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Should All Noises Be Treated Equally: Impact of Input Noise Variability on Neural Network Robustness

arXiv · AI, language, vision and robotics · article · Sep 13, 2026 · UTC

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 deno

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