基于瑞利波频散特性,开展表面波频谱分析法(spectral analysis of surface wave,SASW)检测回填料压实度研究,分析其影响因素。结果表明瑞利波波速与回填料压实度密切相关,可用于压实度检测;SASW法检测与瑞利波信号激发、接收密切相关,...基于瑞利波频散特性,开展表面波频谱分析法(spectral analysis of surface wave,SASW)检测回填料压实度研究,分析其影响因素。结果表明瑞利波波速与回填料压实度密切相关,可用于压实度检测;SASW法检测与瑞利波信号激发、接收密切相关,需设置合适的采集参数;针对不同类型的回填料,需分别率定压实度与波速之间的相关关系;周边环境,包括应用场景的边界条件等影响SASW法检测精度。展开更多
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. Ge...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 un- known 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.展开更多
文摘基于瑞利波频散特性,开展表面波频谱分析法(spectral analysis of surface wave,SASW)检测回填料压实度研究,分析其影响因素。结果表明瑞利波波速与回填料压实度密切相关,可用于压实度检测;SASW法检测与瑞利波信号激发、接收密切相关,需设置合适的采集参数;针对不同类型的回填料,需分别率定压实度与波速之间的相关关系;周边环境,包括应用场景的边界条件等影响SASW法检测精度。
文摘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 un- known 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.
