Based on the analysis of impedance tensor data, tipper data, and the conjugate gradient algorithm, we develop a three-dimensional （3D） conjugate gradient algorithm for inverting magnetotelluric full information data...Based on the analysis of impedance tensor data, tipper data, and the conjugate gradient algorithm, we develop a three-dimensional （3D） conjugate gradient algorithm for inverting magnetotelluric full information data determined from five electric and magnetic field components and discuss the method to use the full information data for quantitative interpretation of 3D inversion results. Results from the 3D inversion of synthetic data indicate that the results from inverting full information data which combine the impedance tensor and tipper data are better than results from inverting only the impedance tensor data （or tipper data） in improving resolution and reliability. The synthetic examples also demonstrate the validity and stability of this 3D inversion algorithm.展开更多
Computation of impedance tensor elements is one of the important steps in magnetotelluric data processing. Conventionally, the impedance tensor is defined as a 2 x 2 matrix with Zxx, Zxy, Zyx, and Zyy as elements. In ...Computation of impedance tensor elements is one of the important steps in magnetotelluric data processing. Conventionally, the impedance tensor is defined as a 2 x 2 matrix with Zxx, Zxy, Zyx, and Zyy as elements. In the present study, the six-element impedance tensor is computed with a 2 × 3 matrix using Zxx, Zxy, Zyx, Zyy, Zxz, and Zyz. The properties of the impedance tensor elements have been analyzed for these above two types. The methodology has been tested with five component magnetotelluric data from the Kutch sedimentary basin, Gujarat, India. From the computation of apparent resistivity computation and phase we observed that there is small difference between the four and six impedance elements of Zxy and Zyx for most of the frequency band. However for longer period data, more than 100 sec, an increase in the apparent resistivity and decrease in the phase is observed. We also note that the tipper magnitude is nearly zero for most of the periods, but gradually shows an increasing trend for longer periods （〉100 see）. The Kutch sedimentary basin geoeleetric section shows near horizontal layers at shallow depths and anomalous high conductivity heterogeneous layers at deeper depths may be responsible for the large Hz component at longer periods. This indicates that the vertical component of the magnetic field, Hz, does play an important role in the estimation of electric field parameters in the region with large 2D/3D structures.展开更多
To better retain useful weak low-frequency magnetotelluric(MT)signals with strong interference during MT data processing,we propose a SVM-CEEMDWT based MT data signal-noise separation method,which extracts the weak MT...To better retain useful weak low-frequency magnetotelluric(MT)signals with strong interference during MT data processing,we propose a SVM-CEEMDWT based MT data signal-noise separation method,which extracts the weak MT signal affected by strong interference.First,the approximate entropy,fuzzy entropy,sample entropy,and Lempel-Ziv(LZ)complexity are extracted from the magnetotelluric data.Then,four robust parameters are used as the inputs to the support vector machine(SVM)to train the sample library and build a model based on the different complexity of signals.Based on this model,we can only consider time series with strong interference when using the complementary ensemble empirical mode decomposition(CEEMD)and wavelet threshold(WT)for noise suppression.Simulation results suggest that the SVM based on the robust parameters can distinguish the time periods with strong interference well before noise suppression.Compared with the CEEMD WT,the proposed SVM-CEEMDWT method retains more low-frequency low-variability information,and the apparent resistivity curve is smoother and more continuous.Moreover,the results better reflect the deep electrical structure in the field.展开更多
基金supported by the National Hi-tech Research and Development Program of China（863Program）（No.2007AA09Z310） National Natural Science Foundation of China（Grant No.40774029 40374024）+1 种基金 the Fundamental Research Funds for the Central Universities（Grant No.2010ZY53） the Program for New Century Excellent Talents in University（NCET）
文摘Based on the analysis of impedance tensor data, tipper data, and the conjugate gradient algorithm, we develop a three-dimensional （3D） conjugate gradient algorithm for inverting magnetotelluric full information data determined from five electric and magnetic field components and discuss the method to use the full information data for quantitative interpretation of 3D inversion results. Results from the 3D inversion of synthetic data indicate that the results from inverting full information data which combine the impedance tensor and tipper data are better than results from inverting only the impedance tensor data （or tipper data） in improving resolution and reliability. The synthetic examples also demonstrate the validity and stability of this 3D inversion algorithm.
文摘Computation of impedance tensor elements is one of the important steps in magnetotelluric data processing. Conventionally, the impedance tensor is defined as a 2 x 2 matrix with Zxx, Zxy, Zyx, and Zyy as elements. In the present study, the six-element impedance tensor is computed with a 2 × 3 matrix using Zxx, Zxy, Zyx, Zyy, Zxz, and Zyz. The properties of the impedance tensor elements have been analyzed for these above two types. The methodology has been tested with five component magnetotelluric data from the Kutch sedimentary basin, Gujarat, India. From the computation of apparent resistivity computation and phase we observed that there is small difference between the four and six impedance elements of Zxy and Zyx for most of the frequency band. However for longer period data, more than 100 sec, an increase in the apparent resistivity and decrease in the phase is observed. We also note that the tipper magnitude is nearly zero for most of the periods, but gradually shows an increasing trend for longer periods （〉100 see）. The Kutch sedimentary basin geoeleetric section shows near horizontal layers at shallow depths and anomalous high conductivity heterogeneous layers at deeper depths may be responsible for the large Hz component at longer periods. This indicates that the vertical component of the magnetic field, Hz, does play an important role in the estimation of electric field parameters in the region with large 2D/3D structures.
基金funded by the National Key R&D Program of China(No.2018YFC0603202)the National Natural Science Foundation of China(No.41404111)+1 种基金Natural Science Foundation of Hunan Province(No.2018JJ2258)Hunan Provincial Science and Technology Project Foundation(No.2018TP1018)
文摘To better retain useful weak low-frequency magnetotelluric(MT)signals with strong interference during MT data processing,we propose a SVM-CEEMDWT based MT data signal-noise separation method,which extracts the weak MT signal affected by strong interference.First,the approximate entropy,fuzzy entropy,sample entropy,and Lempel-Ziv(LZ)complexity are extracted from the magnetotelluric data.Then,four robust parameters are used as the inputs to the support vector machine(SVM)to train the sample library and build a model based on the different complexity of signals.Based on this model,we can only consider time series with strong interference when using the complementary ensemble empirical mode decomposition(CEEMD)and wavelet threshold(WT)for noise suppression.Simulation results suggest that the SVM based on the robust parameters can distinguish the time periods with strong interference well before noise suppression.Compared with the CEEMD WT,the proposed SVM-CEEMDWT method retains more low-frequency low-variability information,and the apparent resistivity curve is smoother and more continuous.Moreover,the results better reflect the deep electrical structure in the field.
