碳酸盐岩岩心图像生物扰动强度智能识别方法研究<sup>*</sup>

doi:10.7605/gdlxb.2025.064

古地理学报 ›› 2025, Vol. 27 ›› Issue (4): 924-936. 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^*

芦碧波¹(), 何佳康¹, 牛永斌²(), 沈文啟¹, 姚康为¹

1 河南理工大学计算机科学与技术学院,河南焦作 454003
2 河南省煤系非常规资源成藏与开发重点实验室,河南理工大学资源环境学院,河南焦作 454003

收稿日期:2025-01-07 修回日期:2025-02-25 出版日期:2025-08-01 发布日期:2025-07-30
通讯作者: 牛永斌,男,1980年生,博士,河南理工大学资源环境学院教授,主要从事应用遗迹学与沉积学研究工作。E-mail: niuyongbin@hpu.edu.cn。
作者简介:
芦碧波,男,1978年生,博士,河南理工大学计算机科学与技术学院教授,主要从事人工智能与图像处理研究工作。E-mail: lubibo@hpu.edu.cn。
基金资助:
*国家自然科学基金项目(41472104); 国家自然科学基金项目(42272178)

Intelligent identification methods for bioturbation intensity in carbonate rock core images

LU Bibo¹(), HE Jiakang¹, NIU Yongbin²(), SHEN Wenqi¹, YAO Kangwei¹

1 School of Computer Science and Technology,Henan Polytechnic University,Henan Jiaozuo 454003,China
2 Henan Key Laboratory of Coal Measure Unconventional Resources Accumulation and Exploitation, School of Resources and Environment, Henan Polytechnic University, Henan Jiaozuo 454003, China

Received:2025-01-07 Revised:2025-02-25 Online:2025-08-01 Published:2025-07-30
Contact: NIU Yongbin,born in 1980,is a professor at School of Resources and Environment,Henan Polytechnic University. He is mainly engaged in applied ichnology and sedimentolgy. E⁃mail: niuyongbin@hpu.edu.cn.
About author:
LU Bibo,born in 1978,is a professor at the School of Computer Science and Technology,Henan Polytechnic University. He is mainly engaged in artificial intelligence and image processing. E-mail: lubibo@hpu.edu.cn.
Supported by:
National Natural Science Foundation of China(41472104); National Natural Science Foundation of China(42272178)

摘要/Abstract

摘要：

生物扰动是(古)生物在生命活动过程中在沉积物表面或内部形成的各种沉积结构或沉积构造,在分析沉积地层古环境、预测其分布规律、评价烃源岩生烃能力和盖层封堵能力、揭示(古)生物对油气储集层的改造机制和改造效应等方面具有重要应用。传统生物扰动强度分析主要依靠人工识别后对照生物扰动指数图版进行半定量划分,因此受主观因素影响大,执行效率低且结果容易产生较大误差。文中通过引入EMA(Efficient Multi-Scale Attention)注意力机制到ResNet-50模型中,提出了一种加入注意力机制的残差网络模型(Res-EMANet)。该模型在训练过程中采用随机梯度下降算法(Stochastic Gradient Descent,SGD),初始学习率为 0.01,权重衰减参数为 0.0001;批次大小设置为16,共执行了300个轮次。从准确率(Accuracy)、精确率(Precision)、召回率(Recall)、F1 分数(F1-score)和混淆矩阵(Confusion Matrix)等5个方面评价了模型结构改进对模型性能的影响,并利用塔里木盆地奥陶系16口取心井3028张含不同等级生物扰动的岩心照片数据集进行了模型检验,结果表明: (1)该模型能够准确划分岩心数字图像上0~5级别的生物扰动强度,准确率高达91%,显著优于传统人工方法和已有的ResNet-50模型。(2)该模型在提升生物扰动等级识别准确度的同时,有效降低了对专家知识的依赖和人工评估生物扰动等级的劳动强度及个人主观性的影响,在生物扰动特征的自动化、智能化和定量化分析等方面展现了显著的应用优势。本研究为生物扰动程度评估和识别的自动化处理提供了一款高效可靠的定量化分析工具,这对油气勘探领域的沉积学和古生物学研究具有重要意义。

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

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.

