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--- |
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language: |
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- en |
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tags: |
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- inversion |
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- seismic |
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- imaging |
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- subsurface |
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size_categories: |
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- 10B<n<100B |
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viewer: false |
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--- |
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<h1 align="center"> |
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<span style="font-family: 'Papyrus', sans-serif; color: red; font-weight: bold;">OpenSWI</span>: |
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<span style="font-family: 'Papyrus', sans-serif; color:rgb(14, 126, 146);">A Massive-Scale Benchmark Dataset for </span> <br> |
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<span style="font-family: 'Papyrus', sans-serif; color:rgb(14, 126, 146);">Surface Wave Dispersion Curve Inversion</span> |
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</h1> |
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<h5 align="center"><a href="https://liufeng2317.github.io/">Feng Liu</a>, Sijie Zhao, Xinyu Gu, Fenghua Ling, Peiqin Zhuang, Yaxing Li*, Rui Su*, Lihua Fang, Jianping Huang, Lei Bai</h5> |
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## 📖 **Overview** |
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**OpenSWI** is a comprehensive 1D dataset for surface-wave dispersion curve inversion, specifically designed for both shallow subsurface exploration (\~3 km) and deep geological studies (\~300 km). The dataset contains synthetic data derived from a variety of geological models, as well as real-world observational data, providing an invaluable resource for assessing and enhancing the generalization capabilities of deep learning models. The dataset includes: |
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* [**OpenSWI-Shallow**](https://huggingface.co/datasets/LiuFeng2317/OpenSWI/tree/main/Datasets/OpenSWI-shallow/0.2-10s-Aug): 1D velocity profiles derived from 2D velocity models (OpenFWI dataset), paired with corresponding surface wave dispersion curves. |
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* [**OpenSWI-Deep**](https://huggingface.co/datasets/LiuFeng2317/OpenSWI/tree/main/Datasets/OpenSWI-deep/1s-100s-Aug): 1D velocity profiles generated from high-resolution 3D geological models, sourced globally and regionally, tailored for deep geological studies. |
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* [**OpenSWI-Real**](https://huggingface.co/datasets/LiuFeng2317/OpenSWI/tree/main/Datasets/OpenSWI-real): AI-ready observational data from Long Beach, USA, and the China Seismological Reference Model Project, with 1D velocity profiles and corresponding surface wave dispersion curves. |
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These datasets are ideal for training and evaluating deep learning models focused on surface-wave dispersion curve inversion tasks. |
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--- |
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## 📊 **OpenSWI Datasets** |
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<table> |
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<thead> |
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<tr> |
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<th>Group</th> |
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<th>Datasets</th> |
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<th>Period Range (s)</th> |
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<th>Depth Range and Interval (km)</th> |
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<th>Extracted 1D Velocity</th> |
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<th>Augmented 1D Velocity</th> |
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</tr> |
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</thead> |
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<tbody> |
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<tr> |
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<td rowspan="5"><a href="https://huggingface.co/datasets/LiuFeng2317/OpenSWI/tree/main/Datasets/OpenSWI-shallow/0.2-10s-Aug">OpenSWI-shallow</a></td> |
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<td>OpenFWI-FlatVelA</td> |
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<td>0.1-10 s</td> |
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<td>0-2.8 km / 0.04 km</td> |
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<td>30,000 x 4 x 70</td> |
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<td>1,490,415 x 4 x 70</td> |
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</tr> |
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<tr> |
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<td>OpenFWI-Flat-FaultA</td> |
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<td>0.1-10 s</td> |
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<td>0-2.8 km / 0.04 km</td> |
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<td>292,941 x 4 x 70</td> |
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<td>2,925,151 x 4 x 70</td> |
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</tr> |
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<tr> |
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<td>OpenFWI-CurveVel</td> |
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<td>0.1-10 s</td> |
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<td>0-2.8 km / 0.04 km</td> |
