Near surface characterization in Southern Oman: Multi-Wave Inversion guided by Machine Learning

Shallow stratigraphy in Southern Oman is characterized by the presence of a shallow anhydrite layer causing a strong velocity inversion which makes seismic imaging particularly tedious. This known shallow sharp velocity inversion cannot be easily captured with reflected waves based techniques or even acoustic full waveform inversion. We propose to recover it applying multi-wave inversion, an approach combining information from first breaks of P waves and from dispersion curves picked on ground-roll. In addition, an unsupervised machine learning technology is used to improve the quality of surface wave dispersion curve picks, crucial for the reliability of the multi-wave inversion results. With this innovative approach, the joint inversion of first breaks and surface waves leads to an enhanced and high resolution P-wave velocity model of the near surface along with an improvement in the depth imaging.