Seismic high-resolution processing plays a critical role in reservoir target detection. As one of the most common approaches, regularization can achieve a high-resolution inversion result. However, the performance of regularization depends on the settings of the associated parameters and constraint functions. Further, it is difficult to solve an objective function with complex constraints, and it requires designing an optimization algorithm. In addition, existing algorithms have high computational complexity, which impedes the inversion of the large data volume. To address these problems, an optimization-inspired deep learning inversion solver is proposed to solve the blind high-resolution inverse (BHRI) problems of various seismic wavelets rapidly, called BHRI-Net. The method builds on ideas from classic regularization theory and recent advances in deep learning, and it makes full use of prior information encoded in the forward operator and noise model to learn an accurate mapping relationship. It unrolls the alternating iterative BHRI algorithm into a deep neural network, and it applies the convolutional neural network to learn proximal mappings, in which all parameters of the BHRI algorithm are learned from training data. Further, the proposed network can be split into two parts and incorporate the transfer learning strategy to invert field data, which increases the flexibility of the proposed network and reduces training time. Finally, the tests on synthetic and field data show that the proposed method can effectively invert the high-resolution data and seismic wavelet from observation data with improved accuracy and high computational efficiency.

中文翻译：

---

以优化为灵感的深度学习高分辨率地震数据反演

地震高分辨率处理在储层目标检测中起着至关重要的作用。作为最常见的方法之一，正则化可以实现高分辨率的反演结果。但是，正则化的性能取决于相关参数和约束函数的设置。此外，难以求解具有复杂约束的目标函数，并且需要设计优化算法。另外，现有算法具有很高的计算复杂度，这阻碍了大数据量的反转。为了解决这些问题，提出了一种以优化为灵感的深度学习反演求解器，以快速解决各种地震子波的盲高分辨率反演（BHRI）问题，称为BHRI-Net。该方法基于经典正则化理论的思想和深度学习的最新进展，并且充分利用了正向算子和噪声模型中编码的先验信息来学习精确的映射关系。它将交替迭代BHRI算法展开为一个深度神经网络，并应用卷积神经网络来学习近端映射，其中从训练数据中学习BHRI算法的所有参数。此外，提出的网络可以分为两部分，并结合转移学习策略来反转现场数据，这增加了提出的网络的灵活性并减少了训练时间。最后，