Geological Everything Model 3D: A Promptable Foundation Model for Unified and Zero-Shot Subsurface Understanding

• URL: http://arxiv.org/abs/2507.00419v2
• Date: Tue, 08 Jul 2025 08:31:54 GMT
• Title: Geological Everything Model 3D: A Promptable Foundation Model for Unified and Zero-Shot Subsurface Understanding
• Authors: Yimin Dou, Xinming Wu, Nathan L Bangs, Harpreet Singh Sethi, Jintao Li, Hang Gao, Zhixiang Guo,
• Abstract summary: Geological Everything Model 3D (GEM) is a unified generative architecture that reformulates tasks as prompt-conditioned inference.<n>GEM achieves zero-shot generalization across tasks with heterogeneous prompt types, without retraining for new tasks or data sources.<n>GEM demonstrates broad applicability across surveys and tasks, including Martian radar stratigraphy analysis, structural interpretation in subduction zones, full seismic stratigraphic interpretation, geobody segmentation, and property modeling.
• Score: 8.832957977030198
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Understanding Earth's subsurface is critical for energy transition, natural hazard mitigation, and planetary science. Yet subsurface analysis remains fragmented, with separate models required for structural interpretation, stratigraphic analysis, geobody segmentation, and property modeling-each tightly coupled to specific data distributions and task formulations. We introduce the Geological Everything Model 3D (GEM), a unified generative architecture that reformulates all these tasks as prompt-conditioned inference along latent structural frameworks derived from subsurface imaging. This formulation moves beyond task-specific models by enabling a shared inference mechanism, where GEM propagates human-provided prompts-such as well logs, masks, or structural sketches-along inferred structural frameworks to produce geologically coherent outputs. Through this mechanism, GEM achieves zero-shot generalization across tasks with heterogeneous prompt types, without retraining for new tasks or data sources. This capability emerges from a two-stage training process that combines self-supervised representation learning on large-scale field seismic data with adversarial fine-tuning using mixed prompts and labels across diverse subsurface tasks. GEM demonstrates broad applicability across surveys and tasks, including Martian radar stratigraphy analysis, structural interpretation in subduction zones, full seismic stratigraphic interpretation, geobody segmentation, and property modeling. By bridging expert knowledge with generative reasoning in a structurally aware manner, GEM lays the foundation for scalable, human-in-the-loop geophysical AI-transitioning from fragmented pipelines to a vertically integrated, promptable reasoning system. Project page: https://douyimin.github.io/GEM

Related papers

• History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions [0.10923877073891446]
  The graph-based variational autoencoder represents an architecture that can handle the uncertainty of different geological scenarios.<n>We offer in-depth analysis of the latent space using tools such as PCA, t-SNE, and TDA to illustrate its structure.
  arXiv  Detail & Related papers  (2025-07-14T12:14:17Z)
• Topology-Aware Modeling for Unsupervised Simulation-to-Reality Point Cloud Recognition [63.55828203989405]
  We introduce a novel Topology-Aware Modeling (TAM) framework for Sim2Real UDA on object point clouds.<n>Our approach mitigates the domain gap by leveraging global spatial topology, characterized by low-level, high-frequency 3D structures.<n>We propose an advanced self-training strategy that combines cross-domain contrastive learning with self-training.
  arXiv  Detail & Related papers  (2025-06-26T11:53:59Z)
• Spatial Understanding from Videos: Structured Prompts Meet Simulation Data [79.52833996220059]
  We present a unified framework for enhancing 3D spatial reasoning in pre-trained vision-language models without modifying their architecture.<n>This framework combines SpatialMind, a structured prompting strategy that decomposes complex scenes and questions into interpretable reasoning steps, with ScanForgeQA, a scalable question-answering dataset built from diverse 3D simulation scenes.
  arXiv  Detail & Related papers  (2025-06-04T07:36:33Z)
• Graph Foundation Models: A Comprehensive Survey [66.74249119139661]
  Graph Foundation Models (GFMs) aim to bring scalable, general-purpose intelligence to structured data.<n>This survey provides a comprehensive overview of GFMs, unifying diverse efforts under a modular framework.<n>GFMs are poised to become foundational infrastructure for open-ended reasoning over structured data.
  arXiv  Detail & Related papers  (2025-05-21T05:08:00Z)
• Evaluating and Explaining Earthquake-Induced Liquefaction Potential through Multi-Modal Transformers [0.0]
  This study presents an explainable parallel transformer architecture for soil liquefaction prediction.<n>The architecture processes data from 165 case histories across 11 major earthquakes.<n>The model achieves 93.75% prediction accuracy on cross-regional validation sets.
  arXiv  Detail & Related papers  (2025-02-11T09:18:07Z)
• PEACE: Empowering Geologic Map Holistic Understanding with MLLMs [64.58959634712215]
  Geologic map, as a fundamental diagram in geology science, provides critical insights into the structure and composition of Earth's subsurface and surface.<n>Despite their significance, current Multimodal Large Language Models (MLLMs) often fall short in geologic map understanding.<n>To quantify this gap, we construct GeoMap-Bench, the first-ever benchmark for evaluating MLLMs in geologic map understanding.
  arXiv  Detail & Related papers  (2025-01-10T18:59:42Z)
• A foundation model enpowered by a multi-modal prompt engine for universal seismic geobody interpretation across surveys [13.150829303910385]
  We introduce a promptable foundation model for interpreting any geobodies across seismic surveys. The model integrates a pre-trained vision foundation model (VFM) with a sophisticated multi-modal prompt engine. Our approach establishes a new paradigm for geoscientific data interpretation, with broad potential for transfer to other tasks.
  arXiv  Detail & Related papers  (2024-09-08T03:44:23Z)
• Self-Supervised Representation Learning for Geospatial Objects: A Survey [21.504978593542354]
  Self-supervised learning (SSL) has garnered increasing attention for its ability to learn effective and generalizable representations directly from data without extensive labeled supervision.<n>This paper presents a survey of SSL techniques specifically applied to or developed for geospatial objects in three primary geometric vector types: Point, Polyline, and Polygon.<n>We examine the emerging trends in SSL for geospatial objects, particularly the gradual advancements towards geospatial foundation models.
  arXiv  Detail & Related papers  (2024-08-22T05:28:22Z)
• ImplicitTerrain: a Continuous Surface Model for Terrain Data Analysis [14.013976303831313]
  ImplicitTerrain is an implicit neural representation (INR) approach for modeling high-resolution terrain continuously and differentiably. Our experiments demonstrate superior surface fitting accuracy, effective topological feature retrieval, and various topographical feature extraction.
  arXiv  Detail & Related papers  (2024-05-31T23:05:34Z)
• Geometric Deep Learning for Structure-Based Drug Design: A Survey [83.87489798671155]
  Structure-based drug design (SBDD) leverages the three-dimensional geometry of proteins to identify potential drug candidates. Recent advancements in geometric deep learning, which effectively integrate and process 3D geometric data, have significantly propelled the field forward.
  arXiv  Detail & Related papers  (2023-06-20T14:21:58Z)
• A General Purpose Neural Architecture for Geospatial Systems [142.43454584836812]
  We present a roadmap towards the construction of a general-purpose neural architecture (GPNA) with a geospatial inductive bias. We envision how such a model may facilitate cooperation between members of the community.
  arXiv  Detail & Related papers  (2022-11-04T09:58:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.