Local phase-constrained convolutional autoencoder network for identifying multivariate geochemical anomalies

IF 4.2 2区 地球科学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Qiliang Liu , Gusheng Wu , Zhankun Liu , Xiancheng Mao , Jie Yang , Min Deng
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引用次数: 0

Abstract

Autoencoder is a powerful tool for identifying multivariate geochemical anomalies. However, existing autoencoder-based geochemical anomaly detection methods primarily rely on a global reconstruction error (e.g., mean square error) to define the lower limit of geochemical anomalies, neglecting the common, local structure information of geochemical data. This limitation inevitably results in the decreased accuracy of geochemical anomaly identification. This study proposed a local Phase-Constrained Convolutional AutoEncoder network (PC-CAE) for the identification of multivariate geochemical anomalies. Initially, we employed a local Fourier transform to extract phase information from both the original and the reconstructed data. Subsequently, a convolutional autoencoder network was utilized to learn the latent representation of geochemical background, using the local phase difference between the original and reconstructed data to preserve the local data structure related to geology setting. Additionally, an adaptive weighting strategy was employed to mitigate the overfitting issue. The training samples with high reconstruction errors were finally identified as anomalies. We tested the validity of PC-CAE using the stream sediment geochemical dataset collected in the Jiaodong gold province, Eastern China. The results demonstrated that PC-CAE outperforms existing convolutional autoencoder network and spectrum–area multifractal model in identifying multivariate geochemical anomalies associated with Au mineralization.

用于识别多元地球化学异常的局部相位约束卷积自动编码器网络
自动编码器是识别多元地球化学异常的强大工具。然而,现有的基于自动编码器的地球化学异常检测方法主要依靠全局重构误差(如均方误差)来定义地球化学异常的下限,忽略了地球化学数据的共性、局部结构信息。这种局限性必然导致地球化学异常识别的准确性下降。本研究提出了一种用于识别多元地球化学异常的局部相位约束卷积自动编码器网络(PC-CAE)。首先,我们采用局部傅立叶变换从原始数据和重建数据中提取相位信息。随后,利用卷积自动编码器网络来学习地球化学背景的潜在表示,利用原始数据和重建数据之间的局部相位差来保留与地质环境相关的局部数据结构。此外,还采用了自适应加权策略来缓解过拟合问题。重建误差较大的训练样本最终被识别为异常。我们使用在中国东部胶东黄金省采集的溪流沉积物地球化学数据集测试了 PC-CAE 的有效性。结果表明,在识别与金矿化相关的多元地球化学异常方面,PC-CAE 优于现有的卷积自动编码器网络和谱区多分形模型。
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来源期刊
Computers & Geosciences
Computers & Geosciences 地学-地球科学综合
CiteScore
9.30
自引率
6.80%
发文量
164
审稿时长
3.4 months
期刊介绍: Computers & Geosciences publishes high impact, original research at the interface between Computer Sciences and Geosciences. Publications should apply modern computer science paradigms, whether computational or informatics-based, to address problems in the geosciences.
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