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Noisy data test

nvel7-9 snvel7-9 noise-misfit sncurve
nvel7-9,snvel7-9,noise-misfit,sncurve
Figure 6.
FWI results of noisy data. (a) Standard FWI; (b) FWI with seislet regularization; (c) normalized model error versus iteration for standard FWI (dot line) and FWI with seislet regularization (solid line); (d) data convergence of FWI using seislet regularization at each frequency.
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To further test the robustness of the proposed method, we generate a noisy dataset by simply adding strong random noise to the original time domain data. Then we transform the noisy data to frequency domain for inversion. We perform two inversions: standard FWI and FWI using seislet regularization, and compare their results in Figures 6a and 6b. In this comparison, we find that seislet regularization suppresses the noise caused by the ambient noise in the data, and helps to get a good inversion result. Figure 6c shows the model convergence curves, which also tell us that FWI using seislet regularization has a faster model convergence rate. Finally, we show the data convergence of FWI using seislet regularization for each frequency inversion in Figure 6d. We can observe that at each separate frequency, the proposed method exhibits a fast data-fitting convergence.


next up previous [pdf]

Next: Conclusions Up: Examples Previous: Encoded data test

2017-10-09