A process for generating a velocity model for a sediment-basement interface of a subsurface region includes receiving seismic data representing acoustic signals that are reflected from regions of the subsurface. The process includes receiving potential fields data comprising potential field values that are mapped to locations in the subsurface. The process includes generating weighted time-depth data pairs. The process includes selecting a velocity model that relates a velocity value to a depth value in a time-depth relationship. The process includes optimizing velocity coefficients of the velocity model by determining, for each velocity model of a set, a set of depth estimates for corresponding time values and comparing the set of depth estimates to depth values of the weighted time-depth data pairs. The process includes adjusting the velocity coefficients of the velocity model. The process includes generating a seismic image of the sediment-basement interface.

A method for picking first breaks on a synthetic seismic dataset for a full wavefield travel-time inversion is provided. This method includes obtaining a synthetic seismic dataset and for each synthetic trace, determining a plurality of synthetic trace amplitude maxima and minima, determining a modified energy ratio trace, and forming a trace mask based at least in part, on an amplitude of the synthetic trace and on an amplitude of the modified energy ratio trace. The method further includes determining for each synthetic trace, a positive estimated first break based on the plurality of synthetic trace amplitude maxima and the trace mask and a negative estimated first bread based on the plurality of synthetic trace amplitude minima and the trace mask. The method concludes with determining a predicted first break for each synthetic trace from the positive estimate first break and the negative estimate first break.

A computer implemented method for processing a seismic image comprising seismic values obtained from seismic measurements performed on a geological formation includes determining a seismic dip image based on the seismic image, said seismic dip image comprising local seismic dips representative of the local gradient of the seismic values of the seismic image; initializing a seismic horizon surface modeled by using a combination of spline functions or by using a triangle mesh; and iteratively modifying coefficients used for combining the spline functions, thereby iteratively modifying the seismic horizon surface to progressively increase alignment between local orientations of the seismic horizon surface and the corresponding local seismic dips, until a predetermined stop criterion is satisfied.

Systems and methods for interpreting multi-Z horizons from seismic data are disclosed. Seismic data is displayed via a graphical user interface (GUI) of an application executable at a user's computing device. User input is received via the GUI for picking surfaces of a multi-Z horizon within a current view of the displayed data. The user's input is tracked as it is received via the GUI over a series of input points within the current view of the displayed seismic data. Based on the tracking, each of a plurality of surfaces for the multi-Z horizon and at least one edge point between the picked surfaces within the current view of the seismic data are determined. The current view of the seismic data within the GUI is dynamically updated to include a visual indication of the plurality of surfaces and the at least one edge point for the multi-Z horizon.

Systems and methods for interpreting multi-Z horizons from seismic data are disclosed. Seismic data is displayed via a graphical user interface (GUI) of an application executable at a user's computing device. User input is received via the GUI for picking surfaces of a multi-Z horizon within a current view of the displayed data. The user's input is tracked as it is received via the GUI over a series of input points within the current view of the displayed seismic data. Based on the tracking, each of a plurality of surfaces for the multi-Z horizon and at least one edge point between the picked surfaces within the current view of the seismic data are determined. The current view of the seismic data within the GUI is dynamically updated to include a visual indication of the plurality of surfaces and the at least one edge point for the multi-Z horizon.

Systems and methods for training a model that refines estimated parameter values include computer processors and non-transitory electronic storage that stores subsurface map data sets that correspond to different subsurface volumes of interest, the system configured to obtain training data including unrefined subsurface map data sets specifying estimated parameter values of a first parameter as a function of position within corresponding subsurface volumes of interest, obtain an initial seismic mapping model, generate a conditioned seismic mapping model, and store the conditioned seismic mapping model in the electronic storage.

A method and apparatus for hydrocarbon management including generating an image of a subsurface formation by: obtaining simultaneous-source survey data, an earth model, and a first and a second velocity model of the subsurface formation; generating synthetic survey data with at least one of the earth model, the first velocity model, and the second velocity model. The method and apparatus may include directly migrating the simultaneous-source survey data; migrating the synthetic survey data; and subtracting the migrated synthetic survey data from the migrated simultaneous-source survey data. The method and apparatus may include subtracting the synthetic survey data from the simultaneous-source survey data; and directly migrating the result of the subtraction. The method and apparatus may include generating an artifact-reduced image.

A method and apparatus for generating attenuation-compensated images of subsurface region, including: computing an image of the region utilizing elastic wave propagation, based on field data and subsurface model; generating forward-modeled data utilizing forward viscoelastic wave propagation, based on the image; computing secondary image by migration; computing NMF based on the images; and applying the NMF to the image to generate the attenuation-compensated image. A method and apparatus includes: iteratively computing attenuation-compensated gradient of the region utilizing an elastic wave propagation operator in the back-propagation and a viscoelastic wave propagation operator in the forward modelling, based on field data and subsurface model; computing search direction based on the attenuation-compensated gradient, searching for an improved model, and checking the improved model for convergence.

A method and apparatus for generating attenuation-compensated images of subsurface region, including: computing an image of the region utilizing elastic wave propagation, based on field data and subsurface model; generating forward-modeled data utilizing forward viscoelastic wave propagation, based on the image; computing secondary image by migration; computing NMF based on the images; and applying the NMF to the image to generate the attenuation-compensated image. A method and apparatus includes: iteratively computing attenuation-compensated gradient of the region utilizing an elastic wave propagation operator in the back-propagation and a viscoelastic wave propagation operator in the forward modelling, based on field data and subsurface model; computing search direction based on the attenuation-compensated gradient, searching for an improved model, and checking the improved model for convergence.

Sonic logging data including a sonic waveform associated with a plurality of shot gathers is accessed. A transformation operator is applied to the sonic logging data to provide a transformed sonic image, the transformation operator including at least one of a short time average long time average (STA/LTA) operator, a phase shift operator, and a deconvolution operator. A machine learning process is performed using the transformed sonic image to determine a sonic slowness associated with the sonic logging data. The sonic slowness is provided as an output.