Machine learning in identifying and mapping the surface rupture of the 2021 <i>M</i><sub>W</sub>7.4 Madoi earthquake, Qinghai

ZENG XianYang; LIU Jing; WANG Wei; YAO WenQian; WU Jing; LIU XiaoLi; HAN LongFei; WANG WengXin; XING YuKun; DU RuiLin; YANG XuQian

doi:10.6038/cjg2022Q0117

2023 Vol. 66, No. 3

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Article navigation > Chinese Journal of Geophysics > 2023 > 66(3): 1098-1112

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ZENG XianYang, LIU Jing, WANG Wei, YAO WenQian, WU Jing, LIU XiaoLi, HAN LongFei, WANG WengXin, XING YuKun, DU RuiLin, YANG XuQian. 2023. Machine learning in identifying and mapping the surface rupture of the 2021 MW7.4 Madoi earthquake, Qinghai. Chinese Journal of Geophysics (in Chinese), 66(3): 1098-1112, doi: 10.6038/cjg2022Q0117

Citation:

ZENG XianYang, LIU Jing, WANG Wei, YAO WenQian, WU Jing, LIU XiaoLi, HAN LongFei, WANG WengXin, XING YuKun, DU RuiLin, YANG XuQian. 2023. Machine learning in identifying and mapping the surface rupture of the 2021 M_W7.4 Madoi earthquake, Qinghai. Chinese Journal of Geophysics (in Chinese), 66(3): 1098-1112, doi: 10.6038/cjg2022Q0117

Machine learning in identifying and mapping the surface rupture of the 2021 M_W7.4 Madoi earthquake, Qinghai

1.
State Key Laboratory of Earthquake Dynamics, Institute of Geology, China Earthquake Administration, Beijing 100029, China
2.
Institude of Surface-Earth System Science, School of Earth System Science, Tianjin University, Tianjin 300072, China
3.
Hangzhou Five Generation Communication and Big Data Research Institute, Hangzhou 310012, China
4.
Key Laboratory of Earthquake Geodesy, Institute of Seismology, China Earthquake Administration, Wuhan 430071, China
Fund Project:
国家重点研发计划（2021YFC3000605-04），国家自然科学基金（U1839203，42030305），中国地震局地质研究所基本科研业务项目（IGCEA1812）和中国地震局地震科技星火计划项目（XH22003C）联合资助

More Information

Corresponding author: LIU Jing, E-mail: liu_zeng@tju.edu.cn

Received Date: 20 February 2022

Revised Date: 28 October 2022

Available Online: 10 March 2023

Abstract

Abstract

High-resolution mapping of coseismic surface rupture of large earthquakes is very important for better understanding the behavior and mechanism of earthquake rupture and for quantifying earthquake hazards. High-resolution UAV imagery and topographic data provide a large volume of valuable images of the surface rupture. Manual mapping of fractures on many high-resolution images could be labor-intensive, time-consuming, and thus inefficient. Machine learning provides more possibilities for the rapid processing of such big-data images. In this paper, we demonstrate the potential of Machine learning techniques to rapid, efficient, and complete identification of fractures of the surface rupture zone using high-precision UAV images of the 2021 Madoi M_W7.4 earthquake. We applied the canny algorithm (based on Convolutional Neural Networks) to discuss the processing flow and key steps of UAV digital orthophoto in detail, including preparing training data, training, and post-processing. By comparing the interpretations of manual mapping and machine recognition, the proposed method can effectively map surface rupture, providing a tool for studying future large earthquakes. Machine learning has advantages and broad prospects in quantitative studies of earthquake geology, surface processes and geomorphology.
- Machine learning /
- Remote sensing /
- Convolutional neural network /
- 2021 M_W7.4 Madoi earthquake /
- Earthquake surface rupture /
- Detailed mapping

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