Disclosed herein is a method of stripping a strong reflection layer based on deep learning. The method establishes a direct mapping relationship between a strong reflection signal and seismic data of a target work area through a nonlinear mapping function of the deep neural network, and strips a strong reflection layer after the strong layer is accurately predicted. A mapping relationship between the seismic data containing the strong reflection layer and an event of the strong reflection layer is directedly found through training parameters. In addition, this method does not require an empirical parameter adjustment, and only needs to prepare a training sample that meets the actual conditions of the target work area according to the described rules.

This disclosure describes processes and systems for generating a high-resolution velocity model of a subterranean formation from recorded seismic data gathers obtained in a marine seismic survey of the subterranean formation. A velocity model is computed by iterative FWI using reflections, resolving the velocity field of deep subterranean targets without requiring ultralong offsets. The processes and systems use of an impedance sensitivity kernel to characterize reflections in a modeled wavefield, and then use the reflections to compute a velocity sensitivity kernel that is used to produce low-wavenumber updates to the velocity model. The iterative process is applied in a cascade such that position of reflectors and background velocity are simultaneously updated. Once the low-wavenumber components of the velocity model are updated, the velocity model is used as an input of conventional FWI to introduce missing velocity components (i.e., high-wavenumber) to increase the resolution of the velocity model.

The current disclosure is directed to methods and systems to determine properties of a subterranean formation located below a body of water. The methods and systems compute synthetic pressure and velocity vector wavefields that represent acoustic energy interactions within a model environment that comprises a model body of water located above a model subterranean formation. The model environment is separated into a stationary region and a time-varying region. The methods and systems include determining properties of the subterranean formation by iteratively adjusting the model environment to approximate the actual subterranean formation. The model environment is iteratively adjusted until a minimum difference between the synthetic pressure and velocity vector wavefields computed for each change to the model environment and actual pressure and velocity wave fields obtained from a marine seismic survey of the subterranean formation is achieved.

A system and method for analyzing geologic features including fluid estimation and lithology discrimination may include the steps of identifying areas of interest on a seismic horizon, computing statistical data ranges for the seismic amplitudes within the areas of interest, and analyzing the geologic features based on the amplitude variation with offset (AVO) or angle (AVA) curves including the statistical data ranges.

Method and system is described for modeling one or more geophysical properties of a subsurface volume. In one embodiment, a method of modeling the subsurface comprises obtaining one or more subsurface volume and obtaining an interpretation of the subsurface volume. One or more flexible geologic concepts are defined and applied to the interpretation of the subsurface volume. The one or more geologic concepts comprise one or more flexible geologic concepts. A modified interpretation of the subsurface volume is obtained based upon the applied geologic concepts.

A method includes receiving, via a processor, input data based upon received seismic data, migrating, via the processor, the input data via a pre-stack depth migration technique to generate migrated input data, encoding, via the processor, the input data via an encoding function as a migration attribute to generate encoded input data having a migration function that is non-monotonic versus an attribute related to the input data, migrating, via the processor, the encoded input data via the pre-stack depth migration technique to generate migrated encoded input data, and generating an estimated common image gather based upon the migrated input data and the migrated encoded input data. The method also includes generating a seismic image utilizing the estimated common image gather, wherein the seismic image represents hydrocarbons in a subsurface region of the Earth or subsurface drilling hazards.

Hydrocarbon exploration and extraction can be facilitated using machine-learning models. For example, a system described herein can receive seismic data indicating locations of geological bodies in a target area of a subterranean formation. The system can provide the seismic data as input to a trained machine-learning model for determining whether the target area of the subterranean formation includes one or more types of geological bodies. The system can receive an output from the trained machine-learning model indicating whether or not the target area of the subterranean formation includes the one or more types of geological bodies. The system can then execute one or more processing operations for facilitating hydrocarbon exploration or extraction based on the seismic data and the output from the trained machine-learning model.

Limitations in accuracy and computing power requirements impeding conventional Kirchhoff migration and reverse time migration are overcome by using the wave-equation Kirchhoff, WEK, technique with Kirchhoff migration. WEK technique includes forward-propagating a low-frequency wavefield from a shot location among pre-defined source locations, calculating an arrival traveltime of a maximum amplitude of the low-frequency wavefield, and applying Kirchhoff migration using the arrival traveltime and the maximum amplitude.

Methods and devices according to various embodiments perform full-wave inversion jointly for datasets acquired at different times over the same underground formation using a time-lag cost function with target regularization terms. This approach improves the 4D signal within reservoirs and suppresses 4D noise outside.

Simulated wells may be selected from a subsurface representation to serve as representation of the corresponding simulated subsurface region. Spatial coverage of a simulated well for the simulated subsurface region may be determined based on extent of similarity between the simulated well and other simulated wells in the subsurface representation. The simulated wells may be selected to achieve desired spatial coverage for the simulated subsurface region and to achieve desired representation of properties of interest for the simulated subsurface region.