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Empirical Predictions of Seafloor Properties Based on Remotely Measured Sediment Impedance

机译:基于远程测量沉积阻抗的海底特性的经验预测

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Numerous acoustic systems have been developed over the past 25 years for remote classification of the seabed. Many systems use inversions of echo returns to estimate seafloor impedance and then use empirical relationships to predict other seabed properties from values of impedance. New regressions are presented, separately for siliciclastic and carbonate sediments, which allow prediction of sediment grain size, porosity, bulk density, percent sand and gravel and sound speed ratio and attenuation from values of an index of impedance (product of sound speed ratio and bulk density). This index is independent of pore water temperature and salinity and water depth. The regressions are based on nearly 800 cores collected from 67 shallow-water sites around the world (12 carbonate and 55 siliciclastic sites). Data are typically restricted to the upper 30 cm of sediment. The regressions based on the nearly 4,500 common data points from core measurements (3,922 for siliciclastic and 621 for carbonate sediments) do not vary significantly from the regressions for siliciclastic sediments first presented by Richardson and Briggs (1993) or between carbonate and siliciclastic sediments suggesting the empirical predictions universally apply to coastal sediments. Sound speed dispersion, sediment disturbance during core collection and measurement, inequalities between sample size (acoustic footprint vs. core diameter), spatial variability, and regression error all affect the accuracy of sediment property predictions.
机译:许多声学系统已经开发在过去25年的海底远程分类。许多系统使用回波返回值反转估计海底阻抗,然后用经验关系从阻抗值预测其他海底性能。新回归给出,分别为硅质碎屑和碳酸盐沉积物,这允许沉积物粒度,孔隙度,堆积密度,百分比砂石和声音速度比和衰减的预测从阻抗的索引的值(声速比和散装产品密度)。该指数是独立的孔隙水温度和盐度和水深。该回归是基于来自世界各地的67浅水网站(12碳酸盐和55个硅质网站)收集了近800芯。数据通常受限于沉积物的上部30厘米。基于从核心测量近4500公共数据点(3922为硅质碎屑和621为碳酸盐沉积物)的回归不显著从回归为硅质碎屑沉积物首先由Richardson和布里格斯(1993)呈现或碳酸盐和硅质碎屑沉积物暗示之间变化经验预测普遍适用于海岸沉积物。声速分散,芯收集和测量期间沉积物干扰,样本大小(声学足迹与纤芯直径),空间变异,并回归误差之间的不平等所有影响沉积物性质的预测的准确性。

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