{'fraction_non_alphanumeric': 0.02261553588987217, 'fraction_numerical': 0.0, 'mean_word_length': 5.741721854304636, '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}}
Cross-Modality Super-Resolution of Satellite Gravity Data for Geophysical Exploration

Gravity surveys have played an essential role in many geoscience fields, especially as an early screening tool for subsurface hydrocarbon exploration. Since land-based gravity surveys are often expensive and difficult to obtain in remote places, we explore the use of satellite-derived gravity data which is available throughout the Earth and updated periodically. Since the accuracy and resolution of the gravity measured from satellites are lower than land-based gravity measurements, the satellite-based data was enhanced through a deep-learning-based super-resolution (SR) technique. The SR-enhanced Bouguer and free-air gravity anomaly data were used for the classification of hydrocarbon regions using supervised machine learning. Results indicate the successful application of supervised machine learning for hydrocarbon classification using SR-enhanced Bouguer and free-air gravity anomalies with high prediction accuracy.