Network Performance Evaluation workflow and test for seismic monitoring of geothermal projects in Switzerland

Switzerland is investing in geothermal energy solutions to decrease CO2 emissions by 2050. However, geothermal energy exploration can carry the risk of induced seismicity. Adequately managing seismic risk is key to establishing safe and economically viable geothermal projects. To monitor possible induced seismicity, dedicated seismic networks in the vicinity of the monitored projects have to be in place. These networks must be sensitive enough to follow the evolution of the microseismicity and allow the operators to run traffic-light systems and take actions before larger events occur. Current geothermal guidelines establish the minimum monitoring requirements of such networks, providing specific values for Magnitude of Completeness (Mc) and location accuracies.

To adequately monitor geothermal projects in Switzerland, we developed a workflow that goes from network geometry planning to its final installation (Antunes et al., 2025). This workflow includes network performance and evaluation procedures in order to ensure the minimum monitoring requirements proposed in the Good Practice Guide for managing induced seismicity in Switzerland (Kraft et al., 2025). To evaluate beforehand the detection sensitivity of a seismic network, we estimate the Bayesian Magnitude of Completeness (BMC), optimised for Switzerland. We additionally estimate the theoretical location uncertainties inside the network by generating and locating a synthetic catalogue of events, using the 3D velocity model for Switzerland. Both approaches consider the background noise level at the stations and the specific network geometry.

In December 2017, a seismic network was installed to monitor the geothermal activities of the AGEPP project in Lavey-les-Bains, Switzerland. This seismic network was in operation until mid 2023, acquiring the natural seismicity of this active alpine area. We use the public seismic catalogue as input for a template matching (QuakeMatch, Toledo et al., 2024) scan to increase the sensitivity, reducing the initial Mc by 2 orders of magnitude. We evaluate and test the network performance tools of our workflow by comparing the results of our numerical estimations with the resulting seismic catalogues (Mc and location errors). Our results show good agreement between the theoretical methods' estimations and the catalogue data registered with the network, proving that our numerical tools are a good approach to estimate the performance of a network when no earthquake information is available, e.g., right after a network installation.

 

References:

Antunes, V., Kraft, T., Toledo Zambrano, T. A., Reyes, C. G., Megies, T., & Wiemer, S. (2025). Optimising Seismic Networks for Enhanced Monitoring of Deep Geothermal Projects in Switzerland. In Proceedings of the European Geothermal Congress 2025. European Geothermal Energy Council. https://doi.org/10.3929/ethz-c-000791611

Kraft, T., Roth, P., Ritz, V., Antunes, V., Toledo Zambrano, T. A., & Wiemer, S. (2025). Good-Practice Guide for Managing Induced Seismicity in Deep Geothermal Energy Projects in Switzerland. ETH Zurich. https://doi.org/10.3929/ethz-b-000714220

Toledo, T., Simon, V., Kraft, T., Antunes, V., Herrmann, M., Diehl, T., & Villiger, L. (2024). The QuakeMatch Toolbox: Using waveform similarity to enhance the analysis of microearthquake sequences at Swiss geothermal projects (No. EGU24-13824). Copernicus Meetings. https:.//doi.org/10.5194/egusphere-egu24-13824