Data‐driven adaptive decomposition of multicomponent seabed recordings

Abstract

Dual‐sensor (hydrophone and three‐component geophone) data recorded on the sea floor allow the elastic wavefield to be decomposed into its upgoing and downgoing P‐ and S‐wave components. Most decomposition algorithms require accurate knowledge of the elastic properties of the sea floor in the vicinity of the receivers and properly calibrated sensors, in order for the data to be a faithful vector representation of the ground motion. We present a multistep adaptive decomposition scheme that provides the necessary information directly from the data by imposing constraints on intermediate decomposition results. The proposed scheme requires no a priori information and only a minimal amount of user‐defined input, thus allowing multicomponent data to be decomposed in an automated data‐driven fashion. The performance of the technique is illustrated using seabed data acquired in the North Sea with prototype single sensors (multicomponent geophones individually sampled). Realistic sea floor properties and sensor calibration operators are obtained, and elastic decomposition of the calibrated data generally yields good results. Dominant water‐layer reverberations are successfully attenuated and primary reflections are substantially enhanced in the computed upgoing P‐wave potential just below the sea floor. In contrast, the result for the upgoing S‐wave potential is somewhat less convincing; although the energy of water‐layer multiples is substantially reduced, notable amounts of undesired multiple energy remain in this section after decomposition, particularly at high offsets. These imperfections may point to inaccuracies in the parametrization of the sea floor or remaining inaccuracies in the vector fidelity of the horizontal geophone recordings. Nevertheless, the results obtained with the extended data‐driven decomposition scheme are at least comparable to previously published results.