| The magnetotelluric strong interference separation technology has always been the research hot and difficulty. So far, it has made a lot of achievements, but with the continuous development of human civilization, environmental noise caused by heavy industry intensive factors and humanities electromagnetic noise caused by human activities factors are growing more and more serious, result in magnetotelluric sounding data are serious pollution and magnetotelluric faced enormous difficulties. Existed magnetotelluric strong interference separation methods show many deficiencies in the practical application and measure. The difficulties and challenges are also increasing in this field. Therefore, in order to improve the quality of magnetotelluric sounding data, suppress the noise interference has become imperative. Studied the magnetotelluric strong interference characteristics and proposed the specific method, are the important significance to improve the magnetotelluric sounding data quality, and the processing and interpretation of the magnetotelluric method detection results. This work are co-funded by the National Scientific and Technological Project of Deep Probing on3D Structure and Geodynamic Process of Ore District (SinoProbe-03) and the National Natural Science Foundation of China of the Research of Magnetotelluric Strong Interference Separation Method based on Mathematical Morphology (Grant No.41104071), and has important theoretical and practical significance using mathematical morphology theory study the magnetotelluric strong interference separation method.Based on the idea of mathematical morphology, we analyze the magnetotelluric strong interference separation and application, and focus on the traditional morphological filtering and the generalized morphological filtering as well as secondary signal-to-noise separation method of top-hat transformation, median filtering and signal subspace enhancement on the basis of morphological filtering. By means of theoretical analysis, simulation, and practical application, we carry out magnetotelluric strong interference separation research. The main contribution and innovation of this work are summarized as follows: (1) Study five typical magnetotelluric strong interference characteristics, and analyze the major noise sources in ore concentration area. Through add the similar square wave interference and the similar charge and discharge triangular wave interference to the measuring point, we study the quality of magnetotelluric data from both time-domain waveform and Cagniard resistivity curve impact on the typical noise.(2) Numerical simulate the typical single noise interference, and study the de-noising performance of different sizes and types of structural elements, moreover, discuss the selection rules of the structural elements sizes and types.(3) According to the V5-2000does not directly provide time series software, analyzing the instrument data acquisition format, realizing the magnetotelluric original material reading and restore. The work proposes the magnetotelluric signal-to-noise separation method based on traditional morphological filtering, and analyzes the de-noising performance of different type structural elements and the same type, different size structural elements.(4) The work constructs the combination generalized morphological filtering, and proposes magnetotelluric strong interference separation method based on the combination generalized morphological filtering. Qaidam basin in Qinghai Province, we carry out test research, and select a representative measuring point by using the combination generalized morphological filtering for processing. Compared with improvement situation both time domain waveform and Cagniard resistivity-phase curve, analyzed de-noising effect of measuring point including comparative single noise. Through the combination generalized morphological filtering to process the strong interference measuring point, comprehensive evaluated the noise suppression capability of strong interference measuring point including complex noise interference types.(5) On the basis of the mathematical morphological filtering, we propose magnetotelluric secondary signal-to-noise separation methods of top-hat transformation, median filtering and signal subspace enhancement. According to the noise contour or reconstructed signal extracted by morphological filtering, and further separated the useful signal which contains large-scale low frequency detail components. Using the secondary signal-to-noise separation to process the strong interference measuring point in ore concentrated area, we comparative analyze the Cagniard resistivity-phase curve improvement situation both the combination generalized morphological filtering and secondary signal-to-noise separation method, and comprehensive evaluate the advantages of the two methods on the reservation of low frequency slow change information, as well as the quality improvement effect for magnetotelluric sounding data.Through the above five aspects research, we show that, magnetotelluric strong interference separation method based on mathematical morphology can effectively eliminate large-scale interference and baseline drift for magnetotelluric strong interference, better restore the magnetotelluric original signal characteristics and improve the quality of magnetotelluric sounding data. Due to the mathematical morphology operation speed is fast, and it has potential advantages. The method provides a new solution for the separation of the mass magnetotelluric signals and strong interference in ore concentrated areas, and has broad application prospect.Finally, we summarize the main contents and innovations of this work, and discuss the deficiencies of mathematical morphology in the magnetotelluric strong interference separation, moreover, put forward some suggestions on the next stage of research work. |
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