降噪
计算机科学
神经编码
稀疏逼近
人工智能
编码(社会科学)
词典学习
噪音(视频)
模式识别(心理学)
大地电磁法
水准点(测量)
K-SVD公司
数据挖掘
地质学
图像(数学)
数学
工程类
电阻率和电导率
统计
大地测量学
电气工程
作者
Jin Li,Luo Yu-cheng,Guang Li,Yecheng Liu,Jingtian Tang
出处
期刊:Geophysics
[Society of Exploration Geophysicists]
日期:2024-01-19
卷期号:: 1-59
标识
DOI:10.1190/geo2023-0205.1
摘要
Audio magnetotelluric (AMT), as a commonly used passive geophysical technique, provides outstanding metal ore exploration capabilities based on the resistivity structure of the Earth. However, the accuracy of AMT in translating geoelectrical structures decreases when the data collected in mining areas are of poor data quality and contain complex anthropogenic noise, leading to distorted apparent resistivity-phase curves and posing significant challenges for mineral exploration. To effectively denoise AMT data, we propose a new denoising method that combines atom-profile updating dictionary learning (APrU) with nucleus sampling attention mechanism sparse coding (NSAM). First, we use APrU to accurately learn the characteristics of the noise in the AMT data; then, we apply the updated dictionary to perform sparse coding on the AMT data by NSAM to obtain the noise; finally, we subtract the noise from the original AMT data to obtain the denoised data. Our experimental results suggest that the proposed method can learn an over-complete dictionary via the to-be-processed AMT data, thereby enabling the sparse representation of the noise within the learned dictionary. We also demonstrate the efficacy of this method with a set of field data collected from the Lu-zong mining area, and the attained denoised data faithfully restores the geoelectrical structures with heightened accuracy. The findings confirm that the proposed method realizes the unsupervised learning of the AMT data and allows us to achieve precise denoising performance.
科研通智能强力驱动
Strongly Powered by AbleSci AI