arXiv AI

Multimodal Auto-regressive Transformer Surrogate for Modeling Variable Operations and Quantifying Uncertainty in Geological Carbon Storage

arXiv:2608. 02629v1 Announce Type: cross Abstract: The use of variable well perforation and injection strategies can improve the efficiency of geological carbon storage operations.

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.

By Shwetha Salimath, Francesca Bugiotti, Sylvain Wlodarczyk, Sohaib Ouzineb

llms rag

arXiv AI

Jun 10

Data assimilation for subsurface flow using latent diffusion model parameterization: performance of ensemble-Kalman and Monte Carlo techniques

arXiv:2606. 11140v1 Announce Type: cross Abstract: Data assimilation (DA) in subsurface flow entails calibrating model parameters to match observed data, typically at wells, while preserving geological realism.

By Guido Di Federico, Wenchao Teng, Louis J. Durlofsky

diffusion

arXiv Machine Learning

Jun 29

Boundary condition fidelity for bottom-hole pressure and CO2 plume prediction in geological carbon storage

arXiv:2606. 27515v1 Announce Type: new Abstract: Accurate prediction of bottom-hole pressure (BHP) and CO2 plume migration is essential for safe geological carbon storage, yet practical simulations often rely on truncated domains where artificial boundaries distort pressure diffusion and CO2 saturation footprints.

By Romal Ramadhan, Seyyed A. Hosseini, Larry W. Lake

diffusion

arXiv Machine Learning

Jun 30

A Trainable-by-Parts Operator Learning Framework: Bridging DeepONet and Karhunen-Loeve Expansions for Large-Scale Applications

arXiv:2606. 28519v1 Announce Type: new Abstract: Training operator-learning models for large-scale problems governed by partial differential equations (PDEs) is challenging due to the curse of dimensionality, memory constraints, and limited training data.

By Christian Munoz, Alexandre Tartakovsky

arXiv Machine Learning

Jun 17

Towards Fast GNN Surrogates for CO2 Migration in Complex Geological Formations

arXiv:2606. 17180v1 Announce Type: new Abstract: This chapter discusses how a data-driven machine learning approach can reproduce key aspects of the physical behavior of multiphase flows in complex geological formations.

By Rodrigo S. Luna, Thiago H. N. Coelho, Luiz S. L. Neto, Roberto M. Velho, Adriano M. A. Cortes, Renato N. Elias, Alexandre G. Evsukoff, Fernando A. Rochinha, Mauricio Araya-Polo, Herve Gross, Alvaro L. G. A. Coutinho

rag diffusion benchmarks

arXiv Machine Learning

Jun 30

Machine learning enhanced data assimilation framework for multiscale carbonate rock characterization

arXiv:2602. 06989v2 Announce Type: replace-cross Abstract: Carbonate reservoirs offer significant capacity for subsurface carbon storage, oil production, and underground hydrogen storage.

By Zhenkai Bo, Ahmed H. Elsheikh, Hannah P. Menke, Julien Maes, Sebastian Geiger, Muhammad Z. Kashim, Zainol A. A. Bakar, Kamaljit Singh

arXiv Machine Learning

Jun 17

Amortized Probabilistic Retrieval of Atmospheric CO2 from OCO-2 Spectra Using Deep Learning with Laplace Approximations and Normalizing Flows

arXiv:2606. 17413v1 Announce Type: new Abstract: Space-based monitoring of atmospheric carbon dioxide (CO2) is essential for constraining the global carbon budget.

By Alejandro Calle-Saldarriaga, Felix Jimenez, Jack Grosskreuz, Jiazheng Wang, Jonathan Hobbs, Matthias Katzfuss

arXiv Machine Learning

Jun 25

Surrogate models for Rock-Fluid Interaction: A Grid-Size-Invariant Approach

arXiv:2602. 22188v2 Announce Type: replace Abstract: Modelling rock-fluid interaction requires solving a set of partial differential equations (PDEs) to predict the flow behaviour and the reactions of the fluid with the rock on the interfaces.

By Nathalie C. Pinheiro, Donghu Guo, Hannah P. Menke, Aniket C. Joshi, Claire E. Heaney, Ahmed H. ElSheikh, Christopher C. Pain

arXiv AI

Jul 22

Now We Know? A Systematic Comparison of TerraMind and THOR

arXiv:2607. 18504v1 Announce Type: cross Abstract: Benchmarks for Geospatial Foundation Models (GFMs) increasingly rank models by aggregate score, but such rankings obscure why models differ: how much of the gap is architecture, how much is decoder capacity, and how much is a use-case-specific artefact?

By Frederick Schindlegger, Kenzo Bounegta, Eva Gmelich Meijling, Johannes Jakubik, Arnt-B{\o}rre Salberg, Theodor Forgaard, Nicolas Longepe, Valerio Marsocci

computer-vision fine-tuning multimodal benchmarks

arXiv Machine Learning

Jun 16

Multi-Modal Spatio-Temporal Graph Neural Network with Mixture of Experts for Soil Organic Carbon Prediction

arXiv:2606. 16580v1 Announce Type: new Abstract: Top-soil organic carbon (SOC) prediction is fundamental to agricultural sustainability, land use policy and fertilization planning.

By Daniele Mos, Felipe Drummond, Anton Bossenbroek, Soufiane el Khinifri

rag fine-tuning multimodal

arXiv Machine Learning

Jul 7

Exploring Convolutional Neural Processes for Weather Downscaling

arXiv:2607. 04190v1 Announce Type: new Abstract: Global reanalysis products such as ERA5-Land provide spatially complete weather fields but at resolutions too coarse for local applications, particularly in mountainous regions where temperature can vary by several degrees over short distances.

By Francisco Passos

benchmarks

arXiv Machine Learning

Jul 28

No Free Lunch in Flow Surrogates under Time-Varying Boundary Conditions: A Two-Regime Study

arXiv:2607. 23667v1 Announce Type: cross Abstract: A flow surrogate validated on a simple regime is often taken as evidence that the approach will carry to a richer one.

By Georg Winkler, Martin Stoll

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