{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T07:40:01Z","timestamp":1773906001625,"version":"3.50.1"},"reference-count":85,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,2,1]],"date-time":"2026-02-01T00:00:00Z","timestamp":1769904000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["22023YFC2906404"],"award-info":[{"award-number":["22023YFC2906404"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Computers & Geosciences"],"published-print":{"date-parts":[[2026,2]]},"DOI":"10.1016\/j.cageo.2025.106090","type":"journal-article","created":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T07:37:41Z","timestamp":1765006661000},"page":"106090","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["An interpretable causal variational autoencoder for geochemical anomalies recognition by regarding ore-controlling factors"],"prefix":"10.1016","volume":"208","author":[{"given":"Fan","family":"Ni","sequence":"first","affiliation":[]},{"given":"Yihui","family":"Xiong","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.cageo.2025.106090_bib1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11053-005-4674-0","article-title":"Measuring the performance of mineral-potential maps","volume":"14","author":"Agterberg","year":"2005","journal-title":"Nat. Resour. Res."},{"issue":"2","key":"10.1016\/j.cageo.2025.106090_bib2","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1111\/j.2517-6161.1982.tb01195.x","article-title":"The statistical analysis of compositional data","volume":"44","author":"Aitchison","year":"1982","journal-title":"J. Roy. Stat. Soc. B"},{"key":"10.1016\/j.cageo.2025.106090_bib3","volume":"vol. 11","author":"Carranza","year":"2008"},{"issue":"3\u20134","key":"10.1016\/j.cageo.2025.106090_bib4","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1016\/j.oregeorev.2007.07.001","article-title":"Selection of coherent deposit-type locations and their application in data-driven mineral prospectivity mapping","volume":"33","author":"Carranza","year":"2008","journal-title":"Ore Geol. Rev."},{"key":"10.1016\/j.cageo.2025.106090_bib7","doi-asserted-by":"crossref","first-page":"270","DOI":"10.3390\/min9050270","article-title":"A multi-convolutional autoencoder approach to multivariate geochemical anomaly recognition","volume":"9","author":"Chen","year":"2019","journal-title":"Minerals"},{"key":"10.1016\/j.cageo.2025.106090_bib8","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.cageo.2019.01.016","article-title":"A spatially constrained multi-Autoencoder approach for multivariate geochemical anomaly recognition","volume":"125","author":"Chen","year":"2019","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.cageo.2025.106090_bib10","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.oregeorev.2006.10.002","article-title":"Mapping singularities with stream sediment geochemical data for prediction of undiscovered mineral deposits in Gejiu, Yunnan province, China","volume":"32","author":"Cheng","year":"2007","journal-title":"Ore Geol. Rev."},{"issue":"3","key":"10.1016\/j.cageo.2025.106090_bib11","first-page":"298","article-title":"Singularity of mineralization and multifractal distribution of mineral deposits","volume":"27","author":"Cheng","year":"2008","journal-title":"Bull. China Soc. Mineral Petrol. Geochem."},{"key":"10.1016\/j.cageo.2025.106090_bib5","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.gexplo.2014.02.013","article-title":"Application of continuous restricted boltzmann machine to identify multivariate geochemical anomaly","volume":"140","author":"Chen","year":"2014","journal-title":"J. Geochem. Explor."},{"key":"10.1016\/j.cageo.2025.106090_bib6","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1144\/geochem2016-024","article-title":"Application of one-class support vector machine to quickly identify multivariate anomalies from geochemical exploration data","volume":"17","author":"Chen","year":"2017","journal-title":"Geochem. Explor. Environ. Anal."