ADMM-Based Method for Estimating Magnetotelluric Impedance in the Time Domain

Guo, Zhenyu; Han, Jiangtao; Gong, Xun; Liu, Lijia; Zhou, Rui; Wu, Yongli

doi:10.1109/tgrs.2022.3171768

Cited by 7 publications

(2 citation statements)

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“…Existing time series processing techniques, such as the least-square method (Sims et al, 1971), remote reference method (Goubau et al, 1978;Gamble et al, 1979b;Gamble et al, 1979a), robust estimation method (Egbert and Booker, 1986;Chave et al, 1987;Larsen et al, 1996;Smirnov, 2003;Chave and Thomson, 2004), maximum likelihood estimation method (Chave, 2014;Chave, 2017), and others methods based on wavelet transform, 10.3389/feart.2023.1086749 Hilbert-Huang transform, variational mode decomposition, and interstation transfer function (Kappler, 2012;Cai, 2014;Campanya et al, 2014;Cai and Chen, 2015;Carbonari et al, 2017;Wang et al, 2017), have different processing performance for different kinds of noise. For example, the least-square method performs poorly in the presence of outliers (Egbert and Booker, 1986), and the remote reference method does not work when noise is correlated at the local and remote sites (Shalivahan and Bhattacharya, 2002;Pomposiello et al, 2009), and the robust estimation method usually fails to work when MT data are contaminated by persistent or coherent noises (Escalas et al, 2013;Carbonari et al, 2017;Li et al, 2020a;Li et al, 2020c;Zhou et al, 2022;Zhang et al, 2022). New time series processing methods for various types of noise are the focus of current research, but there is a lack of criteria to evaluate the effectiveness of various MT time series processing methods in existing studies.…”

Section: Introductionmentioning

confidence: 99%

“…The traditional evaluation method based on curve continuity is proved to be unreliable (Sutarno, 2005). Many studies take the measured low-noise data as the standard data, but because the real response is unknown, it is insufficient to prove the effectiveness of the time series processing method (Li et al, 2020a;Li et al, 2020b;Guo et al, 2022;Zhou et al, 2022). Therefore, reliable standard time series are urgently needed to test the effectiveness of various time series processing methods.…”

Section: Introductionmentioning

confidence: 99%

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Synthesizing magnetotelluric time series based on forward modeling

Wang

Chen²,

Zhang³

2023

Front. Earth Sci.

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The validity of magnetotelluric time-series processing methods has been lacking reasonable testing criteria. Since the time series synthesized by existing techniques are not fully derived from a given model, they are not reliable. In this paper, we present a novel approach to synthesize magnetotelluric time series based on forward modeling and the correspondence between frequency and time domain electromagnetic fields. In this approach, we obtain the electromagnetic response of two orthogonal polarization sources for a given model by magnetotelluric forward modeling, and simulate the randomness of the polarization of the natural field source by a linear combination of the two polarization sources. Based on the correspondence between the frequency and time domain electromagnetic fields, the electromagnetic fields obtained by forward modeling in the frequency domain are transformed into the time domain, and finally the time series are synthesized. The test results on 1D and 3D models validate the effectiveness of the proposed method and the correctness of the procedure. After adding noise to the synthesized time series, we can test the performance of each method by comparing the results of the time series processing methods with the response of the given model. Therefore, the method presented in this paper can be used to construct standard magnetotelluric time series, which can be used as a carrier to construct synthetic data satisfying various noise distributions, and for the study of related methods. This method can also be used to synthesize time series of other frequency-domain electromagnetic methods.

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