Technology
CONTROLLED AMPLITUDE NOISE ATTENUATION (CANA)
CANA is our amplitude-preserving pre-stack noise attenuation workflow. It is most commonly used in our AVO-compliant processing flow. In an AVO-compliant processing flow, we do not perform any single-trace processes, so many conventional noise attenuation methods cannot be used. CANA models seismic noise by its temporal and spatial characteristics. It is especially effective at removing ground roll, spikes and other noise bursts. The noise is adaptively removed and the underlying signal is preserved. CANA is part of our AVO-compliant processing flow that allows us to produce seismic volumes that are ready for subsequent AVO inversion and time-lapse analysis.
5D INTERPOLATION
5D interpolation produces well-sampled Common Offset Vector (COV) or Offset Vector Tile (OVT) datasets that are ideally suited for pre-stack migration. We have developed technologies and workflows that are able to compensate irregular acquisition geometry, equalize geometries from multiple 3D datasets, regularize disparate baseline and monitor surveys for 4D time-lapse analysis.
SIMULTANEOUS SOURCE DEBLENDING
Simultaneous source acquisition leads to overlapping records, or interference, that must be removed prior to subsequent processing. While conventional noise attenuation methods can partially attenuate simultaneous source interference by estimating the underlying signal, inversion-based deblending offers the ability to jointly estimate the signal and interference, achieving a high level of interference attenuation while preserving the underlying signal.
HARMONIC NOISE REMOVAL
Slip-sweep Vibroseis acquisition generates harmonic noise that interferes with preceding shot records. We have developed a process to predict and then subtract the harmonic noise, based upon the relationship between the ideal sweep and the measured ground force.
MULTICHANNEL SINGULAR SPECTRUM ANALYSIS (MSSA)
Multichannel Singular Spectrum Analysis (MSSA) is a powerful noise attenuation technology that exploits the spatial redundancy of data. Incoherent noise is attenuated by thresholding the singular values of data embedded in structured matrices. MSSA can be applied in a variety of domains (including common source gathers, common receiver gathers, cross-spreads, common offset vectors, CDP gathers, and post-stack data), using up to five dimensions. Our implementation of MSSA can optionally use a statistical analysis of the singular values at all times, positions, and frequencies in a survey to optimally discriminate between signal and noise. MSSA is well suited for strong noise environments and regularly achieves a high level of signal preservation. With MSSA, we are able to target a variety of noise types, including random noise, high amplitude spikes, and harmonics, as well as high amplitude, high-frequency scattered energy.
ACQUISITION FOOTPRINT ATTENUATION
Key Seismic's approach for acquisition footprint attenuation detects and eliminates localized spikes in the kx-ky domain associated with the periodicity of the acquisition footprint. Subset ranges of inlines and crosslines are used to determine the amplitude footprint, making this approach adaptive in both time and space.
VELOCITY VARIATION WITH AZIMUTH (VVAZ)
We perform Velocity Variation with Azimuth (VVAZ) analysis to estimate and correct for azimuthal velocity variations. One cause of azimuthal velocity variations is the presence of horizontal transverse isotropic (HTI) media. When seismic waves travel through these media the acquired data can exhibit an azimuthal dependence of arrival time. An HTI medium is characterized by the azimuth of Vfast, ϕfast, and the percentage of anisotropy, which is a measure of the intensity of the anisotropy and is defined as ((Vfast – Vslow) / Vfast * 100).
Diffraction Imaging
Diffraction imaging provides enhanced images of the subtle faults and localized heterogeneities, which are obscured by the much stronger reflection energy. Our approach is based on a focus-defocus algorithm, which identifies and removes specular reflections.
DATA-DRIVEN MULTIPLE ELIMINATION
Our multiple elimination technology encompasses both surface-related multiple elimination (SRME) and model-based water-layer demultiple (MWD), a method tailored to mitigate shallow-water multiples. Additionally, we have created an adaptation of SRME for land data, which addresses the near-surface by incorporating differential static corrections in the prediction process. This approach is known as surface-corrected multiple elimination (SCME). These methods share the advantage of being data-driven, as they do not depend on velocity differences to distinguish between primaries and multiples.