},{"key":"10.1016\/j.cageo.2025.106090_bib9","doi-asserted-by":"crossref","DOI":"10.1016\/j.cageo.2025.105913","article-title":"Geological-knowledge-guided graph self-supervised pretraining framework for identifying mineralization-related geochemical anomalies","volume":"199","author":"Chen","year":"2025","journal-title":"Comput. Geosci."},{"issue":"1","key":"10.1016\/j.cageo.2025.106090_bib12","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1144\/1467-7873\/09-215","article-title":"Major advances in exploration geochemistry, 1998\u20132007","volume":"10","author":"Cohen","year":"2010","journal-title":"Geochem. Explor. Environ. Anal."},{"key":"10.1016\/j.cageo.2025.106090_bib13","doi-asserted-by":"crossref","DOI":"10.1016\/j.cageo.2024.105703","article-title":"Geo-Hgan: unsupervised anomaly detection in geochemical data via latent space learning","volume":"192","author":"Ding","year":"2024","journal-title":"Comput. Geosci."},{"issue":"4","key":"10.1016\/j.cageo.2025.106090_bib14","doi-asserted-by":"crossref","first-page":"1252","DOI":"10.1111\/1755-6724.15053","article-title":"Delineation of integrated Anomaly with generative adversarial networks and deep neural networks in the Zhaojikou Pb\u2010zn ore district, Southeast China","volume":"97","author":"Duan","year":"2023","journal-title":"Acta Geologica Sinica\u2010English Edition"},{"issue":"8","key":"10.1016\/j.cageo.2025.106090_bib15","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit. Lett."},{"issue":"2","key":"10.1016\/j.cageo.2025.106090_bib17","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1144\/geochem2019-031","article-title":"State-of-the-art analysis of geochemical data for mineral exploration","volume":"20","author":"Grunsky","year":"2020","journal-title":"Geochem. Explor. Environ. Anal."},{"issue":"5","key":"10.1016\/j.cageo.2025.106090_bib16","doi-asserted-by":"crossref","first-page":"2225","DOI":"10.1007\/s11053-022-10088-x","article-title":"Recognizing multivariate geochemical anomalies related to mineralization by using deep unsupervised graph learning","volume":"31","author":"Guan","year":"2022","journal-title":"Nat. Resour. Res."},{"key":"10.1016\/j.cageo.2025.106090_bib18","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast-learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"issue":"7","key":"10.1016\/j.cageo.2025.106090_bib19","first-page":"1950","article-title":"Metallogenetic regularity and prospecting direction of granite related li-be-nb-ta deposits in the nanling region, south China","volume":"39","author":"Hou","year":"2023","journal-title":"Acta Petrol. Sin."},{"issue":"9","key":"10.1016\/j.cageo.2025.106090_bib20","first-page":"2625","article-title":"Several progresses in the study of uranium deposits in south China","volume":"35","author":"Hu","year":"2019","journal-title":"Acta Petrol. Sin."},{"issue":"3","key":"10.1016\/j.cageo.2025.106090_bib21","first-page":"291","article-title":"Metallogeneses and their geodynamic settings related to Mesozoic granitoids in the Nanling range","volume":"11","author":"Hua","year":"2005","journal-title":"Geol. J. China Univ."},{"key":"10.1016\/j.cageo.2025.106090_bib22","series-title":"Causal Inference in Statistics, Social, and Biomedical Sciences","author":"Imbens","year":"2015"},{"issue":"10","key":"10.1016\/j.cageo.2025.106090_bib23","doi-asserted-by":"crossref","first-page":"2318","DOI":"10.1109\/TKDE.2017.2720168","article-title":"Theory-guided data science: a new paradigm for scientific discovery from data","volume":"29","author":"Karpatne","year":"2017","journal-title":"IEEE Trans. Knowl. Data Eng."