# WLFM: A Well-Logs Foundation Model for Multi-Task and Cross-Well Geological Interpretation

arXiv: https://arxiv.org/abs/2509.18152 · HTML: https://mlsanity.com/paper/2509.18152/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 34 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 2 of 2)

- [TOTEM: TOkenized Time Series EMbeddings for General Time Series Analysis](https://mlsanity.com/paper/2402.16412/) (2024)
- [Well log data generation and imputation using sequence-based generative adversarial networks](https://mlsanity.com/paper/2412.00718/) (2024)

## Built on by (most cited first, 2 of 2)

- [Weakly supervised multimodal segmentation of acoustic borehole images with depth-aware cross-attention](https://mlsanity.com/paper/2603.20729/) (2026)
- [Autoregressive Drillhole Modelling Under Distribution Shift](https://mlsanity.com/paper/2610.01204/) (2026)

## Abstract (arXiv)

Well-log interpretation is fundamental for subsurface characterization but remains challenged by heterogeneous tool responses, noisy signals, and limited labels. We propose WLFM, a foundation model pretrained on multi-curve logs from 1200 wells, comprising three stages: tokenization of log patches into geological tokens, self-supervised pretraining with masked-token modeling and stratigraphy-aware contrastive learning, and multi-task adaptation with few-shot fine-tuning. WLFM consistently outperforms state-of-the-art baselines, achieving 0.0041 MSE in porosity estimation and 74.13% accuracy in lithology classification, while WLFM-Finetune further improves to 0.0038 MSE and 78.10% accuracy. Beyond predictive accuracy, WLFM exhibits emergent layer-awareness, learns a reusable geological vocabulary, and reconstructs masked curves with reasonable fidelity, though systematic offsets are observed in shallow and ultra-deep intervals. Although boundary detection is not explicitly evaluated here, clustering analyses suggest strong potential for future extension. These results establish WLFM as a scalable, interpretable, and transferable backbone for geological AI, with implications for multi-modal integration of logs, seismic, and textual data.