Marine controlled-source electromagnetic method data de-noising based on compressive sensing

Abstract

The marine controlled-source electromagnetic method (CSEM) is a detection technology predominately used for hydrocarbon exploration and reservoir estimation. However, the signal can be easily contaminated by different types of noises. De-noising is a crucial procedure for improving data accuracy and credibility. Presently, frequently used de-noising methods are the classical windowed Fourier transform and wavelet transform methods. These methods are only effective for Gaussian white noises, and they are not suitable for marine CSEM data. Compressive sensing (CS), a new digital data processing technology, is a new method for solving this problem. By using the orthogonal matching pursuit algorithm with different dictionaries, CS provides an effective way to reduce noise impacts. This work presents the first application of CS to marine CSEM data de-noising, and the outcomes under different dictionaries are compared with those of traditional methods. The de-noising performance of CS is closely related to the selected dictionary, and we separately analyze the results of the discrete sine transform dictionary, wavelet dictionary, and three merged dictionaries. The results illustrate that the CS algorithm under certain dictionaries performs better than the windowed Fourier transform and wavelet transform methods.