Adaptive Deghosting

Deghosting technique for single-sensor marine streamer data that adapts to changing acquisition conditions and 3D crossline effects

Ghost reflections from the sea surface can degrade the frequency content of your marine seismic data through destructive interference with the primary signal. By removing the ghost effects, we produce a high-fidelity wavelet without the amplitude and phase distortions that harm image quality to extend the bandwidth of your seismic data.

Our adaptive deghosting algorithm was developed to remove both the source and receiver side ghosts from single sensor, hydrophone-only marine streamer data and the source-side ghosts from ocean bottom seismic (OBS) data. Adaptive deghosting is suitable for shallow-tow, deep-tow, or slanted marine streamer data and can be used with all acquisition geometries.

Legacy data beforeAfter adapative deghosting applied

Final image without adaptive deghosting (left) and with adaptive deghosting (right). Adaptive deghosting, applied to both source and receiver side ghosts, extends usable bandwidth and greatly improves data resolution.

Overcome uncertainty and 3D effects

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. 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.

Improve image accuracy with a deghosted wavelet

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 (FWI), improving the resolution and accuracy of your final image.

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