{'fraction_non_alphanumeric': 0.021300448430493273, 'fraction_numerical': 0.0, 'mean_word_length': 5.244755244755245, '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}}
Inversion of Self-Potential Anomaly Using Laplace Crossover Operator Based Genetic Algorithm

Summary This paper addresses the approach to invert parameters of different buried structures using their self-potential (SP) anomalies. This approach involves a Laplace Crossover (LX) operator based Genetic Algorithm (GA) to invert synthetic SP anomaly of buried sphere, cylinder and inclined sheet. GA is used to determine depth, shape factor, polarization angle, origin of anomaly, electric current dipole for sphere and cylinder and half width along with the mentioned parameters for inclined sheet. This study of the synthetic examples shows that the proposed GA works very well with both noise free and noise corrupted data. Since it gives good convergence of the inverted data to the original values of synthetic examples, it can also be used in interpretation of real field complex SP data.