This study presents a deep learning-based approach to seismic velocity inversion problem, focusing on both noisy and noiseless training datasets of varying sizes. Our Seismic Velocity Inversion Network (SVInvNet) introduces a novel architecture that contains a multi-connection encoder-decoder structure enhanced with dense blocks. This design is specifically tuned to effectively process time series data, which is essential for addressing the challenges of non-linear seismic velocity inversion. For training and testing, we created diverse seismic velocity models, including multi-layered, faulty, and salt dome categories. We also investigated how different kinds of ambient noise, both coherent and stochastic, and the size of the training dataset affect learning outcomes. SVInvNet is trained on datasets ranging from 750 to 6,000 samples and is tested using a large benchmark dataset of 12,000 samples. Despite its fewer parameters compared to the baseline model, SVInvNet achieves superior performance with this dataset. The performance of SVInvNet was further evaluated using the OpenFWI dataset and Marmousi-derived velocity models. The comparative analysis clearly reveals the effectiveness of the proposed model.

翻译：本研究提出了一种基于深度学习的地震速度反演方法，重点关注不同规模含噪与无噪训练数据集。我们提出的地震速度反演网络（SVInvNet）引入了一种新颖架构，该架构包含通过密集块增强的多连接编码器-解码器结构。此设计专门针对时间序列数据进行优化处理，这对于解决非线性地震速度反演问题至关重要。为进行训练与测试，我们构建了多样化的地震速度模型，包括多层结构、断层构造及盐丘类别。同时探究了不同类型的环境噪声（相干噪声与随机噪声）以及训练数据集规模对学习效果的影响。SVInvNet在750至6,000个样本的数据集上进行训练，并使用包含12,000个样本的大型基准数据集进行测试。尽管相比基线模型参数量更少，SVInvNet在该数据集上仍实现了更优性能。通过OpenFWI数据集及Marmousi衍生的速度模型进一步评估了SVInvNet的性能。对比分析结果清晰地验证了所提模型的有效性。