Computer Science > Machine Learning
arXiv:2603.09165 (cs)
[Submitted on 10 Mar 2026]
Title:GIAT: A Geologically-Informed Attention Transformer for Lithology Identification
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Abstract:Accurate lithology identification from well logs is crucial for subsurface resource evaluation. Although Transformer-based models excel at sequence modeling, their "black-box" nature and lack of geological guidance limit their performance and trustworthiness. To overcome these limitations, this letter proposes the Geologically-Informed Attention Transformer (GIAT), a novel framework that deeply fuses data-driven geological priors with the Transformer's attention mechanism. The core of GIAT is a new attention-biasing mechanism. We repurpose Category-Wise Sequence Correlation (CSC) filters to generate a geologically-informed relational matrix, which is injected into the self-attention calculation to explicitly guide the model toward geologically coherent patterns. On two challenging datasets, GIAT achieves state-of-the-art performance with an accuracy of up to 95.4%, significantly outperforming existing models. More importantly, GIAT demonstrates exceptional interpretation faithfulness under input perturbations and generates geologically coherent predictions. Our work presents a new paradigm for building more accurate, reliable, and interpretable deep learning models for geoscience applications.
| Subjects: | Machine Learning (cs.LG); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2603.09165 [cs.LG] |
| (or arXiv:2603.09165v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09165
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View a PDF of the paper titled GIAT: A Geologically-Informed Attention Transformer for Lithology Identification, by Jie Li and 2 other authors
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