Autoregressive modelling, successful in language tasks, is applied to mineral‑exploration drillholes where lithology is revealed sequentially from shallow to deep. The authors introduce DrillBench, a benchmark of 49,671 Western Australian drillholes for next‑layer prediction and autoregressive stratigraphic generation across a graded transfer spectrum. Experiments show that spatial conditioning helps locally but fails under strong shift, while autoregressive models transfer more robustly; a backbone‑agnostic recipe combining large‑scale pretraining and spatial retrieval improves generalisation, especially in distant geological splits.
arXiv:2609.20978v2 Announce Type: replace Abstract: High-consequence subsurface decisions often rely on sparse data that permit competing geological interpretations. Determining consistency of these...
The paper introduces a workflow that ranks competing geological interpretations by converting them into spatial priors and assessing their consistency with hydraulic‑head observations. Using a text‑to‑image model to generate 1600 geologic images per interpretation, a variational autoencoder encodes them, and a supervised inverse network maps head data into this latent space, producing log‑conductivity fields for flow simulation. The method is validated on a synthetic Johansen Formation benchmark and applied to two conceptual models of the Culebra Dolomite, yielding compatibility scores that align with independent evidence.
arXiv:2607. 22804v1 Announce Type: cross Abstract: Accurate geological characterization of subsurface reservoirs from well log data is essential to support projects such as carbon capture and storage (CCS), geothermal development, and extraction of natural resources.
arXiv:2609.36193v1 Announce Type: new Abstract: Learning from scientific measurements often requires aligning modalities with different spatial support and resolution. Subsurface characterization is...
arXiv:2606. 10069v3 Announce Type: replace Abstract: In this paper we build upon a previous study in which we demonstrated, using XGBoost and earthquake catalogue data from Japan and Chile, that a set of 60 seismic statistical features (SSFs) had much greater predictive value than a set of 428 generic time series features from the tsfresh package.
arXiv:2606. 10069v1 Announce Type: new Abstract: In this paper we build upon a previous study in which we demonstrated, using XGBoost and earthquake catalogue data from Japan and Chile, that a set of 60 seismic statistical features (SSFs) had much greater predictive value than a set of 428 generic time series features from the tsfresh package.
arXiv:2605. 03383v2 Announce Type: replace Abstract: Geological interpretation infers subsurface properties and structures from indirect geophysical observations.
arXiv:2606. 25000v1 Announce Type: new Abstract: To evaluate whether vision-language models can reason about geological histories, it is necessary to construct observations for which the underlying process history is known.
arXiv:2608. 12271v1 Announce Type: new Abstract: Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near-surface conditions also depend on unresolved terrain and land-surface properties.
arXiv:2609.39626v1 Announce Type: cross Abstract: Ensemble smoothers are the most successful and efficient techniques currently available for history matching. However, because these methods rely on...
The paper presents a framework that adapts general-purpose Vision Foundation Models (VFMs) to seismic data denoising using Parameter‑Efficient Fine‑Tuning with Low‑Rank Adaptation (LoRA). It introduces a kurtosis‑guided unsupervised test‑time adaptation module that updates only LoRA parameters to self‑calibrate for site‑specific noise without ground truth. Experiments on exploration seismic images and DAS data demonstrate that the approach matches or surpasses domain‑specific models and generalizes well to unseen cross‑site data.
arXiv:2603. 06829v2 Announce Type: replace Abstract: Subsurface ore detection is of paramount importance given the rising depletion of shallow mineral resources in recent years.