Optimal Network Design for Microseismic Monitoring in Urban Areas - A Case Study in Munich, Germany





Seismic Networks, Induced Seismicity, Microseismic Monitoring, Optimization


Well-designed monitoring networks are crucial for obtaining precise locations, magnitudes and source parameters, both for natural and induced microearthqakes. The performance of a seismic network depends on many factors, including network geometry, signal-to-noise ratio (SNR) at the seismic station, instrumentation and sampling rate. Therefore, designing a high-quality monitoring network in an urban environment is challenging due to the high level of anthropogenic noise and dense building infrastructure, which can impose geometrical limitations and elevated construction costs for sensor siting. To address these challenges, we apply a numerical optimization approach to design a microseismic surveillance network for induced earthquakes in the metropolitan area of Munich (Germany), where several geothermal plants exploit a deep hydrothermal reservoir. First of all, we develop a detailed noise model for the city of Munich, to capture the heterogeneous noise conditions. Then, we calculate the expected location precision for a randomly chosen network geometry from the body-wave amplitudes and travel times of a synthetic earthquake catalog considering the modeled local noise level at each network station. In the next step, to find the optimum network configuration, we use a simulated annealing approach in order to minimize the error ellipsoid volume of the linearized earthquake location problem. The results indicate that a surface station network cannot reach the required location precision (0.5 km in epicentre and 2 km in source depth) and detection capability (magnitude of completeness Mc = 1.0) due to the city´s high seismic noise level. In order to reach this goal, borehole stations need to be added to increase the SNR of the microearthquake recordings, the accuracy of their body-wave arrival times and source parameters. The findings help to better quantify the seismic monitoring requirements for a save operation of deep geothermal projects in urban areas.


Agemar, T., Weber, J., & Schulz, R. (2014). Deep geothermal energy production in Germany. Energies, 7(7), 4397–4416. https://doi.org/10.3390/en7074397

Aki, K. (1976). Signal to noise ratio in seismic measurements. Volcanoes and Tectonos· Phere, Tokai Univ. Press, Tokyo, 187–192.

Antuens, V., Toledo, T., Kraft, T., Reyes, C., & Megies, T. (2023). Optimal Design and Ground Truth Performance Test for Deep Geothermal Seismic Monitoring Networks. (in preparation).

Asten, M. W., & Henstridge, J. (1984). Array estimators and the use of microseisms for reconnaissance of sedimentary basins. Geophysics, 49(11), 1828–1837. https://doi.org/10.1190/1.1441596

Baisch, S., Fritschen, R., Groos, J., Kraft, T., Plenefisch, T., Plenkers, K., Ritter, J. R., & Wassermann, J. (2012). Empfehlungen zur Überwachung induzierter Seismizität-Positionspapier des FKPE. DGG Mitteilungen, 3, 17–31.

Bartal, Y., Somer, Z., Leonard, G., Steinberg, D. M., & Horin, Y. B. (2000). Optimal seismic networks in Israel in the context of the Comprehensive Test Ban Treaty. Bulletin of the Seismological Society of America, 90(1), 151–165. https://doi.org/10.1785/0119980164

Bayerisches Landesamt für Umwelt. (2012). Geothermische Charakterisierung von Karst-Kluft-Aquiferen im Großraum München. Endbericht.

Bondár, I., Myers, S. C., Engdahl, E. R., & Bergman, E. A. (2004). Epicentre accuracy based on seismic network criteria. Geophysical Journal International, 156(3), 483–496. https://doi.org/10.1111/j.1365-246X.2004.02070.x

Bormann, P., & Wielandt, E. (2013). Seismic signals and noise. In New manual of seismological observatory practice 2 (NMSOP2) (pp. 1–62). Deutsches GeoForschungsZentrum GFZ. https://doi.org/10.2312/GFZ.NMSOP-2_ch4

Brune, J. N. (1970). Tectonic stress and the spectra of seismic shear waves from earthquakes. Journal of Geophysical Research, 75(26), 4997–5009. https://doi.org/10.1029/JB075i026p04997

Büttner, G., Feranec, J., Jaffrain, G., Mari, L., Maucha, G., & Soukup, T. (2004). The CORINE land cover 2000 project. EARSeL EProceedings, 3(3), 331–346.

