The research of paleontology is an essential part of contemporary earth science. However, the time-consuming manual identification process has always been cumbrous in the field of paleontology. Since conventional algorithms have limited efficiency in processing images of complicated paleontological fossils. In this work, a combinational machine learning method, which comprises appropriate image preprocessing, Scale-invariant feature transform (SIFT), K-means clustering (K-means), and Support Vector Machine (SVM) are applied to realize automatic recognition of paleontological images under microscope. It is demonstrated that this combined algorithm has superior performance in morphological feature extraction in the case of complex mineral textures. With this technique, the overall average accuracy of image recognition is 84.5%, which significantly improved the efficiency of sample analysis in the field of paleontology.
Artificial intelligence technology has rapidly emerged in various new industries due to its high efficiency and has been successfully used in many fields. However, it has been slow to start in the field of petroleum exploration, under the background of the need for more efficient exploration and development in the petroleum field. In this paper we used the ResNet-18 convolutional neural network to make an attempt to automatically identify rock thin section, and finds that this method can efficiently identify rock thin section and has a higher accuracy rate. In addition, we adopted appropriate image enhancement technology, which can significantly improve the recognition accuracy of the model. It proves that related machine learning technology has broad application prospects in the fields of petroleum exploration and petroleum geology.
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