A method includes receiving seismic data including signals collected using a receiver, separating a downgoing wavefield from an upgoing wavefield in the signals, generating a modified downgoing wavefield by removing direct arrivals from the downgoing wavefield, estimating a first-order multiple reflection signal at least partially by deconvolving the modified downgoing wavefield and the downgoing wavefield, and generating a seismic image based at least in part on the estimated first-order multiple reflection signals.

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.

An object of the present invention is to provide a method of a practical level for performing sensor fusion of multiple vibration sensors such as a seismometer, and in an example, to extend a dynamic range of a high-sensitivity geophone by sensor fusion of the high-sensitivity geophone and a low-sensitivity acceleration geophone. A state related to the high-sensitivity geophone is estimated by capturing, in a Kalman filter, an acceleration record from the low-sensitivity acceleration geophone and a velocity record, or a displacement record, and an acceleration record of the high-sensitivity geophone, and estimating and calculating them as the linear Kalman filter problem with a control input. The high-sensitivity geophone is an actual device, but the state can be estimated using a sensor value of the low-sensitivity acceleration geophone even when the record is saturated. This extends the dynamic range of the high-sensitivity geophone.

Scattered body waves are isolated to primary, shear, and surface waves as a receiver wavefield from recorded near-surface scattered wave data generated by scatters. The isolated receiver wavefield is backward propagated through an earth model from a final to an initial state. A source wavefield and the receiver wavefields are cross-correlated. A source wavefield and the receiver wavefields are stacked, over all time steps and sources, to generate a subsurface image. A display of the subsurface image is initiated.

A distributed acoustic sensor is positioned within a wellbore of a geologic formation. Seismic waves are detected using the distributed acoustic sensor. A raw seismic profile is generated based on the detected seismic waves. Resonant noise is identified and reduced in seismic data associated with the raw seismic profile.

A distributed acoustic sensor is positioned within a wellbore of a geologic formation. Seismic waves are detected using the distributed acoustic sensor. A raw seismic profile is generated based on the detected seismic waves. Resonant noise is identified and reduced in seismic data associated with the raw seismic profile.

A method and a system for implementing the method are disclosed wherein the seismic input data and land acquisition input data may be obtained from a non-flat surface, sometimes mild or foothill topography as well as the shot and receiver lines might not necessarily be straight, and often curve to avoid obstacles on the land surface. In particular, the method and system disclosed, decomposes the cross-spread data into sparse common spread beams, then maps those sparse beams into common-spread depth domain, in order to finally stack them to construct the subsurface depth images. The common spread beam migration and processing have higher signal to noise ratio, as well as faster turn-around processing time, for the cross-spread land acquisition over the common-shot or common offset beam migration/processing. The common spread beam migration method and system disclosed, will eventually help illuminate and interpret the hydro-carbonate targets for the seismic processing.

A method and a system for implementing the method are disclosed wherein the seismic input data and land acquisition input data may be obtained from a non-flat surface, sometimes mild or foothill topography as well as the shot and receiver lines might not necessarily be straight, and often curve to avoid obstacles on the land surface. In particular, the method and system disclosed, decomposes the cross-spread data into sparse common spread beams, then maps those sparse beams into common-spread depth domain, in order to finally stack them to construct the subsurface depth images. The common spread beam migration and processing have higher signal to noise ratio, as well as faster turn-around processing time, for the cross-spread land acquisition over the common-shot or common offset beam migration/processing. The common spread beam migration method and system disclosed, will eventually help illuminate and interpret the hydro-carbonate targets for the seismic processing.

Techniques are disclosed relating to deblending of sources in multi-source geophysical survey data, including marine or land-based data. Multiple sets of deblended receiver traces are generated by iteratively applying a coherency filter to estimated sets of deblended receiver traces and updating a residual until a termination condition is reached. In some embodiments, applying the coherency filter during a current iteration may include determining coefficients of the coherency filter based on estimated sets of deblended receiver traces from an immediately prior iteration. In further embodiments, applying the coherency filter may include applying a 3D projection filter, such as an fxy projection filter.

Techniques are disclosed relating to deblending of sources in multi-source geophysical survey data, including marine or land-based data. Multiple sets of deblended receiver traces are generated by iteratively applying a coherency filter to estimated sets of deblended receiver traces and updating a residual until a termination condition is reached. In some embodiments, applying the coherency filter during a current iteration may include determining coefficients of the coherency filter based on estimated sets of deblended receiver traces from an immediately prior iteration. In further embodiments, applying the coherency filter may include applying a 3D projection filter, such as an fxy projection filter.