A New Compressional Wave Speed Inversion Method Based on Granularity Parameters
A New Compressional Wave Speed Inversion Method Based on Granularity Parameters
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DOI：
10.1109/access.2019.2961115
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发表时间：
2019
期刊：
影响因子：
3.9
通讯作者：
Jingqiang Wang;Z. Hou;Guanbao Li;G. Kan;Xiangmei Meng;Baohua Liu
中科院分区：
文献类型：
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作者：
Jingqiang Wang;Z. Hou;Guanbao Li;G. Kan;Xiangmei Meng;Baohua Liu
如何提高纵波速度的预测精度一直是地声学研究领域的基础性课题之一。由于粒度的稳定性，无论是在实验室还是在海底环境中，纵波速度与粒度的回归关系都是一种重要的声速反演方法。机器学习（ML）为更有效的声速预测系统提供了一种新的解决方案。在这项研究中，两个最大似然算法，随机森林（RF）和支持向量回归（SVR），结合9个粒度参数（平均粒度，中值粒度，偏度，峰度，分选系数，砾石，砂，粉砂，粘土含量分别。）分析了粒度特性对声速的影响。结果表明，基于预测模型得到的声速精度高于回归方程，RF模型的精度高于SVR模型。基于RF预测模型，进行了特征选择，结果表明，最有影响力的粒度参数是平均粒度。此外，RF模型还可以在缺少部分参数的情况下，以较高的精度预测声速，这可以是一个有用的工具，海洋工程和地震反演。
How to improve the prediction accuracy of compressional wave speed has always been one of the basic research subjects in geoacoustics study field. Due to the stability of granularity, whether in the laboratory or in the seabed environment, the regression relationship between compressional wave speed and granularity is an important sound speed inversion method. Machine Learning (ML) provides a new solution for more efficient sound speed prediction systems. In this study, two ML algorithm, Random forest (RF) and Support Vector Regression (SVR), combined with nine granularity parameters (mean grain size, median grain size, skewness, kurtosis, sorting coefficient, gravel, sand, silt, and clay content respectively.) to analysis the effect of granularity property on sound speed. As a result, the sound speed-granularity predictive models were established, and the sound speed accuracy obtained based on the predictive models are higher than that of the regression equations, and the RF model has a higher accuracy than the SVR model. Based on the RF predictive model, the feature selection was conducted and the results show that the most influential parameter of granularity is mean grain size. Furthermore, the RF model can also predict the sound speed with high precision in the absence of partial parameters, which can be a useful tool for ocean engineering and seismic inversion.