Imaging Submarine Fault Zones Using Reflected S-waves Extracted by Ambient Noise Interferometry from Distributed Acoustic Sensing
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1. Data Description This dataset contains the processed data and/or scripts used to generate the figures in the aforementioned publication. The data is organized and named according to the figures in the paper for straightforward reference. Publication Title: Imaging Submarine Fault Zones Using Reflected S-waves Extracted by Ambient Noise Interferometry from Distributed Acoustic SensingCorresponding Author: [Zhufeng Lu/Co-author Xiaodong Yang and Jizhong Yang]Contact Email: [ luzhufeng24@mails.ucas.ac.cn]2. Data Structure and File Naming ConventionAll data files are named using the convention Figure[Figure Number][Sub-figure Letter], which corresponds directly to the figures presented in the manuscript. Examples:Figure2a.mat: Contains the data used to plot Figure 2a. Important Note: Some figures may be composed of multiple sub-figures (e.g., a, b, c). The data for each panel is provided in a separate file. 3. File Format DescriptionData Files: The primary data files are in [.mat / .csv / .h5 / .txt] format. .mat: MATLAB data file. Can be loaded in MATLAB using the load('filename.mat') command. For use in Python, you can use scipy.io.loadmat('filename.mat'). .csv: Comma-separated values. Can be opened with any text editor, spreadsheet software (Excel, Google Sheets), or imported into data analysis environments (Python/pandas, R). .h5: Hierarchical Data Format. Suitable for large and complex datasets. Can be read using h5py in Python or specialized tools in MATLAB and other languages.
