지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2021.1
- 수록면
- 122 - 136 (15page)
이용수
초록· 키워드
오류제보하기
Undoubtedly, forecasting the anomalous values could play an important role in the inchoate stages of exploration. Therefore, it is essential to find the most accurate approach to separate anomalous values from background and afterward use the results to anticipate each arbitrary sample. In this study, results of a structural multivariate method (the combination of Mahalanobis distance and U-statistics algorithms) are used to distinguish anomalous values from background. Then, three data mining methods will be applied to produce practical equations and finally estimate anomalous values. Actually, at the first, separation of geochemical anomalies, based on the combination of the U-statistics and the Mahalanobis distance approaches, would be done. Afterward, three data mining methods, K nearest neighbor (K-NN), decision tree, and naïve Bayes classifier have been applied based on separation results and the other parameters – x and y coordinates and Cu and Mo grades. For this purpose, after separation of anomalous values according to the number of 377 collected surface samples from Parkam porphyry system by applying above combination, the data mining methods would be utilized to anticipate anomalous values for each unknown point. Finally, in order to judge about the designed networks, training samples would be considered as test samples under the application of the networks. Results show that the decision tree method would appear as the more powerful approach than the other due to far fewer number of wrong estimated samples and approving high accuracy of the designed network. Noteworthy is that resubstitution error for this network is noted only 0.0212 based on numbers of wrong estimated samples (8 from 377). Whereas, the numbers of wrong estimated samples for K-NN and naïve Bayes methods are, respectively, 13 and 46 and the rates of error are 0.0345 and 0.122. Moreover, it was observed that the estimated samples (the joint points of Cu and Mo) delineated by decision tree method are closely associated with the defined zone of potassic alteration in the study area. Finally, according to results, it can be said that the combination of decision tree method and the introduced anomaly separation approach could be applied as a reliable and efficient technique to approach worthwhile predictions.
목차
등록된 정보가 없습니다.
참고문헌 (42)
참고문헌 신청
Cheng, Q / 1999 / Spatial and scaling modelling for geochemical anomaly separation / Journal of Geochemical Exploration 65(3):175~194
Cheng, Q / 1994 / The separation of geochemical anomalies from background by fractal methods / Journal of Geochemical Exploration 51:109~130
Cheng, Q / 1996 / A spatial analysis method for geochemical anomaly separation / Journal of Geochemical Exploration 56:183~195
Cheng, Q / 2000 / Integrated spatial and spectrum method for geochemical anomaly separation / Natural Resources Research 9(1):43~52
De Maesschalck, R. D / 2000 / The Mahalanobis distance / Chemometrics and Intelligent Laboratory Systems 50:1~18
Cheng, Q / 1994 / The separation of geochemical anomalies from background by fractal methods / Journal of Geochemical Exploration 51:109~130
Cheng, Q / 1996 / A spatial analysis method for geochemical anomaly separation / Journal of Geochemical Exploration 56:183~195
Cheng, Q / 2000 / Integrated spatial and spectrum method for geochemical anomaly separation / Natural Resources Research 9(1):43~52
De Maesschalck, R. D / 2000 / The Mahalanobis distance / Chemometrics and Intelligent Laboratory Systems 50:1~18
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