Metallogenic model of the Shuangjianzishan Ag-Pb-Zn district, Northeast China: Revealed from integrated geophysical investigation

Wu, Yongli; Han, Jiangtao; Liu, Yunhe; Ma, Guoqing; Han, Fuxing; Yang, Yanchen; Liu, Lijia; Guo, Lei; Guan, Yisheng; Zhang, Yinghui; Guo, Zhenyu; Mu, Qian

doi:10.1016/j.gsf.2021.101321

Cited by 10 publications

(1 citation statement)

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“…Compared with controlled-source signals, natural MT signals have the advantages of a wide frequency range, deep exploration depth, and easy-to-use equipment. Therefore, MT is widely used in many fields, such as marine exploration, volcano monitoring, and geodynamic interpretation [1][2][3]. However, during the period of observation, MT data are vulnerable to distortions from electromagnetic wave emitted by high-voltage lines, communication radio stations, and underground mining machines around monitoring sites.…”

Section: Introductionmentioning

confidence: 99%

De-Noising of Magnetotelluric Signals by Discrete Wavelet Transform and SVD Decomposition

et al. 2021

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Magnetotelluric (MT) sounding data can easily be damaged by various types of noise, especially in industrial areas, where the quality of measured data is poor. Most traditional de-noising methods are ineffective to the low signal-to-noise ratio of data. To solve the above problem, we propose the use of a de-noising method for the detection of noise in MT data based on discrete wavelet transform and singular value decomposition (SVD), with multiscale dispersion entropy and phase space reconstruction carried out for pretreatment. No “over processing” takes place in the proposed method. Compared with wavelet transform and SVD decomposition in synthetic tests, the proposed method removes the profile of noise more completely, including large-scale noise and impulse noise. For high levels or low levels of noise, the proposed method can increase the signal-to-noise ratio of data more obviously. Moreover, application to the field MT data can prove the performance of the proposed method. The proposed method is a feasible method for the elimination of various noise types and can improve MT data with high noise levels, obtaining a recovery in the response. It can improve abrupt points and distortion in MT response curves more effectively than the robust method can.

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Section: Introductionmentioning

confidence: 99%