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Acoustic Pressure and Particle Velocity for Spatial Filtering of Bottom Arrivals

P. Felisberto,
P. Santos,
S.M. Jesus
LARSys, University of Algarve,
Campus de Gambelas, PT-8005-139 Faro, Portugal.

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Ref.: IEEE Journal of Oceanic Engineering, vol 44, Issue 1, pp.179-192, 2019.

This work discusses the combination of acoustic pressure and particle velocity motion for ocean seismic applications. Traditionally, the seismic image is extracted from bottom reflected broadband acoustic signals received in hydrophones arranged as long linear arrays (streamers). Since hydrophones are omnidirectional in nature, the received bottom returns are often contaminated by water born signals, sea surface reflections and noise. A substantial part of the processing of the streamer data is dedicated to filtering out these unwanted signals. Nowadays, off the shelf vector sensors allow to measure both acoustic pressure and particle velocity motion in a single and compact sensor. The combination of pressure and particle velocity measured at a single location or particle velocity and particle velocity gradient at closely spaced locations allow for spatial beam steering to predetermined directions and filter out unwanted replicas from other directions. Moreover this can be done at the sensor level, dramatically decreasing offline processing. The spatial filtering capabilities of various pressure-pressure, particle velocity-particle velocity and pressure-particle velocity combinations are analyzed in view of seismic applications. It is shown that a simple steering procedure combining pressure and particle velocity components of a triaxial sensor allows to determine the tri-dimensional structure of the acoustic field and the separation of the bottom reflections for seismic processing. The spatial selectivity of the various sensor combinations are shown with simulations and verified with experimental data acquired with 10 cm separated vector sensors in the 800-1250 Hz band, during the Makai 2005 sea trial, off Kauai Island, Hawaii (USA).

ACKNOWLEDGMENT: This work was funded under project WiMUST contract 645141, H2020 program of the EU.

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