EASRAPP: An Open-Source Semiautomatic Python GUI-Based Application for Extraction and Analysis of Surface Ruptures in a Large Earthquake

Author:

Li Dongchen1ORCID,Ren Junjie123ORCID

Affiliation:

1. 1National Institute of Natural Hazards, Ministry of Emergency Management of China (MEMC), Beijing, China

2. 2Key Laboratory of Crustal Dynamics, China Earthquake Administration (CEA), Beijing, China

3. 3Key Laboratory of Compound and Chained Natural Hazards Dynamics, MEMC, Beijing, China

Abstract

Abstract Earthquake surface ruptures record the kinematics of the rupture behavior and rheology of the fault zone. General methods of acquiring coseismic surface ruptures, including the field geological survey and the visual interpretation of remote sensing images, are generally time consuming and challenging to obtain detailed features of surface ruptures. Here, we developed an open-source semiautomatic Python graphical user interface-based application named EASRAPP (An Application for Extraction and Analysis of Surface Ruptures). EASRAPP is a graphical Python application that provides an interactive, user-friendly framework for semiautomatically extracting and analyzing earthquake surface ruptures and associated quantitative parameters. It consists of four main modules for obtaining the region of interest for surface ruptures in a remote sensing image, extracting surface ruptures, editing the vector extraction results, and analyzing the width of the surface rupture zone and strikes of all surface ruptures. Moreover, some additional functions are available, including data structure conversion for vector and raster data, vector merging, raster mosaicing, and batch cropping from multiple large images to many small images. EASRAPP is written in Python 3, based on several open-source Python packages such as Tkinter, SciPy, and so forth. Because of its modular design, it is convenient to modify the code and add new functionalities to a collaborative development environment. Furthermore, the output of the editing module may serve as a machine learning or deep learning training dataset, and offer conditions for detailed kinematic analysis and acquiring accurate width of the active fault deformation zone. EASRAPP was tested on a single unmanned aerial vehicle image to demonstrate all modules and tools. In addition, EASRAPP was also applied to other drone and satellite images to extract surface ruptures from recent and historical earthquakes. Our results indicate that: (1) the tool can quickly extract the fine structures of surface fractures, (2) EASRAPP can be used to extract surface ruptures generated by historical events, and (3) it can be applied to high-resolution aerial and satellite images.

Publisher

Seismological Society of America (SSA)

Subject

Geophysics

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Fit-for-Purpose Approach for the Detection and Analysis of Earthquake Surface Ruptures Using Satellite Images;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2023

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