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 complex information, crucial 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, SVInvNet achieves superior performance with this dataset. The outcomes of the SVInvNet are additionally compared to those of the Full Waveform Inversion (FWI) method. 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方法来解决地震速度反演问题，重点研究了不同规模的有噪声和无噪声训练数据集。我们的地震速度反演网络（SVInvNet）引入了一种新颖架构，该架构包含一个由密集块增强的多连接编码器-解码器结构。该设计专门针对处理复杂信息进行了优化，这对于应对非线性地震速度反演的挑战至关重要。在训练和测试过程中，我们创建了多种地震速度模型，包括多层模型、断层模型和盐丘模型。我们还研究了不同类型的环境噪声（包括相干噪声和随机噪声）以及训练数据集规模对学习结果的影响。SVInvNet在750到6000个样本的数据集上进行训练，并使用包含12000个样本的大型基准数据集进行测试。尽管其参数数量少于基线模型，但SVInvNet在该数据集上实现了更优的性能。此外，还将SVInvNet的结果与全波形反演（FWI）方法进行了比较。对比分析清晰地揭示了所提模型的有效性。