Checking Generative Artificial Intelligence-Based Subsurface Models
<p id="p1">This book chapter provides a comprehensive overview of generative artificial intelligence (genAI) applications in subsurface modeling. It introduces a suite of quantitative checks of genAI-based realizations. genAI for subsurface modeling is proposed as an alternative solution to traditio...
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| Autores principales: | , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2025
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/89798 |
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| Sumario: | <p id="p1">This book chapter provides a comprehensive overview of generative artificial intelligence (genAI) applications in subsurface modeling. It introduces a suite of quantitative checks of genAI-based realizations. genAI for subsurface modeling is proposed as an alternative solution to traditional geostatistical workflows because it integrates several sources of information. We detail a minimum acceptance protocol that spans multiple checks of model evaluation: global univariate distribution (examined through histogram overlap, Q-Q plot divergence, Earth Mover’s Distance for continuous data, and total variation distance for categorical data); local uncertainty (assessed via cell-wise standard deviation and Shannon entropy); global uncertainty (evaluated using dimensionality reduction techniques coupled with overlap coefficients); and global spatial continuity (measured with variogram analyses). Additionally, we address local exactitude through conditioning metrics, identify conditioning artifacts, and check dynamic response using flow simulation recovery factors. The checks also include a stability analysis of the latent space via the Structural Similarity Index Measure (SSIM). These quantitative checks aim to enhance the reliability and applicability of genAI subsurface models in critical decision-making processes for resource development. This work is a practical guide for researchers and practitioners seeking to implement robust, uncertainty-aware genAI methodologies in subsurface modeling workflows.</p> |
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| ISBN: | 978-1-83968-501-9 |
| DOI: | 10.5772/intechopen.115623 |