📝 SummarXiv

Abstract

Seismic velocity inversion is a key task in geophysical exploration, enabling the reconstruction of subsurface structures from seismic wave data. It is critical for high-resolution seismic imaging and interpretation. Traditional physics-driven methods, such as Full Waveform Inversion (FWI), are computationally demanding, sensitive to initialization, and limited by the bandwidth of seismic data. Recent advances in deep learning have led to data-driven approaches that treat velocity inversion as a dense prediction task. This research benchmarks three advanced encoder-decoder architectures -- U-Net, U-Net++, and DeepLabV3+ -- together with SeismoLabV3+, an optimized variant of DeepLabV3+ with a ResNeXt50 32x4d backbone and task-specific modifications -- for seismic velocity inversion using the ThinkOnward 2025 Speed \& Structure dataset, which consists of five-channel seismic shot gathers paired with high-resolution velocity maps. Experimental results show that SeismoLabV3+ achieves the best performance, with MAPE values of 0.03025 on the internal validation split and 0.031246 on the hidden test set as scored via the official ThinkOnward leaderboard. These findings demonstrate the suitability of deep segmentation networks for seismic velocity inversion and underscore the value of tailored architectural refinements in advancing geophysical AI models.