Abstract: A method involves detecting primary entities in a document, involving determining that a subset of the primary entities are associated with a first primary entity type, and determining a second primary entity type of one of the primary entities. The method further involves processing the primary entity of the second primary entity type to determine a secondary entity type of the primary entity. The secondary entity type is a subcategory of the second primary entity type. The method also involves hierarchically organizing the primary entities into a document layout structure that includes a top level and a child level. The top level is established by the first subset of primary entities based on the first primary entity type identifying the first subset as headings, and the child level is established by the primary entity based on the second primary entity type, the child level identifying the secondary entity type.

Abstract: A method includes receiving seismic training data comprising a plurality of images each including a plurality of traces, predicting a location of a feature in at least some of the plurality of traces based on a location of an amplitude peak therein, applying labels to the locations, classifying pixels of the plurality of images as representing the feature or not representing the feature, using a semantic segmentation model, adjusting the labels based on the classification of the pixels, training, using the adjusted labels and the seismic training data, a machine-learning model to identify the feature, and identifying the feature in a different seismic data set using the trained machine-learning model.

Abstract: A method involves detecting primary entities in a document, involving determining that a subset of the primary entities are associated with a first primary entity type, and determining a second primary entity type of one of the primary entities. The method further involves processing the primary entity of the second primary entity type to determine a secondary entity type of the primary entity. The secondary entity type is a subcategory of the second primary entity type. The method also involves hierarchically organizing the primary entities into a document layout structure that includes a top level and a child level. The top level is established by the first subset of primary entities based on the first primary entity type identifying the first subset as headings, and the child level is established by the primary entity based on the second primary entity type, the child level identifying the secondary entity type.

Abstract: A method includes receiving seismic training data comprising a plurality of images each including a plurality of traces, predicting a location of a feature in at least some of the plurality of traces based on a location of an amplitude peak therein, applying labels to the locations, classifying pixels of the plurality of images as representing the feature or not representing the feature, using a semantic segmentation model, adjusting the labels based on the classification of the pixels, training, using the adjusted labels and the seismic training data, a machine-learning model to identify the feature, and identifying the feature in a different seismic data set using the trained machine-learning model.