An inversion method for a multilayer seabed geoacoustic parameter in a shallow sea, includes: establishing a plurality of seabed models, different seabed models corresponding to different layer numbers, randomly generating a value of each geoacoustic parameter based on a preset change range corresponding to each geoacoustic parameter, then calculating to obtain a theoretical sound pressure value, and comparing the theoretical sound pressure value with an actual sound pressure value, adjusting and updating the value of each geoacoustic parameter according to the comparison result until the obtained theoretical sound pressure value is matched with the actual sound pressure value, and obtaining a target geoacoustic parameter value; calculating to obtain a BIC value corresponding to each seabed model; and taking the seabed model with the minimum BIC value as a target seabed model, and taking a target geoacoustic parameter value corresponding to the target seabed model as a target inversion parameter value.

Reducing migration distortions in migrated images of the Earth's subsurface. Recorded seismic data may be migrated, using a migration velocity model, to generate a migration image comprising ADCIGs with distortions. Synthetic seismic data may be generated, using the migration velocity model, for a grid of point scatterers. The synthetic seismic data may be migrated, using the migration velocity model, to generate impulse responses for the point scatterers. The impulse responses are used as point spread functions (PSFs) which approximate the blurring operator, e.g., the Hessian. An optimal reflectivity model may be selected using image-domain least-squares migration (LSM), based on the PSFs, with regularization of the difference between the migration image and a reflectivity model and a total variation (TV) regularization of the reflectivity model in the spatial and angular domains. An image of the optimal reflectivity model may be generated with reduced migration distortions compared to the original migration image.

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.

Aspects of the disclosure provide for a method using clusters of sonic peaks from a logging tool to generate a log of an acoustic property of the formation as a function of depth.

Methods and systems for processing seismic data are presented. Primary wave (P) seismic data (PP data) and shear wave (P) seismic data (PS data) are jointly inverted as part of a nonlinear tomography process which adheres to one or more co-depthing constraints.

Methods and systems are disclosed for forming an image of a subterranean region of interest. The method includes receiving an observed seismic dataset and a seismic velocity model for the subterranean region of interest, and generating a simulated seismic dataset based on the seismic velocity model and the geometry of the observed seismic dataset. The method further includes determining a transformed observed seismic dataset by applying a nonlinear amplitude transform to the observed seismic dataset and determining a transformed simulated seismic by applying the same transform to the simulated seismic dataset. The method still further includes forming an objective function based on the transformed observed seismic and the transformed simulated seismic dataset, and determining an updated seismic velocity model based upon finding an extremum of the objective function.

Systems and methods for simulating subterranean regions having multi-scale, complex fracture geometries. Non-intrusive embedded discrete fracture modeling formulations are applied in conjunction with commercial or in-house simulators to efficiently and accurately model subsurface characteristics including temperature profiles in regions having complex hydraulic fractures, complex natural fractures, or a combination of both.

A reflection full waveform inversion method updates separately a density model and a velocity model of a surveyed subsurface formation. The method includes generating a model-based dataset corresponding to the seismic dataset using a velocity model and a density model to calculate an objective function measuring the difference between the seismic dataset and the model-based dataset. A high-wavenumber component of the objective function's gradient is used to update the density model of the surveyed subsurface formation. The model-based dataset is then regenerated using the velocity model and the updated density model, to calculate an updated objective function. The velocity model of the surveyed subsurface formation is then updated using a low-wavenumber component of the updated objective function's gradient. A structural image of the subsurface formation is generated using the updated velocity model.

A computer-implemented method for augmented inversion and uncertainty quantification for characterizing geophysical bodies is disclosed. The method includes machine-learning-augmented inversion that also facilitates the characterization of uncertainties in geophysical bodies. The method may further estimate wavelets without a well-log calibration, thereby enabling a pre-discovery exploration phase when well log data is unavailable. The machine learning component incorporates a priori knowledge about the subsurface and physics, such as distributions of expected rock types and rock properties, geological structures, and wavelets, through learning from examples. The methodology also allows for conditioning the characterization with the information extracted a priori about the geobodies, such as probabilities of rock types, using other analysis tools. Thus, the conditioning strategy may make the inversion more robust even when a priori distributions are not well balanced. Using the method, a scenario testing workflow may evaluate different candidate subsurface models, facilitating the management of uncertainty in decision-making processes.

Methods and systems are disclosed for updating a seismic velocity model of a subterranean region of interest. The method includes receiving an observed seismic dataset and a seismic velocity model, and generating a simulated seismic dataset based on the seismic velocity model and the geometry of the observed seismic dataset, wherein each dataset is composed of a plurality of seismic traces. The method further includes determining a transformed observed seismic dataset and a transformed simulated seismic dataset by determining the instantaneous frequency of at least one member of the plurality of observed seismic traces; and at least one member of the plurality of simulated seismic traces. The method still further includes forming an objective function based on the transformed observed seismic dataset and the transformed simulated seismic dataset and determining an updated seismic velocity model based on an extremum of the objective function.