Sparse-spike deconvolution (SSD) is an important method for seismic resolution enhancement. With the wavelet given, many trace-by-trace SSD methods have been proposed for extracting an estimate of the reflection-coefficient series from stacked traces. The main drawbacks of trace-by-trace methods are that they neither use the information from the adjacent seismograms nor do they take full advantage of the inherent spatial continuity of the seismic data. Although several multitrace methods have been consequently proposed, these methods generally rely on different assumptions and theories and require different parameter settings for different data applications. Therefore, traditional methods demand intensive human-computer interaction. This requirement undoubtedly does not fit the current dominant trend of intelligent seismic exploration. Therefore, we have developed a deep learning (DL)-based multitrace SSD approach. The approach transforms the input 2D/3D seismic data into the corresponding SSD result by training end-to-end encoder-decoder-style 2D/3D convolutional neural networks (CNN). Our key motivations are that DL is effective for mining complicated relations from data, the 2D/3D CNN can take multitrace information into account naturally, the additional information contributes to the SSD result with better spatial continuity, and parameter tuning is not necessary for CNN predictions. We determine the significance of the learning rate for the training process’s convergence. Benchmarking tests on the field 2D/3D seismic data confirm that the approach yields accurate high-resolution results that are mostly in agreement with the well logs, the DL-based multitrace SSD results generated by the 2D/3D CNNs are better than the trace-by-trace SSD results, and the 3D CNN outperforms the 2D CNN for 3D data application.

中文翻译：

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深度学习进行多迹稀疏峰值反卷积

稀疏峰反褶积（SSD）是提高地震分辨率的一种重要方法。在给出小波的情况下，已经提出了许多逐迹线SSD方法，用于从堆叠迹线中提取反射系数序列的估计值。逐迹跟踪方法的主要缺点是它们既不使用相邻地震图的信息，也不充分利用地震数据固有的空间连续性。尽管因此提出了几种多迹线方法，但是这些方法通常依赖于不同的假设和理论，并且对于不同的数据应用需要不同的参数设置。因此，传统方法需要密集的人机交互。无疑，这一要求不符合当前智能地震勘探的主导趋势。所以，我们已经开发了基于深度学习（DL）的多迹SSD方法。该方法通过训练端到端的编码器/解码器样式的2D / 3D卷积神经网络（CNN），将输入的2D / 3D地震数据转换为相应的SSD结果。我们的主要动机是DL可有效地从数据中挖掘复杂的关系，2D / 3D CNN可以自然地考虑多迹信息，附加信息有助于SSD结果具有更好的空间连续性，并且对于CNN预测不需要参数调整。我们确定学习率对于训练过程趋同的重要性。对现场2D / 3D地震数据进行的基准测试证实，该方法可产生准确的高分辨率结果，这些结果大部分与测井结果相符，