@inproceedings{chen-etal-2026-large-language,
title = "Large Language Models for Solid-Earth Geoscience: Applications, Modalities, and Memory-Reason-Act Techniques",
author = "Chen, Yisu and
Zhang, Yiran and
Dai, Yifeng and
Wu, Puzhen and
Li, Sirui and
Liu, Wei and
Ding, Yihao",
editor = "Zhang, Yue and
Xu, Ruifeng and
Purwarianti, Ayu and
Rong, Tong and
Suzuki, Jun",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {IJCNLP}-{AACL} 2026",
month = nov,
year = "2026",
address = "Hengqin, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-aacl.243/",
pages = "4776--4790",
ISBN = "979-8-89176-444-6",
abstract = "Solid Earth geoscience, geology, geochemistry, and geophysics, is increasingly served by large language models (LLMs) and multimodal LLMs (MLLMs) that combine text, geological maps, hyperspectral imagery, 3D structural models, spatial databases, and quantitative measurements into operational decision pipelines. Existing studies are fragmented across tasks, datasets, and system designs. This survey provides a structured review of LLM/MLLM research in solid-Earth geoscience along three axes: applications, modalities, and Memory-Reason-Act techniques. We identify five clusters of application (information extraction, knowledge Q A and retrieval, multimodal map and 3D interpretation, spatial predictive modelling, and agentic pipelines), characterise four modalities (text, visual, spatial, and quantitative signal) and their encoding gaps, and analyse Memory, Reasoning, and Action design choices with their failure modes. We further examine data resources and bias, and identify the absence of closed-loop drill-outcome validation as the central methodological gap blocking next-generation solid-Earth foundation models."
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<abstract>Solid Earth geoscience, geology, geochemistry, and geophysics, is increasingly served by large language models (LLMs) and multimodal LLMs (MLLMs) that combine text, geological maps, hyperspectral imagery, 3D structural models, spatial databases, and quantitative measurements into operational decision pipelines. Existing studies are fragmented across tasks, datasets, and system designs. This survey provides a structured review of LLM/MLLM research in solid-Earth geoscience along three axes: applications, modalities, and Memory-Reason-Act techniques. We identify five clusters of application (information extraction, knowledge Q A and retrieval, multimodal map and 3D interpretation, spatial predictive modelling, and agentic pipelines), characterise four modalities (text, visual, spatial, and quantitative signal) and their encoding gaps, and analyse Memory, Reasoning, and Action design choices with their failure modes. We further examine data resources and bias, and identify the absence of closed-loop drill-outcome validation as the central methodological gap blocking next-generation solid-Earth foundation models.</abstract>
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%0 Conference Proceedings
%T Large Language Models for Solid-Earth Geoscience: Applications, Modalities, and Memory-Reason-Act Techniques
%A Chen, Yisu
%A Zhang, Yiran
%A Dai, Yifeng
%A Wu, Puzhen
%A Li, Sirui
%A Liu, Wei
%A Ding, Yihao
%Y Zhang, Yue
%Y Xu, Ruifeng
%Y Purwarianti, Ayu
%Y Rong, Tong
%Y Suzuki, Jun
%S Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026
%D 2026
%8 November
%I Association for Computational Linguistics
%C Hengqin, China
%@ 979-8-89176-444-6
%F chen-etal-2026-large-language
%X Solid Earth geoscience, geology, geochemistry, and geophysics, is increasingly served by large language models (LLMs) and multimodal LLMs (MLLMs) that combine text, geological maps, hyperspectral imagery, 3D structural models, spatial databases, and quantitative measurements into operational decision pipelines. Existing studies are fragmented across tasks, datasets, and system designs. This survey provides a structured review of LLM/MLLM research in solid-Earth geoscience along three axes: applications, modalities, and Memory-Reason-Act techniques. We identify five clusters of application (information extraction, knowledge Q A and retrieval, multimodal map and 3D interpretation, spatial predictive modelling, and agentic pipelines), characterise four modalities (text, visual, spatial, and quantitative signal) and their encoding gaps, and analyse Memory, Reasoning, and Action design choices with their failure modes. We further examine data resources and bias, and identify the absence of closed-loop drill-outcome validation as the central methodological gap blocking next-generation solid-Earth foundation models.
%U https://aclanthology.org/2026.findings-aacl.243/
%P 4776-4790
Markdown (Informal)
[Large Language Models for Solid-Earth Geoscience: Applications, Modalities, and Memory-Reason-Act Techniques](https://aclanthology.org/2026.findings-aacl.243/) (Chen et al., Findings 2026)
ACL
- Yisu Chen, Yiran Zhang, Yifeng Dai, Puzhen Wu, Sirui Li, Wei Liu, and Yihao Ding. 2026. Large Language Models for Solid-Earth Geoscience: Applications, Modalities, and Memory-Reason-Act Techniques. In Findings of the Association for Computational Linguistics: IJCNLP-AACL 2026, pages 4776–4790, Hengqin, China. Association for Computational Linguistics.