[Truncated abstract] Seismic tomography has been extensively used in geophysics for different purposes such as geological mapping and prospecting for oil and gas. In geophysics, ultrasound or electromagnetic waves are normally used to provide the tomographic information. In the geotechnical area, seismic tomography is emerging as a promising technique that can be used to determine the spatial variability of shear wave velocities and hence the small strain stiffness of geomaterials. Although some studies have been undertaken to incorporate seismic measurement into centrifuge modelling, there has been to date no attempt to build a complete seismic tomography facility with high resolution for use in a geotechnical centrifuge. Such a powerful facility can help in better understanding of soil behaviour by providing a complete picture of the spatial variation of the soil property of concern. The main aim of this study was to develop a high-resolution seismic tomography (ST) system for the beam centrifuge at the University of Western Australia (UWA) by which the shear wave velocity and hence maximum shear modulus could be determined anywhere in the centrifuge model. ... This limitation was the requirement to use an a priori model. The exact solutions in the different examples presented in this chapter were known, and they were used as a priori models into the inversion process. However, in practice the exact solution is unknown, and the aim of the tomographic inversion is to obtain a solution that best describes the measured data. Carrying out inversion without using an a priori model can yield an output model that hints at the nature of the model. This output can then be used as the starting point in an iterative process, in which the output from one step is used as an a priori model for reinverting the original data in a subsequent step. In this case, this process slightly improved the output tomogram and decreased the value of root mean squares of travel time residuals (Rrms). An alternative inversion strategy was proposed based on the results obtained in this study. It involves using a searching algorithm. A searching process can be carried out based on the output from the first iteration (without using an a priori model). The search can involve varying the parameters that describe buried anomalies, such as the size of the anomaly, the velocity value in the anomaly, and the location of the anomaly. The aim is to search for the combination of anomaly parameters that minimises the resulting error parameters (mainly Rrmx, but also the average error and the standard deviation of the error). For more subtle cases, such as the velocity model under a footing, where inversion without using an a priori model did not recover the input model, a searching algorithm involving applying perturbations to the exact Boussinesq model can be performed. Not only can the searching procedure involve adding perturbation to the velocity values in the Boussinesq model, but it can also add perturbation to the shape of the velocity distribution below the footing. The searching process can continue until a model that fits the data with a minimum error is found, i.e., minimising Rrms.
|Qualification||Doctor of Philosophy|
|Publication status||Unpublished - 2008|