Title: Seismic Velocity Inversion from Multi-Source Shot Gathers Using Deep Segmentation Networks: Benchmarking U-Net Variants and SeismoLabV3+ Show Chinese title

Title: 基于多源炮集的地震速度反演使用深度分割网络：U-Net变体和SeismoLabV3+的基准测试

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. Show Chinese abstract

Abstract: 地震速度反演是地球物理勘探中的关键任务，能够从地震波数据中重建地下结构。这对于高分辨率地震成像和解释至关重要。传统的物理驱动方法，如全波形反演（FWI），计算需求大，对初始值敏感，并受地震数据带宽的限制。深度学习的最新进展导致了数据驱动的方法，将速度反演视为密集预测任务。本研究对三种先进的编码器-解码器架构——U-Net、U-Net++和DeepLabV3+——以及SeismoLabV3+进行了基准测试，SeismoLabV3+是具有ResNeXt50 32x4d主干网络和任务特定修改的DeepLabV3+优化变体，用于使用ThinkOnward 2025 Speed & Structure数据集进行地震速度反演，该数据集包含五通道地震炮记录与高分辨率速度图。实验结果表明，SeismoLabV3+表现最佳，在内部验证分割上的MAPE值为0.03025，在隐藏测试集上的MAPE值为0.031246，这是通过官方ThinkOnward排行榜评分得出的。这些发现证明了深度分割网络在地震速度反演中的适用性，并强调了定制架构改进在推进地球物理AI模型中的价值。