A Signal-Noise Separation Method for Magnetotelluric Impedance Tensor Estimate Improvement
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摘要: 在强噪声环境下，由于相关噪声的影响，准确地估算大地电磁张量阻抗越来越困难.研究一种处理相关噪声问题的信噪分离方法，即采用最小二乘Robust方法，利用相关噪声相对较小的远参考站磁场信号求取分离张量，将本地磁场的观测信号分离为MT信号和噪声，估算大地电磁张量阻抗.分别对模拟数据与实测数据进行处理, 结果表明，信噪分离方法能够分离出较强的相关噪声，给出的视电阻率和相位曲线比最小二乘Robust方法和远参考方法获得的更加平滑，处理效果明显.研究表明，在处理受相关噪声影响的MT数据中，信噪分离方法具有明显的优势.Abstract: It is difficult to estimate precise magnetotelluric (MT) impedance tensor in high correlated noise areas. In order to approximate MT impedance tensor, we use remote sites which contain relatively lower correlated noise to compute separation tensor by using Robust least square method. The MT data in three sites which are contaminated by higher and lower correlated noises are processed using signal-noise separation method. The results show that it can separate higher correlated noise and get much smoother apparent resistivity and phase curves than those obtained by using Robust least square method and remote reference method. It is concluded that signal-noise separation method is efficient for much more precise MT impedance tensor.
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Key words:
- signal-noise separation /
- magnetotellurics /
- impedance tensor /
- geophysics
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图 1 信噪分离方法总体流程
Fig. 1. Flow chart for the basic steps used to compute impedance tensor
图 2 原始时间序列的视电阻率和相位曲线
a.Robust方法；b.信噪分离方法
Fig. 2. Apparent resistivity and phase estimates of original time series
图 3 噪声占10%的时间序列的视电阻率和相位曲线
a.Robust；b.信噪分离方法
Fig. 3. Apparent resistivity and phase estimates of time series (10% noise)
图 4 噪声占20%的时间序列的视电阻率和相位曲线
a.Robust方法；b.信噪分离方法
Fig. 4. Apparent resistivity and phase estimates of time series (20% noise)
图 5 实例1中Robust (a)、远参考方法(c)和信噪分离方法(相关噪声(b)、MT信号(d))比较
Fig. 5. Comparison of the results obtained from robust least square(a), robust remote reference(c) and signal-noise separation method (correlated noise(b), MT signal(d)) respectively in the first example
图 6 实例2中Robust方法(a)、远参考方法(b)和信噪分离方法(c)比较
Fig. 6. Comparison of the results obtained from robust least square (a), robust remote reference (b) and signal-noise separation method(c) respectively in the second example
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