Imaging disturbance zones ahead of a tunnel by elastic full-waveform inversion:Adjoint gradient based inversion vs.parameter space reduction using a level-set method  

作　　者：Andre Lamert Luan T.Nguyen Wolfgang Friederich Tamara Nestorovic 

机构地区：[1]Seismology,Institute of Geology,Mineralogy and Geophysics,Ruhr-Uniersity Bochum,Germany [2]Reservoir Engineering and Rock Physics,International Geothermal Center,Bochuon University of Applied Sciences,Germany [3]Mechanics of Adaptive Systems,Institute of Computational Engineering,Rudhr-Umiversity Bochum,Germany

出　　处：《Underground Space》2018年第1期21-33,共13页地下空间（英文）

摘　　要：We present and compare twoflexible and effective methodologies to predict disturbance zones ahead of underground tunnels by using elastic full-waveform inversion.One methodology uses a linearized,iterative approach based on misfit gradients computed with the adjoint method while the other uses iterative,gradient-free unscented Kalmanfiltering in conjunction with a level-set representation.Whereas the former does not involve a priori assumptions on the distribution of elastic properties ahead of the tunnel,the latter intro-duces a massive reduction in the number of explicit model parameters to be inverted for by focusing on the geometric form of potential disturbances and their average elastic properties.Both imaging methodologies are validated through successful reconstructions of simple disturbances.As an application,we consider an elastic multiple disturbance scenario.By using identical synthetic time-domain seismo-grams as test data,we obtain satisfactory,albeit different,reconstruction results from the two inversion methodologies.The computa-tional costs of both approaches are of the same order of magnitude,with the gradient-based approach showing a slight advantage.The model parameter space reduction approach compensates for this by additionally providing a posteriori estimates of model parameter uncertainty.

关 键 词：Tunnel seismics Full waveform inversion Seismic waves Level-set method Adjoint method Kalman filter 

分 类 号：O17[理学—数学]