The Application of S‐transform Spectrum Decomposition Technique in Extraction of Weak Seismic Signals
Authors: Gong DENG , Feng LIANG , Xiao‐Ting LI , Jun‐Meng ZHAO , Hong‐Bing LIU , Xun WANG , Chang‐Hui JU 
Published: April 2016

In processing of deep seismic reflection data, when the frequency band difference between the weak useful signal and noise both from the deep subsurface is very small and hard to distinguish, the usefulness of traditional method of filtering will be limited. To solve this problem, we apply different spectral decomposition methods respectively to experimental data and real data and compare the results from these methods. Our purpose is to find an effective way to protect weak signals during processing deep seismic reflection data.

The spectral decomposition method is based on the discrete Fourier transform, which uses the signal frequency‐amplitude spectrum and other information to generate a high‐resolution seismic image. Typically, it is used to identify the lateral distribution of medium properties, solve spectrum changes within complex media and local phase instability and other issues, such as locating faults and small‐scale complex fractures. S‐transform as a new time‐frequency analysis method, which is a generalization of STFT developed by Stockwell in 1994, has the ability to automatically adjust the resolution. This method has been widely applied to exploration seismic, MT and other geophysical datasets in recent years. It has become one of the effective methods in noise suppressing during geophysical data processing. Comparing with conventional oil reflection seismic data, in order to probe deep structure, deep seismic reflection employs a large quantity of explosives, long observing systems, leading to a phenomenon that valid signals from the deep and noise are mixed together both in time domain and frequency domain. Considering these characteristics of deep reflection data, this paper combines spectral decomposition with S transform technology. First we design a simple pulse function experimental data to confirm the validity of the S transform method. Then we illustrate the effectiveness of spectral decomposition which is influenced by the choice of frequency analysis methods, especially the transform window function which determines the resolving power of the method. On this basis, S transform spectrum decomposition is applied to a single channel of deep reflection seismic data and the stacked profile, then the application results of traditional transform spectral decomposition and S transform spectral decomposition are compared.

Comparison of single channel data shows that compared with traditional spectral decomposition, the S transform spectral decomposition method is able to automatically adjust the resolution, accurately calibrate frequency component of weak signals at different times in deep reflection seismic data. Application to stacked profile data shows that the results obtained by the S transform spectral decomposition and those from other spectral decomposition method are largely consistent, while the results of S transform spectral decomposition clearly depict the characteristics of low‐frequency details which are superimposed by noise in original stacked profile. At the same time, it improves the resolution and enhances the phase axis continuity on the stacked profile. Comparison also clearly indicates that the phase axis on the resultant profile obtained by Gabor transform spectral decomposition is more broken, which is caused by fixed‐length window function used by Gabor transform decomposition, the length of the window function parameters can only be selected before the start of processing and is set to a certain value, while the S transform spectral decomposition method chooses the variable length of the window function according to signal change. It can automatically adjust the frequency characteristics of the signal by the local window length to better characterize the details of each frequency range. Such an effect is very obvious in deep reflection seismic imaging.

Our results show that the key of the spectral decomposition technique is to select the transform window function. The S transform spectral decomposition technology used in real deep reflection seismic data processing can effectively protect the weak low‐frequency signals. It can effectively improve the signal to noise ratio and the resolution of weak reflection signals from the deep subsurface, while depicting the characteristics of low‐frequency details on the stacked section and ultimately obtaining better imaging results.