1. Insititute of Gravity and Magnetic Technology,Chang’an University,Xi’an 710054,China 2. College of Geology Engineering and Geomatics,Chang’an University,Xi’an 710054,China 3. Key Laboratory of Western China’s Mineral Resources and Geological Engineering,Ministry of Education,Chang’an University,Xi’an 710054,China 4. China Aero Geophysivey and Remote Sensing Center for Natural and Resources,Beijing 100083,China
Accurately characterizing rock types and their structural relationships can provide important information for researches on energy and mineral exploration, deep structure and tectonic, and so on. At present, geophysical data can be used to identify lithology through the differences between the corresponding physical property parameters of different rocks (such as density, susceptibility, resistivity, speed,etc.). However, different rock physical properties often coincide at a certain degree, on the other hand the result of lithological identification is not accurate enough when a single physical property is used. Therefore, it is of great significance to use multi-source data for lithological identification. Bayesian method belongs to the statistical classification methods, which relies on probability for classification, and the calculation of probability density depends on the correlation between sample attributes. On such a basis, the authors introduce the Bayesian probability model based on adaptive kernel density estimation to lithology identification. This method has good adaptability for many different types of physical property parameters and such the predicted lithology classification results with probability parameters, fuzzy intervals, and a variety of lithology classification results. This method has a strong scalability that can process both parametric and non-parametric information at the same time to maximally the known geological information and physical parameters. Synthetic models prove that this method has the ability to provide more stable and accurate results of lithology recognition compared with the methods of traditional Gaussian algorithm and fixed bandwidth kernel density estimation.
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