{'fraction_non_alphanumeric': 0.059569074778200254, 'fraction_numerical': 0.035487959442332066, 'mean_word_length': 5.171875, '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}}
Building and Understanding Deep Neural Networks Components for Seismic Processing: Lessons Learned

Introduction Since its recent successes in natural image classification and analysis (LeCun et al., 2015), deep learning (DL) has gained significant attention in many industrial fields, including in seismic processing. There, most of the current investigations consist in learning to mimic seismic processing algorithms or workflows, i.e. to predict the processed image from the “initial” image using deep neural networks (DNN); see e.g. Sun et al. (2018), Mandelli et al. (2019), Richardson and Feller (2019), Ovcharenko et al. (2019), Sen et al. (2019), Yuan et al. (2019). However, seismic processing has its own particularities, which may necessitate adaptations of current DL methods.