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<td>295,773 x 4 x 70</td> |
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<td>2,952,975 x 4 x 70</td> |
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</tr> |
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<tr> |
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<td>OpenFWI-Fold-Fault</td> |
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<td>0.1-10 s</td> |
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<td>0-2.8 km / 0.04 km</td> |
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<td>537,774 x 4 x 70</td> |
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<td>5,369,692 x 4 x 70</td> |
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</tr> |
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<tr> |
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<td>OpenFWI-StyleA</td> |
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<td>0.1-10 s</td> |
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<td>0-2.8 km / 0.04 km</td> |
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<td>2,344,958 x 4 x 70</td> |
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<td>9,345,103 x 4 x 70</td> |
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</tr> |
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<tr> |
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<td rowspan="14"><a href="https://huggingface.co/datasets/LiuFeng2317/OpenSWI/tree/main/Datasets/OpenSWI-deep/1s-100s-Aug">OpenSWI-deep</a></td> |
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<td>LITHO1.0</td> |
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<td>1-100 s</td> |
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<td>0-300 km / 1.0 km</td> |
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<td>40,959 x 4 x 300</td> |
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<td>245,771 x 4 x 301</td> |
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</tr> |
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<tr> |
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<td>USTClitho1.0</td> |
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<td>1-100 s</td> |
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<td>0-300 km / 1.0 km</td> |
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<td>9,125 x 4 x 300</td> |
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<td>54,750 x 4 x 301</td> |
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</tr> |
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<tr> |
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<td>Central-and-Western US</td> |
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<td>1-100 s</td> |
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<td>0-300 km / 1.0 km</td> |
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<td>6,803 x 4 x 300</td> |
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<td>40,818 x 4 x 301</td> |
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</tr> |
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<tr> |
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<td>Continental China</td> |
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<td>1-100 s</td> |
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<td>0-300 km / 1.0 km</td> |
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<td>4,516 x 4 x 300</td> |
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<td>27,096 x 4 x 301</td> |
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</tr> |
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<tr> |
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<td>US Upper-Mantle</td> |
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<td>1-100 s</td> |
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<td>0-300 km / 1.0 km</td> |
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<td>3,678 x 4 x 300</td> |
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<td>22,061 x 4 x 301</td> |
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</tr> |
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<tr> |
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<td>EUcrust</td> |
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<td>1-100 s</td> |
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<td>0-300 km / 1.0 km</td> |
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<td>43,520 x 4 x 300</td> |
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<td>261,155 x 4 x 301</td> |
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</tr> |
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<tr> |
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<td>Alaska</td> |
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<td>1-100 s</td> |
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<td>0-300 km / 1.0 km</td> |
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<td>19,408 x 4 x 300</td> |
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<td>116,448 x 4 x 301</td> |
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</tr> |
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<tr> |
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<td>CSEM-Europe</td> |
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<td>1-100 s</td> |
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<td>0-300 km / 1.0 km</td> |
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<td>21,931 x 4 x 300</td> |
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<td>131,586 x 4 x 301</td> |
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</tr> |
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<tr> |
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<td>CSEM-Eastmed</td> |
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<td>1-100 s</td> |
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<td>0-300 km / 1.0 km</td> |
