Detection of Geologic Anomalies with Monte Carlo Clustering Assemblies
Abstract Authors present new clustering based algorithms for detection of geologic anomalies and results of their testing on the data containing anomalies of two types: (a) high permeability anomaly with regular records containing smaller permeability values, and (b) gas filled sand anomaly with regular records containing data from brine filled sands. Results of algorithms testing, presented in the paper, demonstrate high stability of anomaly detection with false discovery rate below 20% and with the true discovery rate exceeding 73%