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Abstract

Aspects described herein provide for methods and apparatus for interpretation of borehole sonic dispersion data using data-driven machine learning based approaches. Training datasets are generated from two possible sources. First, application of machine learning enabled automatic dipole interpretation (MLADI) and/or machine learning enabled automatic quadrupole interpretation (MLAQI) methods on field data processing will naturally create substantial volume of labeled data, i.e., pairing dispersion data with dispersion modes labeled by MLADI and MLAQI. Second, it is also possible to generate large volume of synthetic dispersion data from known model parameters. These two types of labeled data can be used either separately or in combination to train neural network models. These models can map dispersion data to modal dispersion much more efficiently.

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