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基于自适应掠夺遗传算法的沉积物参数声反演方法。

Acoustic inversion method for parameters of sediments based on adaptive predatory genetic algorithm.

机构信息

Department of Geophysics, East China University of Technology, 418 Guanglan Avenue, Nanchang, Jiangxi Province, 330013, China.

Second Institute of Oceanography, State Oceanic Administration, 36 Baochubei Street, Hang Zhou, Zhejiang Province, 310012, China.

出版信息

J Acoust Soc Am. 2018 Jan;143(1):141. doi: 10.1121/1.5020272.

Abstract

Acoustic inversion for the physical parameters of seafloor sediments is an important and difficult aspect of sediment acoustic research. Submarine surface sediments are typical porous media, which involve many parameters. Thus, the optimization of high-dimensional inversion represents one of the difficulties. An acoustic inversion method to obtain the physical parameters of seafloor sediments is constructed based on the adaptive predatory genetic algorithm and effective density fluid model derived from Biot theory. The method introduces the adaptive process and predatory strategy into the genetic algorithm and uses the norm of the relative difference between the predicted wave number and the measured wave number as the objective function. The method is confirmed to be stable and efficient by simulated data and is also applied to invert porosity, tortuosity, and permeability of the sediments in Hangzhou Bay of China using acoustic data measured by an in situ acoustic measurement system.

摘要

海底沉积物物理参数的声学反演是沉积物声学研究的一个重要而困难的方面。海底表层沉积物是典型的多孔介质,涉及许多参数。因此,高维反演的优化是难点之一。本文基于自适应捕食遗传算法和从 Biot 理论导出的有效密度流模型,构建了一种海底沉积物物理参数的声学反演方法。该方法将自适应过程和捕食策略引入遗传算法,并将预测波数与实测波数的相对差的范数作为目标函数。通过模拟数据验证了该方法的稳定性和有效性,还利用原位声学测量系统测量的声学数据,将该方法应用于中国杭州湾沉积物的孔隙度、迂曲度和渗透率反演。

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