Study On Marine Controlled-Source Electromagnetic Signal Processing

Marine controlled-source electromagnetic (MCSEM) method can detect the hydrocarbon reservoir effectively, but there is not a fundamental breakthrough in China. In order to enhance signal-to-noise of initial data and provide high quality data for subsequent inversion and interpretation, this paper puts forward a set of Marine CSEM data process methods and uses MATLAB language to accomplish Marine CSEM data process visual interface.This paper employs polarized ellipse analysis and component rotation for the correction of receiver direction, while, utilizes hypothetic model to suppress the noise in the air wave. Because Marine CSEM signals are nonlinear and non-stationary, we introduces Hilbert-Huang transformation time-frequency method to Marine CSEM data processing based on the fact that Hilbert-Huang Transformation is an entirely local time-frequency analysis method developed in recent years for dealing with non-linear , non-stationary signals. And it has been proved that selecting signals using Hilbert time-frequency energy spectrum is helpful to effectively suppress most of the noise. And Wavelet Analysis Technique is adopted in this paper to suppress the residual noise on the basis of Hilbert-Huang Transformation, afterward, selecting signals by using Hilbert-Huang Transformation once again, finally enhancing the quality of data processing and information's interpretability.Motion of ocean waves will generate electromagnetic field, which is the one of the main noises in Marine electromagnetic signals. It will affect the accuracy of signals processing and interpretation. In order to improve the effect of the application of marine electromagnetic exploration, this paper introduces Hilbert-Huang transformation time-frequency method to Marine MT signal processing. Based on the character of noises and signals of Marine MT from South China Sea, we compare with the Hilbert time-frequency spectrum and Hilbert Marginal spectrum of original signals and signals after noise suppression. The result shows that using empirical mode decomposition method and multi-scale filter characteristics can effectively suppress the noises generated by the motion of ocean waves. The sounding curves after denoise through HHT is better than the original curves to some degrees. However, there still exists the so called "flying spots". In such case, we resort to the Robust estimation method, and the corresponding result is much more satisfying.