Shot gather before and after DUG Deblend. In this OBN example three triple-source vessels were firing within 15km of each other. (Data courtesy of Carbon Transition and TGS)
Shot gather before and after DUG Deblend. Polarcus XArray™ – Penta source data before and after DUG Deblend.
These land shot records demonstrate the results of DUG Deblend (centre) as applied to blended data (left) compared to data acquired without any interfering shots (right). The non-blended dataset provides a powerful comparison highlighting the efficacy of our deblending solution. Images courtesy of Apache.
Realise the full potential of blended acquisition without compromising data quality or amplitude fidelity with our inversion-based deblending algorithm.
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Up/down deconvolution applies 3D source and receiver deghosting, 3D source designature and 3D surface-related multiple attenuation to the upgoing wavefield. Our exclusive down-down deconvolution solution provides all these benefits to the downgoing wavefield.
Up/down deconvolution and down/down deconvolution. In this figure we compare stacks after pre-stack depth migration. (Data courtesy of Carbon Transition and TGS)
Brute stack before (left) and after (right) source and receiver deghosting with DUG Broad.
Before and After DUG Broad. Shot gather before and after source and receiver deghosting using DUG Broad. Note that the deghosted result has also been redatumed to mean sea level.
DUG’s deghosting technology: DUG Broad removes the amplitude and phase distortion caused by both source and receiver ghosts.
DUG recognises the use of high-quality relevant signatures in the effective deconvolution of the source wavelet from seismic data as an important part of high fidelity broadband processing. We use the NFH data to calculate notional signatures for every shot and subsequently use these for signature deconvolution and zero-phasing.
Input (left) and two zero-phased and debubbled datasets, using far field signatures (FFS) derived from the NFH recordings (right) and a single modelled FFS (centre). Each dataset has been bandpass filtered between 1-6 Hz. The water bottom horizon is shown dipping to the left near the top of the section. Accurate zero-phasing is achieved using the FFS derived from the NFH data, but not with the modelled FFS.
Stack section before (left) and after (right) DUG IME. In this case the water bottom horizon is acting as the multiple generator. Time range of the data shown is from 5300-6400 ms. (Data courtesy Spectrum Geo)
Stack section before interbed demultiple (left) and multiples predicted by DUG IME (right). In this case the water bottom horizon is acting as the multiple generator. Time range of the data shown is from 5300-6400 ms. (Data courtesy Spectrum Geo)
DUG’s interbed multiple elimination process – DUG IME – accurately models and subtracts interbed multiples. It can be used to predict the multiple model from a single generator or it can be applied in a cascaded (top-down) manner to eliminate interbed multiples resulting from a number of different generators.
DUG SW-SRME (3D shallow water surface-related multiple elimination) is our 3D multiple attenuation algorithm for shallow water settings. SW-SRME overcomes the issue of the lack of recorded near offsets using a model of the sea-bed reflection.
Near stack section before and after DUG SW-SRME. NOVAR MC3D dataset courtesy of Multi-Client Resources (MCR).
Before and after acquisition footprint removal. Timeslice (400 ms) before (left) and after (right) acquisition footprint removal.
Before and after anisotropic azimuthal moveout. Timeslice before (left) and after (right) anisotropic azimuthal moveout correction. This data is from Australia’s southern margin (courtesy Origin Energy).
In addition to the standard pre-processing tools, a full suite of conditioning algorithms is available to improve the final images and prepare the gathers for AVO and QI studies.
