Λεπτομέρειες βιβλιογραφικής εγγραφής
| Τίτλος: |
OpenSWI: a massive-scale benchmark dataset for surface wave dispersion curve inversion. |
| Συγγραφείς: |
Liu, Feng, Zhao, Sijie, Gu, Xinyu, Ling, Fenghua, Zhuang, Peiqin, Li, Yaxing, Su, Rui, Fang, Lihua, Zhou, Lianqing, Huang, Jianping, Bai, Lei |
| Πηγή: |
Earth System Science Data; 2026, Vol. 18 Issue 4, p2769-2798, 30p |
| Θεματικοί όροι: |
Geophysical prospecting, Seismic wave velocity, Rayleigh waves, Deep learning, Geological research, Estimation theory |
| Περίληψη: |
Surface wave dispersion curve inversion plays a critical role in both shallow geophysical exploration and deep geological studies, yet it remains hindered by sensitivity to initial models, susceptibility to local minima, and low computational efficiency. Recently, data-driven deep learning methods, inspired by their success in computer vision and natural language processing, have shown promising 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 address this gap, we introduce OpenSWI, a comprehensive benchmark dataset generated through the Surface Wave Inversion Dataset Preparation (SWIDP) pipeline. OpenSWI comprises two synthetic datasets tailored to different research scales and application scenarios, namely OpenSWI-shallow and OpenSWI-deep, as well as an AI-ready real-world dataset for generalization evaluation, OpenSWI-real. OpenSWI-shallow is derived from the 2-D geological model dataset OpenFWI, containing over 22 million 1-D velocity profiles paired with their fundamental-mode phase and group velocity dispersion curves, spanning a broad spectrum of shallow geological structures (e.g., flat layers, faults, folds, and realistic stratigraphy). OpenSWI-deep is built from 14 global and regional 3-D geological models, comprising approximately 1.26 million high-fidelity 1-D velocity-dispersion data pairs for deep earth studies. OpenSWI-real, compiled from open-source projects, contains two sets of observed dispersion curves and their corresponding 1-D reference models, serving as a benchmark for evaluating the generalization of deep learning models. To demonstrate the utility of OpenSWI, we trained deep learning models on OpenSWI-shallow and OpenSWI-deep, and evaluated them on OpenSWI-real. The results show strong agreement between the predicted and reference velocity models, confirming the diversity and representativeness of the OpenSWI dataset. To facilitate the advancement of intelligent surface wave dispersion curve inversion techniques, we release the OpenSWI dataset (10.5281/zenodo.16874111,) and the SWIDP toolbox along with associated resources (10.5281/zenodo.16884901,), providing open resources to support the research community. [ABSTRACT FROM AUTHOR] |
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| Βάση Δεδομένων: |
Complementary Index |