Key words: bioturbation, deep learning, image classification, carbonate reservoir, Ordovician, Tahe oilfield

中图分类号:

TP391.41

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.

LU Bibo, HE Jiakang, NIU Yongbin, SHEN Wenqi, YAO Kangwei. Intelligent identification methods for bioturbation intensity in carbonate rock core images[J]. Journal of Palaeogeography（Chinese Edition）, 2025, 27(4): 924-936.

http://www.gdlxb.cn/CN/Y2025/V27/I4/924

图/表 12

图 1 岩心生物扰动图版

Fig.1 Plate of core bioturbation

图 2 标注岩心生物扰动指数示意图 a—塔河油田,奥陶系,S80井,生物扰动岩心图像(箭头指示的潜穴);b—划分生物扰动等级后的岩心图像块

Fig.2 Schematic diagram of bioturbation index of labeled core

表 1 扩充后的塔里木盆地奥陶系生物扰动岩心图像数据集

Table 1 Expanded core photo dataset of bioturbation from the Ordovician in Tarim Basin

数据集类型	生物扰动指数
	0	1	2	3	4	5
总数据集	533	379	569	567	464	516
训练集	426	199	432	453	371	412
测试集	54	26	54	58	47	53
验证集	53	24	54	56	56	51

图 3 扩充生物扰动岩心图像块 a—生成BI=0图像块; b—生成BI=5图像块。∑表示图像对应位置的像素值相加取平均值;min表示图像对应位置的像素取最小值

Fig.3 Enlarge bioturbation core image block

图 4 数据增强岩心图像结果 a—原图; b—随机旋转; c—翻转;d—平移

Fig.4 Data-augmented core images

图 5 EMA 注意力机制

Fig.5 Efficient multi-scale attention

图 6 Res-EMANet网络结构

Fig.6 Netword structure of Res-EMANet model

表 2 各种注意力机制在塔河油田奥陶系岩心图像数据集数据集上的测试结果

Table 2 Test results of various attention mechanisms on core image dataset of the Ordovician in Tahe Oilfield

方法	主干网络(Backbone)	准确率(A)/%	精确率(P)/%	召回率(R)/%	F1分数/%
Baseline	ResNet-50	86.73	87.22	87.13	87.07
+SE		89.32	89.80	89.70	89.62
+CBAM		89.32	90.25	89.07	89.54
+CA		88.03	88.15	88.83	88.28
+ECA		89.64	90.22	90.06	89.95
+EMA		91.45	92.22	91.42	91.54

图 7 5种模型训练准确率、验证准确率曲线 a—训练集准确率(Train_acc);b—验证集准确率(Val_acc)

Fig.7 Training accuracy and validation accuracy curves of five models

表 3 5种模型实验结果对比

Table 3 Comparison of experimental results for five models

网络模型	准确率(A)/%	精确率(P)/%	召回率(R)/%	F1分数/%
Vgg-19	85.76	86.81	86.81	86.26
DenseNet121	87.38	87.95	87.91	87.72
ResNet-50	86.73	87.22	87.13	87.07
EfficientNet-B0	88.67	89.14	88.98	88.90
Res-EMANet	91.45	92.22	91.42	91.54

图 8 塔河油田奥陶系岩心数据集分类过程中不同注意力机制可视化图像 a—塔河油田,BI=2,岩心图像; b—BI=2,EMA注意力机制热力图; c—BI=2,CA注意力机制热力图; d—BI=2,CBAM注意力机制热力图; e—塔河油田,BI=3,岩心图像; f—BI=3,EMA注意力机制热力图; g—BI=3,CA注意力机制热力图; h—BI=3,CBAM注意力机制热力图; i—塔河油田,BI=4,岩心图像; j—BI=4,EMA注意力机制热力图; k—BI=4,CA注意力机制热力图; l—BI=4,CBAM注意力机制热力图

