Pre-stack seismic inversion is an effective method for elastic parameter inversion using seismic data, which facilitates seismic fluid identification. However, pure PP-wave inversion has issues of strong multi-solution and limited prediction accuracy. Therefore, we propose a seismic fluid identification approach based on the joint PP- and SH–SH-wave stochastic inversion. First, the linearized SH–SH-wave amplitude variation with offset approximation parameterized by shear modulus and density is derived. Numerical simulations demonstrate that the SH–SH-wave amplitude variation with offset approximation has a good accuracy. Reflection coefficient contribution analysis indicates that the new formulation has better parameter sensitivity to shear modulus and density than the PP wave amplitude variation with offset approximation derived by Russell, which helps one to improve the inversion of shear modulus and density. On this basis, we construct a joint inversion equation of PP and SH–SH waves for a Russell fluid indicator, a shear modulus and density and present a novel joint stochastic inversion method based on the ensemble smoother with multiple data assimilation. Stanford VI-E model tests reveal that the Russell fluid indicator factor, shear modulus and density obtained from the joint PP- and SH–SH-wave inversion have higher identification accuracy and smaller relative errors than those from pure PP-wave inversion. Furthermore, field data tests indicate that this method has practical applicability in seismic fluid identification.

中文翻译：

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基于PP波和SH-SH波随机反演的地震流体识别方法

叠前地震反演是利用地震数据进行弹性参数反演的有效方法，有利于地震流体识别。但纯PP波反演存在多解性强、预测精度有限的问题。因此，我们提出了一种基于PP波和SH-SH波随机反演的地震流体识别方法。首先，推导出具有由剪切模量和密度参数化的偏移近似的线性化 SH-SH 波振幅变化。数值模拟表明，偏移近似下的SH-SH波振幅变化具有良好的精度。反射系数贡献分析表明，新公式对剪切模量和密度的参数敏感性优于Russell推导的带有偏移近似的PP波振幅变化，这有助于改进剪切模量和密度的反演。在此基础上，我们构建了Russell流体指标、剪切模量和密度的PP波和SH-SH波的联合反演方程，并提出了一种基于多数据同化的集合平滑器的新型联合随机反演方法。斯坦福VI-E模型试验表明，PP波和SH-SH波联合反演得到的Russell流体指示因子、剪切模量和密度比纯PP波反演具有更高的识别精度和更小的相对误差。此外，现场数据测试表明该方法在地震流体识别中具有实际应用价值。