Shahram Angardi, Ramin Vafaei Poursorkhabi, Ahmad Zarean Shirvanehdeh, Rouzbeh Dabiri
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引用次数: 0
Abstract
Adequate estimation of S-wave velocity (Vs) structure is a significant parameter in the seismic micro zonation studies. To this purpose, different techniques, such as down-hole measurements and inversion of surface wave’s dispersion curves are proposed for modeling VS profile. In the last decade, modeling Vs profile from the Rayleigh wave’s ellipticity curve (H/V) is more applicable owing to its rapid and simple data gathering procedure. However, regarding the ambiguities in the inversion of H/V curves, obtaining the reliable results priori information, such as down-hole measurement, to constrain the final Vs model is vital. This study addressed this challenge, and based on a hybrid artificial intelligence method introduced a new technique to invert the Rayleigh wave ellipticity curve with acceptable performance. To do that, first model parameters (i.e. number of layers and corresponding thicknesses and shear wave velocities) by the ensemble of neural networks (ENN) were predicted, and then further inversion by jellyfish searching (JS) algorithm (named ENN-JS inversion method) was carried out to obtain a more reasonable Vs model. To build the ensemble system, ten base networks were arranged. To train the neural networks, synthetic Rayleigh wave ellipticity data by forward modeling approach were generated. The combination of the outputs of based networks was performed using the averaging method. Then, JS inversion algorithm was applied to estimate the final adequate Vs model. ENNs provide essential information to the JS searching algorithm on the number of layers and proper search spaces for model parameters. Synthetic and actual datasets tested the ENN-JS inversion technique. Findings show the proposed method provides a robust method for the inversion of Rayleigh wave ellipticity data.
充分估计 S 波速度(Vs)结构是地震微区研究中的一个重要参数。为此,人们提出了不同的技术,如井下测量和反演面波频散曲线来模拟 Vs 剖面。在过去的十年中,根据瑞利波的椭圆度曲线(H/V)建立 Vs 剖面模型因其快速和简单的数据收集程序而更加适用。然而,由于 H/V 曲线反演的模糊性,获得可靠的先验结果信息(如井下测量)以约束最终的 Vs 模型至关重要。本研究针对这一挑战,基于混合人工智能方法,引入了一种新技术,以可接受的性能反演瑞利波椭圆度曲线。为此,首先通过神经网络集合(ENN)预测模型参数(即层数和相应厚度以及剪切波速度),然后通过水母搜索(JS)算法(命名为 ENN-JS 反演方法)进行进一步反演,以获得更合理的 Vs 模型。为了建立集合系统,安排了十个基础网络。为训练神经网络,采用前向建模方法生成合成瑞利波椭圆度数据。使用平均法对基础网络的输出进行组合。然后,应用 JS 反演算法估算出最终的适当 Vs 模型。ENN 为 JS 搜索算法提供了关于层数和模型参数适当搜索空间的重要信息。合成数据集和实际数据集测试了 ENN-JS 反演技术。结果表明,所提出的方法为反演瑞利波椭圆度数据提供了一种稳健的方法。
期刊介绍:
pure and applied geophysics (pageoph), a continuation of the journal "Geofisica pura e applicata", publishes original scientific contributions in the fields of solid Earth, atmospheric and oceanic sciences. Regular and special issues feature thought-provoking reports on active areas of current research and state-of-the-art surveys.
Long running journal, founded in 1939 as Geofisica pura e applicata
Publishes peer-reviewed original scientific contributions and state-of-the-art surveys in solid earth and atmospheric sciences
Features thought-provoking reports on active areas of current research and is a major source for publications on tsunami research
Coverage extends to research topics in oceanic sciences
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