Methods of analyzing and optimizing a seismic survey design are described. Specifically, the sampling quality is analyzed as opposed to the overall quality of the whole survey. This allows for analysis of the impact of the offsets, obstacles, and other aspects of the survey on the sampling quality, which will improve the ability to compress the resulting data and minimize acquisition footprints.

Systems and methods include a computer-implemented method includes concurrently outputting, by a computing device to a display of the computing device, a graphical time-domain interpretation of seismic data, a graphical velocity model related to the seismic data, and a graphical depth-domain interpretation of the seismic data. The method may further include identifying, by the computing device, a first alteration to one of the time-domain interpretation, the velocity model, and the depth-domain interpretation. The method may further include identifying, by the computing device based on the first alteration, a second alteration to another of the time-domain interpretation, the velocity model, and the depth-domain interpretation. The method may further include updating, by the computing device based on the first alteration and the second alteration, at least two of the graphical time-domain interpretation, the graphical velocity model, and the graphical depth-domain interpretation. Other embodiments may be described or claimed.

Marine surveys carried out with multiple source arrays comprising three or more sources are discussed. Each source of a multiple source array is an array of source elements, such as air guns. The sources of a multiple source array may be arranged in particular type of configuration that is effectively maintained while the survey vessel travels a sail line. The sources of the multiple source array are activated to acoustically illuminate a subterranean formation with acoustic signals. Two or more sources of a multiple source array may be activated to create blended seismic data. Methods to deblend, source deghost, and attenuate noise in the blended seismic data obtained by using a multiple source array are also discussed.

Marine surveys carried out with multiple source arrays comprising three or more sources are discussed. Each source of a multiple source array is an array of source elements, such as air guns. The sources of a multiple source array may be arranged in particular type of configuration that is effectively maintained while the survey vessel travels a sail line. The sources of the multiple source array are activated to acoustically illuminate a subterranean formation with acoustic signals. Two or more sources of a multiple source array may be activated to create blended seismic data. Methods to deblend, source deghost, and attenuate noise in the blended seismic data obtained by using a multiple source array are also discussed.

The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems for direct gas reservoir detection using frequency amplitude. One computer-implemented method includes spectrally decomposing seismic data associated with a target area into a plurality of mono-frequency volumes. Further, the method includes based on a low-frequency volume of the plurality of volumes, generating a low frequency map of the target area. Yet further, the method includes based on a high-frequency volume of the plurality of volumes, generating a high frequency map of the target area. Additionally, the method includes dividing the low frequency map by the high frequency map to generate a frequency ratio map. The method also includes using the frequency ratio map to identify a subsurface gas reservoir in the target area.

The present disclosure provides a method and a device for imaging diffracted waves based on azimuth-dip angle gathers and a storage medium, which relates to the technical field of seismic exploration, comprising firstly acquiring seismic data and generating target azimuth-dip angle gathers based on the seismic data, wherein the target azimuth-dip angle gathers are a set of all azimuth-dip angle gathers in which the Fresnel zones have been muted, and each of the azimuth-dip angle gathers represents a dip-angle gather corresponding to each azimuth angle; then detecting diffracted waves based on the target azimuth-dip angle gathers, and determining the type of the diffracted waves; and finally, imaging the diffracted waves based on the type of the diffracted waves to obtain a diffracted wave imaging result.

The present disclosure provides a method and a device for imaging diffracted waves based on azimuth-dip angle gathers and a storage medium, which relates to the technical field of seismic exploration, comprising firstly acquiring seismic data and generating target azimuth-dip angle gathers based on the seismic data, wherein the target azimuth-dip angle gathers are a set of all azimuth-dip angle gathers in which the Fresnel zones have been muted, and each of the azimuth-dip angle gathers represents a dip-angle gather corresponding to each azimuth angle; then detecting diffracted waves based on the target azimuth-dip angle gathers, and determining the type of the diffracted waves; and finally, imaging the diffracted waves based on the type of the diffracted waves to obtain a diffracted wave imaging result.

Systems and methods are provided for seismic well tie domain conversion. In one embodiment, a process is provided to integrate well and seismic data for reservoir characterization. System configurations and processes described herein use neural networks to predict sonic well logs in the two way time (TWT) domain from measured well logs in depth, rather than predicting drift function. Embodiments are also directed to systems for reservoir characterization. Domain conversion of data includes receiving input data, preprocessing the data, and training a model to determine a length of an output sequence. The method also includes training the model for conversion of data based on at least one neural network. A sequence length prediction may be output as part of training and to perform modeling/prediction operations. The method also includes outputting sequence length in a TWT domain and output of transformed data.

An automatic seismic facies identification method based on combination of Self-Attention mechanism and U-shape network architecture, including: obtaining and preprocessing post-stack seismic data to construct a sample training and validation dataset; building an encoder through an overlapped patch merging module with down-sampling function and a self-attention transformer module with global modeling function; building a decoder through a patch expanding module with linear upsampling function, the self-attention transformer module, and a skip connection module with multilayer feature fusion function; building a seismic facies identification model using the encoder, the decoder, and a Hypercolumn module, where the seismic facies identification model includes a Hypercolumns-U-Segformer (HUSeg); and building a hybrid loss function; iteratively training the seismic facies identification model with a training and validation set; and inputting test data into a trained identification model to obtain seismic facies corresponding to the test data.

A method includes receiving a data stream comprising content generated by an application executing on a user device. The data stream is received from a guidance service that is separate from the application. The data stream is processed using a set of machine learning models to identify a first set of artifacts within the content. A first state of the application is identified based on the first set of artifacts. First transition data is identified in a logic flow of the application. The first transition data corresponds to transitioning from the first state to a second state of the application. Based on the first transition data, first guidance data is generated that describes user input for transitioning the application from the first state to the second state. The first guidance data is sent to the user device, where it is separately presented from the application by the guidance service.