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Automatic Inversion of Rupture Processes of the Foreshock and Mainshock and Correlation of the Seismicity during the 2019 Ridgecrest Earthquake Sequence

机译:2019年ridGecrest地震序列中止血和主轴的破裂过程的自动反转和地震性的相关性

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摘要

The 2019 Ridgecrest, California, earthquake sequence included an Mw?6.4 foreshock on 4 July, followed by an Mw?7.1 mainshock about 32?hr later. We determined the rupture patterns of the foreshock and mainshock by applying the automatic iterative deconvolution and stacking method to strong‐motion records. The foreshock was characterized by a unilateral rupture toward the southwest, and the shallow portion had a relatively large slip with the maximum value of ~1.4??m?. The mainshock presents an asymmetrical bilateral rupture with an average rupture velocity of 2.0??km/s?. More than 80% of the seismic moment was released on the northwest segment of the fault, producing a maximum slip of ~5.2??m?. With the two inferred slip models, we calculated the Coulomb failure stress change (?ΔCFS?) to analyze the spatial–temporal correlation of the seismicity activity in this sequence. The result shows that the epicenter of the Mw?7.1 mainshock was brought 0.4 bars closer to failure by the Mw?6.4 foreshock, and the stress‐increased zone has a good spatial consistence with the coseismic slip distribution of the mainshock and the aftershock distribution of the foreshock. Besides, the positive ΔCFS induced by the mainshock also enhanced its aftershock activity, especially at depths of 4–10?km where the major rupture occurred, inferring that the mainshock‐induced ΔCFS may be responsible for the occurrence of aftershocks. In addition, we test the effects of different cutoff frequencies and crust velocity structures on the inversion results. The result reveals that the main source rupture characteristics are almost independent of these factors, implying a high reliability of automation inversion of strong‐motion data. Overall, this work indicates that automatic inversion of strong‐motion data can provide reliable and rapid rupture model, which is essential for earthquake emergency responses and tsunami early warnings.
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  • 来源
    《Seismological research letters》 |2020年第3期|共11页
  • 作者单位

    Department of Remote Sensing and Geoinformation Engineering Southwest Jiaotong University;

    School of Earth and Space Sciences Peking University;

    Department of Remote Sensing and Geoinformation Engineering Southwest Jiaotong University;

    Department of Remote Sensing and Geoinformation Engineering Southwest Jiaotong University;

    Department of Remote Sensing and Geoinformation Engineering Southwest Jiaotong University;

    State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology;

    Department of Remote Sensing and Geoinformation Engineering Southwest Jiaotong University;

    Department of Remote Sensing and Geoinformation Engineering Southwest Jiaotong University;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类 地震学;
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