Abstract: A machine learning system efficiently detects faults from three-dimensional (“3D”) seismic images, in which the fault detection is considered as a binary segmentation problem. Because the distribution of fault and nonfault samples is heavily biased, embodiments of the present disclosure use a balanced loss function to optimize model parameters. Embodiments of the present disclosure train a machine learning system by using a selected number of pairs of 3D synthetic seismic and fault volumes, which may be automatically generated by randomly adding folding, faulting, and noise in the volumes. Although trained by using only synthetic data sets, the machine learning system can accurately detect faults from 3D field seismic volumes that are acquired at totally different surveys.

Abstract: A machine learning system efficiently detects faults from three-dimensional (“3D”) seismic images, in which the fault detection is considered as a binary segmentation problem. Because the distribution of fault and nonfault samples is heavily biased, embodiments of the present disclosure use a balanced loss function to optimize model parameters. Embodiments of the present disclosure train a machine learning system by using a selected number of pairs of 3D synthetic seismic and fault volumes, which may be automatically generated by randomly adding folding, faulting, and noise in the volumes. Although trained by using only synthetic data sets, the machine learning system can accurately detect faults from 3D field seismic volumes that are acquired at totally different surveys.

Abstract: A method includes receiving image data that is to be recognized by the at least one neural network. The image data is representative of a fault within a subsurface volume. The image data includes three-dimensional synthetic data. The method also includes generating an output via the at least one neural network based on the received image data. The method also includes comparing the output of the at least one neural network with a desired output; and modifying the neural network so that the output of the neural network corresponds to the desired output.