Methods and systems for processing borehole dispersive waves with a physics-based machine learning analysis

A system for processing borehole wave modes, the system comprising:

• a processor;
• and a memory comprising instructions which, when executed, cause the processor to:
• measure, by an acoustic logging tool within a borehole passing through a formation, an acoustic wave to generate waveform data of the acoustic wave;
• determine wave features from the waveform data, wherein the wave features are associated with a quality of the waveform data;
• generate one or more data-driven scale factors by applying a trained model to the wave features, wherein the one or more data-driven scale factors adaptively balance a data-driven process and a model-based process based on the quality of the waveform data;
• determine data-driven scale parameters from different wave features by comparing wave features generated by the trained model to directly measured wave features;
• determine final parameters of the data-driven scale factor by combining the different wave features;
• generate a formation body wave slowness by performing a hybrid process based on the one or more data driven-scale factors, wherein the hybrid process is a combination of the data-driven process and the model-based process, such that the hybrid process is more reliant on the data-driven model when a larger measured frequency length is present;
• adjusting a downhole operational parameter based at least in part on the formation body wave slowness, wherein the downhole operational parameter comprises one of a drilling parameter, a logging parameter, a completion parameter, and a production parameter;
• and
• providing the formation body wave slowness to a user interface and/or saving the formation body wave slowness to a log, wherein the formation body wave slowness provides wellbore and/or formation characteristics throughout drilling.