A novel Mamba model based on frequency domain enhancement and visual state space for high-precision lithology classification - Yan Zhang; Fan Zhang; Ruifeng Li; Zongguo Zhang (Earth Science Informatics, 2026)

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)

Multi-source data integration modeling and spatial analysis for optimal design of high-level radioactive waste geological repository - Peinan Li; Qingyan Tan; Jingxiao Wang; Yong Ye; Jie Fan; Yan Qiu (Earth Science Informatics, 2025)

Analysis of the temporal and spatial changes of ecological environment quality using the optimization remote sensing ecological index in the middle Yellow River Basin, China - Guanwen Li; Naichang Zhang; Yongxiang Cao; Zhaohui Xia; Chenfang Bao; Liangxin Fan; Sha Xue (Earth Science Informatics, 2024)

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.

Construction and application of mine water inflow prediction model based on multi-factor weighted regression: Wulunshan Coal Mine case - Bo Li; Huang Wu; Pu Liu; Juan Fan; Tao Li (Earth Science Informatics, 2023)

Estimation and predicting of soil water characteristic curve using the support vector machine method - Wenjing Qin; Guisheng Fan; Hongxing-Li (Earth Science Informatics, 2023)

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)

Characterization of seismic information entropy attributes of braided river delta sedimentary microfacies for the upper Shaximiao formation in the Wubaochang area, northeastern Sichuan Basin, China - Yanguo Qiao; Zhigang Liu; Wei Luo; Dianguang Zang; Chenrui Li; Fan Yang; Guoshuai Si (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)