System and method for conditioning seismic data

A computing system comprising:

• one or more processors;
• and
• a memory system including 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 including:
• receiving unconditioned seismic data, the unconditioned seismic data includes a 3D cube representing a subterranean formation;
• introducing transformations into the unconditioned seismic data to generate transformed seismic data, the transformations include a distortion and noise, parameters of the distortion and the noise are bounded, variable, randomized, or a combination thereof, and the parameters control a nature, a strength, or both of the distortion and the noise;
• training a neural network model using the transformed seismic data to attempt to reproduce the unconditioned seismic data, training the neural network model includes determining weights of the neural network model, and the weights are updated using loss functions in a data domain, a frequency domain, or both;
• and
• conditioning the unconditioned seismic data using the trained neural network model to produce conditioned seismic data, conditioning the unconditioned seismic data includes correcting the distortion and the noise, removing the distortion and noise, or both.

A non-transitory computer-readable medium storing instructions that, when executed by at least one processor of a computing system, cause the computing system to perform operations, the operations comprising:

• receiving unconditioned seismic data, wherein the unconditioned seismic data includes a 3D cube representing a subterranean formation;
• introducing transformations into the unconditioned seismic data to generate transformed seismic data, the transformations include a distortion and noise, parameters of the distortion and the noise are bounded, variable, and randomized, the parameters control a nature and a strength of the distortion and the noise, the distortion and the noise are introduced before or after reducing a dynamic range of the unconditioned seismic data, the transformed seismic data, or both, and the distortion and the noise are configured to be introduced in either order or simultaneously;
• training a neural network model using the transformed seismic data to attempt to reproduce the unconditioned seismic data, training the neural network model includes determining weights of the neural network model, the weights are determined in a single or a multi-objective fashion, the weights are determined simultaneously or in a piecemeal iterative fashion, the weights are determined using single or multiple loss functions, the weights are determined with varying relative proportions of the loss functions, and the weights are updated using the loss functions in a data domain, a frequency domain, or both;
• conditioning the unconditioned seismic data using the trained neural network model to produce conditioned seismic data, conditioning the unconditioned seismic data includes correcting and removing the distortion and the noise, and conditioning the unconditioned seismic data enhances geological features;
• and
• displaying the conditioned seismic data.