Japan Geoscience Union Meeting 2022

Presentation information

[E] Poster

S (Solid Earth Sciences ) » S-EM Earth's Electromagnetism

[S-EM14] Electric, magnetic and electromagnetic survey technologies and scientific achievements

Tue. May 31, 2022 11:00 AM - 1:00 PM Online Poster Zoom Room (21) (Ch.21)

convener:Kiyoshi Baba(Earthquake Research Institute, The University of Tokyo), convener:Tada-nori Goto(Graduate School of Life Science, University of Hyogo), Toshihiro Uchida(0), convener:Yuguo Li(Ocean University of China), Chairperson:Kiyoshi Baba(Earthquake Research Institute, The University of Tokyo), Tada-nori Goto(Graduate School of Science, University of Hyogo)

11:00 AM - 1:00 PM

[SEM14-P02] Adaptive filtering estimation of magnetotelluric impedance tensor: Preliminary results

*Yunju Wu1, Yuguo Li1,2 (1.Ocean University of China, 2.Key Lab of Submarine Geosciences and Prospecting Techniques of Ministry of Education)

Keywords:Adaptive filtering algorithm, data processing, Impedance tensor, Magnetotelluric

For estimating the impedance tensor elements of magnetotelluric data, a variety of methods have been developed since 1970s. The least square estimation is the optimal unbiased estimation if errors in MT data are uncorrelated and independent gaussian noise. The robust estimation can eliminate the influence of outliers and leverage points in MT field data, while the remote reference method can reduce the impact of local noise in MT dada. With the use of these estimation methods, one can obtain MT impedance tensor at a number of frequencies, which often are equal distributed at logarithmical scale. The impedance tensor at each frequency is estimated independently. In this paper, we propose a method for estimating MT impedance tensor based on an adaptive filtering algorithm. With the use of this method, the impedance tensors at all frequencies can be obtained and the estimated value of impedance tensor at a frequency is related to that at its neighboring frequency. Some preliminary results of both synthetically and measured MT data will be presented and compared with these obtained by using robust-m method.