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Prediction of Shear Wave Velocity in Underground Layers Using SASW and Artificial Neural Networks

机译:基于SASW和人工神经网络的地下剪切波速预测。

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This research aims at improving the methods of prediction of shear wave velocity in underground layers. We propose and showcase our methodology using a case study on the Mashhad plain in north eastern part of Iran. Geotechnical investigations had previously reported nine measurements of the SASW (Spectral Analysis of Surface Waves) method over this field and above wells which have DHT (Down Hole Test) result. Since SASW utilizes an analytical formula (which suffers from some simplicities and noise) for evaluating shear wave velocity, we use the results of SASW in a trained artificial neural network (ANN) to estimate the unknown nonlinear relationships between SASW results and those obtained by the method of DHT (treated here as real values). Our results show that an appropriately trained neural network can reliably predict the shear wave velocity between wells accurately.
机译:本研究旨在改进地下层剪切波速度的预测方法。我们通过对伊朗东北部马什哈德平原的案例研究提出并展示我们的方法。岩土工程研究以前曾报告过在该领域以及具有DHT(井下测试)结果的井上方对SASW(表面波频谱分析)方法进行的9次测量。由于SASW利用分析公式(存在一些简单性和噪声)来评估剪切波速度,因此我们在经过训练的人工神经网络(ANN)中使用SASW的结果来估计SASW结果与通过该方法获得的结果之间未知的非线性关系。 DHT的方法(此处视为实际值)。我们的结果表明,经过适当训练的神经网络可以可靠地准确预测井之间的剪切波速度。

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