构建真实地震噪声数据集，推动深度学习去噪模型发展。
A Real Benchmark Swell Noise Dataset for Performing Seismic Data Denoising via Deep Learning
- 用真实数据提取噪声并合成地震数据用于模型测试
- 对比两种主流深度学习去噪模型，验证有效性
- 提出新评估指标，捕捉细微性能差异，适合地质勘探研究者
深度学习在计算机视觉领域的进展得益于公开基准数据集的建立。尽管深度学习在地球物理中有广泛应用，但可用于基准测试的真实地震数据集仍十分稀缺，尤其是在真实数据去噪方面——这是油气行业地震数据处理中的核心难题。本文提出一个基准数据集，由合成地震数据与从真实数据滤波中提取的噪声混合而成。在此数据集上，对两种知名的基于深度学习的去噪模型进行了对比实验，旨在加速新型地震数据去噪解决方案的发展。此外，本文引入一种新评估指标，可捕捉模型结果中的微小差异。实验表明，深度学习模型在地震数据去噪中表现有效，但仍存在待解决的问题。
原文摘要 · Abstract (English)
The recent development of deep learning (DL) methods for computer vision has been driven by the creation of open benchmark datasets on which new algorithms can be tested and compared with reproducible results. Although DL methods have many applications in geophysics, few real seismic datasets are available for benchmarking DL models, especially for denoising real data, which is one of the main problems in seismic data processing scenarios in the oil and gas industry. This article presents a benchmark dataset composed of synthetic seismic data corrupted with noise extracted from a filtering process implemented on real data. In this work, a comparison between two well-known DL-based denoising models is conducted on this dataset, which is proposed as a benchmark for accelerating the development of new solutions for seismic data denoising. This work also introduces a new evaluation metric that can capture small variations in model results. The results show that DL models are effective at denoising seismic data, but some issues remain to be solved.
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