Matches in SemOpenAlex for { <https://semopenalex.org/work/W2619107246> ?p ?o ?g. }
- W2619107246 endingPage "651" @default.
- W2619107246 startingPage "639" @default.
- W2619107246 abstract "Mineral targets are local geological anomalies. In a study area of a number of unit cells, mapping mineral prospectivity can be implemented by identifying anomaly cells from the unit cell population. One-class support vector machine (OCSVM) models can yield useful results in anomaly detection in high-dimensional data or without any assumptions on the distribution of the inlying data. The OCSVM model was applied to mapping gold prospectivity of the Laotudingzi-Xiaosiping district, an area with a complex geological background, in Jilin Province, China. The decision function value of each unit cell belonging to an anomaly was computed on the basis of the trained OCSVM model and used to express gold prospectivity of the cell. The receiver operating characteristic (ROC) curve, area under curve (AUC) and data-processing efficiency were used to compare the performance of the OCSVM model and a restricted Boltzmann machine (RBM) model in mapping gold prospectivity. The results show that the OCSVM model outperforms the RBM model in terms of ROC, AUC and data-processing efficiency. Gold targets were optimally delineated by using the Youden index to maximise the spatial association between the delineated gold targets and known gold deposits. The gold targets delineated by the OCSVM model occupy 11% of the study area and contain 88% of the known gold deposits; and the gold targets delineated by the RBM model occupy 10% of the study area and contain 81% of the known gold deposits. Therefore, the OCSVM model is a feasible mineral prospectivity mapping approach." @default.
- W2619107246 created "2017-06-05" @default.
- W2619107246 creator A5072546046 @default.
- W2619107246 creator A5084093126 @default.
- W2619107246 date "2017-05-29" @default.
- W2619107246 modified "2023-09-25" @default.
- W2619107246 title "Mapping mineral prospectivity by using one-class support vector machine to identify multivariate geological anomalies from digital geological survey data" @default.
- W2619107246 cites W1573373265 @default.
- W2619107246 cites W1600588226 @default.
- W2619107246 cites W161635018 @default.
- W2619107246 cites W180171617 @default.
- W2619107246 cites W1968706640 @default.
- W2619107246 cites W1968807024 @default.
- W2619107246 cites W1973237440 @default.
- W2619107246 cites W1973595880 @default.
- W2619107246 cites W1976665493 @default.
- W2619107246 cites W1978053173 @default.
- W2619107246 cites W1979371195 @default.
- W2619107246 cites W1980789580 @default.
- W2619107246 cites W1982998540 @default.
- W2619107246 cites W1984456796 @default.
- W2619107246 cites W1994897523 @default.
- W2619107246 cites W1995631140 @default.
- W2619107246 cites W2001105364 @default.
- W2619107246 cites W2013516207 @default.
- W2619107246 cites W2018366608 @default.
- W2619107246 cites W2022415789 @default.
- W2619107246 cites W2028144932 @default.
- W2619107246 cites W2028953920 @default.
- W2619107246 cites W2036749329 @default.
- W2619107246 cites W2040248838 @default.
- W2619107246 cites W2047361407 @default.
- W2619107246 cites W2048708177 @default.
- W2619107246 cites W2049907150 @default.
- W2619107246 cites W20569399 @default.
- W2619107246 cites W2057591731 @default.
- W2619107246 cites W2058274266 @default.
- W2619107246 cites W2066861002 @default.
- W2619107246 cites W2068075464 @default.
- W2619107246 cites W2068362338 @default.
- W2619107246 cites W2084205924 @default.
- W2619107246 cites W2086156647 @default.
- W2619107246 cites W2104908186 @default.
- W2619107246 cites W2110121831 @default.
- W2619107246 cites W2116925226 @default.
- W2619107246 cites W2132870739 @default.
- W2619107246 cites W2134780026 @default.
- W2619107246 cites W2169638223 @default.
- W2619107246 cites W2191706756 @default.
- W2619107246 cites W220005950 @default.
- W2619107246 cites W2461102141 @default.
- W2619107246 cites W2522879866 @default.
- W2619107246 cites W340152573 @default.
- W2619107246 cites W342324839 @default.
- W2619107246 cites W4253799996 @default.
- W2619107246 cites W44193213 @default.
- W2619107246 cites W620890970 @default.
- W2619107246 cites W866664596 @default.
- W2619107246 cites W92574158 @default.
- W2619107246 doi "https://doi.org/10.1080/08120099.2017.1328705" @default.
- W2619107246 hasPublicationYear "2017" @default.
- W2619107246 type Work @default.
- W2619107246 sameAs 2619107246 @default.
- W2619107246 citedByCount "35" @default.
- W2619107246 countsByYear W26191072462018 @default.
- W2619107246 countsByYear W26191072462019 @default.
- W2619107246 countsByYear W26191072462021 @default.
- W2619107246 countsByYear W26191072462022 @default.
- W2619107246 countsByYear W26191072462023 @default.
- W2619107246 crossrefType "journal-article" @default.
- W2619107246 hasAuthorship W2619107246A5072546046 @default.
- W2619107246 hasAuthorship W2619107246A5084093126 @default.
- W2619107246 hasConcept C109007969 @default.
- W2619107246 hasConcept C119857082 @default.
- W2619107246 hasConcept C121332964 @default.
- W2619107246 hasConcept C12267149 @default.
- W2619107246 hasConcept C124101348 @default.
- W2619107246 hasConcept C127313418 @default.
- W2619107246 hasConcept C12997251 @default.
- W2619107246 hasConcept C151730666 @default.
- W2619107246 hasConcept C153180895 @default.
- W2619107246 hasConcept C154945302 @default.
- W2619107246 hasConcept C17409809 @default.
- W2619107246 hasConcept C26873012 @default.
- W2619107246 hasConcept C41008148 @default.
- W2619107246 hasConcept C55358776 @default.
- W2619107246 hasConcept C58471807 @default.
- W2619107246 hasConcept C66264921 @default.
- W2619107246 hasConcept C739882 @default.
- W2619107246 hasConceptScore W2619107246C109007969 @default.
- W2619107246 hasConceptScore W2619107246C119857082 @default.
- W2619107246 hasConceptScore W2619107246C121332964 @default.
- W2619107246 hasConceptScore W2619107246C12267149 @default.
- W2619107246 hasConceptScore W2619107246C124101348 @default.
- W2619107246 hasConceptScore W2619107246C127313418 @default.
- W2619107246 hasConceptScore W2619107246C12997251 @default.
- W2619107246 hasConceptScore W2619107246C151730666 @default.
- W2619107246 hasConceptScore W2619107246C153180895 @default.