Systems and methods for identifying subsurface features as a function of position in a subsurface volume of interest

A computer-implemented method for training a subsurface feature model to identify subsurface features as a function of position in a subsurface volume of interest, the method being implemented in a computer system that comprises a physical computer processor and a non-transitory storage medium, the method comprising:

• obtaining, from the non-transitory storage medium, training subsurface data comprising a subsurface energy value and corresponding training subsurface feature data, wherein the training subsurface feature data comprises a training structural identification value defined by structural identification parameters and a corresponding training categorization value defined by categorization parameters and the training structural identification value;
• obtaining, from the non-transitory storage medium, an initial subsurface feature model comprising an adversarial loss function, wherein the initial subsurface feature model comprises an initial subsurface feature extraction sub-model, an initial subsurface object detection sub-model, and multiple initial network sub-models;
• generating, with the physical computer processor, a conditioned subsurface feature model by training the initial subsurface feature model using the training subsurface data, the training subsurface feature data, and the adversarial loss function, wherein the conditioned subsurface feature model comprises a conditioned subsurface feature extraction sub-model, a conditioned subsurface object detection sub-model, and multiple conditioned network sub-models;
• and
• storing the conditioned subsurface feature model in the non-transitory storage medium.

A system for identifying subsurface features as a function of position in a subsurface volume of interest, the system comprising:

• non-transitory storage medium;
• and
• a physical computer processor configured by machine readable instructions to:
• obtain, from the non-transitory storage medium, target subsurface data comprising a subsurface energy value;
• obtain, from the non-transitory storage medium, a conditioned subsurface feature model comprising a conditioned subsurface feature extraction sub-model, a conditioned subsurface object detection sub-model, and multiple conditioned network sub-models, the conditioned subsurface feature model having been generated by applying an initial subsurface feature model to training subsurface data and training subsurface feature data, wherein the training subsurface data comprises subsurface energy values, and wherein the training subsurface feature data comprises the training structural identification values defined by structural identification parameters and corresponding training categorization values defined by categorization parameters and one of the training structural identification values;
• apply, with the physical computer processor, the conditioned subsurface feature model to the target subsurface data by:
• generating, with the conditioned feature extraction sub-model, target subsurface feature map layers by convoluting the target subsurface data, wherein a given target subsurface feature map layer corresponds to a given spatial scale of the target subsurface data;
• detecting, with the conditioned subsurface object detection sub-model, potential target subsurface features in the target subsurface feature map layers;
• classifying, with the conditioned subsurface object detection sub-model, the detected target subsurface features;
• and
• linking, with the multiple network sub-models, the classified subsurface features to the target subsurface feature data.