LAUSR

The Chinese Continental Scientific Drilling (CCSD) main hole is located in the Sulu ultrahigh-pressure metamorphic (UHPM) belt, providing significant opportunities for studying the metamorphic strata structure, kinetics process and tectonic evolution. Lithology identification is the primary and crucial stage for above geoscientific researches. To release the burden of log analyst and improve the efficiency of lithology interpretation, many algorithms have been developed to automate the process of lithology prediction. While traditional statistical techniques, such as discriminant analysis and K-nearest neighbors classifier, are incompetent in extracting nonlinear features of metamorphic rocks from complex geophysical log data; artificial intelligence algorithms are capable of solving nonlinear problems, but most of the algorithms suffer from tuning parameters to be global optimum to establish model rather than local optimum, and also encounter challenges in making the balance between training accuracy and generalization ability. Optimization methods have been applied extensively in the inversion of reservoir parameters of sedimentary formations using well logs. However, it is difficult to obtain accurate solution from the logging response equations of optimization method because of the strong overlapping of nonstationary log signals when applied in metamorphic formations. As oxide contents of each kinds of metamorphic rocks are relatively less overlapping, this study explores an approach, set in a metamorphic formation model and using the Broyden Fletcher Goldfarb Shanno (BFGS) optimization algorithm to identify lithology from oxide data. We first incorporate 11 geophysical logs and lab-collected geochemical data of 47 core samples to construct oxide profile of CCSD main hole by using backwards stepwise multiple regression method, which eliminates irrelevant input logs step by step for higher statistical significance and accuracy. Then we establish oxide response equations in accordance with the metamorphic formation model and employ BFGS algorithm to minimize the objective function. Finally, we identify lithology according to the composition content which accounts for the largest proportion. The results show that lithology identified by the method of this paper is consistent with core description. Moreover, this method demonstrates the benefits of using oxide content as an adhesive to connect logging data with lithology, can make the metamorphic formation model more understandable and accurate, and avoid selecting complex formation model and building nonlinear logging response equations.