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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.10962 |
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| _version_ | 1866909786406125568 |
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| author | Sanger, Morgan D. Geyin, Mertcan Maurer, Brett W. |
| author_facet | Sanger, Morgan D. Geyin, Mertcan Maurer, Brett W. |
| contents | Using machine learning (ML), high performance computing, and a large body of geospatial information, we develop surrogate models to predict soil liquefaction across regional scales. Two sets of models - one global and one specific to New Zealand - are trained by learning to mimic geotechnical models at the sites of in-situ tests. Our geospatial approach has conceptual advantages in that predictions: (i) are anchored to mechanics, which encourages more sensible response and scaling across the domains of soil, site, and loading characteristics; (ii) are driven by ML, which allows more predictive information to be used, with greater potential for it to be exploited; (iii) are geostatistically updated by subsurface data, which anchors the predictions to known conditions; and (iv) are precomputed everywhere on earth for all conceivable earthquakes, which allows the models to be executed very easily, thus encouraging user adoption and evaluation. Test applications suggest that: (i) the proposed models outperform others to a statistically significant degree; (ii) the geostatistical updating further improves performance; and (iii) the anticipated advantages of region-specific models may largely be negated by the benefits of learning from larger global datasets. These models are best suited for regional-scale liquefaction hazard simulation and near-real-time response and are accompanied by variance products that convey where, and to what degree, the ML-predicted liquefaction response is influenced by local geotechnical data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_10962 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Mechanics-Informed Machine Learning for Geospatial Modeling of Soil Liquefaction: Global and National Surrogate Models for Simulation and Near-Real-Time Response Sanger, Morgan D. Geyin, Mertcan Maurer, Brett W. Computational Engineering, Finance, and Science Using machine learning (ML), high performance computing, and a large body of geospatial information, we develop surrogate models to predict soil liquefaction across regional scales. Two sets of models - one global and one specific to New Zealand - are trained by learning to mimic geotechnical models at the sites of in-situ tests. Our geospatial approach has conceptual advantages in that predictions: (i) are anchored to mechanics, which encourages more sensible response and scaling across the domains of soil, site, and loading characteristics; (ii) are driven by ML, which allows more predictive information to be used, with greater potential for it to be exploited; (iii) are geostatistically updated by subsurface data, which anchors the predictions to known conditions; and (iv) are precomputed everywhere on earth for all conceivable earthquakes, which allows the models to be executed very easily, thus encouraging user adoption and evaluation. Test applications suggest that: (i) the proposed models outperform others to a statistically significant degree; (ii) the geostatistical updating further improves performance; and (iii) the anticipated advantages of region-specific models may largely be negated by the benefits of learning from larger global datasets. These models are best suited for regional-scale liquefaction hazard simulation and near-real-time response and are accompanied by variance products that convey where, and to what degree, the ML-predicted liquefaction response is influenced by local geotechnical data. |
| title | Mechanics-Informed Machine Learning for Geospatial Modeling of Soil Liquefaction: Global and National Surrogate Models for Simulation and Near-Real-Time Response |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2509.10962 |