A computer-implemented method of enhancing a computer to estimate an uncertainty of an onset of a signal of interest in time-series noisy data. A first mathematical model of first time series data that contains only noise is calculated. A second mathematical model of second time series data that contains the noise and an onset of a signal of interest in the second time series data is calculated. A difference is evaluated between a first combination, being the first mathematical model and the second mathematical model, and a second combination, being the first time series data and the second time series data, wherein evaluating is performed using a generalized entropy metric. A specific time when an onset of the signal of interest occurs is estimated from the difference. An a posteriori distribution is derived for an uncertainty of the specific time at which the onset occurs.

A method for exploring for hydrocarbons, including: simulating a seismic waveform, using a computer, wherein computations are performed on a computational grid representing a subsurface region, said computational grid using perfectly matched layer (PML) boundary conditions that use an energy dissipation operator to minimize non-physical wave reflections at grid boundaries; wherein, in the simulation, the PML boundary conditions are defined to reduce computational instabilities at a boundary by steps including, representing direction of energy propagation by a Poynting vector, and dissipating energy, with the dissipation operator, in a direction of energy propagation instead of in a phase velocity direction; and using the simulated waveform in performing full waveform inversion or reverse time migration of seismic data, and using a physical property model from the inversion or a subsurface image from the migration to explore for hydrocarbons.

A system to detect and control noise in seismic surveys is provided. The system receives, responsive to a seismic wave generated by a source, seismic data detected by a sensor component of a seismic data acquisition unit. The system generates, for windows of the seismic data, Hough tensors for seismic data transforms in multiple dimensions. The system detects, based on a comparison of an eigenvector and eigenvalue of a canonical matrix of the Hough tensors with a historical eigenvector and eigenvalue of a historical canonical matrix of historical Hough tensors of historical seismic data, a first presence of noise in the seismic data. The first presence of noise can correspond to a noisy spectra pattern in a seismic data transform of the seismic data. The system provides, responsive to detection of the first presence of noise in the seismic data, a notification to adjust a characteristic of the seismic survey.

A method includes receiving observed seismic data, determining an envelope or magnitude of the observed seismic data as a first observed value, generating a variable noise term based in part upon the first observed value, and utilizing the variable noise term to determine a likelihood function of a stochastic inversion operation. The method also includes utilizing the likelihood function to generate a posterior probability distribution in conjunction with the stochastic inversion operation and applying the posterior probability distribution to characterize a subsurface region of Earth.

An example method includes at least partially positioning within a wellbore an optical fiber of a distributed acoustic sensing (DAS) data collection system. Seismic data from the DAS data collection system may be received. The seismic data may include seismic traces associated with a plurality of depths in the wellbore. A quality factor may be determined for each seismic trace. One or more seismic traces may be removed from the seismic data based, at least in part, on the determined quality factors.

A system includes an acoustic attenuation interface disposed between a first acoustic transmission conduit and a second acoustic transmission conduit. An acoustic signal source acoustically coupled to the first acoustic transmission conduit generates an acoustic signal. An acoustic noise source acoustically coupled to the second acoustic transmission conduit generates an acoustic noise. A first sensor is configured to detect a first composite signal including the acoustic signal after transmission through at least a portion of the first acoustic transmission conduit and an attenuated acoustic noise. A second sensor is configured to detect a second composite signal including the acoustic signal after transmission through at least a portion of the first acoustic transmission conduit and attenuated by the acoustic attenuation interface and the acoustic noise. An acoustic signal processing system is configured to determine a noise-reduced signal from the first composite signal and the second composite signal.

A computer-implemented method of enhancing a computer to estimate an uncertainty of an onset of a signal of interest in time-series noisy data. A first mathematical model of first time series data that contains only noise is calculated. A second mathematical model of second time series data that contains the noise and an onset of a signal of interest in the second time series data is calculated. A difference is evaluated between a first combination, being the first mathematical model and the second mathematical model, and a second combination, being the first time series data and the second time series data, wherein evaluating is performed using a generalized entropy metric. A specific time when an onset of the signal of interest occurs is estimated from the difference. An a posteriori distribution is derived for an uncertainty of the specific time at which the onset occurs.

Visually enhancing a geological feature in 3D seismic survey data may include selecting a first seismic trace from a 3D seismic survey dataset. Said first seismic trace is subdivided into a plurality of identified characteristic segments. At least one first analytical model function is generated for each of said plurality of identified characteristic segments. At least one adapted wavelet from an existing dictionary is utilized. A matching characteristic is determined between said first seismic trace and said at least one first analytical model function. Said at least one first analytical model function is optimized with respect to said matching characteristic. Both determining a matching characteristic, and optimizing said at least one first analytical model function, are repeated until a predetermined condition is met. A model dataset is generated from said optimized at least one first analytical model function for at least part of said first seismic trace for visual representation.

A method and device for suppressing interference fading noise of optical fibre sensing data are disclosed. The method contains acquiring optical fibre sensing data not subjected to interference fading noise suppression; determining a fading point amplitude threshold based on the optical fibre sensing data; determining a signal fading point based on the fading point amplitude threshold; performing signal interpolation processing on the optical fibre sensing data corresponding to the signal fading point to obtain a signal subjected to interference fading noise suppression; performing phase demodulation and phase unwrapping processing on the signal subjected to interference fading noise suppression to obtain processed optical fibre sensing data.

A computer-implemented method and system provide the ability to detect an earthquake. Distributed acoustic sensing (DAS) data is obtained. A deep neural network model that picks seismic phase arrival times on the DAS data is acquired. A semi-supervised learning approach is utilized to train the deep neural network model. The semi-supervised learning approach utilizes existing labels from a defined seismic dataset to generate pseudo labels on the DAS data. An earthquake is detected by applying the trained deep neural network model to new DAS data.