Shallow stratigraphy in Southern Oman is characterized by the presence of an anhydrite layer causing a strong velocity inversion which makes seismic imaging particularly difficult. This known shallow sharp velocity inversion cannot be easily captured with reflected wave-based techniques or even acoustic full waveform inversion. We propose to recover it by applying multi-wave inversion, an approach combining information from P wave first breaks and ground-roll dispersion curves. In addition, an unsupervised machine learning technique 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 a better high resolution P-wave velocity model of the near surface which enables improved deep imaging.


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