  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.
