Development of technologies for site characterization has grown at a faster pace compared to the development of decisionmaking methods required for the assimilation of inferences they generate. In the case of geophysical surveying, such dephase adds to the dependency on the use of expert's judgment in the interpretation of geophysical mappings. A systematic assimilation of this type of geo-surveying evidence is required, in particular for the integration of spatial geomorphological information (i.e., stratigraphy), characterized from different geophysical methods. This paper presents a methodology to address this challenge by the use of a probabilistic approach. A set of synthetic geophysical mappings are used to illustrate the applicability of the proposed methodology and its potential extrapolation to other scientific imaging disciplines.
This paper introduces a new methodology for addressing the probabilistic determination of geomorphological features and of the material properties, which allows for its uncertainty quantification and the spatial correlation across parameters when information from different geophysical methods is merged. The proposed approach departs from traditional stratigraphic characterizations advancing the possibility of establishing a systematic and reproducible approach that can reduce the subjectiveness and the unnecessary costs related to over conservative estimates for hazard assessment and structural design. A synthetic case study for a 1D heterogeneous media is formulated to illustrate the use of the probabilistic calibration method and of Tarantola's operators, when applied to two geophysical techniques used at the same site. Results show probability distributions of the location of the transition between layers, of the material properties, and of the collaborative operators for merging information content from different geophysical methods.
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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