TY  - JOUR
A1  - Fiedler, Bernhard
A1  - Hainzl, Sebastian
A1  - Zöller, Gert
A1  - Holschneider, Matthias
T1  - Detection of Gutenberg-Richter b-Value Changes in Earthquake Time Series
JF  - Bulletin of the Seismological Society of America
N2  - The Gutenberg-Richter relation for earthquake magnitudes is the most famous empirical law in seismology. It states that the frequency of earthquake magnitudes follows an exponential distribution; this has been found to be a robust feature of seismicity above the completeness magnitude, and it is independent of whether global, regional, or local seismicity is analyzed. However, the exponent b of the distribution varies significantly in space and time, which is important for process understanding and seismic hazard assessment; this is particularly true because of the fact that the Gutenberg-Richter b-value acts as a proxy for the stress state and quantifies the ratio of large-to-small earthquakes. In our work, we focus on the automatic detection of statistically significant temporal changes of the b-value in seismicity data. In our approach, we use Bayes factors for model selection and estimate multiple change-points of the frequency-magnitude distribution in time. The method is first applied to synthetic data, showing its capability to detect change-points as function of the size of the sample and the b-value contrast. Finally, we apply this approach to examples of observational data sets for which b-value changes have previously been stated. Our analysis of foreshock and after-shock sequences related to mainshocks, as well as earthquake swarms, shows that only a portion of the b-value changes is statistically significant.
Y1  - 2018
U6  - https://doi.org/10.1785/0120180091
SN  - 0037-1106
SN  - 1943-3573
VL  - 108
IS  - 5A
SP  - 2778
EP  - 2787
PB  - Seismological Society of America
CY  - Albany
ER  - 
TY  - JOUR
A1  - Fiedler, Bernhard
A1  - Zöller, Gert
A1  - Holschneider, Matthias
A1  - Hainzl, Sebastian
T1  - Multiple Change-Point Detection in Spatiotemporal Seismicity Data
JF  - Bulletin of the Seismological Society of America
N2  - 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 to observational data from Oklahoma and observe statistical significant changes of seismicity in space and time.
Y1  - 2018
U6  - https://doi.org/10.1785/0120170236
SN  - 0037-1106
SN  - 1943-3573
VL  - 108
IS  - 3A
SP  - 1147
EP  - 1159
PB  - Seismological Society of America
CY  - Albany
ER  - 
TY  - THES
A1  - Fiedler, Bernhard
T1  - Change-point detection for seismicity parameters
Y1  - 2019
ER  -