SPBENCH: A MULTI-TASK EVALUATION BENCHMARK FOR EXPLORATION SEISMIC PROCESSING

Exploration seismic processing underpins subsurface imaging and resource exploration, where data quality shapes downstream interpretation and decision making. Learning-based methods have advanced rapidly, but their results remain difficult to compare across studies. Our literature survey of 368 papers reveals widespread reliance on private or difficult-to-reproduce datasets, and only 25 provide public code. This makes it difficult to attribute reported gains to model design rather than differences in experimental settings. To fill this gap, we introduce the Seismic Processing Benchmark (SPBench). SPBench covers six representative processing tasks, including random noise attenuation, trace interpolation, ground-roll noise suppression, multiple suppression, deblending, and first-arrival picking. We reproduce 24 supervised methods on 10 datasets under 43 standardized settings and release datasets, implementations, configurations, evaluation scripts, and results. Global scores hide frequency- and energy-dependent behavior, and per-trace pick errors ignore spatial continuity. We therefore introduce signal-component-resolved evaluation (SCoRE) for reconstruction and the reference-free ridge-curvature score (${\mathrm{RC}}_{\mathrm{norm}}$) for first-arrival picking. Our analyses reveal three patterns. Synthetic rankings do not reliably predict field rankings, and the agreement varies by task when models train within each setting. As degradation strengthens, rankings reorder more under coherent ground roll than under random-like interference. The reference-free ridge score tracks MAE-based model rankings in the evaluated settings (mean Kendall correlation 0.881 across three field surveys), while SCoRE reveals component-dependent differences hidden by global scores. Together, these resources provide a fair and reproducible basis for comparing learning-based seismic-processing methods. The findings further characterize how method advantages vary across the evaluated conditions, and inform model selection.