Method and system for mapping fiber optic distributed acoustic sensing measurements to particle motion

A system for mapping fiber optic distributed acoustic sensing (DAS) measurements to particle motion data, the system comprising:

• a seismic data processing system configured to:
• obtain, from a fiber optic DAS system in a wellbore, a first set of DAS data associated with a first seismic wave;
• obtain, from a discrete seismic receiver in the wellbore, measured particle motion data associated with the first seismic wave, wherein the measured particle motion data comprises electrical signal measurements generated by the discrete seismic receiver;
• generate training data from the first set of DAS data and the measured particle motion data;
• train a machine learning model using the training data;
• obtain a second set of DAS data associated with a second seismic wave;
• generate predicted particle motion data in response to the second seismic wave using the machine learning model applied to the second set of DAS data, wherein the predicted particle motion data is formatted as electrical signal measurements generated by the discrete seismic receiver;
• and
• input the predicted particle motion data to a seismic inversion workflow to determine formation characteristics near the wellbore.

A non-transitory machine-readable medium comprising a plurality of machine-readable instructions executed by one or more processors, the plurality of machine-readable instructions causing the one or more processors to perform operations comprising:

• obtaining, from a fiber optic distributed acoustic sensing (DAS) system in a wellbore, a first set of DAS data associated with a first seismic wave;
• obtaining, from a discrete seismic receiver in the wellbore, measured particle motion data associated with the first seismic wave, wherein the measured particle motion data comprises electrical signal measurements generated by the discrete seismic receiver;
• generating training data from the first set of DAS data and the measured particle motion data;
• training a machine learning model using the training data;
• obtaining a second set of DAS data associated with a second seismic wave;
• generating predicted particle motion data in response to the second seismic wave using the machine learning model applied to the second set of DAS data, wherein the predicted particle motion data is formatted as electrical signal measurements generated by the discrete seismic receiver;
• and
• inputting the predicted particle motion data to a seismic inversion workflow to determine formation characteristics near the wellbore.