A system for processing DAS VSP surveys in real-time is provided. The system includes a DAS data collection system coupled to at least one optical fiber at least partially positioned within a wellbore and configured to repeatedly activate a seismic source of energy. The system further includes an information processing system connected to the DAS data collection system. A seismic dataset is received from the DAS data collection system. The seismic dataset includes a plurality of seismic data records. Two or more of the plurality of seismic data records are combined into a stack. A quality metric indicative of a desired signal-to-noise ratio or incoherence of the stack is determined for each processed seismic dataset collected from a repeated source. Instructions are sent to the DAS data collection system to stop activating the seismic source, in response to determining that the quality metric has reached a predefined threshold.

A device may include a processor that may separate or deblend signals acquired with simultaneous source shooting, in an environment with background noise or other types of noises. The processor may expand a receiver gather before the time of source excitation. The processor may use the expanded time window (e.g., negative time window) to allocate the background noise or other types of noises after removal. The processor may use signal recovery operations to reallocate leaked or misplaced signals created during the separation iterations, including the signals inside the expanded time window, to a correct source excitation and timing. Expanding a receiver gather time window and reallocating leaked or misplaced signals may improve a deblended output used in generating a seismic image.

Various aspects described herein relate to a machine learning based signal recovery. In one example, a computer-implemented method of noise contaminated signal recovery includes receiving, at a server, a first signal including a first portion and a second portion, the first portion indicative of data collected by a plurality of sensors, the second portion representing noise; performing a first denoising process on the first signal to filter out the noise to yield a first denoised signal; applying a machine learning model to determine a residual signal indicative of a difference between the first signal and the first denoised signal; and determining a second signal by adding the residual signal to the first denoised signal, the second signal comprising (i) signals of the first portion with higher magnitudes than the noise in the second portion, and (ii) signals of the first portion having lower magnitudes than the noise in the second portion.

Systems and methods for filtering interface waves from single component seismic data are disclosed. In one embodiment, a method of filtering seismic data includes comparing amplitude coefficients of a matrix storing the seismic data in a time-frequency domain against an amplitude threshold, and comparing frequencies of the matrix against a maximum expected frequency of noise. The method further includes, for each amplitude coefficient having less than the amplitude threshold and an associated frequency less than the maximum expected frequency of noise, scaling the amplitude coefficient to reduce its value. The method also includes performing an inverse time-frequency transformation on the matrix to generate a noise model in a time domain, and subtracting the noise model from the seismic data in the time domain to generate filtered seismic data.

Seismic image processing including filtering a three-dimensional (3D) seismic image for random noise attenuation via multiple processors. The filtering includes receiving a 3D image cube of seismic image data, decomposing the 3D image cube into 3D sub-cubes for parallel computation on the multiple processors, designing and applying a two-dimensional (2D) adaptive filter for image points on 2D image slices of the 3D sub-cubes via the multiple processors to give filtered 3D sub-cubes, and summing the filtered 3D sub-cubes to give a filtered 3D image cube.

A method for performing a formation-related operation based on corrected vertical seismic profile (VSP) data of an earth formation includes performing a VSP survey and applying a normal moveout (NMO) correction equation to the survey data that is a function of source offset to wellhead. The method also includes solving the NMO correction equation using a simulated annealing algorithm having an object function that is a coherence coefficient of semblance analysis of an NMO corrected reflection event within a time window to provide NMO corrected data. The method further includes performing the formation-related operation at at least one of a location, a depth and a depth interval based on the VSP NMO corrected data.

Seismic exploration methods and data processing apparatuses employ a deep neural network to remove seismic interference (SI) noise. Training data is generated by combining an SI model extracted using a conventional model from a subset of the seismic data, with SI free shots and simulated random noise. The trained DNN is used to process the entire seismic data thereby generating an image of subsurface formation for detecting presence and/or location of sought-after natural resources.

The present disclosure is related to methods, systems, and machine-readable media for interference attenuation of a residual portion of seismic data, such as may be recorded in a marine seismic survey. Recorded seismic data can be separated into a portion attributed to a source and a residual portion. Seismic interference attenuation can be performed on the residual portion.

A retrofit valve shutoff device is provided that comprises a coupling key for coupling with an actuator of a shutoff valve on a fluid supply line, an inertial measurement unit for generating one or more signals in response to arrival of seismic waves, a motor for rotating the coupling key and the actuator of the shutoff valve, and a processing unit for receiving the one or more signals from the inertial measurement unit, analyzing the received signals to determine whether to close the shutoff valve, and sending a signal to the motor to rotate the coupling key and the actuator of the shutoff valve to close the shutoff valve based on the analysis of the received signals.

A data set representing features of a geologic formation is formed from two or more signal acquisition data set representing independent aspects of the same wavefield. A wavelet transform is performed on the two or more signal acquisition data sets, and the data sets are further transformed to equalize signal portions of the data sets. Remaining differences in the data sets are interpreted as excess noise and are removed by different methods to improve the signal-to-noise ratio of any resulting data set.