Fig.8 Visualization images of different attention comparisons in classification process of the Ordovician core dataset in Tahe oilfield

图 9 Res-EMANet模型的混淆矩阵

Fig.9 Confusion matrix of Res-EMANet model

参考文献 42

[1]	艾合买提江·阿不都热和曼, 钟建华, 李阳, 陈鑫. 2010. 塔河油田奥陶系缝合线特征及石油地质意义. 中国石油大学学报(自然科学版), 34(1): 7-11,17.
	[Ahmatjan A, Zhong J H, Li Y, Chen X. 2010. Stylolite characteristics and petroleum geology significance of Ordovician carbonate rocks in Tahe Oilfield. Journal of China University of Petroleum(Edition of Natural Science), 34(1): 7-11,17 ]
[2]	金强, 张三, 孙建芳, 魏荷花, 程付启, 张旭栋. 2020. 塔河油田奥陶系碳酸盐岩岩溶相形成和演化. 石油学报, 41(5): 513-525. doi: 10.7623/syxb202005001
	[Jin Q, Zhang S, Sun J F, Wei H H, Cheng F Q, Zhang X D. 2020. Formation and evolution of karst facies of Ordovician carbonate in Tahe Oilfield. Acta Petrolei Sinica, 41(5): 513-525 ] doi: 10.7623/syxb202005001
[3]	刘大锰, 王子豪, 陈佳明, 邱峰, 朱凯, 高羚杰, 周柯宇, 许少博, 孙逢瑞. 2024. 基于ResNet残差神经网络识别的深部煤层显微组分和微裂缝分类: 以鄂尔多斯盆地石炭系本溪组8^#煤层为例. 石油与天然气地质, 45(6): 1524-1536.
	[Liu D M, Wang Z H, Chen J M, Qiu F, Zhu K, Gao L J, Zhou K Y, Xu S B, Sun F R. 2024. Classification of macerals and microfractures in deep coal seams based on ResNet: a case study of the No. 8 coal seam of the Carboniferous Benxi Formation in the Ordos Basin. Oil & Gas Geology, 45(6): 1524-1536 ]
[4]	刘大卫, 李映涛, 韩俊, 张继标, 汝智星, 杨孝群, 王石, 黄诚, 肖重阳. 2025. 塔里木盆地顺北地区奥陶系鹰山组超深层白云岩类型及成储潜力评价. 古地理学报, 27(1): 126-140. doi: 10.7605/gdlxb.2025.00.015
	[Liu D W, Li Y T, Han J, Zhang J B, Ru Z X, Yang X Q, Wang S, Huang C, Xiao C Y. 2025. Ultra-deep dolomite types and their reservoirs potential of the Ordovician Yingshan Formation in Shunbei area,Tarim Basin. Journal of Palaeogeography(Chinese Edition), 27(1): 126-140 ]
[5]	刘合, 任义丽, 李欣, 朱如凯, 胡延旭, 刘茜, 苏乾潇, 吴健平, 李彬. 2024. 岩心智能识别技术内涵与展望. 石油学报, 45(8): 1296-1308. doi: 10.7623/syxb202408011
	[Liu H, Ren Y L, Li X, Zhu R K, Hu Y X, Liu X, Su Q X, Wu J P, Li B. 2024. Connotation and prospect of intelligent recognition technology for cores. Acta Petrolei Sinica, 45(8): 1296-1308 ] doi: 10.7623/syxb202408011
[6]	刘艳如, 吴晓红, 何小海, 罗彬彬, 滕奇志. 2025. 基于改进ResNet50的岩心图像分类研究. 智能计算机与应用, 15(2): 10-16.
	[Liu Y R, Wu X H, He X H, Luo B B, Teng Q Z. 2025. Research on core image classification based on improved ResNet50. Intelligent Com-puter and Applications, 15(2): 10-16 ]
[7]	鲁新便, 胡文革, 汪彦, 李新华, 李涛, 吕艳萍, 何新明, 杨德彬. 2015. 塔河地区碳酸盐岩断溶体油藏特征与开发实践. 石油与天然气地质, 36(3): 347-355.
	[Lu X B, Hu W G, Wang Y, Li X H, Li T, Lü Y P, He X M, Yang D B. 2015. Characteristics and development practice of fault-karst carbonate reservoirs in Tahe area,Tarim Basin. Oil & Gas Geology, 36(3): 347-355 ]
