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The algorithm strives to overcome the following difficulties: (a) singular model inversion, (b) poor signal to noise ratio, (c) feedback, and (d) certain types of non-linear behaviour.
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Fig. 2 1D-layered model inversion results of a model A, b model B, c synthetic sounding curve of the model A, and d synthetic sounding curve of model B. x in horizontal axis is the logarithm of the depth, and the unit of depth is km.
b Location of the open crack and deflation sill models estimated by the model inversion.
(17) Fig. 3 1D-layered model inversion results of different stabilizing functionals with β 2 = 0.1: a inversion results of the model A, b inversion results of the model B, c, d the gradient of the each results, e, f normalized gradient of each results, and g, h convergence of RMS data misfit of each model.
Fig. 3 Distribution of residuals versus epicentral distance before inversion (a) and after joint velocity and anisotropy inversion (b).
Model inversion refers to estimating these (hidden) states from data.
Data conditioning is then, effectively, a part of model inversion.
Model inversion in multivariable systems is non-trivial.
Model inversion is described in Appendix A in detail.
Model inversion was performed within each time window.
Sudomotor nerve parameters from the model inversion are summarised in Table 1.
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