.png){kind=icon}
返回
Local phase-constrained convolutional autoencoder network for identifying multivariate geochemical anomalies
.png){kind=icon}
PRE
AI
DOI：10.1016/j.cageo.2024.105679.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 (PCCAE) 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.
Keyword:
Geochemical exploration
Geochemical anomalies
Local phase constraint
Convolutional autoencoder
Jiaodong peninsular
期刊
C
IF:
4.4
论文数:
5.0K
被引数:
1.5W
机构
引用论文
Improving Flash memory endurance and consumption with ultra-short channel-hot-electron programming pulses通过超短沟道热电子编程脉冲改善闪存耐久性和功耗
.png){kind=icon}
PREAI
分享收藏分享收藏Interpretation of Radio-Echo Returns from Internal Water Bodies in Variegated Glacier, Alaska, U.S.A.美国阿拉斯加杂色冰川内部水体的无线电回波回波解释
OAAI
OAAI分享收藏Identifying multivariate geochemical anomalies via tensor dictionary learning over spatial-elemental dimensionalities通过张量字典学习在空间元素维度上识别多元地球化学异常
PREAI
分享收藏
分享收藏
查看更多内容