupture directivity into probabilistic seismic hazard analysis using a deep learning–based approach

Abstract

Earthquakes with rupture forward directivity effects can produce high amplitude, pulse-like ground shaking, which imposes significantly greater seismic demands on buildings compared to non-pulse-like shaking of similar amplitude. Although theoretical frameworks exist for the incorporation of rupture directivity effects into probabilistic seismic hazard analysis (PSHA), the associated computational burden often makes it impractical for regional-scale studies in practice. Furthermore, many available models to estimate directivity are limited to the application to ruptures of limited geometrical complexity. In this study, we develop a deep learning–based model that provides adjustment terms (moment modifiers) for the mean and standard deviation of ground motion distributions near an earthquake rupture, as predicted by a ground motion model. Thereby the model depends on a chosen distribution of hypocenters along the fault, but not on individual hypocenter locations. Our model is trained on a synthetic dataset generated from an empirical directivity amplification model that includes a wide variety of earthquake ruptures, encompassing diverse seismic properties such as magnitude, dip angle, and faulting style, as well as geometrical complexities ranging from simple, planar ruptures to intricate, multi-segment, branching ruptures with step-overs and strike reversals. This enables our model to provide accurate moment modifiers for a broad spectrum of earthquake ruptures in PSHA and other applications like scenario-based estimates of shaking and loss. The final model can be easily integrated into existing seismic hazard frameworks, as demonstrated by an application in a complete PSHA calculation for Turkey. The results indicate that explicitly incorporating directivity effects can lead to significant changes in long period seismic hazard, and through the use of efficient moment modifier models based on deep learning it can be possible to expand the usage of directivity models in seismic hazard and risk analysis at large regional scales.