| 基于改良信息量法的萤石成矿“甜点区”预测 |
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| 关键词:Gannan region fluorite geological characteristics of the deposit GIS spatial analysis information method metallogenic prediction |
| 基金项目:本文由赣州市社科规划课题(2025年)(编号: 2025-NDJJ19-1528)资助。 |
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| Prediction of Fluorite Mineralization “Sweet Spot” Based on an Improved Information Method |
| In this study, we propose an improved raster-based information method to address the issues of insufficient regionality, low spatial resolution, and high subjectivity in fluorite metallogenic prediction in southern Gannan. Based on the regional metallogenic law, seven types of ore-controlling factors were screened: regional deep-seated faults, faults, stratigraphy, hydrographic networks, topography and geomorphology, and geochemical anomalies of F and CaO. The surface element data were innovatively converted into raster format, and the factor information values were quantified. A multi-factor overlay prediction model was constructed using ArcGIS, and the prediction results were validated using Receiver Operating Characteristic (ROC) curve analysis and supplemental field surveys. Results indicate that the information value of the Yanshanian biotite granite is the highest (1.520). The distance to faults and hydrographic networks shows a negative correlation with the information value, whereas the F geochemical anomalies are significantly and positively correlated with mineralization. The peak information value occurs within the 8–10 km range from deep-seated faults. The CaO factor was deemed ineffective owing to background interference. The prediction delineates Grade I and Grade II sweet spots, accounting for 5.27% and 14.87% of the study area, respectively, and identifies seven key metallogenic belts. The area under the ROC curve is 0.818, and 83.3% of the newly discovered mineral occurrences fall within the predicted target zones, demonstrating the accuracy of the prediction. The improved method significantly enhances the spatial resolution of metallogenic prediction, and the results align well with the geological reality, providing a scientific basis for the planning and exploration allocation of fluorite mining rights in southern Gannan. |
| HUANG Chao,LAN Sheng,YANG Xiang,HUANG Shengji,WEN Peiyan,LUO Linshan,LIU Fengji,CHEN Ling.2026.Prediction of Fluorite Mineralization “Sweet Spot” Based on an Improved Information Method[J].Acta Geoscientica Sinica,47(2):409-420. |
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