5:15 PM - 6:30 PM
[MIS08-P06] Development of Noise Reduction Method using two-stage MSSA (Multichannel Singular Spectrum Analysis) for MT data in Boso, Japan, Evaluation in Time series
Keywords:MT method, Multi-channel Singular Spectrum Analysis, Noise reduction, Boso peninsula
There is a triple junction of the Pacific Plate, the Philippine Sea Plate, and the North American Plate off the coast of the Boso Peninsula and it is one of the most active areas of the crustal activity. To investigate the subsurface resistivity structure of the whole Boso area, magnetotelluric (MT) survey with 41 sites (inter-sites distance of 7 km) has been conducted in 2014-2016, using U43 (12 sites, 1 Hz sampling ; Tierra Technica) and MTU-5, 5A, net (41 sites, 15, 150, and 2400 Hz sampling; Phoenix Geophysics). However, the Boso area is greatly affected by leak current from DC-driven trains, factories, and power lines, so the observed data are severely contaminated by artificial noises. When we tried to apply the conventional noise reduction methods (e.g., remote reference (Gamble et al., 1979) and BIRRP (Chave and Thomson, 2004)) in frequency domain, the obtained MT sounding curve was not ideal. In particular, the phases between the periods of 20 and 400 sec were close to 0 degrees. It suggests that these conventional methods are insufficient to reduce the near-field effects for the Boso data. Thus, we developed a new noise reduction method using MSSA (Multi-channel Singular Spectrum Analysis) as a time domain pre-processing method.
The procedure is as follows;
(1) Decompose 6 component data (Hx, Hy, Ex, Ey, Hxr and Hyr: H and E means magnetic and electric field, respectively, x and y indicates NS and EW component, and r denotes the reference field observed at a quiet station) using MSSA into 6×L principal components (PCs). Here, L shows the window length of MSSA.
(2) Check each PC's contribution and periods and eliminate the PCs corresponding to the longer than 400 sec. That means "detrend" of the original data.
(3) Apply the second MSSA to the detrended time series data to separate signals and noises shorter than 400 sec.
(4) Calculate correlation coefficients between H and Hr and between E and Hr for each PC and select the PCs with higher correlation to "reconstruct" time series data to make MT analysis.
Then, we perform MT analysis by BIRRP to estimate MT response functions,
As a result, the MT sounding curve became smoother than those results by the conventional noise reduction methods. It indicates that the effectiveness of the proposed noise reduction.
In the presentation, we will discuss the evaluation of time series noise reduction in detail.
The procedure is as follows;
(1) Decompose 6 component data (Hx, Hy, Ex, Ey, Hxr and Hyr: H and E means magnetic and electric field, respectively, x and y indicates NS and EW component, and r denotes the reference field observed at a quiet station) using MSSA into 6×L principal components (PCs). Here, L shows the window length of MSSA.
(2) Check each PC's contribution and periods and eliminate the PCs corresponding to the longer than 400 sec. That means "detrend" of the original data.
(3) Apply the second MSSA to the detrended time series data to separate signals and noises shorter than 400 sec.
(4) Calculate correlation coefficients between H and Hr and between E and Hr for each PC and select the PCs with higher correlation to "reconstruct" time series data to make MT analysis.
Then, we perform MT analysis by BIRRP to estimate MT response functions,
As a result, the MT sounding curve became smoother than those results by the conventional noise reduction methods. It indicates that the effectiveness of the proposed noise reduction.
In the presentation, we will discuss the evaluation of time series noise reduction in detail.