Comparison of deep learning algorithms for geochemical anomaly identification
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摘要: 针对选用不同网络结构的深度学习算法进行地球化学异常识别,重构符合成矿分布的地球化学背景时选择依据较少的问题,本文基于闽西南铜锌银成矿区1∶20万水系沉积物数据,采用3种无监督深度学习模型AE、MCAE、FCAE,分别提取了样本中多元素的组合结构特征、空间分布特征以及混合特征,并基于其重构地球化学背景,模拟成矿分布。结果显示,FCAE模型圈定的异常区域与已知铜矿点最贴合,其次是MCAE模型和AE模型,其AUC值分别为0.80、0.78、0.61,且FCAE模型和AE模型对卷积窗口尺寸变化不敏感;说明面向地球化学异常识别构建深度学习算法时,基于提取空间分布特征或混合特征的算法综合表现较好,且基于提取组合结构特征或混合特征的算法对由观测空间尺度变化或不一致引起的噪声有较强抗干扰能力。本文为因地制宜地构建基于深度学习算法的地球化学异常识别模型提供了有效依据。Abstract: There is a lack of selection bases in the geochemical anomaly identification and the reconstruction of the geochemical background conforming to the metallogenic distribution using deep learning algorithms with different network structures. Given this, based on the 1∶200 000 stream sediment data of the copper-zinc-silver metallogenic area in southwestern Fujian Province, this study extracted the combined structural characteristics, spatial distribution characteristics, and mixed characteristics of multiple elements in the samples using three unsupervised deep learning models, i.e., AE, MCAE, and FCAE. Then, these characteristics were used to reconstruct the geochemical background and simulate the metallogenic distribution. The results show that the anomaly areas delineated by the FCAE model were the most consistent with the known copper ore occurrences, followed by the MCAE and AE models. The FCAE, MCAE, and AE models had an area under the curve (AUC) score of 0.80, 0.78, and 0.61, respectively. Moreover, the FCAE and AE models were not sensitive to the change in the convolution window size. These results indicate that when deep learning algorithms are constructed for geochemical anomaly identification, the algorithms based on the extraction of spatial distribution characteristics or mixed characteristics perform well, and those based on the extraction of combined structural characteristics or mixed characteristics have a strong anti-interference ability for the noise caused by the change or inconsistency of the spatial observation scale. This study provides some effective selection bases for constructing geochemical anomaly identification models based on deep learning algorithms.
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