基于压缩感知稀疏信号采样与重构理论，利用AVO反演方法将传统的L2范数改变为L1范数，反演地下地层在L1范数下的稀疏脉冲反射系数。反演得到的稀疏尖峰将局部地下结构通过有限数量的层状结构的叠加来表示，能够提高纵向精度，与传统的AVO反演算法相比提高了薄层的反演效果且具有一定的抗噪性。数值模型及实际数据的结果表明，基于压缩感知原理的L1范数AVO反演方法更加准确、分辨率更高。"/>
Based on the compressed sensing sparse signal sampling and reconstruction theory, the AVO inversion method is used to change the traditional L2 norm to the L1 norm, and the sparse pulse reflection coefficients of the underground stratum under the L1 norm are retrieved. The sparse spikes obtained from the inversion represent the local underground structure by the superposition of a limited number of layered structures, which can improve the longitudinal accuracy. Compared with the traditional AVO inversion algorithm, it improves the inversion effect of thin layers and has a certain anti-noise. The numerical model and actual data results show that the L1 norm AVO inversion method based on the compressed sensing principle is more accurate and has higher resolution."/>
L1 Norm AVO Inversion Algorithm Based on Compressive Sensing Theory
Abstract Based on the compressed sensing sparse signal sampling and reconstruction theory, the AVO inversion method is used to change the traditional L2 norm to the L1 norm, and the sparse pulse reflection coefficients of the underground stratum under the L1 norm are retrieved. The sparse spikes obtained from the inversion represent the local underground structure by the superposition of a limited number of layered structures, which can improve the longitudinal accuracy. Compared with the traditional AVO inversion algorithm, it improves the inversion effect of thin layers and has a certain anti-noise. The numerical model and actual data results show that the L1 norm AVO inversion method based on the compressed sensing principle is more accurate and has higher resolution.
