是否所有噪声都应同等对待：输入噪声变异性对神经网络鲁棒性的影响
Should All Noises Be Treated Equally: Impact of Input Noise Variability on Neural Network Robustness
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中文总结 AI 辅助
本研究通过模拟随钻地震数据，系统评估噪声类型、尺度及复合噪声对神经网络在初至拾取和去噪任务中鲁棒性的影响，发现大尺度噪声和复合噪声训练可提升泛化能力。
中文摘要 AI 辅助
从活跃野外场地采集的地球物理数据常常受到复杂且异质噪声的污染，这些噪声掩盖了微弱的地震事件，并使自动化解释复杂化。尽管深度学习为地震处理提供了有前景的解决方案，但其性能对训练噪声的性质高度敏感，尤其是在分布外（OOD）条件下。本研究探讨了噪声参数（如类型、尺度和复杂性）对神经网络在两个地球物理任务——初至拾取和去噪——中的性能、泛化能力和鲁棒性的影响。我们模拟随钻地震数据，并使用随机生成器应用受控的输入源噪声增强，以改变噪声特征。不同的神经网络在固定噪声类型和尺度上进行训练，然后在已见和未见噪声场景下进行评估。我们通过引入复合噪声混合物逐步增加噪声的复杂性，并在日益具有挑战性的OOD条件下评估模型性能。这产生了一个鲁棒性矩阵，捕捉每个模型相对于其训练配置的泛化能力。结果表明，较大的噪声尺度能提升泛化能力，且噪声类型、任务复杂性和架构之间的有效对齐是最大化泛化增益的关键。此外，使用复合噪声训练可缓解单一噪声训练相关的弱点，充当额外的隐式正则化器以提高鲁棒性。这些发现突出了在噪声地球物理环境中影响模型韧性的关键因素，并为开发能够在多样且不可预测的噪声条件下有效泛化的深度学习模型提供了指导。
英文摘要
Geophysical data collected from active field sites are often contaminated by complex and heterogeneous noise, obscuring weak seismic events, and complicating automated interpretation. Although deep learning offers promising solutions for seismic processing, its performance is highly sensitive to the nature of training noise, especially under out-of-distribution (OOD) conditions. This study investigates the influence of noise parameters, such as type, scale, and complexity on the performance, generalization and robustness of neural networks in two geophysical tasks: first break picking and denoising. We simulate seismic-while-drilling data and apply controlled input source noise augmentation using stochastic generators to vary the noise characteristics. Different neural networks are trained on fixed noise types and scales, then evaluated across both seen and unseen noise scenarios. We incrementally increase the complexity of the noise by introducing compound noise mixtures and assess the performance of the model under increasingly challenging OOD conditions. This yields a robustness matrix that captures the generalizability of each model relative to its training configuration. Results indicate that larger noise scales boost generalization, and that effective alignment between noise type, task complexity, and architecture is key for maximizing generalization gains. In addition, training with compound noises mitigate weaknesses associated with single-noise training, acting as an additional implicit regularizer to improving robustness. These findings highlight key factors influencing model resilience in noisy geophysical environments and offer guidance for developing deep learning models that generalize effectively across diverse and unpredictable noise conditions.
发表机构
- King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）
- Aramco Upstream Research Center at KAUST（沙特阿美上游研究中心（KAUST））
- EXPEC Advanced Research Center, Aramco（沙特阿美EXPEC高级研究中心）
机构由 AI 辅助整理，请以论文原文为准。
