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.

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.

The present method predicts and separates dispersive surface waves from seismic data using dispersion estimation and is completely data-driven and computer automated and no human intervention is needed. The method is capable of predicting and suppressing surface waves from recorded seismic data without damaging the reflections. Nonlinear signal comparison (NLSC) is used to obtain a high resolution and accurate dispersion. Based on the dispersion, surface waves are predicted from the field recorded seismic data. The predicted surface waves are then subtracted from the original data.

A technique includes receiving seismic data indicative of measurements acquired by seismic sensors. The measurements are associated with a measurement noise. The technique includes estimating at least one characteristic of the measurement noise and deghosting the seismic data based at least in part on the estimated characteristic(s) of the measurement noise.

Techniques are disclosed relating to machine learning in the context of noise filters for sensor data, e.g., as produced by geophysical surveys. In some embodiments, one or more filters are applied to sensor data, such a harsh filter determined to cause a threshold level of distortion in measured reflections, a mild filter determined to leave a threshold level of remaining noise signals, or an acceptable filter. In some embodiments, the system trains a machine learning classifier based on outputs of the filtering procedures and uses the classifier to determine whether other filtered sensor data from the same survey exhibits acceptable filtering. This may improve accuracy or performance in detecting unacceptable filtering, in some embodiments.

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.

Denoising seismic data can include extracting a first plurality of overlapping patches corresponding to a respective seismic data window selected from a plurality of seismic data windows, reshaping the first plurality of overlapping patches into patch vectors, and determining sparse approximations of the patch vectors based on a dictionary and a sparse coefficient vector. Denoising seismic data can also include reshaping the sparse approximations into a respective second plurality of overlapping patches, and constructing a denoised version of at least one of the plurality of seismic data windows using the respective second plurality overlapping patches.

A system includes an acoustic logging tool including a transducer configured to: emit a first acoustic pulse in a first direction toward a first acoustic surface; measure a first acoustic signal, wherein the first acoustic signal includes a coherent noise component and a first echo component, wherein the first echo component is due at least in part to an interaction of the first acoustic pulse with the first acoustic surface; emit a second acoustic pulse in a second direction, wherein the second direction is at least partly directed away from the first acoustic surface; and measure a second acoustic signal, wherein the second acoustic signal includes substantially only the coherent noise component. The system also includes a data processing system that includes a processor configured to remove the measurement of the second acoustic signal from the measurement of the first acoustic signal to reduce coherent noise.

An example system for noise removal in distributed acoustic sensing data may include a distributed acoustic sensing (DAS) data collection system and an information handling system coupled thereto. The information handling system may receive seismic information from the DAS data collection system. The seismic information may include seismic traces associated with a plurality of depths in the wellbore. The information handling system may also generate a noise pilot trace by stacking one or more of the seismic traces, and subtract the noise pilot trace from the seismic information received from the DAS data collection system.

A method can include receiving data sets where each of the data sets corresponds to one of a plurality of individual emitter-detector arrangements; calculating a multi-dimensional similarity metric for one of the data sets; and, based at least in part on the multi-dimensional similarity metric, assessing the one data set.