ultiscale full-waveform inversion based on shot subsampling

Conventional full-waveform inversion is computationally intensive because it considers all shots in each iteration. To tackle this, we establish the number of shots needed and propose multiscale inversion in the frequency domain while using only the shots that are positively correlated with frequency. When using low-frequency data, the method considers only a small number of shots and raw data. More shots are used with increasing frequency. The random-in-group subsampling method is used to rotate the shots between iterations and avoid the loss of shot information. By reducing the number of shots in the inversion, we decrease the computational cost. There is no crosstalk between shots, no noise addition, and no observational limits. Numerical modeling suggests that the proposed method reduces the computing time, is more robust to noise, and produces better velocity models when using data with noise.

常规全波形反演利用全部炮集参与计算,反演的计算量巨大。针对这一问题,本文分析了不同频率反演对炮数的需求,进而提出一种基于频率多尺度反演方法的加速策略。该方法利用反演所需炮数与频率正相关的特性,在反演低频数据时,每次迭代只抽取一部分炮集参与反演,频率升高时,相应地引入更多的炮集参与运算,两次迭代之间通过组内随机炮采样的方法实现炮集的轮换,避免炮集信息的丢失。该方法通过降低反演炮数从而减少计算量,由于不涉及炮集的串扰,因此不会引入额外的噪声,也不受限于观测系统。模型测试结果表明,该方法在炮集数量较多时可以明显减少计算时间,同时,该方法具有一定的抗噪能力,对含噪声的地震记录也能得到较好的反演结果。