Articles | Volume 18, issue 4
https://doi.org/10.5194/essd-18-2769-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-18-2769-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
OpenSWI: a massive-scale benchmark dataset for surface wave dispersion curve inversion
School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
Sijie Zhao
Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
School of Geography and Ocean Science, Nanjing University, Nanjing 210023, China
Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
Fenghua Ling
Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
Peiqin Zhuang
Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
Yaxing Li
CORRESPONDING AUTHOR
Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu 610059, China
Rui Su
CORRESPONDING AUTHOR
Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
Lihua Fang
Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China
Lianqing Zhou
Institute of Earthquake Forecasting, China Earthquake Administration, Beijing 100036, China
Jianping Huang
Key Laboratory of Earth Exploration and Information Technology of Ministry of Education, Chengdu University of Technology, Chengdu 610059, China
Lei Bai
Shanghai Artificial Intelligence Laboratory, Shanghai 200232, China
Data sets
OpenSWI-dataset Feng Liu https://doi.org/10.5281/zenodo.16874111
Model code and software
OpenSWI-toolbox Feng Liu https://doi.org/10.5281/zenodo.16884901
Short summary
We introduce a large and diverse dataset that supports the development of machine learning methods for studying Earth structures through surface wave dispersion curves. Existing research has been limited by the absence of such benchmark data. Our dataset includes both computer-generated and real-world examples, allowing models to be tested and compared in a consistent way. By making these resources openly available, we aim to advance research on the shallow and deep Earth.
We introduce a large and diverse dataset that supports the development of machine learning...
Altmetrics
Final-revised paper
Preprint