Seismic analysis for reservoir characterization has been a primary focus for the geophysical community for decades. One of the critical steps in delivering high-quality processed seismic data for seismic analysis is to remove undesirable prestack seismic phenomena prior to amplitude variation with offset (AVO) analysis. Contrary to the conventional approach, which is mainly 2D gather-based and assumes flat events, we have developed a 3D nonlinear approach with a single principle: the 3D geologic structure should be invariant from offset to offset. Trained dictionaries, generated by 3D complex wavelet transformation over pilot volumes, are progressively constructed by stacking over selected offsets or angles. A sparse nonlinear approximation using the L₀ norm is imposed on the data against the trained dictionaries after applying a 3D complex wavelet transform to the data. The final step is to apply an inverse 3D complex wavelet transform to the sparsified coefficients to return to the data space. This workflow is repeated for all offsets or angles. The workflow is automatic and requires minimal user input, resulting in a fast and efficient process. Multiple field data examples have demonstrated significant signal-to-noise ratio uplift, AVO and azimuthal AVO conservation, preservation of steeply dipping structural events, and multiple suppression. The processing time is significantly shorter compared with alternative conventional processes.

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使用训练有素的字典进行3D地震数据处理的非线性解决方案

数十年来，用于储层表征的地震分析一直是地球物理界关注的重点。为地震分析提供高质量处理后的地震数据的关键步骤之一是在进行带偏移的振幅变化（AVO）分析之前，消除不良的叠前地震现象。与主要基于2D聚集并假设平坦事件的常规方法相反，我们开发了一种具有单一原理的3D非线性方法：3D地质结构在偏移之间应保持不变。通过在导频体积上进行3D复数小波变换生成的训练词典，是通过在选定的偏移量或角度上叠加来逐步构建的。使用L _0的稀疏非线性逼近在对数据应用3D复数小波变换之后，将数据针对训练有素的字典强加于数据。最后一步是将3D复数小波逆变换应用于稀疏系数，以返回到数据空间。对所有偏移或角度重复此工作流程。工作流是自动的，需要最少的用户输入，从而实现了快速高效的流程。多个现场数据示例已经证明了显着的信噪比提升，AVO和方位角AVO保持，陡倾结构事件的保留以及多重抑制。与替代的常规方法相比，处理时间明显缩短。