Abstract: Disclosed are methods, systems, and computer-readable medium to perform operations including: receiving an input dataset that represents partial spatial information of an area of interest; providing the input dataset to a spatial context generator, wherein the spatial context generator comprises a machine learning model trained to generate, based on the partial spatial information, contextual spatial information for the area of interest; and using the spatial context generator to generate, based on the partial spatial information, at least one output dataset associated with the area of interest, where each output dataset comprises simulated contextual spatial information for the area of interest.

Abstract: A method for analyzing a geologic sample includes illuminating the geologic sample with a halogen light beam, and capturing an image of the geologic sample on a camera thereby collecting spectra reflected from a surface of the geologic sample. The method further includes processing the image to identify rock properties of the geologic sample and determine a qualitative descriptor of the rock properties. The method further includes analyzing the qualitative descriptor of the rock properties of the geologic sample to determine subsurface lithography.

Abstract: Systems and methods include a computer-implemented method: Seismic data is gathered for a reservoir with unknown fractured and unfractured areas. A structural model is generated. A geomechanical model is built. Geomechanically-estimated fractured areas are determined using the geomechanical model, including: areas where fractures are not likely to exist based on a likelihood lower than a first threshold likelihood, areas where fractures are likely to exist based on a likelihood greater than a second threshold likelihood, and areas where fracturing is unknown based on a likelihood between the first threshold likelihood and the second threshold likelihood. Machine learning-based estimates of a likelihood of a fracture of each area of the reservoir are determined using machine learning based on mathematical calculations of the seismic data.

Abstract: Methods, systems, and computer-readable media are provided for processing seismic data. The method includes receiving a seismic data set associated with a domain, and calculating a seismic attribute associated with the domain from the seismic data set. The method also includes performing one or more mathematical morphology operations on the seismic attribute to generate a processed attribute associated with the domain.

Abstract: Methods, systems, and computer-readable media are provided for processing seismic data. The method includes receiving a seismic data set associated with a domain, and calculating a seismic attribute associated with the domain from the seismic data set. The method also includes performing one or more mathematical morphology operations on the seismic attribute to generate a processed attribute associated with the domain.

Abstract: A method can include providing seismic data for a subsurface region that includes a reflector; processing at least a portion of the seismic data to generate at least one path that extends orthogonally to the reflector; and outputting output data representing the at least one path. Various other apparatuses, systems, methods, etc., are also disclosed.

Abstract: Methods, systems, and computer-readable media for processing seismic data. The method includes receiving seismic traces representing a three-dimensional physical domain, and determining a partition of a similarity matrix based on a subset of the seismic traces. The method also includes determining a transition matrix based on the partition. The method also includes determining one or more eigenvectors and one or more eigenvalues, based on the transition matrix. The method includes calculating one or more approximated eigenvectors for the similarity matrix, based on the partition, the one or more eigenvectors of the transition matrix, and the one or more eigenvalues of the transition matrix. The method includes assigning visual indicator values to the respective seismic traces based on the one or more approximated eigenvectors, and causing a visualization of the three-dimensional physical domain to be displayed in a two-dimensional view, based on the one or more visual indicator values.