In recent years, inspired by the success of computer vision and natural language processing, data-driven deep learning methods have shown great potential to overcome these challenges. However, the lack of large-scale and diverse benchmark datasets remains a major obstacle to the development and evaluation of such methods. To fill this gap, we have launched the OpenSWI comprehensive benchmark dataset, which is generated through the Surface Wave Inversion Dataset Preprocessing (SWIDP) pipeline. OpenSWI includes two synthetic datasets for different research scales and application scenarios - OpenSWI sharow and OpenSWI deep, as well as the AI ready real dataset OpenSWI real for generalization evaluation. OpenSWI real integrates from an open source project, including two sets of observed dispersion curves and their corresponding one-dimensional reference models, as a benchmark dataset for evaluating the generalization ability of deep learning models.
OpenSWI shalow is derived from the 2D geological model dataset OpenFWI, which contains over 22 million sets of one-dimensional velocity profiles and their basic mode phase and group velocity dispersion curves, covering a wide range of shallow geological structures such as flat layers, faults, folds, and real strata. OpenSWI deep is constructed from 14 global and regional 3D geological models, containing approximately 1.26 million high-precision one-dimensional velocity dispersion data pairs, specifically designed for deep Earth research.
Generate pipeline through surface wave inversion dataset preprocessing (SWIDP).
To verify the practicality of OpenSWI, we trained deep learning models based on OpenSWI sharow and OpenSWI deep, and evaluated them through OpenSWI real. The results showed that the predicted model highly matched the baseline velocity model, confirming the diversity and representativeness of the OpenSWI dataset.
This work is licensed under
CC BY 4.0 (Creative Commons Attribution 4.0 International License).
CSTR:11738.11.NCDC.ZENODO.DB7022.2025
10.5281/zenodo.16874111
