基于卷积神经网络的碳酸盐岩生物化石显微图像识别  

Microscopic recognition of micro fossils in carbonate rocks based on convolutional neural network

作　　者：余晓露 叶恺[2] 杜崇娇[2] 宫晗凝 马中良 YU Xiaolu;YE Kai;DU Chongjiao;GONG Hanning;MA Zhongliang(SINOPEC Key Laboratory of Petroleum Accumulation Mechanisms,Wuxi,Jiangsu 214126,China;Wuxi Research Institute of Petroleum Geology,SINOPEC,Wuxi,Jiangsu 214126,China)

机构地区：[1]中国石化油气成藏重点实验室,江苏无锡214126 [2]中国石化石油勘探开发研究院无锡石油地质研究所,江苏无锡214126

出　　处：《石油实验地质》2021年第5期880-885,895,共7页Petroleum Geology & Experiment

基　　金：中国石化优秀青年科技创新项目“岩石(矿物)自动化鉴定分析仪”(P19028);国家自然科学基金(42072156)资助

摘　　要：碳酸盐岩薄片中的生物化石识别对判断沉积环境研究具有重要的意义,但传统的人工鉴定方法对经验要求高,受主观影响较大。该文提出一种基于ResNet卷积神经网络的碳酸盐岩生物化石显微图像识别方法,通过图像预处理、设计模型、训练模型等步骤,实现了薄片图像中生物化石的智能识别,识别准确率为86%;并同时提出进阶YOLO(You Only Look Once)目标检测模型,可实现薄片图像中生物化石所在区域的检测和识别,识别准确率为85%。该方法验证了使用数字图像处理和深度学习方法对碳酸盐岩生物化石显微图像进行智能识别的可行性,可作为传统人工鉴定方法的有益补充,具有一定的实际应用价值。The identification of microfossils in carbonate rocks with thin-section observation is of great signifi-cance for the study of sedimentary environment,but the traditional method by manual identification is highly experience required and is greatly affected by subjective factors.In this paper,a method for microscopic recognition of carbonate rocks based on ResNet convolutional neural network was introduced.Through image preprocessing,model design,model training etc.,the intelligent recognition of fossils of organisms within thin section images were realized,and the recognition accuracy showed to be 86%.Meanwhile,an advanced YOLO(You Look Only Once)object detection model was proposed,which could realize the detection and recognition of the area where the organism locates in thin section image,and the recognition accuracy appeared to be 85%.This method veri-fied the feasibility of using digital image processing algorithm and deep learning method to intelligently identify biological microscopic images of carbonate rocks.It can be regarded as a useful supplement to traditional manual identification methods and has certain practical application value.

关 键 词：卷积神经网络 ResNet YOLO 显微图像识别 生物化石 碳酸盐岩 

分 类 号：TE135[石油与天然气工程—油气勘探]