发表机构
Delft University of Technology; VIRIDIEN (UK) Ltd（代尔夫特理工大学; VIRIDIEN（英国）有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究展示深度学习工作流程在安哥拉Camie油田大规模地震干扰衰减中的实际应用，通过监督学习在共炮点域实现高效SI去除，相比传统算法具有更高精度和更少信号泄漏。
AI 中文摘要
在海洋地震采集中，当来自附近外部震源的能量被捕获时，就会发生地震干扰（SI）。它通常表现为具有线性或非线性运动且在不同测线间振幅变化的相干噪声。SI普遍存在，对地震数据处理构成挑战。我们介绍了一个先前提出的基于深度神经网络（DNN）的工作流程在安哥拉Camie油田一个海洋地震区块中用于SI衰减的案例历史。该油田勘探面积超过345平方公里，其特点是存在多种SI类型。所采用的基于DNN的工作流程在共炮点域基于监督学习框架执行SI衰减：首先，通过传统地球物理算法处理一小部分受SI污染的数据以获得SI噪声的估计，然后将其与同一勘探中无SI的共炮点道集手动混合以生成训练对。为确保信号保真度，应用了多种技术来提高DNN的性能。该案例历史的一个关键亮点是其规模：这代表了一个真实世界的大规模处理项目，我们提供了基于DNN的工作流程与传统地球物理算法在整个勘探区块上的全面比较，重点关注处理质量和处理时间。结果表明，所采用的基于DNN的工作流程表现出色，实现了更高的SI去除精度，信号泄漏更少，SI去除更完整。这一应用的令人鼓舞的结果也为将深度学习整合到其他地震去噪任务中开辟了可能性。此外，我们讨论了该案例历史的局限性，旨在为该领域的未来研究和应用提供见解。
英文摘要
In marine seismic acquisition, seismic interference (SI) occurs when energy from nearby external seismic source(s) is captured. It typically appears as coherent noise with linear or non-linear movement and varying amplitudes across different sail lines. SI is commonly observed and poses a challenge for seismic data processing. We present a case history of a previously proposed deep neural network (DNN)-based workflow applied for SI attenuation across a marine seismic block in the Camie Field of Angola. This field survey covers over 345 km2 and is marked by the challenge of multiple SI types. The employed DNN-based workflow performs SI attenuation in the common shot domain based on a supervised learning framework: a small subset of the SI-contaminated data was first processed by a conventional geophysical algorithm to obtain an estimate of the SI noise, which was then manually blended with the SI-free common shot gathers from the same survey to generate the training pairs. To ensure signal fidelity, several techniques were applied to improve the DNN's performance. A key highlight of this case history is its scale: this represents a real-world, large-scale processing project and we present a comprehensive comparison of the DNN-based workflow with the conventional geophysical algorithm across the entire survey block, focusing on both processing quality and processing time. The results demonstrate the outstanding performance of the employed DNN-based workflow, which achieved higher SI removal accuracy, with less signal leakage and more complete SI removal. The promising results of this application also open up possibilities for integrating deep learning into other seismic denoising tasks. In addition, we discuss the limitations of this case history, aiming to provide insights for future research and applications in the field.
