A method includes receiving, via a processor, a first seismic dataset generated using a first type of survey system. The method further includes receiving, via the processor, a second seismic dataset generated using a second type of survey system. The method additionally includes determining a frequency band in which to combine the first seismic dataset with the second seismic dataset to generate a combined dataset and generating a seismic image based upon the combined dataset, wherein the seismic image represents hydrocarbons in a subsurface region of the Earth or subsurface drilling hazards.

Visually enhancing a geological feature in 3D seismic survey data may include selecting a first seismic trace from a 3D seismic survey dataset. Said first seismic trace is subdivided into a plurality of identified characteristic segments. At least one first analytical model function is generated for each of said plurality of identified characteristic segments. At least one adapted wavelet from an existing dictionary is utilized. A matching characteristic is determined between said first seismic trace and said at least one first analytical model function. Said at least one first analytical model function is optimized with respect to said matching characteristic. Both determining a matching characteristic, and optimizing said at least one first analytical model function, are repeated until a predetermined condition is met. A model dataset is generated from said optimized at least one first analytical model function for at least part of said first seismic trace for visual representation.

The present disclosure describes methods and systems, including computer-implemented methods, computer program products, and computer systems for applying orthogonalization filtering to wavefield separation. One computer-implemented method includes obtaining multi-component wavefields, performing wavefield separation on the multi-component wavefields to obtain separated wavefields, and applying a local orthogonalization weight (LOW) filtering to the separated wavefields to obtain filtered wavefields.

Post-stack seismic data is received. Transformed seismic data is created from the received post-stack seismic data, including performing a super-resolution radon transform on the post-stack seismic data. Signal and noise regions are separated using the transformed seismic data, including using a defined muting function to remove unwanted noise. An inverse radon transform is performed using the separated signal and noise regions, outputting only signals.

In certain embodiments, a system includes a processor and a memory storing machine readable instructions executable by the processor, the machine readable instructions including a seismic image analysis tool having a wavelet packet transformer to generate sub-images of a received seismic image, a sub-image filter to perform noise suppression and edge detection on each sub-image, an inverse wavelet packet transformer to reconstruct higher level sub-images or seismic image from filtered sub-images, and a sub-image merger to recursively merge a sub-image and a reconstructed sub-image.

Seismic shot gather data is received from a computer data store for processing. The received seismic shot gather data is separated into downgoing and upgoing wavefields, a time-frequency-wavenumber (t-f-k) three-dimensional (3D) data cube comprising multiple time-frequency (t-f) slices is formed. The downgoing wavefields are wavelet transformed from a time (t) domain to a t-f domain and the upgoing wavefields are wavelet transformed from the t domain to the t-f domain. A wavelet cross-correlation is performed between the downgoing wavefields in the t-f domain and the upgoing wavefields in a t-f-k domain to generate wavelet cross-correlated data. Soft-threshold filtering if performed for each t-f slice of the t-f-k 3D data cube. An inverse wavelet transform is performed to bring wavelet cross-correlated data from the t-f-k domain to a time-receiver (t-x) domain. All seismic shots of the received seismic shot gather data are looped over and the wavelet cross-correlated data is stacked as a virtual source gather.

Seismic data is deblended by performing, for each receiver, a first inversion and a second inversion in a transform domain. The first inversion is formulated to minimize a number of non-zero coefficients of the first inversion result. A sub-domain of the transform domain is defined by vectors of a transform domain basis for which the first inversion has yielded the non-zero coefficients. The second inversion is performed in this sub-domain. The solution of the second inversion is used to extract deblended seismic datasets corresponding to each of the distinct signals, from the seismic data.

A method, apparatus, and program product analyze time-series data such as seismic data collected from a subsurface formation by splitting a time-series data set such as an individual seismic trace into a plurality of spectral components, each having an associated frequency, determining an instantaneous frequency for each spectral component, determining a frequency difference for each spectral component based at least in part on the associated and instantaneous frequencies therefor, and determining a tuning parameter based at least in part on the determined frequency difference of each spectral component. Doing so enables, for example, thin-bed structures in the subsurface formation to be identified, and in some instances, thicknesses of such structures to be determined.

A method can include receiving seismic data that has an associated bandwidth; for a number of frequency bands, for a number of frequency bands, iteratively filtering and adjusting the seismic data by applying band-pass filters to extract information associated with each of the frequency bands where the adjusting the seismic data includes, after each iteration, subtracting extracted information from the seismic data prior to a subsequent iteration; balancing the extracted information to generate spectrally balanced seismic data; and outputting the spectrally balanced seismic data.

The present disclosure relates to a method of processing seismic signals comprising: receiving a set of seismic signals, applying a wavelet transformation to the set of signals and generating transformed signals across a plurality of scales. Then for each scale determining coherence information indicative of the transformed signals and generating a comparison matrix comparing the transformed signals, then outputting seismic attribute information based on combined coherence information.