Seismic Adaptive Deghosting | SLB

Adaptive Deghosting

Improve image frequency content for single-sensor hydrophone-only marine streamer and ocean bottom seismic data

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Simultaneously removes source- and receiver-side ghosts

Ghost reflections from the sea surface can degrade the frequency content of your marine seismic data through destructive interference with the primary signal. Our adaptive deghosting algorithm simultaneously removes both the source- and receiver-side ghosts from single-sensor hydrophone-only marine streamer data and source-side ghosts from ocean bottom seismic (OBS) data. The result is a high-fidelity wavelet without amplitude and phase distortions that harm image quality, thereby extending the bandwidth of your seismic data.

Accounts for uncertainties in recorded cable depths

Adaptive deghosting accounts for uncertainties in recorded cable depths making it uniquely suited to extend the usable bandwidth of both legacy and newly acquired seismic datasets. Adaptability is an important feature of this deghosting technique which solves for both the upgoing wavefield and ghost delay time using an iterative data-adaptive approach.

Corrects for 3D crossline effects

When used in single-streamer adaptive mode the algorithm can handle variations in the ghost delay due to unexpected streamer depth and water velocity variations. In multicable mode it can iterate through many parameters to find the ideal solution to correct for crossline 3D effects. In both modes it is robust to noise, stable at low frequencies, and can combine source and receiver deghosting in a single pass of the algorithm, resulting in efficient, effective deghosting of your data.

Seismic data before adaptive deghosting Seismic data after adaptive deghosting

Improve image frequency content for single-sensor hydrophone-only marine streamer data and ocean bottom seismic data. Suitable for shallow-tow, deep-tow, or slanted marine streamer data and all acquisition geometries.

Provides a simpler deghosted wavelet for subsequent processing steps

Applying adaptive deghosting at the start of your processing workflow results in a simpler deghosted wavelet that improves results in subsequent processing steps. Adaptively deghosted data provides higher-fidelity signal content when used as input for demultiple and velocity analysis processes. It also improves the visibility of weak signal with depth and provides better low-frequency content for full-waveform inversion, improving the resolution and accuracy of your final image.