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| Citation: | CHEN Zhi-jun, CHENG Qiu-ming, CHEN Jian-guo, 2009. Comparison of Different Models for Anomaly Recognition of Geochemical Data by Using Sample Ranking Method. Earth Science, 34(2): 353-364.
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The geochemical anomaly recognition is the key to geochemical prospecting.Many new models are brought forward to identify and extract the geochemical weak anomalies from the complex background.How to compare the differences in aspect of the anomaly recognition effect of these different models? The authors advance the sample ranking method to discuss this problem.The gliding anomaly contrast and the local singularity analysis are applied to the Cu element data of the stream sediment samples from Gejiu area, Yunnan Province, China.The ranks for the raw data, contrast value and the local singularity exponents, denoted by RANK (Raw), RANK (CV) and RANK (Δα) respectively, are calculated by ordering the samples from the high anomaly to the low anomaly.Three ways are employed to compare the RANK (CV) and RANK (Δα) with the RANK (Raw) : (1) the characteristics of ranks for samples with the strong background and the weak background; (2) the characteristics of ranks for samples where the Cu deposits occur; and (3) the spatial correlation between the locations of Cu deposits and the cumulative area with the same thresholds of the upper rank values.The results demonstrate that the local singularity analysis is a useful model for the weak geochemical anomaly recognition, whose effect corresponds with the gliding anomaly contrast model or even better.The prospective areas delineated by means of weights of evidence method on the basis of local singularity exponents can provide new information and may be significant for the prediction of the undiscovered mineral deposits, which is significantly superior to the results on the basis of raw concentration data.The local singularity analysis has the advantage of the perspicuous principle, convenience and effective performance, and we can substitute it for the gliding contrast value method for the anomaly recognition of geochemical data.
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CHEN Zhi-jun, CHENG Qiu-ming, CHEN Jian-guo, 2009. Comparison of Different Models for Anomaly Recognition of Geochemical Data by Using Sample Ranking Method. Earth Science, 34(2): 353-364.
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