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Surface Consistent Processing

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Surface Consistent Deconvolution
Modern processing workflows for onshore seismic acquisitions include surface consistent (SC) deconvolution techniques. SC deconvolution is used to counter the filtering effects of the source itself, the receiver station (the summation of the individual geophone responses, coupling, etc.) and the unique wave propagation effects of the weathering layer(s).

Single-trace deconvolution creates operators which attempt to remove the effects of the wavelet and flatten the spectrum. These operators are sensitive to the noise on individual traces, and errors in the output phase spectrum commonly occur.

Geotrace’s robust SC deconvolution techniques mitigate these noise effects and stabilize the solution. Source, receiver and offset terms are commonly employed for the surface consistent solution. This implementation may also be constrained to design operators on the band-limited signal component of the data only.

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Surface Consistent Scaling
Relative amplitude processing is imperative for projects proceeding through AVO/AVA analysis. Surface consistent (SC) amplitude analysis and scaling is performed to recover from the spherical divergence effects of the source wavefield and the attenuation effects of the weathering layer(s) at each unique source and receiver location.

Noise rejection is often required to attenuate as much noise as possible prior to amplitude analysis. The measured amplitudes from individual traces are reconciled to yield surface consistent components for subsequent application to the data. The surface consistent solution may be decomposed into 5 terms, i.e. source, receiver, offset, CMP, and channel components.

As a surface consistent process, SC scaling is often performed at multiple stages in the processing workflow. It is usually applied pre-deconvolution, with subsequent analysis and refinement after deconvolution, and after selected noise-rejection stages. In this manner, a robust relative amplitude recovery result may be achieved to satisfy the relative amplitude requirements of reservoir attribute processing.

 

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