Modeling the waveforms of induced seismicity sequences with application to Utah FORGE

We present a modeling workbench for generating hours-long synthetic sequences of induced seismic events. As input, this tool takes observed or synthetic catalogs of induced seismicity including the location, seismic moment and source mechanism of the induced events. It outputs synthetic seismic waveform recordings with added noise.

The purpose of this tool is two-fold: First, it serves to generate synthetic test data that can be used to train detection and location algorithms for induced seismicity monitoring, and to test their efficacy. Monitoring induced seismicity is a key task for the risk management of enhanced geothermal systems, but the relative scarcity of manually labeled data hampers rigorous testing of newly developed algorithms. We intend to partially close the gap in labeled data with synthetics, which can furthermore be used to enhance training data sets for deep learning approaches.

Second, the tool serves as an extension to physics-based models of induced seismicity in geothermal reservoirs, such as hydro-mechanical models, providing an efficient way of turning their induced event clouds into seismic “recordings”.

The workflow is based on the spectral-element solver Salvus and on Python, and relies on source-receiver reciprocity to decrease computational cost for large sets of induced events. As first application site, we chose the Utah Frontier Observatory for Research in Geothermal Energy – FORGE. This first application comprises a realistic digital representation of the reservoir and its surroundings, including topography, geologic structure and crustal scattering. We will show exemplary synthetic induced seismicity sequences based on an observed catalog from the 2022 stimulation at the Utah FORGE, as well as on a hydro-mechanically modeled synthetic catalog. The output waveform sequences are to be publicly shared for benchmarking monitoring workflows for induced events, in an effort to contribute to the de-risking of enhanced geothermal systems.