Relative Focal Mechanism Inversion and Its Application to Ridgecrest Sequence

Author:

Kuang Wenhuan123ORCID,Yuan Congcong4ORCID,Zhang Jie5ORCID,Zhang Wei123ORCID

Affiliation:

1. 1Shenzhen Key Laboratory of Deep Offshore Oil and Gas Exploration Technology, Southern University of Science and Technology, Shenzhen, China

2. 2Guangdong Provincial Key Laboratory of Geophysical High-Resolution Imaging Technology, Southern University of Science and Technology, Shenzhen, China

3. 3Department of Earth and Space Sciences, Southern University of Science and Technology, Shenzhen, Guangdong, China

4. 4Department of Earth and Planetary Sciences, Harvard University, Cambridge, Massachusetts, U.S.A.

5. 5Department of Geophysics, University of Science and Technology of China, Hefei, Anhui, China

Abstract

AbstractEarthquake focal mechanisms are important for characterizing the subsurface faulting geometry and evaluating stress distributions. Existing approaches usually strive to determine the absolute focal mechanisms and may be subject to large uncertainties due to incomprehensive knowledge of the velocity model, particularly for moderate-to-small earthquakes. Alternatively, difficulties that arise from the velocity model can be largely mitigated by inverting the relative data variations in a series of earthquakes, because effects from the velocity model are systematic among all events in the vicinity. In this study, we propose a novel relative focal mechanism inversion (RFMI) method to invert the second-order variations in a series of focal mechanisms utilizing a well-constrained primary event. We test the RFMI method on both synthetic data and 251 real earthquakes (M ≥3) in the 2019 Ridgecrest sequence. The synthetic test results show that the RFMI method is robust and insusceptible to location errors (<2 km) and systematic velocity errors (5%). The real data application results demonstrate improved consistency among the inverted focal mechanisms, resulting in better characterization of the fault orientations than the Southern California Seismic Network (SCSN) focal mechanism catalog. The retrieved earthquake depths are also well correlated with the depths of the Mw 6.4 and 7.1 mainshocks. Waveform cross-correlation analysis verifies the reliability of the results. Furthermore, dynamic stress monitoring is enabled with decent resolution. The proposed RFMI method paves a new path toward achieving a rich number of reliable earthquake focal mechanisms, which will benefit the investigation of the earthquake process.

Publisher

Seismological Society of America (SSA)

Subject

Geophysics

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