New application of wavelets in magnetotelluric data processing: reducing impedance bias

Abstract

Magnetotelluric (MT) data consist of the sum of several types of natural sources including transient and quasiperiodic signals and noise sources (instrumental, anthropogenic) whose nature has to be taken into account in MT data processing. Most processing techniques are based on a Fourier transform of MT time series, and robust statistics at a fixed frequency are used to compute the MT response functions, but only a few take into account the nature of the sources. Moreover, to reduce the influence of noise in the inversion of the response functions, one often sets up another MT station called a remote station. However, even careful setup of this remote station cannot prevent its failure in some cases. Here, we propose the use of the continuous wavelet transform on magnetotelluric time series to reduce the influence of noise even for single site processing. We use two different types of wavelets, Cauchy and Morlet, according to the shape of observed geomagnetic events. We show that by using wavelet coefficients at clearly identified geomagnetic events, we are able to recover the unbiased response function obtained through robust remote processing algorithms. This makes it possible to process even single station sites and increase the confidence in data interpretation.

Theoretical Consideration on the Application of Continuous Wavelet Transform to Time-Series Processing for Magnetotelluric Data

“…As shown in Figure 5, the Paul function, whose width along the time axis is narrower than that of the Morlet function, satisfies the admissibility condition for any value of (De Moortel et al, 2004). Larnier et al(2016) reported that CWT with a wavelet with a high time resolution, such as the Paul function, can accurately detect the pulsive and transient signal, at frequencies above 7 Hz, where lightning activity is dominant. Thus, we investigated the effect of using the Paul function on the quality of MT responses obtained in the ULF band.…”

“…They proposed a method that contained both CWT and robust estimation algorithm. Larnier et al (2016) processed time-series data by applying different types of wavelet functions to the higher band (above about 7 Hz) and the lower band (below 0.1 Hz) respectively. They showed improvement in the quality of the spectral data by comparing the results of their proposed method with those of the BIRRP algorithm (Chave and Thomson, 2004).…”

Improved shift-invariant sparse coding for noise attenuation of magnetotelluric data

“…Robust statistic estimate method requires that most of the data should be noise-free, while some types of noise are persistent and appear during the entire observation process. In this case, the robust statistic method may lead to worse results (Escalas et al 2013;Campanya et al 2014;Larnier et al 2016;Tang et al 2018).…”

“…By removing noises in time domain, the time-series editing methods can directly and effectively improve Li et al Earth, Planets and Space (2020) 72:45 the quality of MT data (Trad and travassos 2000;Tang et al 2013Tang et al , 2018Neukirch and Garcia 2014;Larnier et al 2016). The most representative time-series editing method is the wavelet transform-based scheme.…”

Feasibility to Use Continuous Magnetotelluric Observations for Monitoring Hydrothermal Activity. Numerical Modeling Applied to Campi Flegrei Volcanic System (Southern Italy)

“…Although MT is widely used to characterize hydrothermal and geothermal systems (e.g., Stanley et al, 1977;Volpi et al, 2003;Heise et al, 2008;Newman et al, 2008;Munoz, 2014;Didana et al, 2015;Amatyakul et al, 2016;Peacock et al, 2016;Samrock et al, 2018), using MT as monitoring tool is still a new field of research, likely due to its sensitivity to anthropogenic noises. However, the advancements reached over the last few decades in the MT data processing make the method reliable also in presence of high level of cultural noise (e.g., Bahr, 1988;Egbert, 1997;Caldwell et al, 2004;Chave and Thomson, 2004;Weckmann et al, 2005;Larnier et al, 2016;Carbonari et al, 2018;Platz and Weckmann, 2019). To our knowledge, continuous MT measurements over a long period of time were carried out only by Aizawa et al (2011), who monitored temporal changes in electrical resistivity at Sakurajima volcano for 180 days.…”