GeoBrain

Model

seismic-interpolation-benchmark

GeoBrain

Deep-learning-based missing-trace reconstruction on pre-stack seismic shot gathers. Given a shot gather with masked (missing) traces, the model reconstructs the full gather. Each experiment is one architecture trained on one missing-trace scenario with one random seed. Model directories are suffixed with the dataset the model was trained on: - mobil — Mobil field dataset (pre-stack seismic shot gathers) - segc3 — SEG C3 synthetic dataset (wiki.seg.org/wiki/C3): 9 regular shots, 201 traces x 625 time samples, dt = 2 ms - Chai2020 UNet (chai2020unet) — 2D U-Net (Chai et al., 2020, IEEE TGRS, DOI 10.1109/TGRS.2019.2961015): 50 layers = 19 convolutions (5x5, same padding) + 18 ReLU + 4 max-pool…