Startseite Technik Automated near-field deformation detection from mobile laser scanning for the 2014 Mw 6.0 South Napa earthquake
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Automated near-field deformation detection from mobile laser scanning for the 2014 Mw 6.0 South Napa earthquake

  • Xinxiang Zhu ORCID logo EMAIL logo , Craig L. Glennie ORCID logo und Benjamin A. Brooks
Veröffentlicht/Copyright: 25. November 2021
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Abstract

Quantifying off-fault deformation in the near field remains a challenge for earthquake monitoring using geodetic observations. We propose an automated change detection strategy using geometric primitives generated using a deep neural network, random sample consensus and least squares adjustment. Using mobile laser scanning point clouds of vineyards acquired after the magnitude 6.0 2014 South Napa earthquake, our results reveal centimeter-level horizontal ground deformation over three kilometers along a segment of the West Napa Fault. A fault trace is detected from rows of vineyards modeled as planar primitives from the accumulated coseismic response, and the postseismic surface displacement field is revealed by tracking displacements of vineyard posts modeled as cylindrical primitives. Interpreted from the detected changes, we summarized distributions of deformation versus off-fault distances and found evidence of off-fault deformation. The proposed framework using geometric primitives is shown to be accurate and practical for detection of near-field off-fault deformation.

Funding source: U.S. Geological Survey

Award Identifier / Grant number: 1347092

Award Identifier / Grant number: 1830734

Funding statement: Funding for this research was provided by a cooperative agreement from the USGS and two grants from the National Science Foundation (1347092 and 1830734).

Acknowledgment

We thank Preston Hartzell for guidance in point cloud processing. We are grateful to Chelsea Scott and the other two anonymous reviewers whose constructive criticism helped to improve the manuscript.

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Received: 2021-04-07
Accepted: 2021-11-04
Published Online: 2021-11-25
Published in Print: 2022-01-27

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Heruntergeladen am 31.12.2025 von https://www.degruyterbrill.com/document/doi/10.1515/jag-2021-0023/html
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