Abstract: A method and apparatus for seismic analysis include obtaining an initial geophysical model and seismic data for a subsurface region; producing a subsurface image of the subsurface region with the seismic data and the geophysical model; generating a map of one or more geologic features of the subsurface region by automatically interpreting the subsurface image; and iteratively updating the geophysical model, subsurface image, and map of geologic features by: building an updated geophysical model based on the geophysical model of a prior iteration constrained by one or more geologic features from the prior iteration; imaging the seismic data with the updated geophysical model to produce an updated subsurface image; and automatically interpreting the updated subsurface image to generate an updated map of geologic features.

Abstract: A method for subsurface fault extraction using undirected graphs is provided. Extracting faults in the subsurface may assist in various stages of geophysical prospecting. To that end, an undirected graph may be used in order to identify distinctive fault branches in the subsurface. Fault probability data, from seismic data, may be used to establish connections in the undirected graph. Thereafter, some of the connections in the undirected graph may be removed based on analyzing one or more attributes, such as dip, azimuth, or context, associated with the connections or nodes associated with the connections. After which, the undirected graph may be analyzed in order to extract the faults in the subsurface.

Abstract: A method and apparatus for automated seismic interpretation (ASI), including: obtaining trained models comprising a geologic scenario from a model repository, wherein the trained models comprise executable code; obtaining test data comprising geophysical data for a subsurface region; and performing an inference on the test data with the trained models to generate a feature probability map representative of subsurface features. A method and apparatus for machine learning, including: an ASI model; a training dataset comprising seismic images and a plurality of data portions; a plurality of memory locations, each comprising a replication of the ASI model and a different data portion of the training dataset; a plurality of data augmentation modules, each identified with one of the plurality of memory locations; a training module configured to receive output from the plurality of data augmentation modules; and a model repository configured to receive updated models from the training module.

Abstract: A method to automatically interpret a subsurface feature within geophysical data, the method including: storing, in a computer memory, geophysical data obtained from a survey of a subsurface region; and extracting, with a computer, a feature probability volume by processing the geophysical data with one or more fully convolutional neural networks, which are trained to relate the geophysical data to at least one subsurface feature, wherein the extracting includes fusing together outputs of the one or more fully convolutional neural networks.

Abstract: A method for subsurface fault extraction using undirected graphs is provided. Extracting faults in the subsurface may assist in various stages of geophysical prospecting. To that end, an undirected graph may be used in order to identify distinctive fault branches in the subsurface. Fault probability data, from seismic data, may be used to establish connections in the undirected graph. Thereafter, some of the connections in the undirected graph may be removed based on analyzing one or more attributes, such as dip, azimuth, or context, associated with the connections or nodes associated with the connections. After which, the undirected graph may be analyzed in order to extract the faults in the subsurface.

Abstract: A method and apparatus for seismic analysis include obtaining an initial geophysical model and seismic data for a subsurface region; producing a subsurface image of the subsurface region with the seismic data and the geophysical model; generating a map of one or more geologic features of the subsurface region by automatically interpreting the subsurface image; and iteratively updating the geophysical model, subsurface image, and map of geologic features by: building an updated geophysical model based on the geophysical model of a prior iteration constrained by one or more geologic features from the prior iteration; imaging the seismic data with the updated geophysical model to produce an updated subsurface image; and automatically interpreting the updated subsurface image to generate an updated map of geologic features.

Abstract: A method and apparatus for automated seismic interpretation (ASI), including: obtaining trained models comprising a geologic scenario from a model repository, wherein the trained models comprise executable code; obtaining test data comprising geophysical data for a subsurface region; and performing an inference on the test data with the trained models to generate a feature probability map representative of subsurface features. A method and apparatus for machine learning, including: an ASI model; a training dataset comprising seismic images and a plurality of data portions; a plurality of memory locations, each comprising a replication of the ASI model and a different data portion of the training dataset; a plurality of data augmentation modules, each identified with one of the plurality of memory locations; a training module configured to receive output from the plurality of data augmentation modules; and a model repository configured to receive updated models from the training module.

Abstract: A method to automatically interpret a subsurface feature within geophysical data, the method including: storing, in a computer memory, geophysical data obtained from a survey of a subsurface region; and extracting, with a computer, a feature probability volume by processing the geophysical data with one or more fully convolutional neural networks, which are trained to relate the geophysical data to at least one subsurface feature, wherein the extracting includes fusing together outputs of the one or more fully convolutional neural networks.

Abstract: A method and apparatus for heat-dissipation in an electronics chassis can include a housing having an interior and exterior, at least two walls, at least one of which is a thermally conductive wall, a heat spreader operably coupled to at least a portion of the housing to dissipate or spread heat from a heat producing component.

Abstract: An in vitro method for genotyping genetic variations in a individual, and products for use in the method.

Abstract: Lotioned fibrous structures and sanitary tissue products comprising such fibrous structures are provided. More particularly, fibrous structures comprising two or more different compositions arranged on a surface of the fibrous structure such that one of the compositions is sandwiched between the fibrous structure surface and the other composition is provided. Sanitary tissue products comprising such fibrous structures and methods for making such fibrous structures are also provided.