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Near Surface Characterization in Southern Oman: Multi-Wave Inversion Guided by Machine LearningNormal access

Authors: S. Masclet, T. Bardainne, V. Massart and H. Prigent
Event name: 81st EAGE Conference and Exhibition 2019
Session: Near Surface Technologies I
Publication date: 03 June 2019
DOI: 10.3997/2214-4609.201900968
Organisations: EAGE
Language: English
Info: Extended abstract, PDF ( 5.13Mb )
Price: € 20

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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