[8]	牛永斌, 崔胜利, 胡亚洲, 钟建华, 王培俊. 2017. 塔里木盆地塔河油田奥陶系数字岩心图像中生物扰动的定量表征. 古地理学报, 19(2): 353-363. doi: 10.7605/gdlxb.2017.02.027
	[Niu Y B, Cui S L, Hu Y Z, Zhong J H, Wang P J. 2017. Quantitative characterization of bioturbation based on digital image analysis of the Ordovician core from Tahe Oilfield of Tarim Basin. Journal of Palaeogeography(Chinese Edition), 19(2): 353-363 ]
[9]	牛永斌, 崔胜利, 胡亚洲, 钟建华, 潘结南. 2018. 塔河油田奥陶系生物扰动型储集层的三维重构及启示意义. 古地理学报, 20(4): 691-702. doi: 10.7605/gdlxb.2018.04.050
	[Niu Y B, Cui S L, Hu Y Z, Zhong J H, Pan J N. 2018. Three-dimensional reconstruction and their significance of bioturbation-type reservoirs of the Ordovician in Tahe Oilfield. Journal of Palaeogeography(Chinese Edition), 20(4): 691-702 ]
[10]	牛永斌, 徐资璐, 刘圣鑫, 钟建华, 赵佳如, 王培俊. 2020. 塔河油田奥陶系生物扰动碳酸盐岩储集层微观孔隙结构的数字化表征与连通性分析. 古地理学报, 22(4): 785-798. doi: 10.7605/gdlxb.2020.04.053
	[Niu Y B, Xu Z L, Liu S X, Zhong J H, Zhao J R, Wang P J. 2020. Digital characterization and connectivity analysis of microcosmic pore structures of the Ordovician bioturbated carbonate rock reservoirs in Tahe Oilfield. Journal of Palaeogeography(Chinese Edition), 22(4): 785-798 ]
[11]	牛永斌, 赵佳如, 钟建华, 王敏, 徐资璐, 程梦园. 2021. 基于神经网络模型的生物扰动碳酸盐岩储集层识别与孔隙度预测: 以塔里木盆地塔河油田奥陶系生物扰动碳酸盐岩储集层为例. 地质论评, 67(6): 1898-1909.
	[Niu Y B, Zhao J R, Zhong J H, Wang M, Xu Z L, Cheng M Y. 2021. Identification of the bioturbated carbonate reservoir and their porosity prediction based on conventional well logging data using artificial neural networks: take the Ordovician bioturbated carbonate reservoir in Tahe oilfield,Tarim Basin,as an example. Geological Review, 67(6): 1898-1909 ]
[12]	牛永斌, 荆楚涵, 邵威猛, 程怡高, 李志远. 2023. 生物扰动油气水储层的研究现状及展望. 沉积学报, 41(6): 1934-1953.
	[Niu Y B, Jing C H, Shao W M, Cheng Y G, Li Z Y. 2023. A review and perspective of bioturbated hydrocarbon and water reservoirs. Acta Sedimentologica Sinica, 41(6): 1934-1953 ]
[13]	张抗. 1999. 塔河油田的发现及其地质意义. 石油与天然气地质, 20(2): 24-28.
	[Zhang K. 1999. The discovery of Tahe oilfield and its geological significance. Oil & Gas Geology, 20(2): 24-28 ]
[14]	赵佳如, 牛永斌, 王敏, 徐资璐, 崔胜利, 王培俊. 2021. 塔河油田奥陶系生物扰动型碳酸盐岩储集层特征及其孔隙度计算样本检验模型. 沉积学报, 39(2): 482-492.
	[Zhao J R, Niu Y B, Wang M, Xu Z L, Cui S L, Wang P J. 2021. Reservoir characteristics and porosity calculation sample inspection model of Ordovician bioturbated carbonate reservoirs in Tahe oilfield. Acta Sedimentologica Sinica, 39(2): 482-492 ]
[15]	周程阳, 刘伟, 吴天润, 李骜, 韩霄松. 2024. 基于混合专家模型的岩石薄片图像分类. 吉林大学学报(理学版), 62(4): 905-914.
