arXiv Machine Learning
Physical Cross-Modal Masked Autoencoding for Seismic-to-Well Representation Learning
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arXiv Machine Learning
Sep 23
Parameter-Efficient Adaptation of Pre-Trained Vision Foundation Models for Active and Passive Seismic Data Denoising
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 Machine Learning
Sep 23
Ranking Competing geologic interpretations via foundation-model-assisted generative hydrologic inversion
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...
arXiv Machine Learning
Sep 18
Seismic Site Response Prediction from Sparse Observations Using Finite-Element-Pretrained Latent Dynamics
The paper introduces FLARE‑T, a Transfer‑Enabled Forced Latent Autoencoder for Response Equations, which learns low‑dimensional latent dynamics from dense finite‑element simulations and calibrates them with sparse field observations. By mapping simulated sensor responses into a learned coordinate system, FLARE‑T improves multi‑depth acceleration predictions and pseudo‑acceleration spectra, reducing errors across various sensor locations and motion intensities. Evaluation on a layered‑soil centrifuge test and the Lotung field array demonstrates that FLARE‑T achieves comparable accuracy with different source models, indicating less reliance on precise prior calibration.
arXiv AI
Jul 28
LithoFormer: A Robust Framework for Stratigraphic Inference via Transformers
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 AI
Aug 11
Depth-Aware Implicit Neural Representation Priors for 3D Gravity Inversion
arXiv:2608. 08959v1 Announce Type: new Abstract: Gravimetry images subsurface density contrasts associated with geological structures, geothermal systems, and intrusive bodies.
arXiv Computer Vision
Sep 7
MEOX: Compact Multimodal Mixture-of-Experts for Earth Observation
MEOX is a compact multimodal masked autoencoder designed for Earth Observation that uses a 2.939 million‑parameter encoder and 3.115 million total parameters. It incorporates sensor‑specific adapters, explicit validity signals, and a shared sparse‑expert block to maintain modality‑dependent processing before a learned patch‑wise fusion, followed by fourteen encoder blocks that process a single spatial sequence with four metadata tokens. Pretrained on 1.228 million MMEarth64 samples, MEOX achieves strong performance on GEO‑Bench tasks, surpassing prior CSMoE results, and demonstrates effective sensor‑flexible representation learning with a modest parameter budget.