Seismic acoustic impedance inversion plays an important role in reservoir prediction. However, single-trace inversion methods often suffer from spatial discontinuities and instability due to poor-quality seismic records with spatially variable signal-to-noise ratios or missing traces. The specified hyperparameters for seismic inversion cannot be suitable to all seismic traces and subsurface structures. In addition, conventional multichannel inversion imposes lateral continuity with a prespecified mathematical model. However, the inversion results constrained with specified lateral regularization are inferior when the subsurface situations violate the hypothesis. A data-driven multichannel acoustic impedance inversion method with patch-ordering regularization is introduced, in which the spatial correlation of seismic reflection is used. The method decomposes the seismic profile into patches and constructs the patch-ordering matrix based on the similarity among seismic patches to record the impedance structural extension. So the patch-ordering matrix can record the spatial extension of the acoustic impedance. Then, a simple regularization with difference operators of varying weights can reduce the random noise presented in the inverted impedance profile, stabilize the inversion result, and enhance the spatial continuity of the layer extension. The objective function for multichannel poststack seismic impedance inversion can be constructed by integrating the observed seismic record and the spatial continuity in the form of patch-ordering regularization, and it can be solved effectively with the limited-memory BFGS algorithm. The synthetic and field data tests illustrate the improvement of accuracy and lateral continuity of inverted results with our method, compared to conventional model-based inversion results.

中文翻译：

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通过补丁排序正则化的数据驱动多通道叠后地震阻抗反演

地震声阻抗反演在储层预测中起着重要作用。但是，单迹反演方法由于质量差的地震记录具有空间可变的信噪比或缺少迹线而经常遭受空间不连续性和不稳定性的困扰。为地震反演指定的超参数不能适用于所有地震道和地下结构。另外，常规的多通道反演通过预定的数学模型强加了横向连续性。但是，当地下情况违反假设时，受特定横向正则性约束的反演结果则较差。介绍了一种基于补丁序正则化的数据驱动多通道声阻抗反演方法，该方法利用了地震反射的空间相关性。该方法将地震剖面分解为斑块，并根据地震斑块之间的相似性构造斑块排序矩阵，以记录阻抗结构的扩展。因此，斑块排序矩阵可以记录声阻抗的空间扩展。然后，使用权重不同的差分算子进行简单的正则化可以减少反演阻抗分布中出现的随机噪声，稳定反演结果，并增强层扩展的空间连续性。通过将观测到的地震记录和空间连续性以斑块有序正则化的形式整合起来，可以构建多通道叠后地震阻抗反演的目标函数，并可以通过有限内存BFGS算法有效地解决。