{'fraction_non_alphanumeric': 0.016006739679865205, 'fraction_numerical': 0.006739679865206402, 'mean_word_length': 5.0, '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}}
Spatial variability of SPT data using ordinary and disjunctive kriging

The purpose of this study is to develop a geostastistical model based on ordinary and disjunctive kriging techniques to estimate spatial variability of SPT (N) data in the three-dimensional subsurface of Bangalore. The database consists of 766 boreholes spread over a 220 sq km area, with several N values in each of them. The analysis has been done for corrected SPT (Nc) value. Ordinary kriging produces a linear estimator, whereas disjunctive kriging produces a nonlinear estimator. Knowledge of the semivariogram of the SPT data is used in the kriging theory to estimate the values at points in the subsurface of Bangalore where field measurements are not available. The capability of disjunctive kriging to be a nonlinear estimator and an estimator of conditional probability is explored. A cross-validation (Q1 and Q2) analysis is also done for the developed ordinary and disjunctive kriging models. For the data sets used in this study, disjunctive kriging has shown to be a better estimator than ordinary kriging in terms of reduced kriging variance and the comparison between an estimated and actual value.