},{"issue":"4","key":"10.1016\/j.cageo.2025.106090_bib24","doi-asserted-by":"crossref","first-page":"241","DOI":"10.2113\/gselements.11.4.241","article-title":"Applied geochemistry in mineral exploration and mining","volume":"11","author":"Kyser","year":"2015","journal-title":"Elements"},{"key":"10.1016\/j.cageo.2025.106090_bib28","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"10.1016\/j.cageo.2025.106090_bib26","doi-asserted-by":"crossref","DOI":"10.1016\/j.apgeochem.2020.104747","article-title":"Convolutional neural network and transfer learning based mineral prospectivity modeling for geochemical exploration of Au mineralization within the Guandian\u2013Zhangbaling area, Anhui Province, China","volume":"122","author":"Li","year":"2020","journal-title":"Appl. Geochem."},{"key":"10.1016\/j.cageo.2025.106090_bib27","doi-asserted-by":"crossref","DOI":"10.1007\/s11004-025-10218-0","article-title":"Coupling graph attention networks with causal discovery for geochemical anomaly recognition","author":"Liang","year":"2025","journal-title":"Math. Geosci."},{"issue":"8","key":"10.1016\/j.cageo.2025.106090_bib29","doi-asserted-by":"crossref","first-page":"426","DOI":"10.1038\/s42256-020-0218-x","article-title":"When causal inference meets deep learning","volume":"2","author":"Luo","year":"2020","journal-title":"Nat. Mach. Intell."},{"key":"10.1016\/j.cageo.2025.106090_bib30","doi-asserted-by":"crossref","DOI":"10.1016\/j.apgeochem.2020.104710","article-title":"Recognition of geochemical anomalies using a deep variational autoencoder network","volume":"122","author":"Luo","year":"2020","journal-title":"Appl. Geochem."},{"key":"10.1016\/j.cageo.2025.106090_bib31","doi-asserted-by":"crossref","DOI":"10.1016\/j.apgeochem.2021.105043","article-title":"Detection of geochemical anomalies related to mineralization using the GANomaly network","volume":"131","author":"Luo","year":"2021","journal-title":"Appl. Geochem."},{"issue":"3","key":"10.1016\/j.cageo.2025.106090_bib32","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1007\/s11053-023-10200-9","article-title":"Metallogenic-factor variational autoencoder for geochemical anomaly detection by ad-hoc and post-hoc interpretability algorithms","volume":"32","author":"Luo","year":"2023","journal-title":"Nat. Resour. Res."},{"issue":"1","key":"10.1016\/j.cageo.2025.106090_bib33","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1007\/s11004-024-10153-6","article-title":"Causal discovery and deep learning algorithms for detecting geochemical patterns associated with gold-polymetallic mineralization: a case study of the Edongnan region","volume":"57","author":"Luo","year":"2025","journal-title":"Math. Geosci."},{"issue":"3","key":"10.1016\/j.cageo.2025.106090_bib34","first-page":"420","article-title":"Mesozoic large\u2010scale mineralization and multiple lithospheric extensions in south China","volume":"80","author":"Mao","year":"2006","journal-title":"Acta Geologica Sinica\u2010English Edition"},{"issue":"3","key":"10.1016\/j.cageo.2025.106090_bib35","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.oregeorev.2010.05.008","article-title":"Translating the mineral systems approach into an effective exploration targeting system","volume":"38","author":"McCuaig","year":"2010","journal-title":"Ore Geol. Rev."},{"key":"10.1016\/j.cageo.2025.106090_bib36","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1016\/j.oregeorev.2014.09.007","article-title":"Receiver operating characteristics (ROC) as validation tool for prospectivity models\u2014A magmatic Ni\u2013Cu case study from the central Lapland Greenstone Belt, northern Finland","volume":"71","author":"Nyk\u00e4nen","year":"2015","journal-title":"Ore Geol. Rev."