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

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

Global quieting of high-frequency seismic noise due to COVID-19 pandemic lockdown measures

Human activity causes vibrations that propagate into the ground as high-frequency seismic waves. Measures to mitigate the COVID-19 pandemic caused widespread changes in human activity, leading to a months-long reduction in seismic noise of up to 50%. The 2020 seismic noise quiet period is the longest and most prominent global anthropogenic seismic noise reduction on record. While the reduction is strongest at surface seismometers in populated areas, this seismic quiescence extends for many kilometers radially and hundreds of meters in depth. This provides an opportunity to detect subtle signals from subsurface seismic sources that would have been concealed in noisier times and to benchmark sources of anthropogenic noise. A strong correlation between seismic noise and independent measurements of human mobility suggests that seismology provides an absolute, real-time estimate of population dynamics

ObsPy – What can it do for data centers and observatories?

Data acquisition by seismic centers relies on real-time systems, like SeisComP3, Antelope and Earthworm. However, these are complex systems that are designed for fast and precisely defined standard real-time analyses. Therefore, it is not a simple task to access or modify internal routines, and to integrate them into custom-processing workflows or to perform in-depth data analyses. Often a library is necessary that provides convenient access to data and allows easy control over all of the operations that are to be performed on the data. ObsPy is such a library, which is designed to access and process seismological waveform data and metadata. We use short and simple examples here to demonstrate how effective it is to use Python for seismological data analysis. Then, we illustrate the general capabilities of ObsPy, and highlight some of its specific aspects that are relevant for seismological data centers and observatories, through presentation of real-world examples. Finally, we demonstrate how the ObsPy library can be used to develop custom graphical user interface applications.<br /&gt

ObsPy : a bridge for seismology into the scientific Python ecosystem

info:eu-repo/semantics/publishe

German Seismic and Infrasound Networks Contributing to the European Integrated Data Archive (EIDA)

Germany has a long history in seismic instrumentation. The installation of the first station sites was initiated in those regions with seismic activity. Later on, with an increasing need for seismic hazard assessment, seismological state services were established over the course of several decades, using heterogeneous technology. In parallel, scientific research and international cooperation projects triggered the establishment of institutional and nationwide networks and arrays also focusing on topics other than monitoring local or regional areas, such as recording global seismicity or verification of the compliance with the Comprehensive Nuclear-Test-Ban Treaty. At each of the observatories and data centers, an extensive analysis of the recordings is performed providing high-level data products, for example, earthquake catalogs, as a base for supporting state or federal authorities, to inform the public on topics related to seismology, and for information transfer to international institutions. These data products are usually also accessible at websites of the responsible organizations. The establishment of the European Integrated Data Archive (EIDA) led to a consolidation of existing waveform data exchange mechanisms and their definition as standards in Europe, along with a harmonization of the applied data quality assurance procedures. In Germany, the German Regional Seismic Network as national backbone network and the state networks of Saxony, Saxony-Anhalt, Thuringia, and Bavaria spearheaded the national contributions to EIDA. The benefits of EIDA are attracting additional state and university networks, which are about to join the EIDA community now