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<td>12,782 x 4 x 300</td> |
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<td>76,692 x 4 x 301</td> |
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</tr> |
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<tr> |
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<td>CSEM-Iberian</td> |
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<td>1-100 s</td> |
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<td>0-300 km / 1.0 km</td> |
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<td>9,102 x 4 x 300</td> |
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<td>54,612 x 4 x 301</td> |
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</tr> |
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<tr> |
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<td>CSEM-South Atlantic</td> |
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<td>1-100 s</td> |
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<td>0-300 km / 1.0 km</td> |
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<td>7,371 x 4 x 300</td> |
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<td>44,226 x 4 x 301</td> |
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</tr> |
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<tr> |
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<td>CSEM-North Atlantic</td> |
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<td>1-100 s</td> |
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<td>0-300 km / 1.0 km</td> |
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<td>14,541 x 4 x 300</td> |
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<td>87,246 x 4 x 301</td> |
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</tr> |
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<tr> |
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<td>CSEM-Japan</td> |
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<td>1-100 s</td> |
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<td>0-300 km / 1.0 km</td> |
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<td>14,641 x 4 x 300</td> |
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<td>87,846 x 4 x 301</td> |
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</tr> |
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<tr> |
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<td>CSEM-Astralasia</td> |
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<td>1-100 s</td> |
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<td>0-300 km / 1.0 km</td> |
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<td>4,131 x 4 x 300</td> |
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<td>24,786 x 4 x 301</td> |
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</tr> |
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<tr> |
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<td rowspan="2"><a href="https://huggingface.co/datasets/LiuFeng2317/OpenSWI/tree/main/Datasets/OpenSWI-real/">OpenSWI-real</a></td> |
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<td><a href="https://huggingface.co/datasets/LiuFeng2317/OpenSWI/tree/main/Datasets/OpenSWI-real/LongBeach">LongBeach</a></td> |
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<td>0.263 - 1.666 s</td> |
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<td>0-1.4 km / 0.04 km</td> |
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<td>5,297 x 4 x 35</td> |
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<td>-</td> |
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</tr> |
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<tr> |
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<td><a href="https://huggingface.co/datasets/LiuFeng2317/OpenSWI/tree/main/Datasets/OpenSWI-real/CSRM">CSRM</a></td> |
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<td>8 - 70 s</td> |
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<td>0-120 km / 1.0 km</td> |
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<td>12,901 x 4 x 120</td> |
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<td>-</td> |
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</tr> |
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</tbody> |
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</table> |
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> Details of the source of datasets can be found at [OpenSWI](./Datasets/README.md) |
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### 🏞️ **OpenSWI-Shallow** |
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* **Resource Models**: Includes diverse geological features such as flat layers, faults, folds, and their combinations, representing typical shallow subsurface structures. |
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* **Profiles**: |
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* **~22 million 1D velocity profiles**, each paired with corresponding **Rayleigh wave dispersion curves**. |
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* The dataset spans a **period range from 0.2 to 10 seconds** and covers **100 sampling points** (including uniform, random, and logarithmic distributions) for each dispersion curve. This variety ensures robust training and evaluation across different geological scenarios. |
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* **Geological Diversity**: The models include a broad spectrum of real-world shallow subsurface structures, such as: |
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* **Flat Layers** |
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* **Faulted Layers (Flat-Fault)** |
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* **Folds** |
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* **Folds with Faults (Fold-Fault)** |
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* **Real Style (Field)** |
