update: readme

Datasets/README.md

@@ -1,5 +1,5 @@
 <h1 align="center">OpenSWI: A Massive-Scale Benchmark Dataset for Surface Wave Dispersion Curve Inversion</h1>
-<h5 align="center">Feng Liu, Sijie Zhao, Xinyu Gu, Fenghua Lin, Yaxing Li*, Rui Su*, Jianping Huang, Lei Bai</h5>
+<h5 align="center"><a href="https://liufeng2317.github.io/">Feng Liu</a>, Sijie Zhao, Xinyu Gu, Fenghua Lin, Peiqin Zhuang, Rui Su*, Yaxing Li*, Jianping Huang, Lei Bai</h5>
 
 ### Source of the OpenSWI
 

README.md

@@ -12,7 +12,7 @@ viewer: false
 ---
 
 <h1 align="center">OpenSWI: A Massive-Scale Benchmark Dataset for Surface Wave Dispersion Curve Inversion</h1>
-<h5 align="center"><a href="https://liufeng2317.github.io/">Feng Liu</a>, Sijie Zhao, Xinyu Gu, Fenghua Lin, Yaxing Li*, Rui Su*, Jianping Huang, Lei Bai</h5>
+<h5 align="center"><a href="https://liufeng2317.github.io/">Feng Liu</a>, Sijie Zhao, Xinyu Gu, Fenghua Lin, Peiqin Zhuang, Rui Su*, Yaxing Li*, Jianping Huang, Lei Bai</h5>
 
 ![](./Figure/Figure1.png)
 
@@ -210,9 +210,9 @@ These datasets are ideal for training and evaluating deep learning models focuse
   * 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)**
+  * **Flat Layers**
   * **Faulted Layers (Flat-Fault)**
-  * **Folds (Fold)**
+  * **Folds**
   * **Folds with Faults (Fold-Fault)**
   * **Real Style (Field)**
 
@@ -228,22 +228,22 @@ These diverse models make the dataset highly applicable for both synthetic and r
   * **~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)
+* **Geological Diversity**: The 3D models come from various sources, including well-established models such as:
+  * **LITHO1.0** (Pasyanos et al., 2014)
+  * **USTClitho1.0** (Xin et al., 2018)
   * **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**
+  * **US Upper-Mantle Model** (Xie et al., 2018)
+  * **EUcrust Model** (Lu et al., 2018)
+  * **Alaska Model** (Berg et al., 2020)
+  * **CSEM-Europe Model** (Blom et al., 2020; Fichtner et al., 2018; Çubuk-Sabuncu et al., 2017)
+  * **CSEM Eastern Mediterranean Model** (Blom et al., 2020; Fichtner et al., 2018)
+  * **CSEM Western Mediterranean Model** (Fichtner et al., 2018; Fichtner et al., 2015)
+  * **CSEM South Atlantic Model** (Fichtner et al., 2018; Colli et al., 2013)
+  * **CSEM North Atlantic Model** (Fichtner et al., 2018; Krischer et al., 2018)
+  * **CSEM Japanese Island Model** (Fichtner et al., 2018； Simutė et al., 2016)
+  * **CSEM Australasian Model** (Fichtner et al., 2018; Fichtner et al., 2010)
 
 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.