Imaging Submarine Fault Zones Using Reflected S-waves Extracted by Ambient Noise Interferometry from Distributed Acoustic Sensing Article Swipe
Lu Zhufeng
,
Yang Xiao-dong
,
Yang Jizhong
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.17724186
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5281/zenodo.17724186
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: [ [email protected]]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. 4. CodesThe repository also includes MATLAB scripts used for data processing and figure generation. These scripts are organized by data type and plotting purpose (e.g., Figure 4), allowing users to reproduce the main results of the paper. ACFs.m is used to visualize all ambient-noise autocorrelation functions (ACFs). NCFs.m is used to plot noise cross-correlation functions (NCFs), as shown for example in Figure 6. VSGs.m and Dispersion_energy.m are used together to generate virtual shot gathers from the noise cross-correlations and to compute the corresponding dispersion-energy images (e.g., Figure 5). Plt_tf.m is a general plotting routine for both time-domain and frequency-domain representations of the data and is used to produce figures such as Figure 2a, 2b, and Figure 7.
Related Topics
Concepts
Scripting language
MATLAB
Python (programming language)
Computer science
Software
NetCDF
Data file
Header
File format
Computer graphics (images)
Data processing
Noise (video)
Submarine
Skew
Plot (graphics)
Data format
Data acquisition
Ambient noise level
Java
Remote sensing
Data mining
Unix
Data type
Theodolite
Data visualization
Database
USB
Data structure
Metadata
- Type
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- Language
- en
- Landing Page
- https://doi.org/10.5281/zenodo.17724186
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7107874111
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7107874111Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.17724186Digital Object Identifier
- Title
-
Imaging Submarine Fault Zones Using Reflected S-waves Extracted by Ambient Noise Interferometry from Distributed Acoustic SensingWork title
- Type
-
otherOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-11-26Full publication date if available
- Authors
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Lu Zhufeng, Yang Xiao-dong, Yang JizhongList of authors in order
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https://doi.org/10.5281/zenodo.17724186Publisher landing page
- Open access
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
- OA URL
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https://doi.org/10.5281/zenodo.17724186Direct OA link when available
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Scripting language, MATLAB, Python (programming language), Computer science, Software, NetCDF, Data file, Header, File format, Computer graphics (images), Data processing, Noise (video), Submarine, Skew, Plot (graphics), Data format, Data acquisition, Ambient noise level, Java, Remote sensing, Data mining, Unix, Data type, Theodolite, Data visualization, Database, USB, Data structureTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.figures | 15, 29, 86, 103, 321 |
| abstract_inverted_index.gathers | 286 |
| abstract_inverted_index.general | 304 |
| abstract_inverted_index.primary | 131 |
| abstract_inverted_index.produce | 320 |
| abstract_inverted_index.purpose | 236 |
| abstract_inverted_index.results | 246 |
| abstract_inverted_index.routine | 306 |
| abstract_inverted_index.scripts | 10, 219, 228 |
| abstract_inverted_index.values. | 167 |
| abstract_inverted_index.virtual | 284 |
| abstract_inverted_index.Acoustic | 52 |
| abstract_inverted_index.Contains | 92 |
| abstract_inverted_index.Letter], | 80 |
| abstract_inverted_index.Sheets), | 179 |
| abstract_inverted_index.Suitable | 192 |
| abstract_inverted_index.Xiaodong | 57 |
| abstract_inverted_index.[Zhufeng | 55 |
| abstract_inverted_index.allowing | 240 |
| abstract_inverted_index.analysis | 184 |
| abstract_inverted_index.command. | 156 |
| abstract_inverted_index.composed | 106 |
| abstract_inverted_index.contains | 5 |
| abstract_inverted_index.directly | 83 |
| abstract_inverted_index.generate | 13, 283 |
| abstract_inverted_index.imported | 181 |
| abstract_inverted_index.includes | 217 |
| abstract_inverted_index.multiple | 108 |
| abstract_inverted_index.plotting | 235, 305 |
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| abstract_inverted_index.separate | 123 |
| abstract_inverted_index.software | 176 |
| abstract_inverted_index.together | 281 |
| abstract_inverted_index.Extracted | 45 |
| abstract_inverted_index.Reflected | 43 |
| abstract_inverted_index.Structure | 66 |
| abstract_inverted_index.Submarine | 39 |
| abstract_inverted_index.according | 26 |
| abstract_inverted_index.datasets. | 197 |
| abstract_inverted_index.functions | 258, 267 |
| abstract_inverted_index.organized | 23, 230 |
| abstract_inverted_index.presented | 87 |
| abstract_inverted_index.processed | 7 |
| abstract_inverted_index.reproduce | 243 |
| abstract_inverted_index.visualize | 254 |
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| abstract_inverted_index.<p>4. | 213 |
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| abstract_inverted_index.publication. | 19 |
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| abstract_inverted_index.5).</p> | 300 |
| abstract_inverted_index.Figure[Figure | 78 |
| abstract_inverted_index.R).</p> | 187 |
| abstract_inverted_index.ambient-noise | 256 |
| abstract_inverted_index.corresponding | 295 |
| abstract_inverted_index.<p>.csv: | 165 |
| abstract_inverted_index.<p>.mat: | 144 |
| abstract_inverted_index.Interferometry | 49 |
| abstract_inverted_index.aforementioned | 18 |
| abstract_inverted_index.<p>ACFs.m | 250 |
| abstract_inverted_index.<p>NCFs.m | 260 |
| abstract_inverted_index.<p>VSGs.m | 276 |
| abstract_inverted_index.(Python/pandas, | 186 |
| abstract_inverted_index.Comma-separated | 166 |
| abstract_inverted_index.autocorrelation | 257 |
| abstract_inverted_index.file.</p> | 124 |
| abstract_inverted_index.representations | 312 |
| abstract_inverted_index.straightforward | 34 |
| abstract_inverted_index.frequency-domain | 311 |
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| abstract_inverted_index.<p>Plt_tf.m | 301 |
| abstract_inverted_index.(ACFs).</p> | 259 |
| abstract_inverted_index.cross-correlation | 266 |
| abstract_inverted_index.dispersion-energy | 296 |
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| abstract_inverted_index.Number][Sub-figure | 79 |
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| abstract_inverted_index.Dispersion_energy.m | 278 |
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| abstract_inverted_index.Sensing<br>Corresponding | 53 |
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| [email protected]]<br>2. | 64 |
| abstract_inverted_index.scipy.io.loadmat('filename.mat').</p> | 164 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile |