This paper introduces a probabilistic approach to significantly improve offshore site characterization from integrated geophysical, geological and geotechnical survey data, and from different technologies used from within each of these disciplines. The proposed Bayesian stratigraphy integration methodology is based on the sequential integration of available evidence (experimental observations, model predictions and experts’ beliefs), which allows for the reduction of uncertainty and improve the quality of geospatial analysis translated into higher stratigraphy resolution and higher confidence on the determination of sediments’ mechanical characteristics. A synthetic case study for a 2D heterogeneous shallow offshore soil media is presented to illustrate the overall methodology. One application of probabilistic cluster identification based on geological data is discussed (e.g. 1D density upscaling profile), as this is then transferred to a probabilistic geophysical inversion, including the corresponding uncertainty propagation and.
Geophysical seismic surveys have been applied to marine geo-site characterization to create images of the complex geological conditions under the seafloor. Accurate knowledge of the ground conditions is critical for geo-risk assessment purposes such as mapping shallow gas hydrate deposits, over-pressured zones, or geological anomalies. Traditional seismic reflection profiling is a relatively fast and flexible method of processing seismic data to recover information on the spatial variation in facies boundaries and subsurface structure. However, the method usually does not provide quantitative information on the composition of the sediments and their physical properties. Seismic inversion is a method to convert the wave signals from time-to space-domain and derive specific material properties by using iterative numerical modeling. In this paper, we introduce a probabilistic seismic inversion scheme to recover the vertical profiles of the shallow soil bulk density from marine seismic survey data. This acoustic impedance inversion is based on the geophysical seismic convolution method and the reversible jump Markov chain Monte Carlo (rj-MCMC) method. The rj-MCMC is a recently developed stochastic sampling technique that allows the modeling to free the number of layers under the seafloor. Hence the number of soil units is estimated from the data in an objective manner. We applied this new approach to a single trace of post-stack seismic data, collected from the Hydrate Ridge area on the west coast of Oregon. Since the purpose of this shallow seismic inversion is to support the design of the offshore foundation, we focused on the relatively short length of seismic signals near the seafloor. The inverted results of the bulk densities along the depth compare well with the field measurements performed at the nearby drilled borehole. This study introduces an advantage of the probabilistic seismic inversion approach to support shallow marine site characterization from low-frequency data, and we discuss the benefits of this new approach on geotechnical site characterization.
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