Rock thin-section identification is an indispensable geological exploration tool for understanding and recognizing the composition of the earth.It is also an important evaluation method for oil and gas exploration and...Rock thin-section identification is an indispensable geological exploration tool for understanding and recognizing the composition of the earth.It is also an important evaluation method for oil and gas exploration and development.It can be used to identify the petrological characteristics of reservoirs,determine the type of diagenesis,and distinguish the characteristics of reservoir space and pore structure.It is necessary to understand the physical properties and sedimentary environment of the reservoir,obtain the relevant parameters of the reservoir,formulate the oil and gas development plan,and reserve calculation.The traditional thin-section identification method has a history of more than one hundred years,which mainly depends on the geological experts'visual observation with the optical microscope,and is bothered by the problems of strong subjectivity,high dependence on experience,heavy workload,long identification cycle,and incapability to achieve complete and accurate quantification.In this paper,the models of particle segmentation,mineralogy identification,and pore type intelligent identification are constructed by using deep learning,computer vision,and other technologies,and the intelligent thinsection identification is realized.This paper overcomes the problem of multi-target recognition in the image sequence,constructs a fine-grained classification network under the multi-mode and multi-light source,and proposes a modeling scheme of data annotation while building models,forming a scientific,quantitative and efficient slice identification method.The experimental results and practical application results show that the thin-section intelligent identification technology proposed in this paper does not only greatly improves the identification efficiency,but also realizes the intuitive,accurate and quantitative identification results,which is a subversive innovation and change to the traditional thin-section identification practice.展开更多
岩石薄片的岩性识别是地质分析中不可或缺的一环,其精准度直接影响后续地层岩石种类、性质和矿物成分等信息的确定,对于地质勘探和矿产开采具有重要意义。为了快速准确地识别岩性,本文提出了一种改进的MobileNetV2轻量化模型,通过选取5...岩石薄片的岩性识别是地质分析中不可或缺的一环,其精准度直接影响后续地层岩石种类、性质和矿物成分等信息的确定,对于地质勘探和矿产开采具有重要意义。为了快速准确地识别岩性,本文提出了一种改进的MobileNetV2轻量化模型,通过选取5种岩石类型共3 700张岩石薄片图像进行岩性识别。在MobileNetV2的倒残差结构中嵌入坐标注意力机制,融合图像中多种矿物的全局特征信息。此外,改进MobileNetV2中的分类器,降低模型的参数量和计算复杂度,从而提高模型的运算速度和效率,并采用带泄露线性整流函数(leaky rectified linear unit, Leaky ReLU)作为激活函数,避免网络训练中的梯度消失问题。实验结果表明,本文提出的改进后的MobileNetV2模型大小仅为2.30 MB,在测试集上的精确率、召回率、F_(1)值分别为91.24%、90.18%、90.70%,具有较高的准确性,相比于SqueezeNet、ShuffleNetV2等同类型的轻量化网络,分类效果最好。展开更多
基金supported by the Project of Basic Science Center for the National Natural Science Foundation of China(Grant No.72088101)。
文摘Rock thin-section identification is an indispensable geological exploration tool for understanding and recognizing the composition of the earth.It is also an important evaluation method for oil and gas exploration and development.It can be used to identify the petrological characteristics of reservoirs,determine the type of diagenesis,and distinguish the characteristics of reservoir space and pore structure.It is necessary to understand the physical properties and sedimentary environment of the reservoir,obtain the relevant parameters of the reservoir,formulate the oil and gas development plan,and reserve calculation.The traditional thin-section identification method has a history of more than one hundred years,which mainly depends on the geological experts'visual observation with the optical microscope,and is bothered by the problems of strong subjectivity,high dependence on experience,heavy workload,long identification cycle,and incapability to achieve complete and accurate quantification.In this paper,the models of particle segmentation,mineralogy identification,and pore type intelligent identification are constructed by using deep learning,computer vision,and other technologies,and the intelligent thinsection identification is realized.This paper overcomes the problem of multi-target recognition in the image sequence,constructs a fine-grained classification network under the multi-mode and multi-light source,and proposes a modeling scheme of data annotation while building models,forming a scientific,quantitative and efficient slice identification method.The experimental results and practical application results show that the thin-section intelligent identification technology proposed in this paper does not only greatly improves the identification efficiency,but also realizes the intuitive,accurate and quantitative identification results,which is a subversive innovation and change to the traditional thin-section identification practice.
文摘岩石薄片的岩性识别是地质分析中不可或缺的一环,其精准度直接影响后续地层岩石种类、性质和矿物成分等信息的确定,对于地质勘探和矿产开采具有重要意义。为了快速准确地识别岩性,本文提出了一种改进的MobileNetV2轻量化模型,通过选取5种岩石类型共3 700张岩石薄片图像进行岩性识别。在MobileNetV2的倒残差结构中嵌入坐标注意力机制,融合图像中多种矿物的全局特征信息。此外,改进MobileNetV2中的分类器,降低模型的参数量和计算复杂度,从而提高模型的运算速度和效率,并采用带泄露线性整流函数(leaky rectified linear unit, Leaky ReLU)作为激活函数,避免网络训练中的梯度消失问题。实验结果表明,本文提出的改进后的MobileNetV2模型大小仅为2.30 MB,在测试集上的精确率、召回率、F_(1)值分别为91.24%、90.18%、90.70%,具有较高的准确性,相比于SqueezeNet、ShuffleNetV2等同类型的轻量化网络,分类效果最好。
