Deep neural networks perform very well in learning high-level representations in support of multivariate geochemical anomaly recognition. Geochemical exploration data typically contain a proportion of large variations and missing values, which motivated us to construct a network architecture optimized to deal with these data. Our approach adopted a stacked convolutional denoising autoencoder (SCDAE) to extract robust features and decreased the level of sensitivity to partially corrupted data, that is, input data that are partially missing. SCDAE parameters, which include the network depth, number of convolution layers, number of convolution kernels, and convolution kernel size, were optimized using trial-and-error experiments. The optimal SCDAE architecture was then used to recognize multivariate geochemical anomalies related to mineralization in a case study in southwestern Fujian Province, based on the differences in the reconstruction errors between sample populations. The spatial distribution of high reconstruction errors in the anomaly map was closely related to most known Fe deposits, indicating the effectiveness of the SCDAE at recognizing geochemical anomalies related to Fe mineralization. A comparative study between the SCDAE and a stacked convolutional autoencoder (SCAE) with different corruption levels showed that the SCDAE exhibited reduced sensitivity to stochastic disturbances with different corruption proportions, and had an enhanced ability to recognize geochemical anomalies varying in a reasonable range. The robustness of the SCDAE makes it applicable to a wide variety of geochemical exploration scenarios, particularly in areas with incomplete or missing data.

在支持多变量地球化学异常识别方面，深度神经网络在学习高级表示方面的表现非常出色。地球化学勘探数据通常包含一定比例的大变化和缺失值，这促使我们构建了优化的网络架构来处理这些数据。我们的方法采用了堆叠卷积去噪自动编码器（SCDAE）来提取鲁棒的特征，并降低了对部分损坏的数据（即部分丢失的输入数据）的敏感度。使用试错实验优化了SCDAE参数，包括网络深度，卷积层数，卷积核数和卷积核大小。然后，基于样本种群之间重建误差的差异，在福建省西南部的一个案例研究中，最佳SCDAE体系结构被用于识别与成矿有关的多元地球化学异常。异常图中高重建误差的空间分布与大多数已知的铁矿床密切相关，这表明SCDAE在识别与铁矿化有关的地球化学异常方面是有效的。SCDAE与具有不同损坏级别的堆叠式卷积自编码器（SCAE）之间的比较研究表明，SCDAE对具有不同损坏比例的随机干扰的敏感性降低，并且具有识别在合理范围内变化的地球化学异常的能力。