Marine geomodels from high-resolution seismic reflection data – a model ensemble approach

Prior geological knowledge is crucial in a wide range of geophysical case studies aiming at data processing and interpretation. In this regard, geological modeling methods can be considered as efficient tools to facilitate petrophysical parametrization and constrain geophysical inversions as a priori information over a defined model space. We use the open-source library GemPy (De la Varga et al., 2019) which offers a community driven alternative based on the potential-field method and specialized in probabilistic modeling.

In this work, we show an exemplary case study of high-resolution marine geophysical data comprising single-trace reflection and underwater refraction seismics validated with geotechnical data. Two approaches are suggested: (1) digitalization of geophysical imagery, i.e., spatial information from interpreted horizons and inherent uncertainties, and (2) pseudo depth migration of picked reflection seismic travel times using a simple velocity model from parallel recorded underwater refraction data. Next to a deterministic “best-fit” solution, the model is interpreted following probabilistic distributions of input data and classified after their identified certitudes (e.g., depth range of an observed seismic horizon), where prior knowledge is optionally included using Bayesian Networks. Finally, global uncertainties are estimated by multiple model realizations allowing for improved data assessment and enhanced decision making.

The further outlook of this study is the creation of a variety of digital twins taking into account realistic conditions in terms of both, the geological environment as well as data acquisition, as a solid prior for future data exploitation by inverse processes of wave propagation in shallow marine environments.