},{"issue":"1","key":"10.1016\/j.cageo.2025.106090_bib37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1144\/GSL.SP.2006.264.01.01","article-title":"Compositional data and their analysis: an introduction","volume":"264","author":"Pawlowsky-Glahn","year":"2006","journal-title":"Geological Society"},{"key":"10.1016\/j.cageo.2025.106090_bib38","series-title":"Causality: Objectives and Assessment","first-page":"39","article-title":"Causal inference","author":"Pearl","year":"2010"},{"key":"10.1016\/j.cageo.2025.106090_bib39","series-title":"Hydrothermal Processes and Mineral Systems","author":"Pirajno","year":"2008"},{"issue":"7743","key":"10.1016\/j.cageo.2025.106090_bib40","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41586-019-0912-1","article-title":"Deep learning and process understanding for data-driven Earth system science","volume":"566","author":"Reichstein","year":"2019","journal-title":"Nature"},{"issue":"5","key":"10.1016\/j.cageo.2025.106090_bib41","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","article-title":"Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead","volume":"1","author":"Rudin","year":"2019","journal-title":"Nat. Mach. Intell."},{"issue":"1","key":"10.1016\/j.cageo.2025.106090_bib42","doi-asserted-by":"crossref","first-page":"6236","DOI":"10.1038\/s41598-024-56644-8","article-title":"Geologically-constrained GANomaly network for mineral prospectivity mapping through frequency domain training data","volume":"14","author":"Sabbaghi","year":"2024","journal-title":"Sci. Rep."},{"issue":"1","key":"10.1016\/j.cageo.2025.106090_bib43","doi-asserted-by":"crossref","first-page":"3","DOI":"10.2113\/gsecongeo.105.1.3","article-title":"Porphyry copper systems","volume":"105","author":"Sillitoe","year":"2010","journal-title":"Econ. Geol."},{"key":"10.1016\/j.cageo.2025.106090_bib44","series-title":"Causation, Prediction, and Search","author":"Spirtes","year":"2000"},{"key":"10.1016\/j.cageo.2025.106090_bib49","doi-asserted-by":"crossref","DOI":"10.1016\/j.oregeorev.2019.103305","article-title":"Regional metallogeny of tungsten-tin-polymetallic deposits in Nanling region, South China","volume":"120","author":"Wang","year":"2020","journal-title":"Ore Geol. Rev."},{"issue":"3","key":"10.1016\/j.cageo.2025.106090_bib45","first-page":"209","article-title":"Genesis of granitoids and crustal evolution in southeast China","volume":"10","author":"Wang","year":"2003","journal-title":"Earth Sci. Front."},{"issue":"5\u20136","key":"10.1016\/j.cageo.2025.106090_bib46","doi-asserted-by":"crossref","first-page":"704","DOI":"10.1080\/00206814.2010.503736","article-title":"Mesozoic large magmatic events and mineralization in SE China: oblique subduction of the Pacific plate","volume":"53","author":"Wang","year":"2011","journal-title":"Int. Geol. Rev."},{"key":"10.1016\/j.cageo.2025.106090_bib50","doi-asserted-by":"crossref","DOI":"10.1016\/j.oregeorev.2021.104658","article-title":"Incorporation of geological constraints into geochemical anomaly identification using BME-GWR: a case study from Inner Mongolia of China","volume":"141","author":"Wang","year":"2022","journal-title":"Ore Geol. Rev."},{"key":"10.1016\/j.cageo.2025.106090_bib48","doi-asserted-by":"crossref","DOI":"10.1016\/j.apgeochem.2020.104668","article-title":"Assessing geochemical anomalies using geographically weighted lasso","volume":"119","author":"Wang","year":"2020","journal-title":"Appl. Geochem."},{"key":"10.1016\/j.cageo.2025.106090_bib47","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1016\/j.gexplo.2012.06.017","article-title":"Tectonic\u2013geochemical exploration modeling for characterizing geo-anomalies in southeastern Yunnan district, China","volume":"122","author":"Wang","year":"2012","journal-title":"J. Geochem. Explor."},{"issue":"1","key":"10.1016\/j.cageo.2025.106090_bib51","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0375-6742(97)00029-0","article-title":"Geochemical mapping in China","volume":"60","author":"Xie","year":"1997","journal-title":"J. Geochem. Explor."