A method for processing seismic data includes receiving blended seismic data from one or more seismic sources. The method also includes applying a transform to the blended seismic data to decompose the blended seismic data into different parameters. The method also includes applying one or more independent sparse inversions to the different parameters. The method also includes defining a set of prior information techniques to be used within the one or more independent sparse inversions. The method also includes determining an energy part of the blended seismic data that is greater than a first predetermined threshold based at least partially upon the multiple independent sparse inversions, the set of prior information techniques, or both. The method also includes removing the energy part from the blended seismic data to produce modified seismic data.

A method and product for reducing or eliminating noise in waveforms for the purpose of improving the subsequent acoustic log processing products. The present disclosure identifies the noise signature and removes the noise signature from the formation signal through a process of deconvolution. This product and method is particularly effective where the noise signal overlaps in the time domain and in the frequency domain with the formation signal.

Leveraging migration and demigration, here we propose a learning-based approach for fast denoising with applications to fast-track processing. The method is designed to directly work on raw data without separating each noise type and character. The automatic attenuation of noise is attained by performing migration/demigration guided sparse inversion. By discussing examples from a Permian Basin dataset with very challenging noise issues, we attest the feasibility of this learning-based approach as a fast turnaround alternative to conventional denoising methodology.

A technique includes receiving sensor data; sorting the data into a gather representation that corresponds to a plurality of shots of an energy source; and determining a signal cone based at least in part on at least one characteristic of the gather representation. The technique includes processing the sensor data in a processor-based machine to attenuate noise to generate data representing a signal based at least in part on the determined signal cone and the gather representation.

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 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.

A tangible, non-transitory, machine-readable media, includes instructions configured to cause a processor to determine a residual associated with input seismic data received from a seismic source. The residual is indicative of a difference between expected input seismic data and the input seismic data, and wherein the input seismic data is configured to be combed with an expanded window such that the expanded window comprises data generated by an earlier seismic source excitation and received before a time of a seismic source excitation that generated an input seismic trace corresponding to the input seismic data. The instructions are also configured to cause the processor to determine a deblended output based at least in part on the residual. In addition, the instructions are configured to cause a processor to update the deblended output based at least in part on a result from performing one or more recovery operations configured to recover coherent signals from non-coherent signals of the deblended output. The coherent signals comprise a matching parameter. Further, the instructions are configured to cause a processor to filter the deblended output to remove a portion of the deblended output that is before the time of the seismic source excitation or before a predicted earliest arrival time of a seismic wave travelling from the seismic source to a receiver, to generate an improved deblended output comprising less noise than the deblended output. Still further, the instructions configured to cause a processor to transmit the filtered deblended output for use in generating a seismic image. The seismic image represents hydrocarbons in a subsurface region of Earth or subsurface drilling hazards.