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Abstract

Summary

We employ a recently developed data-driven approach to exemplary infer a probabilistic 2D sleeve friction model constrained by ill-posed geophysical tomographic imaging and laterally sparse cone penetration logging data. The integration and inference approach is based on fuzzy concepts and can fully cope with unknown and even non-unique inter-relations between geotechnical parameters, such as sleeve friction, and multiple physical properties imaged by fully non-linear geophysical tomography, e.g. ensembles of equivalent seismic or radar velocity models. Such data-driven integration and inference approaches can be applied to complex databases and do not require the a priori selection of tomographic data sets believed to be particularly closely linked to the target parameter, e.g., sleeve friction and seismic shear wave velocity tomograms. However, in the sense of error propagation incorporation of all available tomographic data sets may inflate the range of the final probabilistic prediction, which is not desirable. In turn, discarding data sets not expected to be physically linked to the target parameter may hamper predictions and potentially result in overseeing weak and yet unrecognized, but eventually existing, physical links, which could have improved the inference of probabilistic geotechnical target parameter models.

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/content/papers/10.3997/2214-4609.201600795
2016-05-30
2024-04-20

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References

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About Data-driven Integration of Ill-posed Geophysical Tomography and Geotechnical Logging Data
Conference Proceedings 2016, 1 (2016); https://doi.org/10.3997/2214-4609.201600795
/content/papers/10.3997/2214-4609.201600795
/content/papers/10.3997/2214-4609.201600795
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