},{"issue":"3\u20134","key":"10.1016\/j.cageo.2025.106090_bib52","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1144\/1467-7873\/08-184","article-title":"Multi-scale geochemical mapping in China","volume":"8","author":"Xie","year":"2008","journal-title":"Geochem. Explor. Environ. Anal."},{"key":"10.1016\/j.cageo.2025.106090_bib53","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.cageo.2015.10.006","article-title":"Recognition of geochemical anomalies using a deep autoencoder network","volume":"86","author":"Xiong","year":"2016","journal-title":"Comput. Geosci."},{"issue":"3","key":"10.1016\/j.cageo.2025.106090_bib54","doi-asserted-by":"crossref","first-page":"623","DOI":"10.1007\/s11004-021-09935-z","article-title":"Robust feature extraction for geochemical anomaly recognition using a stacked convolutional denoising autoencoder","volume":"54","author":"Xiong","year":"2022","journal-title":"Math. Geosci."},{"issue":"4","key":"10.1016\/j.cageo.2025.106090_bib55","doi-asserted-by":"crossref","first-page":"783","DOI":"10.1007\/s11004-021-09979-1","article-title":"A physically constrained variational autoencoder for geochemical pattern recognition","volume":"54","author":"Xiong","year":"2022","journal-title":"Math. Geosci."},{"issue":"4","key":"10.1016\/j.cageo.2025.106090_bib56","first-page":"1133","article-title":"Fundamental geological features and metallogenic geological backgrounds of Nanling tectonic belt","volume":"46","author":"Xu","year":"2021","journal-title":"Earth Sci."},{"key":"10.1016\/j.cageo.2025.106090_bib57","first-page":"573","article-title":"Early Mesozoic geotectonic processes in south China","volume":"36","author":"Xu","year":"2009","journal-title":"Geology in China"},{"key":"10.1016\/j.cageo.2025.106090_bib58","doi-asserted-by":"crossref","DOI":"10.1016\/j.apgeochem.2023.105722","article-title":"The graph attention network and its post-hoc explanation for recognizing mineralization-related geochemical anomalies","volume":"155","author":"Xu","year":"2023","journal-title":"Appl. Geochem."},{"key":"10.1016\/j.cageo.2025.106090_bib59","doi-asserted-by":"crossref","DOI":"10.1016\/j.apgeochem.2024.106137","article-title":"Geologically constrained unsupervised dual-branch deep learning algorithm for geochemical anomalies identification","volume":"174","author":"Xu","year":"2024","journal-title":"Appl. Geochem."},{"issue":"1","key":"10.1016\/j.cageo.2025.106090_bib60","article-title":"Geological knowledge\u2010guided dual\u2010branch deep learning model for identification of geochemical anomalies related to mineralization","volume":"2","author":"Xu","year":"2025","journal-title":"J. Geophys. Res.: Machine Learning and Computation"},{"key":"10.1016\/j.cageo.2025.106090_bib25","article-title":"Auto-encoding variational bayes","author":"Kingma","year":"2013","journal-title":"arXiv:1312.6114"},{"key":"10.1016\/j.cageo.2025.106090_bib61","doi-asserted-by":"crossref","DOI":"10.1016\/j.gexplo.2023.107274","article-title":"An interpretable attention branch convolutional neural network for identifying geochemical anomalies related to mineralization","volume":"252","author":"Yang","year":"2023","journal-title":"J. Geochem. Explor."},{"key":"10.1016\/j.cageo.2025.106090_bib62","doi-asserted-by":"crossref","unstructured":"Yang, M., Liu, F., Chen, Z., Shen, X., Hao, J., Wang, J., 2020. CausalVAE: disentangled representation learning via neural structural causal models. 2021 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 9588-9597.","DOI":"10.1109\/CVPR46437.2021.00947"},{"key":"10.1016\/j.cageo.2025.106090_bib63","doi-asserted-by":"crossref","DOI":"10.1016\/j.gexplo.2025.107850","article-title":"Unbiased and augmentation-free self-supervised graph contrastive learning for detecting mineralization-related geochemical anomalies","author":"Yang","year":"2025","journal-title":"J. Geochem. Explor."