Abstract: A method for capturing long-range dependencies in geo-physical data sets involves dependency-training a first backpropagation-enabled process, followed by interdependency-training the dependency-trained backpropagation-enabled process. Dependency-training computes spatial relationships for each input channel of a geophysical data set. Interdependency-training computes inter-feature and spatial relationships between each of the featurized input channels. The output conditional featurized input channels are fused to produce a combined representation of the conditional featurized input channels. The combined representation is inputted to a second backpropagation-enabled process to compute a prediction selected from the group consisting of a geologic feature occurrence, a geophysical property occurrence, a hydrocarbon occurrence, an attribute of subsurface data, and combinations thereof.

Abstract: A method for capturing long-range dependencies in seismic images involves dependency-training a backpropagation-enabled process, followed by label-training the dependency-trained backpropagation-enabled process. Dependency-training computes spatial relationships between elements of the training seismic data set. Label-training computes a prediction selected from an occurrence, a value of an attribute, and combinations thereof. The label-trained backpropagation-enabled process is used to capture long-range dependencies in a non-training seismic data set by computing a prediction selected from the group consisting of a geologic feature occurrence, a geophysical property occurrence, a hydrocarbon occurrence, an attribute of subsurface data, and combinations thereof.

Abstract: A method for improving a backpropagation-enabled process for identifying subsurface features from seismic data involves a model that has been trained with an initial set of training data. A target data set is used to compute a set of initial inferences on the target data set that are combined with the initial training data to define updated training data. The model is trained with the updated training data. Updated inferences on the target data set are then computed. A set of further-updated training data is defined by combining at least a portion of the initial set of training data and at least a portion of the target data and associated updated inferences. The set of further-updated training data is used to train the model. Further-updated inferences on the target data set are then computed and used to identify the occurrence of a user-selected subsurface feature in the target data set.

Abstract: A method for improving a backpropagation-enabled process for identifying subsurface features from seismic data involves a model that has been trained with an initial set of training data. A target data set is used to compute a set of initial inferences on the target data set that are combined with the initial training data to define updated training data. The model is trained with the updated training data. Updated inferences on the target data set are then computed. A set of further-updated training data is defined by combining at least a portion of the initial set of training data and at least a portion of the target data and associated updated inferences. The set of further-updated training data is used to train the model. Further-updated inferences on the target data set are then computed and used to identify the occurrence of a user-selected subsurface feature in the target data set.