{'fraction_non_alphanumeric': 0.020316027088036117, 'fraction_numerical': 0.004514672686230248, 'mean_word_length': 5.117241379310345, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
A MAP algorithm for AVO seismic inversion based on the mixed (L2, non-L2) norms to separate primary and multiple signals in slowness space

AVO (Amplitude Vs Offset) seismic inversion is a technique of tomographic seismic imaging for creating a model in stack-velocity space that can correctly reconstruct the measured AVO seismic dataset. This is usually implemented by minimizing a least squares inversion algorithm. This algorithm has limitations because it reconstructs seismic images with artifacts yield by impulsive noise contained in the input raw seismic dataset. Recently, superior seismic images were reconstructed using a MAP (Maximum A Posterior) approach, based on the Norm Lp. In this paper, we demonstrate similar results and even superior ones via minimizing a MAP approach built through L2 norm of dataset misfit and a non-L2 Lorentzian error norm of the model energy.