GEOBEST2020+ baseline seismic monitoring workflow and network performance evaluation for deep geothermal projects in Switzerland

Deep geothermal is a clean and renewable source of energy with a high potential for heat and electricity production which can help Switzerland meet its energy and climate objectives. Worldwide, several geothermal projects have been successfully operated for decades. Unfortunately, some projects have also been suspended due to unexpected levels of induced seismicity. Thus, adequate risk management is essential to establish safe and economically viable geothermal projects.

In Switzerland, the subsurface is under the sovereignty of the cantonal authorities. Within the GEOBEST2020+ project, the Swiss Seismological Service (SED) supports the cantons in adequately handling the risk of induced seismicity associated with deep geothermal projects. Funded by the Federal Office of Energy (SFOE) in the scope of its SwissEnergy program, the GEOBEST2020+ program aims to provide operator-independent seismological consulting and baseline seismic monitoring services to the cantonal authorities.

In this framework, we deploy dedicated seismic networks in the vicinity of the monitored projects. These networks must be sensitive enough to follow the evolution of microseismicity and allow the operators to run traffic-light systems and take action before larger events occur. Before the station installation, we perform a careful site survey analysis, considering the background noise conditions and evaluating the signal-to-noise ratio at each site. To evaluate beforehand the detection sensibility of a seismic network, we estimate the Bayesian Magnitude of Completeness (BMC), optimized for Switzerland. We additionally estimate the theoretical location uncertainties inside the network considering the background noise level at the stations and the network geometry.

Here we show the comparison results between our methodology to the ground truths of several years of continuous monitoring data at Lavey-les-Bains, canton of Vaud. We use a combination of three types of detection methods: Machine Learning combined with migration methods (MALMI), coherence (Pyrocko/Lassie) and template matching (QuakeMatch), to produce a high-quality, manually revised seismic catalog. We compare our theoretical methods of network performance to the real data measured at the geothermal site.