	[Zhou C Y, Liu W, Wu T R, Li A, Han X S. 2024. Classification of rock thin section images based on mixture of expert models. Journal of Jilin University(Science Edition), 62(4): 905-914 ]
[16]	周永章, 左仁广, 刘刚, 袁峰, 毛先成, 郭艳军, 肖凡, 廖杰, 刘艳鹏. 2021. 数学地球科学跨越发展的十年: 大数据、人工智能算法正在改变地质学. 矿物岩石地球化学通报, 40(3): 556-573.
	[Zhou Y Z, Zuo R G, Liu G, Yuan F, Mao X C, Guo Y J, Xiao F, Liao J, Liu Y P. 2021. The great-leap-forward development of mathematical geoscience during 2010-2019: big data and artificial intelligence algorithm are changing mathematical geoscience. Bulletin of Mineralogy,Petrology and Geochemistry, 40(3): 556-573 ]
[17]	Ayranci K, Yildirim I E, Waheed U B, MacEachern J A. 2021. Deep learning applications in geosciences: insights into ichnological analysis. Applied Sciences, 11(16): 7736.
[18]	Dey J, Sen S. 2017. Impact of bioturbation on reservoir quality and production: a review. Journal of the Geological Society of India, 89(4): 460-470.
[19]	Dorador J, Rodríguez-Tovar F J, Expedition I. 2014. Quantitative estimation of bioturbation based on digital image analysis. Marine Geology,349: 55-60.
[20]	Eltom H A, Syahputra M R N, El-Husseiny A, La Croix A D. . 2023. Spatial complexity of burrow attributes and their impact on porosity and permeability distributions in bioturbated reservoirs. Sedimentary Geology,450: 106395.
[21]	He K M, Zhang X Y, Ren S Q, Sun J. 2016. Deep residual learning for image recognition. 2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). June 27-30,2016,Las Vegas,NV,USA. IEEE: 770-778.
[22]	Hou Q B, Zhou D Q, Feng J S. 2021. Coordinate attention for efficient mobile network design. 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). June 20-25,2021. Nashville,TN,USA. IEEE: 13713-13722.
[23]	Hu J, Shen L, Sun G. 2018. Squeeze-and-excitation networks. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. June 18-23,2018. Salt Lake City,UT,USA. IEEE: 7132-7141.
[24]	Huang G, Liu Z, Van Der Maaten L, Weinberger K Q. 2017. Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition: 4700-4708.
[25]	Hülse D, Vervoort P, van de Velde S J, Kanzaki Y, Boudreau B, Arndt S, Bottjer D J, Hoogakker B, Kuderer M, Middelburg J J, Volkenborn N, Kirtland Turner S, Ridgwell A. 2022. Assessing the impact of bioturbation on sedimentary isotopic records through numerical models. Earth-Science Reviews,234: 104213.
[26]	Kikuchi K, Naruse H. 2024. Abundance of trace fossil phycosiphon incertum in core sections measured using a convolutional neural network. Sedimentary Geology,461: 106570.
[27]	Knaust D. 2012. Methodology and Techniques. Developments in Sedimentology. Elsevier: 245-271.
