Towards blending Physics-Based numerical simulations and seismic databases using Generative Adversarial Network

Abstract

This paper presents a new strategy to blend the outcome of physics-based numerical simulations with massive but poorly-labelled experimental databases such as in-situ data routinely recorded for monitoring purposes. The proposed approach relies on a set of adversarial learning techniques with a twofold purpose: (1) finding two reduced-dimensional non-linear representations of both synthetic and experimental data; (2) training a stochastic generator of fake experimental responses conditioned by the physics-based simulation results.This methodology is applied to earthquake ground motion prediction. Indeed, regional three-dimensional high-fidelity numerical models accounting for both extended sources and complex geology are still limited to a low-frequency range. Moreover, they are prone to significant uncertainties induced by a lack of data on small scale geological structures and rupture processes. Databases of broadband seismic signals recorded worldwide at seismological networks are used to retrieve some pieces of information on these small scale data to generate realistic broadband signals from synthetic ones.Outstanding performances in encoding seismic signals are demonstrated, together with efficient generation capabilities, provided that the physics-based results carry enough information to properly condition the stochastic generator. In addition, this paper shows that the proposed method, fed only with raw data from both databases and numerical models, outperforms other random signal generators based on pre-existing expertise such as prescribed spectra and more or less complex phenomenological models.