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| Absorption compensation based on self-adaptive signal identification |
| MA Xiong1,2, LI Guofa3, LIU Libin4, GUI Zhixian1,2 |
1. College of Geophysics and Petroleum Resources, Yangtze University, Wuhan, Hubei 430100, China;
2. Key Laboratory of Exploration Technologies for Oil and Gas Resources (Yangtze University), Ministry of Education, Wuhan, Hubei 430100, China;
3. School of Geophysics, China University of Petroleum(Beijing), Beijing 102249, China;
4. Institute of Geophysical Research, Shengli Oil Field, Dongying, Shandong 257022, China |
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Abstract The absorption attenuation effect of subsurface media seriously reduces the detection depth of seismic signals and further hinders the development of high-resolution seismic exploration. Inverse-Q filtering is a useful tool for compensating for the attenuation of strata and improving the seismic resolution. However, the traditional inverse-Q filtering cannot identify seismic signals, which will result in the amplification of noises in the compensation process of seismic signals. To alleviate this issue, this paper proposes a multichannel absorption compensation method based on self-adaptive signal identification. The core of this approach is to improve the stability and accuracy of the absorption compensation algorithm by using the spatial structure characteristics of seismic signals. The implementation of the method includes four steps:firstly, the paper expresses the spatial structure features of the reflected signal by the spatial prediction operator. Then, the paper uses the signal prediction operator to construct the constraint term of spatial reflection structure. Next, the paper adds the reflection structure constraint term to the multichannel absorption compensation objective function for obtaining the absorption compensation inversion system. Finally, the paper solves the objective function to obtain the absorption compensation result. The synthetic data tests and field data examples show that the proposed method can not only improve the resolution of seismic data but also protect the spatial structure characteristics and signal-to-noise ratio of compensation results, and it has strong stability and certain practicability.
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Received: 07 November 2022
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2023, Vol. 58 
