Research on geochemical anomaly classification based on Weka platform
Geochemical data is an important part of applied geochemical research and the basic achievement of exploration.The machine learning algorithm of geological data has an important reference in the study of geochemical anomaly classifica-tion.In this paper,based on soil geochemical data of 13 elements,such as Au and Cu,in a certain area of the Province,and the J48 decision tree algorithm of Weka platform,the geochemical anomalies of the target research area are classified.The results showed that under supervised conditions,the accuracy of the trained test set is 89.8％and the error rate is 10.1％,with the trained model showing high accuracy in anomaly classification.The most effective parameters include ab-normal area,nap value,faults,concentration centers,and number of mineral deposits.
NETL
NSTL
万方数据
维普
