Abstract: A method for enhancing properties of geophysical data with deep learning networks. Geophysical data may be acquired by positioning a source of sound waves at a chosen shot location, and measuring back-scattered energy generated by the source using receivers placed at selected locations. For example, seismic data may be collected using towed streamer acquisition in order to derive subsurface properties or to form images of the subsurface. However, towed streamer data may be deficient in one or more properties (e.g., at low frequencies). To compensate for the deficiencies, another survey (such as an Ocean Bottom Nodes (OBN) survey) may be sparsely acquired in order to train a neural network. The trained neural network may then be used to compensate for the towed streamer deficient properties, such as by using the trained neural network to extend the towed streamer data to the low frequencies.

Abstract: A method for directional Q compensation of seismic data may comprise calculating angle-dependent subsurface travel times; applying directional Q compensation to the prestack seismic data to obtain Q-compensated data in time-space domain, wherein the directional Q compensation is based on the angle-dependent subsurface travel times; and using the Q-compensated data to generate an image of the subsurface. Directional Q compensation may comprise determining an angle-dependent forward E operator and an angle-dependent adjoint E* operator using the angle-dependent subsurface travel times; and applying a sparse inversion algorithm using the angle-dependent operators to obtain a model of Q-compensated data.

Abstract: A method for learning and applying a similarity measure between geophysical objects is provided. Similarity measures may be used for a variety of geophysics applications, including inverse problems. For example, an inverse problem may seek to minimize or maximize an associated objective function, which summarizes the degree of similarity between observed data and simulated data. However, when comparing between two or more geophysical objects in the context of the inverse problem, it is difficult to determine whether the observed difference between the two or more geophysical objects is due to noise or intrinsic dissimilarity between the objects. In this regard, an application-specific similarity measure, which may be tailored to the specific application, such as the specific inverse problem, may be generated and applied in order to better solve the inverse problem.

Abstract: A method for enhancing properties of geophysical data with deep learning networks. Geophysical data may be acquired by positioning a source of sound waves at a chosen shot location, and measuring back-scattered energy generated by the source using receivers placed at selected locations. For example, seismic data may be collected using towed streamer acquisition in order to derive subsurface properties or to form images of the subsurface. However, towed streamer data may be deficient in one or more properties (e.g., at low frequencies). To compensate for the deficiencies, another survey (such as an Ocean Bottom Nodes (OBN) survey) may be sparsely acquired in order to train a neural network. The trained neural network may then be used to compensate for the towed streamer deficient properties, such as by using the trained neural network to extend the towed streamer data to the low frequencies.

Abstract: A method and apparatus for generating a high-resolution seismic image, including extracting a reflectivity distribution from a geological model; utilizing the reflectivity distribution to label features of the model; generating forward-modeled data from the model; migrating the forward-modeled data to create a migrated image; and training a deep neural network with the labeled synthetic geological model and the migrated image to create a reflectivity prediction network. A method and apparatus includes: selecting a first subset of the field data; applying a low-pass filter to the first subset to generate a first filtered dataset; migrating the first filtered dataset to create a first migrated image; applying a high-pass filter to the first subset to generate a second filtered dataset; migrating the second filtered dataset to create a second migrated image; and training a deep neural network to predict a target distribution of high-frequency signal.

Abstract: A methodology for extending bandwidth of geophysical data is disclosed. Geophysical data, obtained via a towed streamer, may have significant noise in a certain band (such as less than 4 Hz), rendering the data in the certain band unreliable. To remedy this, geophysical data, from a band that is reliable, may be extended to the certain band, resulting in bandwidth extension. One manner of bandwidth extension comprises using machine learning to generate a machine learning model. Specifically, because bandwidth may be viewed as a sequence, machine learning configured to identify sequences, such as recurrent neural networks, may be used to generate the machine learning model. In particular, machine learning may use a training dataset acquired via ocean bottom nodes in order to generate the machine learning model. After which, the machine learning model may be used to extend the bandwidth of a test dataset acquired via a towed streamer.

Abstract: A method for directional Q compensation of seismic data may comprise calculating angle-dependent subsurface travel times; applying directional Q compensation to the prestack seismic data to obtain Q-compensated data in time-space domain, wherein the directional Q compensation is based on the angle-dependent subsurface travel times; and using the Q-compensated data to generate an image of the subsurface. Directional Q compensation may comprise determining an angle-dependent forward E operator and an angle-dependent adjoint E* operator using the angle-dependent subsurface travel times; and applying a sparse inversion algorithm using the angle-dependent operators to obtain a model of Q-compensated data.