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diff --git a/README.md b/README.md
index 7da79263d0e701a53a0d662d245c4fc3db39b287..850e499738e77c4083a9fca418b08066b04cd99e 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,124 @@
----
-license: cc-by-4.0
----
+# 🌍 **OpenSWI: A Massive Surface-Wave Dispersion Curve Inversion Benchmark Dataset**
+
+## 📖 **Overview**
+
+**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:
+
+* **OpenSWI-Shallow**: 1D velocity profiles derived from 2D velocity models (OpenFWI dataset), paired with corresponding surface wave dispersion curves.
+* **OpenSWI-Deep**: 1D velocity profiles generated from high-resolution 3D geological models, sourced globally and regionally, tailored for deep geological studies.
+* **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.
+
+These datasets are ideal for training and evaluating deep learning models focused on surface-wave dispersion curve inversion tasks.
+
+---
+
+## 📊 **Datasets**
+
+### 🏞️ **OpenSWI-Shallow**
+
+* **Resource Models**: Includes diverse geological features such as flat layers, faults, folds, and their combinations, representing typical shallow subsurface structures.
+* **Profiles**:
+
+  * **~22 million 1D velocity profiles**, each paired with corresponding **Rayleigh wave dispersion curves**.
+  * 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.
+* **Geological Diversity**: The models include a broad spectrum of real-world shallow subsurface structures, such as:
+
+  * **Flat Layers (FlatVel)**
+  * **Faulted Layers (Flat-Fault)**
+  * **Folds (Fold)**
+  * **Folds with Faults (Fold-Fault)**
+  * **Real Style (Field)**
+
+These diverse models make the dataset highly applicable for both synthetic and real-world seismic data inversion tasks.
+
+---
+
+### 🌍 **OpenSWI-Deep**
+
+* **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.
+* **Profiles**:
+
+  * **~1.26 million 1D velocity profiles**, derived from the 3D models.
+  * 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.
+  * These profiles offer high-resolution data suitable for deep geological studies and support advanced seismic inversion techniques.
+* **Geological Diversity**: The 3D models come from various sources, including well-established models such as:
+
+  * **LITHO1.0** (global crust and mantle structure)
+  * **USTClitho1.0** (high-resolution model for China)
+  * **Central and Western US Models** (Shen et al., 2013)
+  * **Continental China** (Shen et al., 2016)
+  * **US Upper-Mantle Model**
+  * **EUcrust Model**
+  * **Alaska Model**
+  * **CSEM-Europe Model**
+  * **CSEM Eastern Mediterranean Model**
+  * **CSEM Western Mediterranean Model**
+  * **CSEM South Atlantic Model**
+  * **CSEM North Atlantic Model**
+  * **CSEM Japanese Island Model**
+  * **CSEM Australasian Model**
+
+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.
+
+---
+
+### 🏔️ **OpenSWI-Real**
+
+* **Long Beach, USA (Fu et al., 2022)**:
+
+  * Contains **5,297 stations**, each with phase velocity dispersion curves.
+  * The **period range** for the curves is **0.263 to 1.666 seconds**, focusing on shallow subsurface structures.
+  * This dataset provides real-world observational data for evaluating model performance and generalization in seismic inversion tasks.
+
+* **China Seismological Reference Model (Xiao et al., 2024)**:
+
+  * Includes **12,901 grid points**, each with phase and group velocity dispersion curves.
+  * The **period range** for these curves is **8 to 70 seconds**, ideal for studying deeper geological structures.
+  * Data is sourced from a dense network of seismic stations across mainland China, offering comprehensive coverage for advanced inversion tasks.
+
+---
+
+
+## 🔍 **Related Works**
+<details>
+<summary>Click to expand related works</summary>
+
+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).
+   
+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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+10. **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).
+
+...
+
+</details>
+
+## 📚 **Citation**
+
+If you use this dataset in your research, please cite:
+
+```bibtex
+@article{your_citation,
+  title={OpenSWI: A Massive Surface-Wave Dispersion Curve Inversion Benchmark Dataset},
+  author={Feng Liu, Sijie Zhao},
+  year={2025},
+}
+```
+
+## 📝 **License**
+
+Released under the **CC BY 4.0 License**. See the full license in the repository.