{'fraction_non_alphanumeric': 0.02385008517887564, 'fraction_numerical': 0.0034071550255536627, 'mean_word_length': 5.606741573033708, '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}}
Extremal methods in mine detection and classification

This paper concerns general statistical properties of mine detection systems utilizing high (e.g. acoustic) returns in the presence of reverberation, modeled as a (background) random field. Recently developed extensions of the 1D theory of high level stochastic excursions are used to describe the occurrences of high peaks of a 2D background reverberation field by a (theoretically justified) Poisson model. This model and its further refinements are then used in discussing false alarm, detection, and classification probabilites.