Coles, D., & Curtis, A. (2011). Efficient nonlinear Bayesian survey design using DN optimization. Geophysics, 76(2), Q1–Q8. https://doi.org/10.1190/1.3552645

D’Alessandro, A., Luzio, D., D’Anna, G., & Mangano, G. (2011). Seismic network evaluation through simulation: An application to the Italian National Seismic Network. Bulletin of the Seismological Society of America, 101(3), 1213–1232. https://doi.org/10.1785/0120100066

De Landro, G., Picozzi, M., Russo, G., Adinolfi, G. M., & Zollo, A. (2020). Seismic networks layout optimization for a high-resolution monitoring of induced micro-seismicity. Journal of Seismology, 24, 953–966. https://doi.org/10.1007/s10950-019-09880-9

Edwards, B., Fäh, D., & Giardini, D. (2011). Attenuation of seismic shear wave energy in Switzerland. Geophysical Journal International, 185(2), 967–984. https://doi.org/10.1111/j.1365-246X.2011.04987.x

Eulenfeld, T., & Wegler, U. (2016). Measurement of intrinsic and scattering attenuation of shear waves in two sedimentary basins and comparison to crystalline sites in Germany. Geophysical Journal International, 205(2), 744–757. https://doi.org/10.1093/gji/ggw035

Evans, K. F., Zappone, A., Kraft, T., Deichmann, N., & Moia, F. (2012). A survey of the induced seismic responses to fluid injection in geothermal and CO2 reservoirs in Europe. Geothermics, 41, 30–54. https://doi.org/10.1016/j.geothermics.2011.08.002

Fowler, C. M. R. (1990). The solid earth: an introduction to global geophysics. Cambridge University Press.

Groos, J., & Ritter, J. (2009). Time Domain Classification and Quantification of Seismic Noise. Noise and Diffuse Wavefields: Extended Abstracts of the Neustadt Workshop; Neustadt an Der Weinstraße, Germany, 5-8 July 2009. Ed.: Ch. Sens-Schönfelder, 25. https://doi.org/10.1111/j.1365-246X.2009.04343.x

Grünthal, G., & Wahlström, R. (2003). An M w based earthquake catalogue for central, northern and northwestern Europe using a hierarchy of magnitude conversions. Journal of Seismology, 7, 507–531. https://doi.org/10.1023/B:JOSE.0000005715.87363.13

Hardt, M., & Scherbaum, F. (1994). The design of optimum networks for aftershock recordings. Geophysical Journal International, 117(3), 716–726. https://doi.org/10.1111/j.1365-246X.1994.tb02464.x

Häring, M. O., Schanz, U., Ladner, F., & Dyer, B. C. (2008). Characterisation of the Basel 1 enhanced geothermal system. Geothermics, 37(5), 469–495. https://doi.org/10.1016/j.geothermics.2008.06.002

Havskov, J., Ottemöller, L., Trnkoczy, A., & Bormann, P. (2012). Seismic networks. In New Manual of Seismological Observatory Practice 2 (NMSOP-2) (pp. 1–65). Deutsches GeoForschungsZentrum GFZ. https://doi.org/10.2312/GFZ.NMSOP-2_ch8

Hecht, C., & Pletl, C. (2015). Das Verbundprojekt GRAME–Wegweiser für eine geothermische Wärmeversorgung urbaner Ballungsräume. Geothermische Energie, 82(2), 02.

Hirschberg, S., Wiemer, S., & Burgherr, P. (2014). Energy from the Earth: Deep Geothermal as a Resource for the Future? (Vol. 62). vdf Hochschulverlag AG.

Kijko, A. (1977). An algorithm for the optimum distribution of a regional seismic network—I. Pure and Applied Geophysics, 115(4), 999–1009. https://doi.org/10.1007/BF00881222

Kirkpatrick, S., Gelatt Jr, C. D., & Vecchi, M. P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680. https://doi.org/10.1126/science.220.4598.671

Kraft, T. (2014). A high-resolution ambient seismic noise model for Europe. EGU General Assembly, EGU2014-2282, 27.

Kraft, T. (2016). A high-resolution and calibrated model of man-made seismic noise for Europe. Jahrestagung Der Deutschen Geophysikalischen Gesellschaft, S2-A.

Kraft, T., Mignan, A., & Giardini, D. (2013). Optimization of a large-scale microseismic monitoring network in northern Switzerland. Geophysical Journal International, 195(1), 474–490. https://doi.org/10.1093/gji/ggt225

Kraft, T., Roth, P., & Wiemer, S. (2020). Good Practice Guide for Managing Induced Seismicity in Deep Geothermal Energy Projects in Switzerland: Version 2. https://doi.org/10.3929/ethz-b-000453228

Lentsch, D., & Schweingruber, M. (2022). First Multilateral Deep Geothermal Well in the South German Molasse Basin. Eropean Geothermal Congress.

