Effective denoising of magnetotelluric (MT) data using a combined wavelet method

• Autorzy: Ling Zhenbao , Wang Peiyuan , Wan Yunxia , Li Tonglin

Identyfikatory

DOI

10.1007/s11600-019-00296-0

• Języki publikacji: EN

Abstrakty

EN

Noise interference, especially from human noise, seriously affects the quality of magnetotelluric (MT) data. Strong human noise distorts the apparent resistivity curve, known as the near-source effect, causing poor reliability of MT data inversion. Based on analyzing the frequency characteristics of human noise resulting from the surrounding environment, a new waveletbased denoising method is proposed for both synthetic and real MT data in this paper. The new technique combines multiresolution analysis with a wavelet threshold algorithm based on Bayes estimation and has a remarkable effect on denoising at all band frequencies. The multi-resolution analysis method was employed to reduce long-period noise, and a wavelet threshold algorithm was used to eliminate strong high-frequency noise. In this research, the improved algorithm was assessed via simulated experiments and field measurements with regard to the reduction in human noises. This study demonstrates that the new denoising technique can increase the signal-to-noise ratio by at least 112% and provides an extensive analysis method for mineral resource exploration.

Słowa kluczowe

EN

wavelet transform; apparent resistivity; Combined Denoising Method; magnetotellurics

PL

transformacja falkowa; oporność pozorna

• Wydawca: Instytut Geofizyki PAN ; Springer
• Czasopismo: Acta Geophysica
• Rocznik: 2019
• Tom: Vol. 67, no. 3
• Strony: 813--824
• Opis fizyczny: Bibliogr. 27 poz.

Twórcy

autor

Ling Zhenbao

• College of Instrumentation & Electrical Engineering, Jilin University, Changchun, China

autor

Wang Peiyuan

• Shanxi Engineering Vocational College, Taiyuan, China

autor

Wan Yunxia

• College of Instrumentation & Electrical Engineering, Jilin University, Changchun, China

autor

Li Tonglin

• College of Geoexploration Science & Technology, Jilin University, Changchun, China

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).