Seismic exploration methods and data processing apparatuses employ a deep neural network to remove seismic interference (SI) noise. Training data is generated by combining an SI model extracted using a conventional model from a subset of the seismic data, with SI free shots and simulated random noise. The trained DNN is used to process the entire seismic data thereby generating an image of subsurface formation for detecting presence and/or location of sought-after natural resources.

Systems and methods are disclosed for identifying and displaying geostructural properties as a function of lithology, horizons, and faults interpreted from well and seismic data. Exemplary implementations may include obtaining an initial fracture distribution grid model; obtaining training structural deformation data; obtaining training subsurface lithology parameter data; obtaining training fracture attribute data; and training the initial fracture distribution grid model to generate a trained fracture distribution grid model.

The present disclosure belongs to the technical field of seismic exploration imaging, and relates to an interpretive-guided velocity modeling seismic imaging method and system, a medium and a device. The method comprises the following steps: S1. performing first imaging on a given initial velocity model to obtain a first imaging result; S2. performing relative wave impedance inversion on the first imaging result to obtain a relative wave impedance profile; S3. performing Curvelet filtering on the relative wave impedance profile to obtain a first interpretation scheme; S4. superposing the first interpretation scheme and the initial velocity model to obtain a new migration velocity field; S5. performing second imaging on a new migration velocity field to obtain a second imaging result; and S6. repeating steps S2-S4 for the obtained second imaging result until a final seismic imaging result is obtained.

A method for processing seismic data may include receiving, via a processor, the seismic data acquired via a seismic survey. The seismic survey may include seismic sources that emit seismic wavefields at different locations. Each of the seismic sources may change a directivity pattern of a respective seismic wavefield based on a respective location of the respective seismic source. The seismic survey may also include seismic receivers that may receive the seismic data. The method may also include generating one or more basis functions that correspond to measurements of the seismic data, modelling a signal component of the seismic data as a sum of the one or more basis functions, and storing the signal component in a storage component. The signal component may be used to acquire an image of a subsurface region of the earth for identifying a feature in the subsurface region of the earth.

A method is described for seismic imaging including receiving a pre-stack seismic dataset and an earth model at one or more computer processors; performing least-squares reverse time migration of the pre-stack seismic dataset using the earth model to create a digital seismic image, wherein the least-squares reverse time migration includes wave-equation forward modeling based on an asymptotic expression for reflection in a subsurface Kirchhoff integral; and generating a display of the digital seismic image on a graphical user interface.

Systems, methods, and apparatuses for generating a subsurface image using diffraction energy information are disclosed. The systems, methods, and apparatuses may include converting a shot gather into one or more plane-wave gather using a Radon transform. The plane-wave gathers may be extrapolated into source-side wavefields and receiver-side wavefields and further generate a pseudo dip-angle gather. The diffraction energy information may be extracted from the pseudo dip-angle gather, and an image containing subsurface features may be generated from the extracted diffraction energy information. The receiver-side wavefields may be decomposed using a recursive Radon transform.

Methods and systems for forming a three-dimensional (“3D”) seismic image of a subterranean region of interest is disclosed. The method includes obtaining a seismic dataset a seismic trace for each of a plurality of pairs of one source and one receiver location and obtaining a 3D travel-time cube for each source location and each receiver location. The method further includes dividing the seismic dataset into a plurality of seismic subsets composed of set of source locations, set of receiver locations a seismic trace for each pair of source and receiver location and the 3D travel-time cube for each source for each receiver location. The method still further includes transmitting, to a random-access memory block of a computer processing unit the seismic subset, and forming a seismic partial image based on the seismic subset, and determining the 3D seismic image based on a combination of the seismic partial images.

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.

Methods and systems for identifying a multiple artifact are disclosed. The method includes obtaining a post-stacked seismic image of a subterranean region and identifying a horizon with the post-stacked seismic image. The method further includes determining a spectral section over the horizon by applying spectral decomposition to the post-stacked seismic image. The method still further includes detecting a frequency anomaly within the spectral section by comparing the spectral section to a reference spectral section and identifying the multiple artifact based on the frequency anomaly.

Hybrid seismic inversion methods and apparatuses perform wave equation inversion and stochastic inversion to generate one or more final models for the reservoir characterization of the survey region. A method may include retrieving seismic data using seismic data recording sensors; storing the seismic data in the database; retrieving well data using the well born sensor in the wellbore; storing the seismic data in the database; storing geology integration information and one or more background models in the database; retrieving the seismic data and processing the seismic data to mitigate the seismic data for a seismic hybrid inversion; and performing the seismic hybrid inversion including performing wave equation inversion and stochastic inversion to generate the one or more final models for the reservoir characterization of the survey region.