Multichannel Analysis of Surface Waves (MASW) is commonly used to determine the
shallow subsurface velocity structure. The obtained velocities provide valuable informa-
tion for foundation design and other geotechnical applications. The conventional method
of picking surface-wave dispersion curves, which are subsequently inverted to obtain the
desired velocity profiles, can be a labour-intensive task. In this thesis, we make use of
recent advances in Deep Learning (DL), utilizing Convolutional Neural Networks (CNNs)
to estimate dispersion curves directly from shot gathers or their corresponding dispersion
spectra. For this, we first test the proposed approach on various synthetic data, suc-
cessfully addressing challenges such as geological models with velocity inversion, different
noise levels, and the estimation of the first higher mode. When applied to field data,
our results indicate that training the CNN on dispersion spectra provides more accurate
and reliable predictions compared to training on shot gathers directly. We improve the
predictions using shot gathers as training data by modifying the original CNN architec-
ture and applying transfer learning techniques. The enhanced CNN models demonstrate
significant improvements, indicating that this approach could aid the analysis of MASW
data. To fully realize the potential of this method, future efforts should focus on increas-
ing the similarity between synthetic and field data, for example by incorporating realistic
noise.