Multiple Change-Point Detection in Spatiotemporal Seismicity Data
- Earthquake rates are driven by tectonic stress buildup, earthquake-induced stress changes, and transient aseismic processes. Although the origin of the first two sources is known, transient aseismic processes are more difficult to detect. However, the knowledge of the associated changes of the earthquake activity is of great interest, because it might help identify natural aseismic deformation patterns such as slow-slip events, as well as the occurrence of induced seismicity related to human activities. For this goal, we develop a Bayesian approach to identify change-points in seismicity data automatically. Using the Bayes factor, we select a suitable model, estimate possible change-points, and we additionally use a likelihood ratio test to calculate the significance of the change of the intensity. The approach is extended to spatiotemporal data to detect the area in which the changes occur. The method is first applied to synthetic data showing its capability to detect real change-points. Finally, we apply this approach toEarthquake rates are driven by tectonic stress buildup, earthquake-induced stress changes, and transient aseismic processes. Although the origin of the first two sources is known, transient aseismic processes are more difficult to detect. However, the knowledge of the associated changes of the earthquake activity is of great interest, because it might help identify natural aseismic deformation patterns such as slow-slip events, as well as the occurrence of induced seismicity related to human activities. For this goal, we develop a Bayesian approach to identify change-points in seismicity data automatically. Using the Bayes factor, we select a suitable model, estimate possible change-points, and we additionally use a likelihood ratio test to calculate the significance of the change of the intensity. The approach is extended to spatiotemporal data to detect the area in which the changes occur. The method is first applied to synthetic data showing its capability to detect real change-points. Finally, we apply this approach to observational data from Oklahoma and observe statistical significant changes of seismicity in space and time.…
Author details: | Bernhard FiedlerORCiDGND, Gert ZöllerORCiDGND, Matthias HolschneiderORCiDGND, Sebastian HainzlORCiDGND |
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DOI: | https://doi.org/10.1785/0120170236 |
ISSN: | 0037-1106 |
ISSN: | 1943-3573 |
Title of parent work (English): | Bulletin of the Seismological Society of America |
Publisher: | Seismological Society of America |
Place of publishing: | Albany |
Publication type: | Article |
Language: | English |
Date of first publication: | 2018/03/01 |
Publication year: | 2018 |
Release date: | 2021/11/29 |
Volume: | 108 |
Issue: | 3A |
Number of pages: | 13 |
First page: | 1147 |
Last Page: | 1159 |
Funding institution: | DFGGerman Research Foundation (DFG) [SFB 1294] |
Organizational units: | Mathematisch-Naturwissenschaftliche Fakultät / Institut für Mathematik |
DDC classification: | 5 Naturwissenschaften und Mathematik / 51 Mathematik / 510 Mathematik |
Peer review: | Referiert |
Publishing method: | Open Access / Green Open-Access |