},{"issue":"10","key":"10.1016\/j.cageo.2025.106090_bib64","doi-asserted-by":"crossref","DOI":"10.1093\/petrology\/egac093","article-title":"Rare-metal enrichment and Nb\u2013Ta fractionation during magmatic\u2013hydrothermal processes in rare-metal granites: evidence from zoned micas from the yashan Pluton, South China","volume":"63","author":"Yin","year":"2022","journal-title":"J. Petrol."},{"key":"10.1016\/j.cageo.2025.106090_bib65","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.gexplo.2013.01.013","article-title":"Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling","volume":"128","author":"Yousefi","year":"2013","journal-title":"J. Geochem. Explor."},{"issue":"4","key":"10.1016\/j.cageo.2025.106090_bib66","first-page":"361","article-title":"Complexity and self-organized criticality of solid earth system","volume":"4","author":"Yu","year":"1988","journal-title":"Geol. J. China Univ."},{"key":"10.1016\/j.cageo.2025.106090_bib67","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.gexplo.2019.04.007","article-title":"Application of hierarchical clustering, singularity mapping, and Kohonen neural network to identify ag-au-pb-zn polymetallic mineralization associated geochemical anomaly in Pangxidong district","volume":"203","author":"Yu","year":"2019","journal-title":"J. Geochem. Explor."},{"key":"10.1016\/j.cageo.2025.106090_bib68","series-title":"International Conference on Machine Learning","first-page":"7154","article-title":"DAG-GNN: DAG structure learning with graph neural networks","author":"Yu","year":"2019"},{"key":"10.1016\/j.cageo.2025.106090_bib69","first-page":"172","article-title":"Earth system, metallogenic system to exploration system","volume":"14","author":"Zhai","year":"2007","journal-title":"Earth Sci. Front."},{"key":"10.1016\/j.cageo.2025.106090_bib70","doi-asserted-by":"crossref","DOI":"10.1016\/j.apgeochem.2021.104994","article-title":"Detection of the multivariate geochemical anomalies associated with mineralization using a deep convolutional neural network and a pixel-pair feature method","volume":"130","author":"Zhang","year":"2021","journal-title":"Appl. Geochem."},{"key":"10.1016\/j.cageo.2025.106090_bib71","doi-asserted-by":"crossref","DOI":"10.1016\/j.cageo.2022.105100","article-title":"A geologically-constrained deep learning algorithm for recognizing geochemical anomalies","volume":"162","author":"Zhang","year":"2022","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.cageo.2025.106090_bib72","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1016\/j.cageo.2019.05.011","article-title":"Integration of auto-encoder network with density-based spatial clustering for geochemical anomaly detection for mineral exploration","volume":"130","author":"Zhang","year":"2019","journal-title":"Comput. Geosci."},{"key":"10.1016\/j.cageo.2025.106090_bib73","article-title":"Interpretability-enhanced mineral prospectivity models: a synergistic approach using large language models, knowledge graphs, and machine learning","author":"Zhang","year":"2025","journal-title":"Math. Geosci."},{"key":"10.1016\/j.cageo.2025.106090_bib74","doi-asserted-by":"crossref","first-page":"628","DOI":"10.1016\/j.oregeorev.2013.08.005","article-title":"Application of geographically weighted regression to identify spatially non-stationary relationships between Fe mineralization and its controlling factors in eastern tianshan, China","volume":"57","author":"Zhao","year":"2014","journal-title":"Ore Geol. Rev."