Using transfer learning to enhance void detection and shear wave velocity model inversion from near-surface seismic shot gathers

A Convolutional Neural Network (CNN) has been designed to delineate the shear-wave velocity ( Vs ) models and detect subsurface void locations. Addressing the processing and interpretation challenges posed on real seismic data, our strategy emphasizes that leveraging the ground truth, which is the void location in this study, enables the CNN to catch the identical features in real waveforms. Initially, a synthetic dataset is employed, imparting foundational knowledge to the CNN regarding the Vs model and void locations. Drawing inspiration from transfer learning, this pre-trained CNN serves as an initial model and is refined using a real dataset focused on void locations. After refining, the CNN shows enhanced reliability to detect the void and extract the Vs model, as evidenced by the improved alignment between forward modeling and real waveforms. Our findings underscore how leveraging the ground truth can actualize the potential of CNN on velocity model extraction. • A CNN is applied to delineate the Vs models and detect void locations. • Transfer learning with real dataset is applied to enhance Vs model inversion. • Forward modeling waveforms based on new Vs model highly consistent with real one.