古地理学报2025，Vol.27Issue(4)：924-936,13.DOI:10.7605/gdlxb.2025.064
碳酸盐岩岩心图像生物扰动强度智能识别方法研究
Intelligent identification methods for bioturbation intensity in carbonate rock core images
摘要
Abstract
Bioturbation refers to various sedimentary textures or structures formed on sediment surfaces or within sediments due to biological activity.It plays a crucial role in analyzing paleoenvironmental conditions in sedimentary strata,predicting distribution patterns,evaluating the hydrocarbon generation capacity of source rocks,assessing the sealing capacity of caprocks,and revealing the mechanisms and effects of bioturbation on hydrocarbon reservoirs.Traditional methods for analyzing bioturbation intensity mainly relies on manual identification,followed by semi-quantitative classification using bioturbation index charts.This approach is highly subjective,inefficient,and prone to large errors.In this paper,we proposed a residual network model that incorporates an attention mechanism(Res-EMANet)by integrating the Efficient Multi-Scale Attention(EMA)mechanism into the ResNet-50 model.During training,the model employs stochastic gradient descent(SGD)with an initial learning rate of 0.01,a weight decay parameter of 0.0001,a batch size of 16,and a total of 300 epochs.Model performance improvements are evaluated based on five aspects:accuracy,precision,recall,F1-score,and the confusion matrix.We validated the model using a dataset of 3,028 core images from 16 wells of the Ordovician in the Tarim Basin,which contain various levels of bioturbation.The results show that:(1)The model can accurately classify bioturbation intensities ranging from level 0 to 5 in digital core images,achieving an accuracy of up to 91％.This significantly outperforms traditional manual methods as well as the original ResNet-50 model.(2)The model not only improves the accuracy of bioturbation grade recognition but also effectively reduces dependence on expert knowledge,as well as the labor intensity and subjectivity associated with manual bioturbation assessments.It demonstrates significant advantages in the automation,intelligence,and quantification of bioturbation feature analysis.This research offers an efficient and reliable quantitative analysis tool for the automated processing of bioturbation degree assessment and identification,which is of great significance to the sedimentology and paleontology studies in the field of oil and gas exploration.关键词
生物扰动/深度学习/图像分类/碳酸盐岩储集层/奥陶系/塔河油田Key words
bioturbation/deep learning/image classification/carbonate reservoir/Ordovician/Tahe oilfield分类
信息技术与安全科学引用本文复制引用
芦碧波,何佳康,牛永斌,沈文啟,姚康为..碳酸盐岩岩心图像生物扰动强度智能识别方法研究[J].古地理学报,2025,27(4):924-936,13.基金项目
国家自然科学基金项目(编号:41472104,42272178)资助.[Financially supported by the National Natural Science Foundation of China(Nos.41472104,42272178)] （编号:41472104,42272178）
