Automated geological modeling with ArcGIS Engine: a methodological framework for surface and subsurface data integration - Guoxu Chen; Suzanne Hurter; Li Cao; Muyuan Lu; Yongqi Fan; Kesheng Zuo (Earth Science Informatics, 2026)

Precipitation nowcasting leveraging spatial correlation feature extraction and deep spatio-temporal fusion network - Wenbin Yu; Yangsong Li; Cheng Fan; Daoyong Fu; Chengjun Zhang; Yadang Chen; Ming Qian; Jie Liu; Gaoping Liu (Earth Science Informatics, 2024)

Exploring neuro-symbolic AI applications in geoscience: implications and future directions for mineral prediction - Weilin Chen; Xiaogang Ma; Zhe Wang; Wenjia Li; Chao Fan; Jiyin Zhang; Xiang Que; Chenhao Li (Earth Science Informatics, 2024)
The integration of machine learning (ML) and deep learning (DL) into geoscience has experienced a pronounced uptick in recent years, a trend propelled by the intricate nature of geosystems and the abundance of data they produce. These computational methods have been harnessed across a spectrum of geoscientific challenges, from climate modeling to seismic analysis, exhibiting notable efficacy in extracting valuable insights from intricate geological datasets for applications such as mineral prediction. A thorough analysis of the literature indicates a marked escalation in AI-centric geoscience research starting in 2018, characterized by a predictive research orientation and a persistent focus on key computational terms. The thematic network and evolution analyses underscore the enduring prominence of “deep learning” and “machine learning” as pivotal themes, alongside progressive developments in “transfer learning” and “big data”. Despite these advancements, other methodologies have garnered comparatively lesser focus. While ML and DL have registered successes in the realm of mineral prediction, their amalgamation with domain-specific knowledge and symbolic reasoning could further amplify their interpretability and operational efficiency. Neuro-Symbolic AI (NSAI) emerges as a cutting-edge approach that synergizes DL’s robust capabilities with the precision of symbolic reasoning, facilitating the creation of models that are both powerful and interpretable. NSAI distinguishes itself by surmounting traditional ML constraints through the incorporation of expert insights and delivering explanatory power behind its predictive prowess, rendering it particularly advantageous for mineral prediction tasks. This literature review delves into the promising potential of NSAI, alongside ML and DL, within the geoscientific domain, spotlighting mineral prediction as a key area of focus. Despite the hurdles associated with infusing domain expertise into symbolic formats and mitigating biases inherent in symbolic reasoning, the application of NSAI in the realm of critical mineral prediction stands to catalyze a paradigm shift in the field. By bolstering prediction accuracy, enhancing decision-making processes, and fostering sustainable resource exploitation, NSAI holds the potential to significantly reshape geoscience’s future trajectory.

Land subsidence calculation model under the coupling effect of groundwater and coal mining - Yiguo Xue; Hong Chen; Fanmeng Kong; Zhiqiang Li; Daohong Qiu; Qiqi Chen; Fan Yang; Xudong Jiang (Earth Science Informatics, 2022)

SHRIMPDB: a new geoanalytical database for U-Th-Pb geochronological data from SHRIMP measurements - Yutong He; Di Tian; Ranran Gao; Runlong Fan; Li Yao; Pengfei Chen (Earth Science Informatics, 2018)