The paper presents a real‑time, AI‑driven geosteering workflow that combines Generative Adversarial Networks for geological parameterization, ensemble methods for model updating, and dynamic programming optimization for decision support during directional drilling. The framework uses offline GAN training to generate realistic geology realizations and a Forward Neural Network to predict Logging‑While‑Drilling tool responses, enabling progressive reduction of subsurface uncertainty around the drilling bit. Tested on a low‑net‑to‑gross drilling scenario, the prototype delivers steering recommendations and automatically maps formation boundaries along the well path.
arXiv:2605. 24844v2 Announce Type: replace Abstract: While general-purpose Large Language Models (LLMs) applied to Geology often hallucinate when reasoning about subsurface structures and deep-time evolution, current AI in Earth sciences predominantly targets surface remote sensing and GIS.
arXiv:2607. 05830v1 Announce Type: cross Abstract: The increasing uncertainty from flexible demand and renewable generation has made distributionally robust optimization (DRO) an important tool for robust power system dispatch.
arXiv:2606. 24679v1 Announce Type: cross Abstract: Data preparation pipelines improve data quality in machine learning by transforming raw tables into learning-ready data through sequential cleaning and feature transformation operators.
arXiv:2608.22068v1 Announce Type: new Abstract: Geothermal well arrays, which organize multiple geothermal wells into carefully planned geometric configurations, provide opportunities to enhance ener...
arXiv:2606. 19138v1 Announce Type: new Abstract: Neural Controlled Differential Equations (NCDE) provide a powerful continuous-time framework for forecasting time series, but standard graph-based extensions typically learn spatial structure purely from data, even in settings where a directed graph structure is known a priori.
The paper examines how varying input noise characteristics—type, scale, and complexity—affect neural network robustness in geophysical tasks such as first break picking and denoising. By training models on fixed noise settings and testing them on both seen and unseen noise scenarios, the study constructs a robustness matrix that reveals how larger noise scales improve generalization and how aligning noise type with task complexity and architecture maximizes performance. Training with compound noise mixtures further mitigates weaknesses of single-noise training, acting as an implicit regularizer that enhances robustness under out‑of‑distribution conditions.
GenONet introduces a Spatio-Temporal U-DeepONet architecture that serves as a generator in a GAN framework for high‑resolution precipitation nowcasting up to three hours ahead. By learning continuous‑time precipitation dynamics with a Deep Operator Network and enforcing physics through a moisture‑conservation loss, the model produces sharp, physically consistent forecasts that outperform baselines, especially for high‑intensity events and longer lead times. Ablation studies confirm the added value of the physics‑informed regularizer and the synergy of operator learning with adversarial training.
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.
arXiv:2608. 10494v1 Announce Type: new Abstract: Earth observation (EO) agents construct scientifically valid tool workflows and ground their conclusions in current geospatial evidence.
arXiv:2607. 01171v1 Announce Type: new Abstract: Sample-based generative models are increasingly used for probabilistic forecasting in high-stakes decision settings, yet their training objectives are blind to the decision maker's cost structure.
arXiv:2606. 13695v1 Announce Type: cross Abstract: Mineral prospectivity modelling (MPM) underpins exploration economics, yet most operational pipelines reduce to data-driven classifiers trained on shallow surface proxies.
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.