We present here for the first time,the Raman and infrared spectroscopic investigation of amphiboles from the World's deepest borehole,the Kola super-deep borehole,at the depth of 11.66 km.The Kola Super-deep boreh...We present here for the first time,the Raman and infrared spectroscopic investigation of amphiboles from the World's deepest borehole,the Kola super-deep borehole,at the depth of 11.66 km.The Kola Super-deep borehole(SG-3)(henceforth referred as KSDB)is located in the northwest of the Kola Peninsula in the northern frame of the Pechenga structure,Russia.It was drilled in the north-eastern part of the Baltic Shield(69о5’N,30о44’E)and reached a depth of 12.262 km.It has been drilled in the northern limb of the Pechenga geosyncline composed of rhythmically inter-bedded volcanogenic and tuffaceous-sedimentary strata extending to the NW at 300°–310°and dipping to SW at angles of 30°–50°.The SG-3 geological section is represented by two complexes–Proterozoic and Archaean.Amphibolite facies is dominant in the depth region from 6000 m to 12,000m to the deepest.The Raman spectra of the sample reveal abundant presence of plagioclase and amphiboles.The most distinct Raman peak in this study indicates the tremolite-ferro-actinolite rich enrichment of the borehole samples at this depth corroborating earlier conventional petrographic studies.展开更多
In the process of geothermal exploitation and utilization, the reinjection amount of used geothermal water in super-deep and porous reservoir is small and significantly decreases over time. This has been a worldwide p...In the process of geothermal exploitation and utilization, the reinjection amount of used geothermal water in super-deep and porous reservoir is small and significantly decreases over time. This has been a worldwide problem, which greatly restricts the exploitation and utilization of geothermal resources. Based on a large amount of experiments and researches, the reinjection research on the tail water of Xianyang No.2 well, which is carried out by combining the application of hydrogeochemical simulation, clogging mechanism research and the reinjection experiment, has achieved breakthrough results. The clogging mechanism and indoor simulation experiment results show: Factors affecting the tail water reinjection of Xianyang No.2 well mainly include chemical clogging, suspended solids clogging, gas clogging, microbial clogging and composite clogging, yet the effect of particle migration on clogging has not been found; in the process of reinjection, chemical clogging was mainly caused by carbonates(mainly calcite), silicates(mainly chalcedony), and a small amount of iron minerals, and the clogging aggravated when the temperature rose; suspended solids clogging also aggravated when the temperature rose, which showed that particles formed by chemical reaction had a certain proportion in suspended solids.展开更多
目的:通过深度学习算法对核磁图像进行超分辨率重建,实现对前列腺癌风险分层的精准化预测。方法:回顾性收集2018年1月至2023年9月在我院病理确诊为前列腺癌的209例患者的临床及影像资料,按照7∶3的比例将患者分为训练集和测试集。分别...目的:通过深度学习算法对核磁图像进行超分辨率重建,实现对前列腺癌风险分层的精准化预测。方法:回顾性收集2018年1月至2023年9月在我院病理确诊为前列腺癌的209例患者的临床及影像资料,按照7∶3的比例将患者分为训练集和测试集。分别从每位患者原始和重建的T2-WI、DWI、ADC等序列进行感兴趣区的标注,并提取影像组学特征;使用正则化及Lasso算法进行特征的降维、构建;然后基于LR、NaiveBayes、SVM、KNN、MLP等分类器构建机器学习预测模型;绘制受试者工作特征(receiver operating characteristic,ROC)曲线并计算ROC曲线下面积(area under the ROC curve,AUC),评估模型的预测性能;决策曲线分析(decision curve analysis,DCA)被用来评估模型的临床价值。结果:采用原始图像的影像组学模型其最佳AUC为0.849,采用超分辨率重建图像的影像组学模型最佳AUC为0.864;DCA曲线分析表明采用超分重建图像的模型拥有更好的临床应用价值。结论:基于深度学习超分重建技术的MRI影像组学可以更精准化地预测前列腺癌的风险分层。展开更多
基金National Institute of advanced Studies (NIAS)Indian National Science Academy (INSA) for the support in under the INSA senior Scientist scheme.
文摘We present here for the first time,the Raman and infrared spectroscopic investigation of amphiboles from the World's deepest borehole,the Kola super-deep borehole,at the depth of 11.66 km.The Kola Super-deep borehole(SG-3)(henceforth referred as KSDB)is located in the northwest of the Kola Peninsula in the northern frame of the Pechenga structure,Russia.It was drilled in the north-eastern part of the Baltic Shield(69о5’N,30о44’E)and reached a depth of 12.262 km.It has been drilled in the northern limb of the Pechenga geosyncline composed of rhythmically inter-bedded volcanogenic and tuffaceous-sedimentary strata extending to the NW at 300°–310°and dipping to SW at angles of 30°–50°.The SG-3 geological section is represented by two complexes–Proterozoic and Archaean.Amphibolite facies is dominant in the depth region from 6000 m to 12,000m to the deepest.The Raman spectra of the sample reveal abundant presence of plagioclase and amphiboles.The most distinct Raman peak in this study indicates the tremolite-ferro-actinolite rich enrichment of the borehole samples at this depth corroborating earlier conventional petrographic studies.
基金funded by National Science Foundation Project in 2015 (No.41472221)
文摘In the process of geothermal exploitation and utilization, the reinjection amount of used geothermal water in super-deep and porous reservoir is small and significantly decreases over time. This has been a worldwide problem, which greatly restricts the exploitation and utilization of geothermal resources. Based on a large amount of experiments and researches, the reinjection research on the tail water of Xianyang No.2 well, which is carried out by combining the application of hydrogeochemical simulation, clogging mechanism research and the reinjection experiment, has achieved breakthrough results. The clogging mechanism and indoor simulation experiment results show: Factors affecting the tail water reinjection of Xianyang No.2 well mainly include chemical clogging, suspended solids clogging, gas clogging, microbial clogging and composite clogging, yet the effect of particle migration on clogging has not been found; in the process of reinjection, chemical clogging was mainly caused by carbonates(mainly calcite), silicates(mainly chalcedony), and a small amount of iron minerals, and the clogging aggravated when the temperature rose; suspended solids clogging also aggravated when the temperature rose, which showed that particles formed by chemical reaction had a certain proportion in suspended solids.
文摘目的:通过深度学习算法对核磁图像进行超分辨率重建,实现对前列腺癌风险分层的精准化预测。方法:回顾性收集2018年1月至2023年9月在我院病理确诊为前列腺癌的209例患者的临床及影像资料,按照7∶3的比例将患者分为训练集和测试集。分别从每位患者原始和重建的T2-WI、DWI、ADC等序列进行感兴趣区的标注,并提取影像组学特征;使用正则化及Lasso算法进行特征的降维、构建;然后基于LR、NaiveBayes、SVM、KNN、MLP等分类器构建机器学习预测模型;绘制受试者工作特征(receiver operating characteristic,ROC)曲线并计算ROC曲线下面积(area under the ROC curve,AUC),评估模型的预测性能;决策曲线分析(decision curve analysis,DCA)被用来评估模型的临床价值。结果:采用原始图像的影像组学模型其最佳AUC为0.849,采用超分辨率重建图像的影像组学模型最佳AUC为0.864;DCA曲线分析表明采用超分重建图像的模型拥有更好的临床应用价值。结论:基于深度学习超分重建技术的MRI影像组学可以更精准化地预测前列腺癌的风险分层。
