Mineral Identification in Sandstone SEM Images Based on Multi-scale Deep Kernel Learning

Identifying sandstone images and judging the types of minerals play an important role in oil and gas reservoir exploration and evaluation. Multiple kernel learning (MKL) method has shown high performance in solving some practical applications. While this method belongs to a shallow structure and cannot handle relatively complex problems well. With the development of deep learning in recent years, many researchers have proposed a deep multiple layer multiple kernel learning (DMLMKL) method based on deep structure. While the existing DMLMKL method only considers the deep representation of the data but ignores the shallow representation between the data. Therefore, this paper propose a multiple scale multiple layer multiple kernel learning (MS-DKL) method that "richer" feature data by fusing deep and shallow representations of mineral image features. Mineral recognition results show that MS-DKL algorithm is higher accuracy in mineral recognition than the MKL and DMLMKL methods.