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These diverse models make the dataset highly applicable for both synthetic and real-world seismic data inversion tasks. |
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--- |
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### 🌍 **OpenSWI-Deep** |
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* **Resource Models**: Includes over **14 global and regional 3D geological models**, derived from high-resolution seismic data. These models represent deep geological structures, from the crust to the mantle, spanning a variety of tectonic settings and geological environments. |
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* **Profiles**: |
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* **~1.26 million 1D velocity profiles**, derived from the 3D models. |
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* The profiles span a **period range from 1 to 100 seconds**, covering **300 sampling points** (including uniform, random, and logarithmic distributions) for each dispersion curve. |
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* These profiles offer high-resolution data suitable for deep geological studies and support advanced seismic inversion techniques. |
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* **Geological Diversity**: The 3D models come from various sources, including well-established models such as: |
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* **LITHO1.0** (Pasyanos et al., 2014) |
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* **USTClitho1.0** (Xin et al., 2018) |
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* **Central and Western US Models** (Shen et al., 2013) |
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* **Continental China** (Shen et al., 2016) |
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* **US Upper-Mantle Model** (Xie et al., 2018) |
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* **EUcrust Model** (Lu et al., 2018) |
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* **Alaska Model** (Berg et al., 2020) |
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* **CSEM-Europe Model** (Blom et al., 2020; Fichtner et al., 2018; Çubuk-Sabuncu et al., 2017) |
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* **CSEM Eastern Mediterranean Model** (Blom et al., 2020; Fichtner et al., 2018) |
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* **CSEM Western Mediterranean Model** (Fichtner et al., 2018; Fichtner et al., 2015) |
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* **CSEM South Atlantic Model** (Fichtner et al., 2018; Colli et al., 2013) |
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* **CSEM North Atlantic Model** (Fichtner et al., 2018; Krischer et al., 2018) |
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* **CSEM Japanese Island Model** (Fichtner et al., 2018； Simutė et al., 2016) |
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* **CSEM Australasian Model** (Fichtner et al., 2018; Fichtner et al., 2010) |
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These models provide a comprehensive representation of both regional and global deep geological structures, enhancing the dataset’s value for training deep learning models on complex inversion tasks. |
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--- |
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### 🏔️ **OpenSWI-Real** |
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* **Long Beach, USA (Fu et al., 2022)**: |
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* Contains **5,297 stations**, each with phase velocity dispersion curves. |
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* The **period range** for the curves is **0.263 to 1.666 seconds**, focusing on shallow subsurface structures. |
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* This dataset provides real-world observational data for evaluating model performance and generalization in seismic inversion tasks. |
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* **China Seismological Reference Model (Xiao et al., 2024)**: |
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* Includes **12,901 grid points**, each with phase and group velocity dispersion curves. |
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* The **period range** for these curves is **8 to 70 seconds**, ideal for studying deeper geological structures. |
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* Data is sourced from a dense network of seismic stations across mainland China, offering comprehensive coverage for advanced inversion tasks. |
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---- |
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## 📧 **Contact** |
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**Principal Developer** |
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[Feng Liu](https://liufeng2317.github.io/) |
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🏛️ Affiliations: |
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- Shanghai Artificial Intelligence Laboratory |
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- Shanghai Jiao Tong University |
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📧 **Contact Information**: |
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- [](mailto:[email protected]) |
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- [](mailto:[email protected]) |
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---- |
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## 📚 **Citation** |
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If you use this dataset in your research, please cite: |
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```bibtex |
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@software{liufeng_2025_openswi, |
