用深度卷积网络降噪去混叠，提升海陆地震数据处理效率
Using Convolutional Neural Networks for Denoising and Deblending of Marine Seismic Data
- 在共道域使用CNN处理，打破噪声相干性并减小输入体积
- 相比炮域处理，共道域去混叠效果更好，GPU内存利用率更高
- 适用于需要高效处理海量地震数据的勘探场景
海洋地震数据处理计算量大，包含多个耗时步骤。基于神经网络的方法理论上可显著缩短处理时间，有望改变传统处理流程。本文采用深度卷积神经网络（CNN）去除地震干扰噪声并实现去混叠。训练此类网络需大量计算内存，因单个炮集包含超过10⁶个数据样本。初步结果在降噪与去混叠方面均表现良好，但性能受信噪比（SnR）影响。将数据转换至共道域可破坏噪声相干性，同时减小输入规模，使网络更易区分信号与噪声，并提升GPU内存利用效率，支持多核并行处理。在共道域使用CNN进行去混叠取得了较好效果，优于炮域处理。
原文摘要 · Abstract (English)
Processing marine seismic data is computationally demanding and consists of multiple time-consuming steps. Neural network based processing can, in theory, significantly reduce processing time and has the potential to change the way seismic processing is done. In this paper we are using deep convolutional neural networks (CNNs) to remove seismic interference noise and to deblend seismic data. To train such networks, a significant amount of computational memory is needed since a single shot gather consists of more than 106 data samples. Preliminary results are promising both for denoising and deblending. However, we also observed that the results are affected by the signal-to-noise ratio (SnR). Moving to common channel domain is a way of breaking the coherency of the noise while also reducing the input volume size. This makes it easier for the network to distinguish between signal and noise. It also increases the efficiency of the GPU memory usage by enabling better utilization of multi core processing. Deblending in common channel domain with the use of a CNN yields relatively good results and is an improvement compared to shot domain.
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