The Method Of Seismic Random Noise Blind Separation Based On ICA

In seismic exploration, seismic data denoising is almost throughout each aspect in seismic data processing. Especially in complex geological environments, records of seismic exploration often contain strong background noise, which demands Seismic data processing work reach higher level and will directly affect the reliability of seismic data and the accuracy of parameter extraction, etc. So how to improve the signal to noise ratio of seismic data gradually becomes a goal. This paper studies the method of the elimination of random noise in the seismic exploration.ICA is a new multi-dimensional signal processing method based on high level statistics, which is able to achieve separation of source signals in the absence of priori information. Observed signal will be established the objective function in accordance with the principles of statistics independence. Through the optimization algorithm, observed signal will be divided into a number of independent signal components, thus helping to enhance and analyze the signals.Seismic data usually contains random noise, it is generated from a wide variety of unpredictable factors together and isn't regular. This article applies ICA to remove random noise based on the seismic exploration data analysis, and sets up the ICA model for random noise blind separation, and analyzes the assumptions and inherent uncertainties of ICA. This paper uses the improved preprocessing algorithm first remove the additive white Gaussian Noise (AWGN), and then uses JADE algorithm to process the preprocessed data and blindly separate the effective signal from non-Gaussian distribution of random noise. Furthermore, the paper sets up criteria to identify effective signal to resolve the order uncertainty problem in ICA and achieve an effective signal extraction. Simulation experiments and the actual seismic data processing experiments show that the algorithm proposed in this paper effectively remove the random noise.