Disclosed are methods, systems, and computer-readable medium to perform operations including: generating, using a source wavelet and a current velocity model, modeled seismic data of the subterranean formation; applying a pre-condition to a seismic data residual calculated using the modeled seismic data and acquired seismic data from the subterranean formation; generating a velocity update using the source wavelet and the pre-conditioned seismic data residual; updating, using the velocity update, the current velocity model to generate an updated velocity model; determining that the current velocity model satisfies a predetermined condition; and responsively determining that the updated velocity model is the velocity model of the subterranean formation.

Various embodiments herein relate to systems and methods for detecting seismic events. Systems may include inertial sensors distributed on or in communication with a network of optically switchable windows in the building. In some systems, inertial sensors are located within a window controller, within an insulated glass unit, or in some way rigidly attached to the structure of a building. Logic is described for leveraging sensed inertial data to predicted a seismic event and/or evaluate the structural health of the building. In some cases, logic may be used to issue an alert to building occupants about impending shear waves that will arrive at the building's location. In some cases, a window network may respond to a detected seismic event by, e.g., changing the optical state of windows and/or providing occupants with evacuation instructions.

In a first step, a defined scope value is selected for each of a plurality of hydrodynamic input parameters. A simulated topographical result is generated using the selected scope values and a forward model. A detailed seismic interpretation is generated to represent specific seismic features or observed topography. A calculated a misfit value representing a distance between the simulated topographical result and a detailed seismic interpretation is minimized. An estimated optimized sand ratio and optimized hydrodynamic input parameters are generated. In a second step, a genetic algorithm is used to determine a proportion of each grain size in the estimated optimized sand ratio. A misfit value is used that is calculated from thickness and porosity data extracted from well data and a simulation result generated by the forward model to generate optimized components of different grain sizes. Optimized hydrodynamic input parameters and optimized components of different grain sizes are generated.

A method of seismic exploration above a region of the subsurface of the earth containing structural or stratigraphic features conducive to the presence, migration, or accumulation of hydrocarbons comprises setting a tow depth of a resonant seismic source, producing a resonant frequency at a first amplitude with the resonant seismic source at the tow depth, detecting a depth excursion from the tow depth, reducing an amplitude of the mass from the first amplitude to a second amplitude, preventing the mass from contacting at least one of the first end stop or the second end stop based on reducing the amplitude to the second amplitude, correcting the depth excursion to return the resonant seismic source to the tow depth, and increasing the amplitude from the second amplitude to produce the resonant frequency with the resonant seismic source at the tow depth.

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.

The present disclosure provides a method and a system for high-resolution seismic fault detection by means of an adversarial neural network, including following steps of: training a target adversarial neural network based on a preset training sample set, so as to obtain a trained target adversarial neural network, wherein the preset training sample set includes seismic data and fault labels, the target adversarial neural network includes: a segmentation module, a feature fusion module, and a discriminator module, the segmentation module is a module configured for obtaining a fault feature based on the preset training sample set, and the feature fusion module is a module configured for fusing the fault feature and the seismic data into a global feature map; and performing seismic fault detection on a target seismic image based on the trained target adversarial neural network.

A method of detecting an event by: obtaining a first sample data set; determining a frequency domain feature(s) of the first sample data set over a first time period; determining a first threshold for the a frequency domain feature(s) using the first sample data set; determining that the frequency domain feature(s) matches the first threshold; determining the presence of an event during the first time period based on determining that the frequency domain feature(s) matches the first threshold; obtaining a second sample data set; determining a frequency domain feature(s) of the second sample data set over a second time period; determining a second threshold for the frequency domain feature(s) using the second sample data set; determining that the frequency domain feature(s) matches the second threshold; and determining the presence of the event during the second time period based on determining that the frequency domain feature(s) matches the second threshold.

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.

Methods and systems for determining a property of a tubular are described. Measurement data of cross-sectional shapes of the tubular at a plurality of depth positions is provided. A three-dimensional mesh representing the tubular based on the cross-sectional shapes is generated. A stress simulation using the three-dimensional mesh to provide an integrity assessment of the tubular is performed.

A method for estimating surface waves generates incident, back-scattered, virtual back-scattered and super-virtual back-scattered traces. The stacked super-virtual back-scattered traces are an estimate of the surface waves.