A method for surveying, may include receiving, by a processor, first survey data from a first source, the first source comprising a first signal generated by a subsurface earth formation in response to a passive-source electromagnetic signal, wherein the electromagnetic signal is generated by an electroseismic or seismoelectric conversion of the passive-source electromagnetic signal. The method may also include receiving, by the processor, second survey data from a second source and processing the first survey data and the second survey data to determine one or more properties of a subsurface earth formation.

A method, system and non-transitory computer-readable medium for forming a seismic image of a geological structure are provided. After obtaining seismic wave data including a plurality of seismic wave traces at a first region of the geological structure, a predicted time dispersion error of an actual time dispersion error that results from a use of a finite difference approximation in calculating predicted seismic wave data at a second region of the geological structure as if a seismic wave propagates from the first region to the second region of the geological structure, is calculated. A corrected predicted seismic wave data at the second region of the geological structure is calculated by applying the finite difference approximation to the seismic wave data at the first region of the geological structure compensated with the predicted time dispersion error. A seismic image of the second region of the geological structure is generated using the corrected predicted seismic wave data, such that the actual time dispersion error is negated by the predicted time dispersion error.

A method, system and non-transitory computer-readable medium for forming a seismic image of a geological structure are provided. After obtaining seismic wave data including a plurality of seismic wave traces at a first region of the geological structure, a predicted time dispersion error of an actual time dispersion error that results from a use of a finite difference approximation in calculating predicted seismic wave data at a second region of the geological structure as if a seismic wave propagates from the first region to the second region of the geological structure, is calculated. A corrected predicted seismic wave data at the second region of the geological structure is calculated by applying the finite difference approximation to the seismic wave data at the first region of the geological structure compensated with the predicted time dispersion error. A seismic image of the second region of the geological structure is generated using the corrected predicted seismic wave data, such that the actual time dispersion error is negated by the predicted time dispersion error.

Methods and systems for identifying a pay zone in a subterranean formation can include: logging a well extending into the subterranean formation including measuring bulk density, compressional wave travel time and shear wave travel time at different depths in the subterranean formation; calculating elastic attributes including acoustic impedance and compressional velocity-shear velocity ratio at different depths in the subterranean formation; and displaying and analyzing the calculated elastic attributes to identify the low resistivity pay zones.

According to one aspect, a computer-implemented method for detecting an earthquake includes detecting vibrations in a trackpad of a computing device using an inductive element and force sensing circuitry of the trackpad and, processing, by a microcontroller of the computing device, the vibrations for detection of an earthquake vibration signal. In response to detecting the earthquake vibration signal, communicating, by the computing device, the earthquake vibration signal to a remote server and receiving, at the computing device, an earthquake alert from the remote server.

Examples described herein provide a computer-implemented method that includes analyzing a first dataset by applying the first dataset to a first model to generate a first result. The method further includes analyzing a second dataset by applying the second dataset to a second model to generate a second result. The method further includes performing validation on the first model and the second model by comparing the first result to the second result. The method further includes, responsive to determining that the first result and the second result match, modifying an operational action of a surface assembly based on at least one of the first result or the second result. The method further includes, responsive to determining that the first result and the second result do not match, updating at least one of the first model or the second model.

To generate dispersion curves for acoustic waves in a radially layered system, a matrix M containing solutions to the wave equation subject to the boundary conditions of the system is constructed. The reciprocal condition number (RCN) of the matrix M is determined as a function of acoustic wave frequency and slowness. The local minima of the RCN in the frequency-slowness plane produces the dispersion curves corresponding to allowable acoustic modes in the system. A sensitivity analysis which identifies the dispersion curves dependent on a selected parameter. The dispersion curves independent of the perturbed parameters are eliminated by perturbing the modeling parameters and generating the RCN of the perturbed matrix M and then subtracting the RCN values of the unperturbed matrix M, leaving the dispersion curves that exhibit dependence on the selected parameter.

A method for waveform inversion, the method including receiving observed data d, wherein the observed data d is recorded with sensors and is indicative of a subsurface of the earth; calculating estimated data p, based on a model m of the subsurface; calculating, using a trained neural network, a misfit function J.sub.ML; and calculating an updated model m.sub.t+1 of the subsurface, based on an application of the misfit function J.sub.ML to the observed data d and the estimated data p.

A method for seismic processing includes extracting, using a first machine learning model, one or more seismic features from seismic data representing a subsurface domain, receiving one or more well logs representing one or more subsurface properties in the subsurface domain, and predicting, using a second machine learning model, the one or more subsurface properties in the subsurface domain at a location that does not correspond to an existing well based on the seismic data, the one or more well logs, and the one or more seismic features that were extracted from the seismic data.

The present invention discloses systems and methods for attenuation of coherent environmental and source-generated noise in a continuously recorded domain of seismic survey testing. Rather than applying universal de-noising techniques to conventional gathers after source de-blending, the system and methods discussed herein focus on estimating and removing noise directly on continuous records by leveraging the noise characteristics in the domain of natural recording. Such techniques may equally be applied to coherent environmental and source-generated noises on seismic data as well as other data and noise types. Driven by the noise types encountered in the field, the methods of noise attenuation may be based upon time-frequency domain rank reduction techniques. Further, to model signal and/or noise, low-rank approximations are employed in conjunction with other techniques such as operator design and unsupervised learning.