De-noising magnetotelluric data based on machine learning

IF 2.2 3区 地球科学 Q2 GEOSCIENCES, MULTIDISCIPLINARY Journal of Applied Geophysics Pub Date : 2024-10-17 DOI:10.1016/j.jappgeo.2024.105538
Tuanfu Gui , Juzhi Deng , Guang Li , Hui Chen , Hui Yu , Min Feng
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Abstract

The magnetotelluric (MT) sounding is a common geophysical exploration technique, but it is highly polluted by various types of cultural noise. In the realm of MT data processing, traditional techniques often rely on the quality of the measured MT data. Conventional MT time domain denoising methods tend to eliminate valuable signals, potentially leading to unreliable resistivity estimates. To address this concern, we propose employing machine learning to effectively suppress strong noise interference in MT data, thereby preventing the loss of valuable signals. We augment this approach with mathematical morphological filtering (MMF) to capture low-frequency signals, preserving their integrity. We constructed a signal sample library based on a substantial volume of signal samples. Through consistent training, we establish a support vector machine (SVM) classification model that distinguishes high-quality signal fragments from noisy signals. Subsequently, we use adaptive K-singular value decomposition (K-SVD) dictionary learning to extract noise profiles and suppress noisy signals. To validate the feasibility of our method, we apply machine learning to measured data from two distinct observation areas. The measured data were analyzed and processed, and the results were compared with the robust results. This method can effectively eliminate large-scale strong interference in time domain sequences and preserve more low-frequency slow change information and high-quality signals in the reconstructed signals. The apparent resistivity phase curve of synthetic data is smoother and more continuous, and the data quality in the low-frequency range is significantly improved. The results can more accurately and reliably reflect underground electrical structure information.
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基于机器学习的磁突扰数据去噪技术
磁电探测(MT)是一种常见的地球物理勘探技术,但它受到各种文化噪音的严重污染。在 MT 数据处理领域,传统技术通常依赖于测量 MT 数据的质量。传统的 MT 时域去噪方法往往会消除有价值的信号,从而可能导致不可靠的电阻率估算。为了解决这个问题,我们建议采用机器学习来有效抑制 MT 数据中的强噪声干扰,从而防止丢失有价值的信号。我们通过数学形态学滤波 (MMF) 来增强这种方法,以捕捉低频信号并保持其完整性。我们在大量信号样本的基础上构建了一个信号样本库。通过持续的训练,我们建立了一个支持向量机 (SVM) 分类模型,该模型可将高质量信号片段与噪声信号区分开来。随后,我们使用自适应 K-singular 值分解(K-SVD)字典学习来提取噪声轮廓并抑制噪声信号。为了验证我们方法的可行性,我们将机器学习应用于两个不同观测区域的测量数据。我们对测量数据进行了分析和处理,并将结果与鲁棒结果进行了比较。该方法能有效消除时域序列中的大尺度强干扰,并在重建信号中保留更多的低频慢变信息和高质量信号。合成数据的视电阻率相位曲线更加平滑、连续,低频范围的数据质量显著提高。结果能更准确、可靠地反映地下电气结构信息。
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来源期刊
Journal of Applied Geophysics
Journal of Applied Geophysics 地学-地球科学综合
CiteScore
3.60
自引率
10.00%
发文量
274
审稿时长
4 months
期刊介绍: The Journal of Applied Geophysics with its key objective of responding to pertinent and timely needs, places particular emphasis on methodological developments and innovative applications of geophysical techniques for addressing environmental, engineering, and hydrological problems. Related topical research in exploration geophysics and in soil and rock physics is also covered by the Journal of Applied Geophysics.
期刊最新文献
Magnetic diagnosis model for heavy metal pollution in beach sediments of Qingdao, China An improved goal-oriented adaptive finite-element method for 3-D direct current resistivity anisotropic forward modeling using nested tetrahedra Deep learning-based geophysical joint inversion using partial channel drop method Advanced predictive modelling of electrical resistivity for geotechnical and geo-environmental applications using machine learning techniques Editorial Board
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