Random noise suppression method for parallel epicentre seismic data based on PCA-EMD

Abstract

The invention relates to a random noise suppression method for parallel epicentre seismic data based on PCA-EMD. According to the method, firstly, the spectral range of a useful signal is estimated according to spectrum analysis, secondly, modal components dominated by the useful signal in modal components obtained through EMD decomposition are selected according to the useful signal spectrum range, thirdly, the modal components dominated by the useful signal and the remainder are reconstructed to obtain the reconstruction result, and lastly, a Hankel matrix is constructed for the reconstruction result based on the phase space theory, and PCA decomposition and recovery of the useful signal are further carried out. As verified, the data processing speed is fast, compared with an EMD suppression random noise method, the random noise can be suppressed in the whole frequency range, in the signal-noise band aliasing process, not only can noise energy be suppressed, but also signal details can be effectively protected; through the good signal-to-noise ratio improvement capability, the target data positioning error after processing is smaller, data processing cost is reduced, the qualityof the parallel epicentre seismic data can be effectively improved, and the method is better than other methods under strong voice conditions.

Description

Technical field: [0001] The invention relates to a seismic data processing method in geophysical prospecting, in particular to a PCA-EMD-based method for suppressing random noise of parallel seismic source seismic data. Background technique: [0002] In order to improve efficiency and reduce costs, the current seismic exploration technology is developing from single-source seismic exploration to parallel seismic exploration. However, parallel source seismic exploration is often affected by random noise during the acquisition process, and the acquired parallel seismic data is often accompanied by random noise, so the quality of the obtained seismic data is not high, which affects the later seismic data processing and interpretation and migration imaging quality . Parallel source seismic data involves a wide range of space and a large time span, resulting in large differences in energy strength and stability of random noise from different seismic traces. However, the random ...

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Problems solved by technology

However, parallel source seismic exploration is often affected by random noise during the acquisition process, and the acquired parallel seismic data is often accompanied by random noise, so the quality of the obtained seismic data is not high, which affects the later seismic data processing and interpretation and migration imaging quality

Parallel source seismic data involves a wide range of space and a large time span, resulting in large differences in energy strength and stability of random noise from different seismic traces. However, the random noise received by the conventional single-source exploration method is relatively stable in terms of energy intensity, stationarity, and noise frequency distribution. The significant difference in the random noise received by the two exploration methods makes the conventional single-source seismic data Noise suppression method is not suitable for parallel source seismic data noise suppression

At present, for the parallel seismic source acquisition method, the signal-to-noise ratio can only be improved by increasing the number of coverages, but this method will significantly increase the acquisition cost

However, there are few literatures that specialize in random noise suppression methods for parallel seismic source data. In other fields, there are two main types of random noise suppression methods: frequency domain methods and time domain methods. Frequency domain methods such as wavelet filtering and Wiener filtering are in Noise suppression in the frequency domain, although this type of method is simple, but when the useful signal frequency band and the noise frequency band are aliased, this type of method is easy to damage the useful signal details

Although time-domain methods such as SVD and K-L can effectively protect useful signal details, these methods are only suitable for weak random noise conditions

It can be seen that the above methods are not suitable for the suppression of random noise in parallel seismic source data.

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