Academic Journal
OpenSWI: a massive-scale benchmark dataset for surface wave dispersion curve inversion.
| 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] |
| Copyright of Earth System Science Data is the property of Copernicus Gesellschaft mbH and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Βάση Δεδομένων: | Complementary Index |
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| Items | – Name: Title Label: Title Group: Ti Data: OpenSWI: a massive-scale benchmark dataset for surface wave dispersion curve inversion. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Liu%2C+Feng%22">Liu, Feng</searchLink><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Sijie%22">Zhao, Sijie</searchLink><br /><searchLink fieldCode="AR" term="%22Gu%2C+Xinyu%22">Gu, Xinyu</searchLink><br /><searchLink fieldCode="AR" term="%22Ling%2C+Fenghua%22">Ling, Fenghua</searchLink><br /><searchLink fieldCode="AR" term="%22Zhuang%2C+Peiqin%22">Zhuang, Peiqin</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Yaxing%22">Li, Yaxing</searchLink><br /><searchLink fieldCode="AR" term="%22Su%2C+Rui%22">Su, Rui</searchLink><br /><searchLink fieldCode="AR" term="%22Fang%2C+Lihua%22">Fang, Lihua</searchLink><br /><searchLink fieldCode="AR" term="%22Zhou%2C+Lianqing%22">Zhou, Lianqing</searchLink><br /><searchLink fieldCode="AR" term="%22Huang%2C+Jianping%22">Huang, Jianping</searchLink><br /><searchLink fieldCode="AR" term="%22Bai%2C+Lei%22">Bai, Lei</searchLink> – Name: TitleSource Label: Source Group: Src Data: Earth System Science Data; 2026, Vol. 18 Issue 4, p2769-2798, 30p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Geophysical+prospecting%22">Geophysical prospecting</searchLink><br /><searchLink fieldCode="DE" term="%22Seismic+wave+velocity%22">Seismic wave velocity</searchLink><br /><searchLink fieldCode="DE" term="%22Rayleigh+waves%22">Rayleigh waves</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Geological+research%22">Geological research</searchLink><br /><searchLink fieldCode="DE" term="%22Estimation+theory%22">Estimation theory</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Earth System Science Data is the property of Copernicus Gesellschaft mbH and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.5194/essd-18-2769-2026 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 30 StartPage: 2769 Subjects: – SubjectFull: Geophysical prospecting Type: general – SubjectFull: Seismic wave velocity Type: general – SubjectFull: Rayleigh waves Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Geological research Type: general – SubjectFull: Estimation theory Type: general Titles: – TitleFull: OpenSWI: a massive-scale benchmark dataset for surface wave dispersion curve inversion. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Liu, Feng – PersonEntity: Name: NameFull: Zhao, Sijie – PersonEntity: Name: NameFull: Gu, Xinyu – PersonEntity: Name: NameFull: Ling, Fenghua – PersonEntity: Name: NameFull: Zhuang, Peiqin – PersonEntity: Name: NameFull: Li, Yaxing – PersonEntity: Name: NameFull: Su, Rui – PersonEntity: Name: NameFull: Fang, Lihua – PersonEntity: Name: NameFull: Zhou, Lianqing – PersonEntity: Name: NameFull: Huang, Jianping – PersonEntity: Name: NameFull: Bai, Lei IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 04 Text: 2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 18663508 Numbering: – Type: volume Value: 18 – Type: issue Value: 4 Titles: – TitleFull: Earth System Science Data Type: main |
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