In general, seismic waveform inversion adopts an objective function based on the L2-norm. However, waveform inversion using the L2-norm produces distorted results because the L2-norm is sensitive to statistically invalid data such as outliers. As an alternative, there have been several studies applying L1 -norm-based objective functions to waveform inversion. Although waveform inversion based on the L1-norm is known to produce robust inversion result against specific outliers in the time domain, its effectiveness is not yet studied in the frequency domain. This paper proposes an algorithm for L1-norm-based waveform inversion in the frequency domain. The proposed algorithm employs a structure identical to those used in conventional frequency-domain waveform inversion algorithms that exploit the back-propagation technique, but displays robustness against outliers, which has been confirmed through the inversion of the synthetic Marmousi model. The characteristics and advantages of the Ll-norm were analyzed by comparing it with the L2-norm. In addition, inversion was performed on data containing outliers to examine the robustness against outliers. The effectiveness of removing outliers was verified by using the L1 -norm to calculate the residual wavefield and its spectrum for the data containing outliers.
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