Lomax, A., Michelini, A., Curtis, A., & Meyers, R. (2009). Earthquake location, direct, global-search methods. Encyclopedia of Complexity and Systems Science, 5, 2449–2473.

Lund, J. W., & Toth, A. N. (2021). Direct utilization of geothermal energy 2020 worldwide review. Geothermics, 90, 101915. https://doi.org/10.1016/j.geothermics.2020.101915

Mahani, A. B., Kao, H., Walker, D., Johnson, J., & Salas, C. (2016). Performance evaluation of the regional seismograph network in northeast British Columbia, Canada, for monitoring of induced seismicity. Seismological Research Letters, 87(3), 648–660. https://doi.org/10.1785/0220150241

Megies, T., Kraft, T., & Reyes, C. (2023). pyNetOpt3D. Zenodo, https://doi.org/10.5281/zenodo.7638856.

Megies, T., & Wassermann, J. (2017). Verbundprojekt MAGS2 - Vom Einzelsystem zur großräumigen Nutzung- EP2: Untersuchungen zur optimierten seismischen Überwachung hydrogeothermaler Systeme bei dichter räumlicher Lage der Bohrerlaubnisfelder am Beispiel der Situation im Süden Münchens. Abschlussbericht.

Megies, Tobias, & Wassermann, J. (2014). Microseismicity observed at a non-pressure-stimulated geothermal power plant. Geothermics, 52, 36–49. https://doi.org/10.1016/j.geothermics.2014.01.002

Myers, S. C., & Schultz, C. A. (2000). Improving sparse network seismic location with Bayesian kriging and teleseismically constrained calibration events. Bulletin of the Seismological Society of America, 90(1), 199–211. https://doi.org/10.1785/0119980171

Neuffer, T., & Kremers, S. (2017). How wind turbines affect the performance of seismic monitoring stations and networks. Geophysical Journal International, 211(3), 1319–1327. https://doi.org/10.1093/gji/ggx370

Podvin, P., & Lecomte, I. (1991). Finite difference computation of traveltimes in very contrasted velocity models: a massively parallel approach and its associated tools. Geophysical Journal International, 105(1), 271–284. https://doi.org/10.1111/j.1365-246X.1991.tb03461.x

Riedl, C. (2017). A Seismic Noise Map for the Greater Munich Area. LMU Munich.

Schmittbuhl, J., Lambotte, S., Lengliné, O., Grunberg, M., Jund, H., Vergne, J., Cornet, F., Doubre, C., & Masson, F. (2021). Induced and triggered seismicity below the city of Strasbourg, France from November 2019 to January 2021. Comptes Rendus. Géoscience, 353(S1), 561–584. https://doi.org/10.5802/crgeos.71

Seithel, R., Gaucher, E., Mueller, B., Steiner, U., & Kohl, T. (2019). Probability of fault reactivation in the Bavarian Molasse Basin. Geothermics, 82, 81–90. https://doi.org/10.1016/j.geothermics.2019.06.004

Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb01338.x

Stabile, T., Iannaccone, G., Zollo, A., Lomax, A., Ferulano, M., Vetri, M., & Barzaghi, L. (2013). A comprehensive approach for evaluating network performance in surface and borehole seismic monitoring. Geophysical Journal International, 192(2), 793–806. https://doi.org/10.1093/gji/ggs049

Steinberg, D. M., & Rabinowitz, N. (2003). Optimal seismic monitoring for event location with application to on site inspection of the comprehensive nuclear test ban treaty. Metrika, 58(1), 31–57. https://doi.org/10.1007/s001840200222

Wawerzinek, B., Buness, H., von Hartmann, H., & Tanner, D. C. (2021). S-wave experiments for the exploration of a deep geothermal carbonate reservoir in the German Molasse Basin. Geothermal Energy, 9(1), 1–21. https://doi.org/10.1186/s40517-021-00189-w

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How to Cite

Keil, S., Wassermann, J., Megies, T., & Kraft, T. (2023). Optimal Network Design for Microseismic Monitoring in Urban Areas - A Case Study in Munich, Germany. Seismica, 2(2). https://doi.org/10.26443/seismica.v2i2.1030