},{"key":"10.1016\/j.cageo.2025.106090_bib75","article-title":"Dags with no tears: continuous optimization for structure learning","volume":"31","author":"Zheng","year":"2018","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.cageo.2025.106090_bib76","doi-asserted-by":"crossref","first-page":"26","DOI":"10.18814\/epiiugs\/2006\/v29i1\/004","article-title":"Petrogenesis of Mesozoic granitoids and volcanic rocks in south China: a response to tectonic evolution","volume":"29","author":"Zhou","year":"2006","journal-title":"Episodes"},{"key":"10.1016\/j.cageo.2025.106090_bib77","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1016\/j.gexplo.2008.03.004","article-title":"Neuro-fuzzy modelling in mining geochemistry: identification of geochemical anomalies","volume":"100","author":"Ziaii","year":"2009","journal-title":"J. Geochem. Explor."},{"issue":"1","key":"10.1016\/j.cageo.2025.106090_bib78","doi-asserted-by":"crossref","DOI":"10.1038\/srep27127","article-title":"A nonlinear controlling function of geological features on magmatic\u2013hydrothermal mineralization","volume":"6","author":"Zuo","year":"2016","journal-title":"Sci. Rep."},{"key":"10.1016\/j.cageo.2025.106090_bib79","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s11053-017-9345-4","article-title":"Machine learning of mineralization-related geochemical anomalies: a review of potential methods","volume":"26","author":"Zuo","year":"2017","journal-title":"Nat. Resour. Res."},{"key":"10.1016\/j.cageo.2025.106090_bib80","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/s11053-017-9357-0","article-title":"Big data analytics of identifying geochemical anomalies supported by machine learning methods","volume":"27","author":"Zuo","year":"2018","journal-title":"Nat. Resour. Res."},{"key":"10.1016\/j.cageo.2025.106090_bib81","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.earscirev.2019.02.023","article-title":"Deep learning and its application in geochemical mapping","volume":"192","author":"Zuo","year":"2019","journal-title":"Earth Sci. Rev."},{"key":"10.1016\/j.cageo.2025.106090_bib82","doi-asserted-by":"crossref","first-page":"3415","DOI":"10.1007\/s11053-020-09700-9","article-title":"Geodata science-based mineral prospectivity mapping: a review","volume":"29","author":"Zuo","year":"2020","journal-title":"Nat. Resour. Res."},{"key":"10.1016\/j.cageo.2025.106090_bib83","doi-asserted-by":"crossref","DOI":"10.1016\/j.apgeochem.2021.105072","article-title":"The processing methods of geochemical exploration data: past, present, and future","volume":"132","author":"Zuo","year":"2021","journal-title":"Appl. Geochem."},{"issue":"9","key":"10.1016\/j.cageo.2025.106090_bib84","doi-asserted-by":"crossref","first-page":"2864","DOI":"10.1007\/s11430-024-1309-9","article-title":"Explainable artificial intelligence models for mineral prospectivity mapping","volume":"67","author":"Zuo","year":"2024","journal-title":"Sci. China Earth Sci."},{"issue":"3","key":"10.1016\/j.cageo.2025.106090_bib85","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1130\/G52970.1","article-title":"A novel data-knowledge dual-driven model coupling artificial intelligence with a mineral systems approach for mineral prospectivity mapping","volume":"53","author":"Zuo","year":"2025","journal-title":"Geology"}],"container-title":["Computers & Geosciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098300425002407?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0098300425002407?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T03:48:16Z","timestamp":1773892096000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0098300425002407"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2]]},"references-count":85,"alternative-id":["S0098300425002407"],"URL":"https:\/\/doi.org\/10.1016\/j.cageo.2025.106090","relation":{},"ISSN":["0098-3004"],"issn-type":[{"value":"0098-3004","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An interpretable causal variational autoencoder for geochemical anomalies recognition by regarding ore-controlling factors","name":"articletitle","label":"Article Title"},{"value":"Computers & Geosciences","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cageo.2025.106090","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"106090"}}