האם כל רעשים צריכים להיות טופלים באופן שווה: השפעת שונות הרעש הכניסה על רובוסטנסיית רשתות עצביות

תקציר מקורי באנגליתarXiv:2609.14504v1 Announce Type: new Abstract: 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 charac