地震叠前逆时偏移中的去噪与存储

地震叠前逆时偏移是当前公认的地震成像的有效途径,然而它面临着计算量甚巨,低频成像噪音以及存储量大等问题,因此,业内科研工作者对其研究乐此不疲.借助GPU/CPU协同计算可以有效解决计算量的难点,笔者已在另文中阐述,本文着重探讨成像噪音抑制以及存储问题.文中分析了叠前逆时偏移产生成像噪音的机制,据此提出在叠前地震资料中先对数据进行相位与振幅校正,进而在成像后运用拉普拉斯算子滤波法消除成像噪音,从而有效去除成像所产生的低频噪音;针对存储量,采用随机边界,用计算换存储,并借助GPU实现,节省了GPU与CPU之间的数据通讯,数值实验结果表明,采用随机边界方法的逆时偏移结果与直接存储波场的方法得到的结果差别甚小.

The problems of denoise and storage in seismic reverse time migration

Pre-stack reverse time migration (RTM) is a very useful tool for seismic imaging, however, it has some problems such as highly intensive computation cost, imaging noise, and massy memory demand, which the researchers in the field have been striving to solve. The problem of intensive computation cost could be solved by GPU/CPU collaborative computing and has been discussed in the last paper and the other two problems will be emphasized in this paper. At the beginning, we analyze the generation principle of imaging noise and propose that the phase and frequency spectra of seismic data should be modified before migration and a Laplacian filter could be used to remove the low wave-number noise efficiently. Aiming at the problem of massy memory demand, we adopt the random boundary condition which reduces the memory demand but sacrifice the computation cost. The implementation could be performed on GPU and the communication between CPU and GPU could be saved which could reduce the computation cost in another way. The tests on synthetic data examples illustrate that the difference between the migration result from the method of random boundary condition and that from the traditional method of saving the footprint of the wave field could be overlooked.