A technique includes receiving first data acquired by at least a particle motion gradient sensor or a rotation sensor of a streamer that is subject to vibration due to towing of the streamer; and receiving second data acquired by at least one particle motion sensor of the streamer and being indicative of particle motion and vibration noise. The technique includes processing the second data in a processor-based machine to, based at least in part on the first data, attenuate the vibration noise indicated by the second data to generate third data indicative of the particle motion.

A method can include receiving data in a data domain where a first portion of the data domain includes a signal to noise ratio that exceeds a signal to noise ratio in a second portion of the data domain; generating a model; in a transform domain, based at least in part on the model, filtering at least a portion of the data in the second portion of the data domain; and, based at least in part on the filtering, outputting noise attenuated data for at least a portion of the data in the second portion of the data domain.

Various implementations described herein are directed to methods for processing seismic data, including estimating a spectral noise power of multi-measurement seismic data received from a multi-dimensional seismic sensor array having multiple seismic sensors. The methods may include receiving a shot record of multi-measurement seismic data in time-domain, partitioning the shot record into overlapping time-space windows, and computing a frequency-domain spectrum for each time-space window. The methods may include computing a signal presence probability for each time-space window using the frequency-domain spectrum and prior probabilities of signal presence and absence for each time-space window. The methods may include iteratively updating a collective spectral noise power by recursively estimating the spectral noise power of a current time-space window based on the frequency spectrum for the current time-space window, the signal presence probability computed for the current time-space window, and a previously estimated spectral noise power of a previous time-space window.

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.

A vibration detection system includes a seismic source device that generates a vibration wave repeated with a predetermined period, a vibration receiving device that receives a response wave due to the vibration wave transmitted via the ground, and a signal processing apparatus that processes measured vibration signals received by the vibration receiving device. The signal processing apparatus includes a separating part that separates individual periodic signals having a period according to a periodicity of the vibration wave generated by the seismic source from the measured vibration signals, the calculating part that calculates the standard periodic signal from the separated individual periodic signals, and the generating part that subtracts the standard periodic signal from the measured vibration signals and generates differential signals.

Techniques are disclosed relating to reducing noise in geophysical marine survey data. According to some embodiments, a complex-valued, directional, multi-resolution (CDM) transform may be applied to marine survey input data and a noise template. Global adaptation constraints and, optionally, local adaptation constraints may be generated dependent on the transformed marine survey data and transformed noise template. The transformed noise template may be adapted dependent upon the global (and, optionally, local) constraints, and the adapted transformed noise template may be subtracted from the transformed marine survey data to remove noise. An inverse CDM transform may be performed on the resulting data to generate reduce-noise marine survey data in the input domain.

A system and method for multicomponent noise attenuation of a seismic wavefield is provided. Embodiments may include receiving, at one or more computing devices, seismic data associated with a seismic wavefield over at least one channel of a plurality of channels from one or more seismic sensor stations. Embodiments may further include identifying a noise component on the at least one channel of the plurality of channels and attenuating the noise component on the at least one channel of the plurality of channels based upon, at least in part, the seismic data received from the one or more seismic sensor stations.

A method of evaluating a subterranean formation includes conveying a tool along a borehole. The tool includes a transmitter that transmits a drive pulse and a receiver that receives at least one formation response to the drive pulse. The method further includes calculating a signal-to-noise ratio of the at least one formation response and comparing the signal-to-noise ratio to a programmable threshold. The method further includes determining, based on the comparing, an adjusted drive pulse to transmit and transmitting the adjusted drive pulse. The method further includes and receiving at least one formation response to the adjusted drive pulse and deriving formation data from the at least one formation response to the adjusted drive pulse. The method further includes displaying a representation of the formation based on the formation data.

One or more first sensors may be configured to sense seismic signals and one or more second sensors may be configured to sense electromagnetic crosstalk signals. The second sensors are not responsive to the seismic signals. The data from the first and second sensors may be recorded as first data and second data, respectively. The first data may be modified based on the second data to remove the electromagnetic crosstalk noise form the seismic 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.