Evaluation of Seismic Artificial Intelligence with Uncertainty
Fuente:
arXiv
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913869259079680 |
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| author | Myren, Samuel Parikh, Nidhi Rael, Rosalyn Flynn, Garrison Higdon, Dave Casleton, Emily |
| author_facet | Myren, Samuel Parikh, Nidhi Rael, Rosalyn Flynn, Garrison Higdon, Dave Casleton, Emily |
| contents | Artificial intelligence has transformed the seismic community with deep learning models (DLMs) that are trained to complete specific tasks within workflows. However, there is still lack of robust evaluation frameworks for evaluating and comparing DLMs. We address this gap by designing an evaluation framework that jointly incorporates two crucial aspects: performance uncertainty and learning efficiency. To target these aspects, we meticulously construct the training, validation, and test splits using a clustering method tailored to seismic data and enact an expansive training design to segregate performance uncertainty arising from stochastic training processes and random data sampling. The framework's ability to guard against misleading declarations of model superiority is demonstrated through evaluation of PhaseNet [1], a popular seismic phase picking DLM, under 3 training approaches. Our framework helps practitioners choose the best model for their problem and set performance expectations by explicitly analyzing model performance with uncertainty at varying budgets of training data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_14809 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Evaluation of Seismic Artificial Intelligence with Uncertainty Myren, Samuel Parikh, Nidhi Rael, Rosalyn Flynn, Garrison Higdon, Dave Casleton, Emily Machine Learning Artificial Intelligence Geophysics Artificial intelligence has transformed the seismic community with deep learning models (DLMs) that are trained to complete specific tasks within workflows. However, there is still lack of robust evaluation frameworks for evaluating and comparing DLMs. We address this gap by designing an evaluation framework that jointly incorporates two crucial aspects: performance uncertainty and learning efficiency. To target these aspects, we meticulously construct the training, validation, and test splits using a clustering method tailored to seismic data and enact an expansive training design to segregate performance uncertainty arising from stochastic training processes and random data sampling. The framework's ability to guard against misleading declarations of model superiority is demonstrated through evaluation of PhaseNet [1], a popular seismic phase picking DLM, under 3 training approaches. Our framework helps practitioners choose the best model for their problem and set performance expectations by explicitly analyzing model performance with uncertainty at varying budgets of training data. |
| title | Evaluation of Seismic Artificial Intelligence with Uncertainty |
| topic | Machine Learning Artificial Intelligence Geophysics |
| url | https://arxiv.org/abs/2501.14809 |