Seismic survey data is received, indexed into index sets, and each index set partitioned into data blocks. For each particular data block of a particular index set, the particular data block is sliced into frequency slices. For each particular frequency slice of the particular data block, the particular frequency slice is processed to remove random and erratic noise by: forming a Hankel matrix from the particular frequency slice: determining an optimal rank for the Hankel matrix, determining a clean signal and erratic noise from the ranked Hankel matrix, and returning the clean signal and erratic noise for the particular frequency slice. A clean signal is assembled from the index sets.

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.

Systems and methods include a computer implemented method for evaluating rock physical properties. Drilling acoustic signals are received in real time during a drilling operation through rock at a drilling location. Transformed data is generated in a frequency domain from the drilling acoustic signals. The transformed data includes frequency and amplitude information for the drilling acoustic signals. De-noised transformed data is generated from the transformed data by filtering noise including background noise generated in a recording system and top drive rotation generated traces. A lithological significant frequency range that includes de-noised significant data points is determined from the de-noised transformed data. Physical properties of the rock are determined in real time using drill bit rotation rates and the amplitudes of the de-noised significant data points.

Computing device, computer instructions and method for removing cross-talk noise from seismic data and generating an image of a surveyed subsurface. The method includes receiving input seismic data D generated by firing one or more seismic sources so that source energy is overlapping, and the input seismic data D is recorded with seismic sensors over the subsurface; generating a cross-talk noise model N by replacing at least one original shot gather with a reconstructed shot gather; subtracting the cross-talk noise model N from the input seismic data D to attenuate coherent cross-talk noise to obtain processed seismic data D.sub.p; deblending the processed seismic data D.sub.p with a deblending algorithm to attenuate a residual noise to obtain deblended seismic data D.sub.d; and generating the image of the subsurface based on the deblended seismic data D.sub.d.

A method and system for removing intrinsic transducer noises. The method may comprise disposing a measurement assembly into a wellbore, performing a measurement operation at a depth in the wellbore with the measurement assembly to record two or more raw reflected waveforms, identifying one or more intrinsic transducer noises in the two or more raw reflected waveforms, dividing the two or more raw reflected waveforms into one or more subsections, and identifying one or more incoherent measurements in the one or more subsections. The method may further comprise deriving a noise model for each of the one or more incoherent measurements, performing an inversion for each noise model, and applying an adaptive subtraction to remove the one or more intrinsic transducer noises from the two or more raw reflected waveforms.

Methods are presented for determining the location of underground features (e.g., CO.sub.2). One method includes capturing, by sensors distributed throughout a region, seismic traces associated with seismic signals generated by a seismic source. For multiple sensors, active noise is identified or passive noise is measured within each seismic trace and values for attributes associated with the active or passive noise are determined. Further, an unsupervised machine-learning model, based on the values of the attributes, is utilized to determine noise characteristics for multiple sensors. The sensors are grouped in clusters based on the noise characteristics for each sensor. For multiple clusters, a noise filter is created based on the noise characteristics of the sensors in the cluster, and the noise filter of the cluster is applied, for multiple sensors, to the seismic traces of the sensor. Additionally, the filtered seismic traces are analyzed to determine a location of CO.sub.2 underground.

Systems and methods of performing a seismic survey are described. The system can receive seismic data in a first domain, and transform the seismic data into a tau-p domain. The system can identify a value on an envelope in the tau-p domain, select several values on the tau-p envelope using a threshold, and then generate a masking function. The system can combine the masking function with the tau-p transformed seismic data, and then perform an inverse tau-p transform on the combined seismic data. The system can adjust amplitudes in the inverse tau-p transformed combined seismic data, and identify one or more coherent events corresponding to subsea lithologic formations or hydrocarbon deposits.

A method of enhancing seismic images includes receiving a seismic gather. The seismic gather includes a plurality of seismic traces. A feature trace is generated based on the plurality of seismic traces in the seismic gather. For each of the plurality of seismic traces in the seismic gather, a correlation trace is generated based on that seismic trace and the feature trace, the correlation trace is modified using an activation function, and an enhanced trace is generated by multiplying that seismic trace with the modified correlation trace.

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.

Methods are presented for determining the location of underground features (e.g., CO.sub.2). One method includes capturing, by sensors distributed throughout a region, seismic traces associated with seismic signals generated by a seismic source. For multiple sensors, active noise is identified or passive noise is measured within each seismic trace and values for attributes associated with the active or passive noise are determined. Further, an unsupervised machine-learning model, based on the values of the attributes, is utilized to determine noise characteristics for multiple sensors. The sensors are grouped in clusters based on the noise characteristics for each sensor. For multiple clusters, a noise filter is created based on the noise characteristics of the sensors in the cluster, and the noise filter of the cluster is applied, for multiple sensors, to the seismic traces of the sensor. Additionally, the filtered seismic traces are analyzed to determine a location of CO.sub.2 underground.