Machine learning training based on dual loss functions

A computing system, comprising:

• one or more processors;
• and
• a memory system comprising one or more non-transitory, computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising:
• receiving a seismic training input image;
• generating, using a first portion of a machine learning model of a subterranean formation, a first output based at least in part on the seismic training input image;
• generating, using a second portion of the machine learning model of the subterranean formation, a second output based at least in part on the seismic training input image;
• generating a loss function based at least in part on comparing at least two of:
• the first output;
• a deterministic first label synthetically generated and representing a deterministic ground truth for the first output;
• the second output;
• and
• a non-deterministic second label representing a non-deterministic ground truth for the second output;
• refining the first portion, the second portion, or both of the machine learning model based at least in part on the loss function by:
• training the machine learning model based on deterministic first labels and non-deterministic second labels;
• applying the seismic training input image to a deterministic interpretation section to obtain a deterministic interpretation output;
• applying the training input to a non-interpretation section to obtain a non-deterministic output;
• and
• correcting errors in the non-deterministic output on subsequent iterations using the loss function;
• generating, based on the refined first portion and the refined second portion, a plurality of geological interpretation options representing alternative models of the subterranean formation;
• ranking the plurality of geological interpretation options using scores determined from outputs of the refined portions;
• selecting, from among the plurality of geological interpretation options, a selected geological interpretation option;
• determining, based on the selected geological interpretation option, a drilling action comprising at least one mitigating action associated with the selected geological interpretation option;
• and
• transmitting a signal to a controller to cause a drilling tool to perform the drilling action, the signal being at least one of generated or transmitted by the refined first portion, the refined second portion, or both of the machine learning model, the drilling action comprising modifying a drilling direction toward hydrocarbons or away from obstructions identified in the selected geological interpretation option.

A computing system, comprising:

• one or more processors;
• and
• a memory system comprising one or more non-transitory, computer-readable media storing instructions that, when executed by at least one of the one or more processors, cause the computing system to perform operations, the operations comprising:
• receiving a seismic training input image;
• generating, using a first portion of a machine learning model of a subterranean formation, a first output based at least in part on the seismic training input image;
• generating, using a second portion of the machine learning model of the subterranean formation, a second output based at least in part on the seismic training input image;
• generating a loss function based at least in part on comparing:
• the first output;
• a deterministic first label synthetically generated and representing a deterministic ground truth for the first output;
• the second output;
• and
• a non-deterministic second label representing a non-deterministic ground truth for the second output;
• refining the first portion and the second portion of the machine learning model based at least in part on the loss function by:
• training the machine learning model based on deterministic first labels and non-deterministic second labels;
• applying the seismic training input image to a deterministic interpretation section to obtain a deterministic interpretation output;
• applying the training input to a non-interpretation section to obtain a non-deterministic output;
• and
• correcting errors in the non-deterministic output on subsequent iterations using the loss function;
• generating, based on the refined first portion and the refined second portion, a plurality of geological interpretation options representing alternative models of the subterranean formation;
• ranking the plurality of geological interpretation options using scores determined from outputs of the refined portions;
• selecting, from among the plurality of geological interpretation options, a selected geological interpretation option;
• determining, based on the selected geological interpretation option, a drilling action comprising at least one mitigating action associated with the selected geological interpretation option;
• and
• transmitting a signal to a controller to cause a drilling tool to perform the drilling action, the signal being at least one of generated or transmitted by the refined first portion, the refined second portion, or both of the machine learning model, the drilling action comprising modifying a drilling direction toward hydrocarbons or away from obstructions identified in the selected geological interpretation option.