ESTIMATING GEOACOUSTIC BOTTOM PROPERTIES FROM TOWED ARRAY DATA

Abstract

Estimating the seabottom geophysical structure from the analysis of acoustic returns of an explosive source (air-gun, sparker,…) has been used for a longtime as a routine survey technique. Recent work showed the possibility of using well-suited numerical models to invert the acoustic field for estimating detailed geoacoustic sediment properties. Common implementations used long synthetic aperture arrays (up to 2 km and more) in order to resolve potential environmental ambiguities of the acoustic field. Others, used vertical arrays of sensors covering a significant part of the water column to identify the channel normal mode structure and thus gather information for the bottom physical relevant properties. This paper investigates, with simulated data, the concept of using a moderate aperture physical line array and a sound source simultaneously towed by a single ship for inverting the bottom geoacoustic structure from the acoustic returns received on the array. First, bottom parameter estimators are derived and their system sensitivity is investigated. In particular, it is shown that such a system may be used to sense compressional and shear velocities on the bottom first layers. Density and attenuations (both compressional and shear) have in general small influence on the acoustic field structure and are therefore difficult to estimate. Increasing the signal frequency bandwidth by incoherent module averaging has no significant influence on sensitivity. Mismatch cases, mainly those related to array/source relative position, showed that deviations of more than λ/3 in range and λ/5 in depth may give erroneous extremum location and therefore biased final estimates. Second, two bottom parameter estimators are compared and their performance tested on a typical shallow water environment. In order to solve the underlying multiparameter inverse problem, global search optimization is used. In particular, it is shown that the use of an adaptive genetic algorithm may, in conjunction with a well-suited maximum likelihood based parameter estimator, rapidly converge to the surface extremum. Inversion results are in agreement with the predictions obtained from the sensitivity study. The mean relative error at 10 dB signal-to-noise ratio is within 1% for the compressional velocity, while greater errors are reported for the shear velocity, Comparison with recent results obtained with a radial basis functions (RBF) inversion strategy showed similar performance. Finally, results obtained with a 156 m aperture towed array showed a good agreement between the inverted compressional velocities and the ground truth measurements.