Conditional acoustic library generation for distributed fiber optic sensor

A system, comprising:

• a hardware processor;
• and
• a memory that stores a computer program which, when executed by the hardware processor, causes the hardware processor to:
• calibrate physical parameters of acoustic data using a deterministic model related to hardware configurations that generated the acoustic data to provide an intermediate layer of data;
• calibrate the intermediate layer of data using environmental factors related to the acoustic data by employing machine learning to provide a multichannel data output;
• and
• optimize a loss between the multichannel data output and multichannel distributed-optic fiber sensing (DFOS) data to train a hybrid transfer model to translate between DFOS data and acoustic data.

A computer program product, the computer program product comprising a computer readable storage medium storing program instructions embodied therewith, the program instructions executable by a hardware processor to cause the hardware processor to:

• calibrate physical parameters of acoustic data using a deterministic model related to hardware configurations that generated the acoustic data to provide an intermediate layer of data;
• calibrate the intermediate layer of data using environmental factors related to the acoustic data by employing machine learning to provide a multichannel data output;
• and
• optimize a loss between the multichannel data output and multichannel distributed-optic fiber sensing (DFOS) data to train a hybrid transfer model to translate between DFOS data and acoustic data.