Abstract:
A method for processing seismic data may include receiving input seismic data (di) comprising N spatial coordinates, where the input seismic data is in a first spatial domain, expanding the N spatial coordinates of the input seismic data (di) to N′ modified spatial coordinates, where N′ is greater than N, to provide spatially expanded seismic data (de) that is in a second spatial domain, transforming the spatially expanded seismic data (de) to a model domain to provide model domain data (dm), and generating a final image (df) of a subsurface using the model domain data (dm).
Abstract:
Methods and systems for processing data acquired using a variable-depth streamer, obtain up-going and down-going wavefields at a predetermined datum, and use them to identify multiples included in the up-going wavefield. An image of a geological formation under the seabed is then generated using the data from which the multiples have been removed, and/or the multiples.
Abstract:
A method for processing seismic data includes receiving seismic data and a velocity model (c(x)) for a plurality of locations (x), scaling a dimension of the seismic data according to the velocity model (c(x)) to provide a velocity normalized seismic data, and generating a final image (S(x)) of the subsurface using the velocity normalized seismic data. The velocity normalized seismic data may be a reverse-time migration image (I(x,ξ)) corresponding to the plurality of locations (x) and a plurality of propagation distance offsets (ξ). The method may also include transforming the reverse-time migration image (I(x,ξ)) for the plurality of selected positions (x) to a wavenumber domain to provide velocity normalized wavenumber data (I(k,ψ)) and suppressing data components corresponding to non-physical or undefined reflection angles to provide enhanced wavenumber data (I′(k,ψ)) and using the enhanced wavenumber data (I′(k,ψ)) to generate the final image (S(x)). A corresponding apparatus is also disclosed herein.
Abstract:
A non-blended dataset related to a same surveyed area as a blended dataset is used to deblend the blended dataset. The non-blended dataset may be used to calculate a model dataset emulating the blended dataset, or may be transformed in a model domain and used to derive sparseness weights, model domain masking, scaling or shaping functions used to deblend the blended dataset.
Abstract:
Computing device and methods process seismic data sets associated with the same surveyed subsurface but recorded with different spatial sampling and temporal bandwidths. The first seismic data is used to guide processing of the second seismic data. An image of the subsurface is generated based on processed second seismic data.
Abstract:
A device, medium and method for de-blending seismic data associated with a subsurface of the earth. The method includes a step of receiving seismic data “d” recorded with one or more land receivers, wherein the seismic data includes shot recordings generated by plural sources that are simultaneously actuated; a step of forming either a continuous receiver trace or trace segments from the received seismic data; a step of selecting plural overlapping spatial blocks that cover the surface shot locations; a step of assigning the shot recordings to the plural overlapping spatial blocks; a step of applying a mathematical technique to the recordings to determine de-blended data; and a step of generating an image of the subsurface based on the de-blended data.
Abstract:
A device, medium and method for deblending seismic data associated with a subsurface of the earth. The method includes receiving an input dataset generated by first and second sources S1 and S2 that are operating as simultaneous sources; arranging the input dataset based on the firing times of source S1; applying with a computing system an annihilation filter to the arranged input dataset to estimate cross-talk noise; convolving the cross-talk noise estimate with an operator to form a signal estimate using the firing times of S1 and S2; and generating an image of the subsurface based on the signal estimate.
Abstract:
Methods (700) and devices (600) for seismic data processing estimate (720) signal-to-noise ratios of data in a spatio-temporal block of data, determine (730) data-domain weights associated to the data based on the estimated signal-to-noise ratios, and then generate (740) a model of the signal and/or a model of the noise using the data-domain weights.
Abstract:
Computing device, computer instructions and method for up-down separation of seismic data. The method includes receiving the seismic data, which includes hydrophone data and particle motion data; performing a first up-down separation, which is independent of a ghost model, using as input the hydrophone data and the particle motion data, to obtain first up-down separated data; performing a second up-down separation by using as input a combination of (i) the hydrophone data and/or the particle motion data and (ii) the first up-down separated data, wherein an output of the second up-down separation is second up-down separated data; and generating an image of the subsurface based on the second up-down separated data.
Abstract:
Computing device, computer instructions and method for denoising input seismic data d. The method includes receiving the input seismic data d recorded in a first domain by seismic receivers, wherein the input seismic data d includes pure seismic data ss relating to an exploration source and coherent noise data n generated by a man-made device; generating a model m in a second domain to describe the input seismic data d; and processing the model m to obtain an output seismic dataset d′ indicative of seismic data substantially free of the coherent noise data n generated by the man-made device.