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title={OpenSWI: A Massive-Scale Benchmark Dataset for Surface Wave Dispersion Curve Inversion}, |
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author={Feng Liu, Sijie Zhao, Xinyu Gu, Fenghua Ling, Peiqin Zhuang, Yaxing Li, Rui Su, Lihua Fang, Jianping Huang, Lei Bai}, |
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year={2025}, |
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} |
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``` |
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---- |
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## 📝 **License** |
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Released under the **CC BY 4.0 License**. See the full license in the repository. |
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--- |
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## 🔍 **Reference** |
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<details> |
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<summary>Click to expand related works</summary> |
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1. **X. Xiao et al.**, "CSRM‐1.0: A China Seismological Reference Model," *JGR Solid Earth*, vol. 129, no. 9, p. e2024JB029520, Sept. 2024, [doi: 10.1029/2024JB029520](https://doi.org/10.1029/2024JB029520). |
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2. **L. Fu et al.**, "Improved high‐resolution 3D vs model of Long Beach, CA: Inversion of multimodal dispersion curves from ambient noise of a dense array," *Geophys. Res. Lett.*, vol. 49, no. 4, p. e2021GL097619, Feb. 2022, [doi: 10.1029/2021GL097619](https://doi.org/10.1029/2021GL097619). |
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3. **C. Deng et al.**, “OpenFWI: Large-scale multi-structural benchmark datasets for full waveform inversion,” *Neural Information Processing Systems*, Nov. 2021, Accessed: Feb. 29, 2024. [Online]. Available: [https://www.semanticscholar.org/paper/2d13799dcbd0aefb08c379f58cd6004b1376ca33](https://www.semanticscholar.org/paper/2d13799dcbd0aefb08c379f58cd6004b1376ca33) |
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4. **N. Blom et al.**, "Seismic waveform tomography of the central and eastern Mediterranean upper mantle," *Solid Earth*, vol. 11, no. 2, pp. 669–690, Apr. 2020, [doi: 10.5194/se-11-669-2020](https://doi.org/10.5194/se-11-669-2020). |
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5. **E. M. Berg et al.**, "Shear velocity model of Alaska via joint inversion of Rayleigh wave ellipticity, phase velocities, and receiver functions across the Alaska transportable array," *J. Geophys. Res.: Solid Earth*, vol. 125, no. 2, Feb. 2020, [doi: 10.1029/2019jb018582](https://doi.org/10.1029/2019jb018582). |
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6. **H. Xin et al.**, "High‐resolution lithospheric velocity structure of continental China by double‐difference seismic travel‐time tomography," *Seismol. Res. Lett.*, vol. 90, no. 1, pp. 229–241, Jan. 2019, [doi: 10.1785/0220180209](https://doi.org/10.1785/0220180209). |
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7. **J. Xie et al.**, "3-D upper-mantle shear velocity model beneath the contiguous United States based on broadband surface wave from ambient seismic noise," *Pure Appl. Geophys.*, vol. 175, no. 10, pp. 3403–3418, Oct. 2018, [doi: 10.1007/s00024-018-1881-2](https://doi.org/10.1007/s00024-018-1881-2). |
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8. **Y. Lu et al.**, "High-resolution surface wave tomography of the European crust and uppermost mantle from ambient seismic noise," *Geophys. J. Int.*, vol. 214, no. 2, pp. 1136–1150, Aug. 2018, [doi: 10.1093/gji/ggy188](https://doi.org/10.1093/gji/ggy188). |
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9. **L. Krischer et al.**, "Automated large‐scale full seismic waveform inversion for North America and the North Atlantic," *J. Geophys. Res.: Solid Earth*, vol. 123, no. 7, pp. 5902–5928, July 2018, [doi: 10.1029/2017JB015289](https://doi.org/10.1029/2017JB015289). |
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10. **A. Fichtner et al.**, "The collaborative seismic earth model: generation 1," *Geophys. Res. Lett.*, vol. 45, no. 9, pp. 4007–4016, May 2018, [doi: 10.1029/2018gl077338](https://doi.org/10.1029/2018gl077338). |
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11. **Y. Çubuk-Sabuncu et al.**, "3-D crustal velocity structure of western Turkey: constraints from full-waveform tomography," *Phys. Earth Planet. Inter.*, vol. 270, pp. 90–112, Sept. 2017, [doi: 10.1016/j.pepi.2017.06.014](https://doi.org/10.1016/j.pepi.2017.06.014). |
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12. **S. Simutė et al.**, "Full‐waveform inversion of the Japanese islands region," *J. Geophys. Res.: Solid Earth*, vol. 121, no. 5, pp. 3722–3741, May 2016, [doi: 10.1002/2016jb012802](https://doi.org/10.1002/2016jb012802). |
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13. **W. Shen et al.**, "A seismic reference model for the crust and uppermost mantle beneath China from surface wave dispersion," *Geophys. J. Int.*, vol. 206, no. 2, pp. 954–979, Aug. 2016, [doi: 10.1093/gji/ggw175](https://doi.org/10.1093/gji/ggw175). |
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14. **A. Fichtner and A. Villaseñor**, "Crust and upper mantle of the western Mediterranean – constraints from full-waveform inversion," *Earth Planet. Sci. Lett.*, vol. 428, pp. 52–62, Oct. 2015, [doi: 10.1016/j.epsl.2015.07.038](https://doi.org/10.1016/j.epsl.2015.07.038). |
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15. **M. E. Pasyanos et al.**, "LITHO1.0: An updated crust and lithospheric model of the Earth," *JGR Solid Earth*, vol. 119, no. 3, pp. 2153–2173, Mar. 2014, [doi: 10.1002/2013JB010626](https://doi.org/10.1002/2013JB010626). |
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</details> |