[28]	Knaust D. 2017. Atlas of Trace Fossils in Well Core: Appearance, Taxonomy and Interpretation. Springer: 1-209.
[29]	Miguez-Salas O, Dorador J, Rodríguez-Tovar F J. 2019. Introducing Fiji and ICY image processing techniques in ichnological research as a tool for sedimentary basin analysis. Marine Geology,413: 1-9.
[30]	Miller M F, Smail S E. 1997. A semiquantitative field method for evaluating bioturbation on bedding planes. Palaios, 12(4): 391-396.
[31]	Niu Y B, Cheng M Y, Zhang L J, Zhong J H, Liu S X, Wei D, Xu Z L, Wang P J. 2022. Bioturbation enhanced petrophysical properties in the Ordovician carbonate reservoir of the Tahe Oilfield,Tarim Basin,NW China. Journal of Palaeogeography, 11(1): 31-51.
[32]	Ouyang D L, He S, Zhang G Z, Luo M Z, Guo H Y, Zhan J, Huang Z J. 2023. Efficient multi-scale attention module with cross-spatial learning. ICASSP 2023-2023 IEEE International Conference on Acoustics,Speech and Signal Processing: 1-5.
[33]	Simonyan K, Zisserman A. 2014. Very deep convolutional networks for large-scale image recognition. Arxiv Preprint: 1409.1556(publishied as a coference paper at ICLR 2015).
[34]	Sterling S N. 2011. Cores and core logging for geoscientists. Environmental and Engineering Geoscience,17: 307-308.
[35]	Tan M, Le Q. 2019. Efficientnet: rethinking model scaling for convolutional neural networks. International conference on machine learning. PMLR: 6105-6114.
[36]	Taylor A M, Goldring R. 1993. Description and analysis of bioturbation and ichnofabric. Journal of the Geological Society, 150(1): 141-148.
[37]	Taylor A, Goldring R, Gowland S. 2003. Analysis and application of ichnofabrics. Earth-Science Reviews, 60(3-4): 227-259.
[38]	Timmer E R, Gingras M K, Zonneveld J P. 2016. Pychno: a core-image quantitative ichnology logging software. Palaios, 31(11): 525-532.
[39]	Timmer E, Knudson C, Gingras M. 2021. Applying deep learning for identifying bioturbation from core photographs. AAPG Bulletin, 105(4): 631-638. doi: 10.1306/08192019051
[40]	Wang Q L, Wu B G, Zhu P F, Li P H, Zuo W M, Hu Q H. 2020. Eca-Net: efficient channel attention for deep convolutional neural networks. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition(CVPR). June 13-19,2020. Seattle,WA,USA. IEEE: 11534-11542.
[41]	Woo S, Park J, Lee J Y, Kweon I S. 2018. Cbam: convolutional block attention module. Proceedings of the European Conference on Computer vision(ECCV): 3-19.
[42]	Reineck H E. 1963. Sedimentgefüge im Bereich der südlichen Nordsee. Abhandlungen der se Ckenbergischen Naturforschenden Gesellschaft,505: 1-138.

