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Compressive sensing aided seismic geometry design for offshore acquisition

Huang Xiaogang, Zhang Jinmiao, Du Xiangdong, Zhu Zhenyu, Li Weixin, Zhang Yiming, Ding Jicai, Liu Zhipeng, Zhang Jianfeng

Acta Geophysica

2022

Vol. 70, no 2

547--562

Seismic acquisition guided by the compressive sensing theory can significantly improve seismic data acquisition efficiency and reduce the cost. After reviewing the basic principles of compressive sensing, we propose an optimized random sampling method that can control the maximum sampling interval and improve the design flexibility. We analyze several factors that can introduce reconstruction errors from compressive sensed data and learn that besides sampling method, reconstruction errors increase with decimation degree and the complexity of structures and also depend on the reconstruction workflow. In addition, we provide a basic workflow of the geometry design of compressive sensing acquisition. We analyze the feasibility of the three types of receiving equipment that are widely used in marine environment and discuss the potential cost reduction and efficiency gain. Our field example demonstrates the detailed working process and the feasibility of the combination of random sailing line intervals and random shot intervals and verifies the effect of cost saving and efficiency increasing.

Adaptive iterative deblending of simultaneous source seismic data based on sparse inversion

Wei Yajie, Cao Jingjie, Huang Xiaogang, Chen Xue, Cai Zhicheng

Acta Geophysica

2021

Vol. 69, no. 2

497--507

Deblending of simultaneous-source seismic data is becoming more popular in seismic exploration since it can greatly improve the efciency of seismic acquisition and reduce acquisition cost. At present, the deblending methods of simultaneous-source seismic data are mainly divided into two types: fltering method and sparse inversion method. Compared with the fltering method, the sparse inversion method has higher precision, but the selection of its parameter value mainly depends on experience, which is not suitable for large-scale seismic data processing. In this paper, an adaptive iterative deblending method based on sparse inversion is proposed. By improving the original iterative solution method of regularization inversion model, the efective signal and blending noise are weakened simultaneously in the iterative process, so that the energy intensity of blending noise is consistent with that of the efective signal in each iterative, so as to ensure the consistency of the regular parameter calculation method of each iteration. By analyzing the distribution of coefcients in the curvelet domain of pseudo-deblending data and blending noise, it is concluded that the value of regular parameters is the maximum amplitude of residual pseudo-deblending data in the curvelet domain multiplied by a coefcient between 0 and 1. In the process of iterative deblending, the regularized parameters are obtained adaptively from the data itself. It not only ensures the accuracy of the calculation results, but also improves the calculation efciency, which is suitable for large-scale seismic data processing.