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 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 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 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: Methods for updating a physical properties model of a subsurface region that combine advantages of FWI and tomography into a joint inversion scheme are provided. One method comprises obtaining measured seismic data; generating simulated seismic data using an initial model; computing at least one of an FWI gradient and a tomography gradient; minimizing a joint objective function E, wherein the objective function E is based on a combination of the FWI gradient and the tomography gradient or preconditioning of the FWI gradient or the tomography gradient; generating a final model based on the minimized joint objective function E; and using the final model to generate a subsurface image. The joint objective function E may be a function of one or both a FWI objective function CFWI and a tomography objective function CTomo. The joint objective function E may be defined as a weighted sum of CFWI and CTomo.

Abstract: A method, including: generating updated velocity models, each corresponding to one of a plurality of initial velocity models of intersecting 2D seismic survey lines, wherein updates to the plurality of initial velocity models are computed by imposing a seismic-tie regularization constraint on an inversion process that inverts for the updates to the plurality of initial velocity models, and the seismic-tie regularization constraint causes the updated velocity models to have consistent values for depth of seismic reflectivity at intersecting spatial locations.

Abstract: Methods for updating a physical properties model of a subsurface region that combine advantages of FWI and tomography into a joint inversion scheme are provided. One method comprises obtaining measured seismic data; generating simulated seismic data using an initial model; computing at least one of an FWI gradient and a tomography gradient; minimizing a joint objective function E, wherein the objective function E is based on a combination of the FWI gradient and the tomography gradient or preconditioning of the FWI gradient or the tomography gradient; generating a final model based on the minimized joint objective function E; and using the final model to generate a subsurface image. The joint objective function E may be a function of one or both a FWI objective function CFWI and a tomography objective function CTomo. The joint objective function E may be defined as a weighted sum of CFWI and CTomo.

Abstract: Systems and methods for polarization converters are disclosed. An example wireless communication system includes a first transceiver module of a wireless communication system configured to form one or more linearly polarized communication links with a second transceiver module of the wireless communication system, and a polarization converter positioned between the first and second transceiver modules and configured to convert the one or more linearly polarized communication links to circularly polarized communication links. The polarization converter includes first and second frequency selective surfaces (FSSs) formed from respective first and second metalized layers of a printed circuit board (PCB), each FSS includes an array of capacitive patches and inductive traces forming an array of unit cells, and each unit cell of the second FSS is aligned with each unit cell of the first FSS.

Abstract: Systems and methods for dual channel polarization filter structures are disclosed. An example wireless communication system includes a first transceiver module of a wireless communication system configured to form one or more linearly polarized communication links with a second transceiver module of the wireless communication system, and a dual channel polarization filter structure positioned between the first and second transceiver modules and configured to filter the one or more linearly polarized communication links to produce corresponding one or more filtered linearly polarized communication links. The dual channel polarization filter structure includes first and second filter channels each formed from three structural layers including at least one metalized layer printed circuit board (PCB) disposed between the remaining two structural layers, and each filter channel includes an array of filter elements each comprising at least one metamaterial absorber arrangement.

Abstract: A method for generating seismic attribute gathers, the method including: computing, with a computer, seismic images with a field dataset; generating, with a computer, synthetic data corresponding to the seismic images; computing, with a computer, an attribute volume by applying an expectation method to the synthetic data; mapping, with a computer, the attribute volume to the seismic images; and generating, with a computer, seismic attribute gathers by stacking the seismic images mapped to the attribute volume.

Abstract: A method, including: generating updated velocity models, each corresponding to one of a plurality of initial velocity models of intersecting 2D seismic survey lines, wherein updates to the plurality of initial velocity models are computed by imposing a seismic-tie regularization constraint on an inversion process that inverts for the updates to the plurality of initial velocity models, and the seismic-tie regularization constraint causes the updated velocity models to have consistent values for depth of seismic reflectivity at intersecting spatial locations.

Abstract: A method for generating seismic attribute gathers, the method including: computing, with a computer, seismic images with a field dataset; generating, with a computer, synthetic data corresponding to the seismic images; computing, with a computer, an attribute volume by applying an expectation method to the synthetic data; mapping, with a computer, the attribute volume to the seismic images; and generating, with a computer, seismic attribute gathers by stacking the seismic images mapped to the attribute volume.

Abstract: Method for obtaining rock parameters such as porosity and vshale directly from inversion of seismic data corresponding to a single trace location. This method is distinguished from existing methods that obtain elastic properties from inversion of seismic data, then relate the elastic parameters to rock lithology parameters such as porosity or vshale because it is accomplished in one step, can incorporate anisotropy and does not require multiple trace locations for stability. The data are separated into partial stacks, and a wavelet is specified for each stack. A set of linearized equations are constructed relating seismic reflectivity to changes in elastic parameters, and another set of linearized equations is constructed relating the changes in elastic parameters to the lithologic parameters. The linearized reflectivity equations are combined with the linearized rock physics equations, convolved with the specified wavelets, and equated to the seismic data.

Abstract: Method for obtaining rock parameters such as porosity and vshale directly from inversion of seismic data corresponding to a single trace location. This method is distinguished from existing methods that obtain elastic properties from inversion of seismic data, then relate the elastic parameters to rock lithology parameters such as porosity or vshale because it is accomplished in one step, can incorporate anisotropy and does not require multiple trace locations for stability. The data are separated into partial stacks, and a wavelet is specified for each stack. A set of linearized equations are constructed relating seismic reflectivity to changes in elastic parameters, and another set of linearized equations is constructed relating the changes in elastic parameters to the lithologic parameters. The linearized reflectivity equations are combined with the linearized rock physics equations, convolved with the specified wavelets, and equated to the seismic data.