Sidique Gawusu , Benatus Norbert Mvile , Mahamuda Abu , John Desderius Kalimenze
{"title":"基于机器学习的勘探定位:东非坦桑尼亚中部金矿发现案例","authors":"Sidique Gawusu , Benatus Norbert Mvile , Mahamuda Abu , John Desderius Kalimenze","doi":"10.1016/j.oreoa.2024.100065","DOIUrl":null,"url":null,"abstract":"<div><p>Soil geochemical analyses from central Tanzania reveal significant gold (Au) values, highlighting the potential for further exploration in the region. This study employs ensemble machine learning models—XGBoost-RF, XGBoost-SVM, and XGBoost-ANN—to enhance predictions of Au distribution. Among these, the XGBoost-ANN model showed the highest accuracy during the training phase, achieving a Mean Absolute Percentage Error (MAPE) of 1.275, a Root Mean Square Error (RMSE) of 0.031, an R² of 0.999, and a Pearson Correlation Coefficient (PCC) of 0.999. However, its performance declined in the testing phase with a MAPE of 0.0668 and an RMSE of 0.2491, indicating reduced predictiveness on new data. Spatial analyses using Global and Local Moran's I tests revealed no significant global spatial autocorrelation but identified localized clusters of high and low Au concentrations. Specific areas showed significant spatial dependence, enhancing our understanding of the complex geospatial distribution of Au. These findings support the combined use of predictive modeling and spatial statistical methods to refine mineral exploration strategies, highlighting the value of advanced analytics in identifying promising exploration targets.</p></div>","PeriodicalId":100993,"journal":{"name":"Ore and Energy Resource Geology","volume":"17 ","pages":"Article 100065"},"PeriodicalIF":0.0000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Machine learning based prospect targeting: A case of gold occurrence in central parts of Tanzania, East Africa\",\"authors\":\"Sidique Gawusu , Benatus Norbert Mvile , Mahamuda Abu , John Desderius Kalimenze\",\"doi\":\"10.1016/j.oreoa.2024.100065\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>Soil geochemical analyses from central Tanzania reveal significant gold (Au) values, highlighting the potential for further exploration in the region. This study employs ensemble machine learning models—XGBoost-RF, XGBoost-SVM, and XGBoost-ANN—to enhance predictions of Au distribution. Among these, the XGBoost-ANN model showed the highest accuracy during the training phase, achieving a Mean Absolute Percentage Error (MAPE) of 1.275, a Root Mean Square Error (RMSE) of 0.031, an R² of 0.999, and a Pearson Correlation Coefficient (PCC) of 0.999. However, its performance declined in the testing phase with a MAPE of 0.0668 and an RMSE of 0.2491, indicating reduced predictiveness on new data. Spatial analyses using Global and Local Moran's I tests revealed no significant global spatial autocorrelation but identified localized clusters of high and low Au concentrations. Specific areas showed significant spatial dependence, enhancing our understanding of the complex geospatial distribution of Au. These findings support the combined use of predictive modeling and spatial statistical methods to refine mineral exploration strategies, highlighting the value of advanced analytics in identifying promising exploration targets.</p></div>\",\"PeriodicalId\":100993,\"journal\":{\"name\":\"Ore and Energy Resource Geology\",\"volume\":\"17 \",\"pages\":\"Article 100065\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ore and Energy Resource Geology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666261224000270\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ore and Energy Resource Geology","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666261224000270","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machine learning based prospect targeting: A case of gold occurrence in central parts of Tanzania, East Africa
Soil geochemical analyses from central Tanzania reveal significant gold (Au) values, highlighting the potential for further exploration in the region. This study employs ensemble machine learning models—XGBoost-RF, XGBoost-SVM, and XGBoost-ANN—to enhance predictions of Au distribution. Among these, the XGBoost-ANN model showed the highest accuracy during the training phase, achieving a Mean Absolute Percentage Error (MAPE) of 1.275, a Root Mean Square Error (RMSE) of 0.031, an R² of 0.999, and a Pearson Correlation Coefficient (PCC) of 0.999. However, its performance declined in the testing phase with a MAPE of 0.0668 and an RMSE of 0.2491, indicating reduced predictiveness on new data. Spatial analyses using Global and Local Moran's I tests revealed no significant global spatial autocorrelation but identified localized clusters of high and low Au concentrations. Specific areas showed significant spatial dependence, enhancing our understanding of the complex geospatial distribution of Au. These findings support the combined use of predictive modeling and spatial statistical methods to refine mineral exploration strategies, highlighting the value of advanced analytics in identifying promising exploration targets.