Earthquake fissure feature extraction from high resolution optical remote sensing image: taking West Kunlun Mountain earthquake as an example

doi:10.7523/j.ucas.2021.0054

Abstract

Abstract: As one of the common forms of coseismic surface rupture, earthquake fissures will cause cracking and collapse of buildings, threatening the safety of people's lives and property. To find out the distribution law of earthquake fissures is helpful for understanding the basic information such as properties of seismogenic fault, state of tectonic movement, and earthquake rupture process. Different from mining-induced fractures and landslide fissures, earthquake fissures formed by seismogenic fault dislocation basically coincide with the seismogenic fault zone and have obvious spectral and spatial characteristics. Taking the West Kunlun Mountain earthquake surface rupture zone as the research area, and using the GF-2 as the data source, this paper proposes a seismic fracture extraction method based on spectral features and spatial features, and compares it with the noval edge detection algorithm and Gaussian matched filter algorithm. The results show that the proposed method is the best, followed by Gaussian matched filter algorithm, the result of the noval edge detection algorithm is poor. Combined with Riedel shear pattern, the spatial distribution of earthquake fissures and the combination relationship with other rupture types are analyzed in order to grasp the spatial distribution law of surface ruptures, determine the direction of main fracture zone and regional stress field, and guide the detailed investigation of surface ruptures, post disaster emergency rescue and research related to earthquake science.

Key words: optical remote sensing, image filtering, surface rupture, earthquake fissure, Riedel shear pattern

CLC Number:

TP79

LI Jiannan, WEI Yongming, CHEN Yu, GAO Jinfeng. Earthquake fissure feature extraction from high resolution optical remote sensing image: taking West Kunlun Mountain earthquake as an example[J]. Journal of University of Chinese Academy of Sciences, 2023, 40(3): 388-396.

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