[S-EM15] Electric, magnetic and electromagnetic survey technologies and scientific achievements

[SEM15-13] Investigation of signal discrimination method for Noisy MT data

*Shu Kaneko¹, Toru Mogi², Chie Yoshino², Katsumi Hattori^2,3,4 (1.Graduate School of Science and Engineering, Chiba University, 2.Graduate School of Science, Chiba University, 3.Center for Environmental Remote Sensing, Chiba University, 4.Disaster Medicine Research Institute, Chiba University)

Keywords:MT method, Multi-channel Singular Spectrum Analysis (MSSA), Noise reduction

The MT method estimates electrical resistivity from the ratio of the natural electromagnetic field variation. However, the data observed around high human activity includes artificial noise (e.g., the leak current from the DC-driven railways). The noise affects the data in the frequency domain because the waveform of the noise appears rectangular-like. Moreover, the noise collapses the assumption that the MT source comes from a far field. Using the conventional MT noise reduction method, the estimated MT response shows the noise response and /or makes the estimation error large.

To estimate the reasonable MT response, we tried to develop a new noise reduction method in the time domain based on MSSA (Multi-channel Singular Spectrum Analysis) as a preprocess before the conventional noise reduction method in the frequency domain. MSSA is the decomposition method for non-stationary time series. In this sense, MSSA is suitable for MT data.

On the other hand, a wavelet transform-based method using wavelet transform was developed to discriminate the ULF band electromagnetic signal. In this presentation, we consider and will suggest the appropriate signal discrimination method for noisy data by investigating the performance of relatively clean data (Kakioka (Japan Meteorological Agency) data sets) and noisy data at the Boso peninsula.