1887

Abstract

Summary

We present a new approach for 2 D probabilistic prediction of sparsely measured target parameters, e.g., measured by direct push technology or borehole logging. Geophysical tomography is used to constrain the prediction. The presented approach fully accounts for tomographic ambiguity and transduces it into prediction uncertainty. Furthermore, errors of the logging data can be considered to avoid overfitting when learning the optimal link between tomograms and logging data by means of Artificial Neural Networks.

Consideration of errors results in improved predictions, which we exemplary illustrate here by 2D sleeve friction prediction.

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/content/papers/10.3997/2214-4609.201601402
2016-05-30
2024-04-28
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References

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