An Evaluation Method of Travel Time Tomography with Incomplete Data on Elastic Wave

2013 ◽  
Vol 10 (15) ◽  
pp. 5013-5020
Author(s):  
Yali Guo
Keyword(s):  
Elastic Wave ◽  
Travel Time ◽  
Incomplete Data ◽  
Evaluation Method ◽  
Travel Time Tomography

Numerical Solution of a Linearized Travel Time Tomography Problem With Incomplete Data

2020 ◽  
Vol 42 (5) ◽  
pp. B1173-B1192
Author(s):  
Michael V. Klibanov ◽  
Thuy T. Le ◽  
Loc H. Nguyen
Keyword(s):  
Numerical Solution ◽  
Travel Time ◽  
Incomplete Data ◽  
Travel Time Tomography

scholarly journals Spatiotemporal Monitoring and Evaluation Method for Sand-Filling of Immersed Tube Tunnel Foundation

2021 ◽  
Vol 11 (3) ◽  
pp. 1084
Author(s):  
Peng Wu ◽  
Ailan Che
Keyword(s):  
Neural Network ◽  
Elastic Wave ◽  
Frequency Domain ◽  
Bp Neural Network ◽  
Evaluation Method ◽  
Monitoring And Evaluation ◽  
Strong Nonlinearity ◽  
Filling Process ◽  
Time Frequency ◽  
Feature Parameters

The sand-filling method has been widely used in immersed tube tunnel engineering. However, for the problem of monitoring during the sand-filling process, the traditional methods can be inadequate for evaluating the state of sand deposits in real-time. Based on the high efficiency of elastic wave monitoring, and the superiority of the backpropagation (BP) neural network on solving nonlinear problems, a spatiotemporal monitoring and evaluation method is proposed for the filling performance of foundation cushion. Elastic wave data were collected during the sand-filling process, and the waveform, frequency spectrum, and time–frequency features were analysed. The feature parameters of the elastic wave were characterized by the time domain, frequency domain, and time-frequency domain. By analysing the changes of feature parameters with the sand-filling process, the feature parameters exhibited dynamic and strong nonlinearity. The data of elastic wave feature parameters and the corresponding sand-filling state were trained to establish the evaluation model using the BP neural network. The accuracy of the trained network model reached 93%. The side holes and middle holes were classified and analysed, revealing the characteristics of the dynamic expansion of the sand deposit along the diffusion radius. The evaluation results are consistent with the pressure gauge monitoring data, indicating the effectiveness of the evaluation and monitoring model for the spatiotemporal performance of sand deposits. For the sand-filling and grouting engineering, the machine-learning method could offer a better solution for spatiotemporal monitoring and evaluation in a complex environment.

Three-Dimensional Acoustic Travel-Time Tomography of the Atmosphere

2008 ◽  
Vol 94 (3) ◽  
pp. 349-358 ◽  
Author(s):  
Sergey N. Vecherin ◽  
Vladimir E. Ostashev ◽  
Keith D. Wilson
Keyword(s):  
Travel Time ◽  
Three Dimensional ◽  
Travel Time Tomography ◽  
Acoustic Travel Time
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