选择文件类型/文献管理软件名称

选择包含的内容

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

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 42

相关文章 15

编辑推荐

Metrics

本文评价

[1]	廖崇杰, 陈雷, 古志斌, 刘丙晓, 杨莉, 谭秀成, 熊敏, 曹剑. 海相富有机质页岩储集层特征、成因类型和机制: 以川南长宁五峰组—龙马溪组为例^*[J]. 古地理学报, 2025, 27(3): 714-730.
[2]	张梅, 王贵文, 赖锦, 庞小娇, 杨柳. 湖相碳酸盐岩储集层测井识别方法及应用: 以桑托斯盆地A油田白垩系BVE组为例^*[J]. 古地理学报, 2025, 27(2): 517-527.
[3]	李阳. 鄂尔多斯盆地南缘旬探1井奥陶系天文旋回信号提取及三级层序的判识^*[J]. 古地理学报, 2025, 27(1): 209-224.
[4]	汪彦, 王诺宇, 杨德彬, 张恒, 张娟, 张长建, 张晓. 塔河油田西北部奥陶系古水系结构特征及演化^*[J]. 古地理学报, 2024, 26(6): 1467-1482.
[5]	李华, 何幼斌, 姚凤南, 何一鸣, 姜纯伟, 张显坤, 吴吉泽, 徐艳霞, 梁建设. 鄂尔多斯盆地西北缘奥陶系克里摩里组等深流沉积^*[J]. 古地理学报, 2024, 26(4): 962-971.
[6]	时志强, 彭深远, 赵子腾. 上奥陶统五峰组海底麻坑沉积的首次识别及其地质意义^*[J]. 古地理学报, 2024, 26(2): 255-268.
[7]	刘国庆, 苏中堂, 郝志磊, 魏柳斌, 任军锋, 廖慧鸿, 吴浩文. 鄂尔多斯盆地马家沟组碳酸盐岩-膏盐岩共生体系沉积微相与沉积模式^*[J]. 古地理学报, 2024, 26(2): 279-295.
[8]	熊加贝, 何登发, 成祥, 罗瑀峰. 鄂尔多斯盆地南缘奥陶系顶部碳酸盐岩风化壳特征及其成因机制^*[J]. 古地理学报, 2024, 26(1): 100-118.
[9]	毛丹凤, 何登发, 包洪平, 魏柳斌, 成祥, 苟钧壹, 石婧, 许艳华. 鄂尔多斯盆地怀远运动期不整合类型及其分布特征^*[J]. 古地理学报, 2024, 26(1): 119-131.
[10]	隋少强, 杨志波, 赵雅静, 贾艳雨, 宿宇驰, 王茜, 高飞, 季汉成, 鲍志东. 鲁西豫东地区奥陶系岩溶热储形成演化及主控因素分析^*[J]. 古地理学报, 2023, 25(6): 1364-1378.
[11]	牛永斌, 程梦园, 程怡高, 邵威猛, 荆楚涵. 琼东南盆地北部新近系三亚组Ophiomorpha-Thalassinoides遗迹组构的储层改造效应^*[J]. 古地理学报, 2023, 25(6): 1407-1420.
[12]	季汉成, 陈亮, 孙予舒, 史燕青, 向鹏飞, 杨志波. 冀中坳陷杨税务地区奥陶系岩相古地理特征及其对潜山内幕储集层的控制作用^*[J]. 古地理学报, 2023, 25(5): 976-991.
[13]	于洲, 胡子见, 王前平, 赵静, 吴东旭, 吴兴宁, 李维岭, 鲁慧丽, 朱文博. 鄂尔多斯盆地中东部奥陶系深层白云岩储集层特征及主控因素^*[J]. 古地理学报, 2023, 25(4): 931-944.
[14]	丁奕, 张立军. 古海洋氧化还原条件的遗迹化石定量表征特征:以华南二叠纪末生物大灭绝事件为例^*[J]. 古地理学报, 2023, 25(2): 405-418.
[15]	郭绪杰, 吴兴宁, 金武弟, 林世国, 吴东旭, 于洲. 鄂尔多斯盆地马家沟组四段岩相古地理新认识及勘探区带优选^*[J]. 古地理学报, 2023, 25(1): 105-118.

模态框（Modal）标题

选择文件类型/文献管理软件名称

选择包含的内容

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

RichHTML

PDF

可视化

被引次数

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 42

相关文章 15

编辑推荐

Metrics

本文评价

碳酸盐岩岩心图像生物扰动强度智能识别方法研究^*