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快速点特征直方图(FPFH)三维配准-化工大学毕业设计(外文翻译)

快速点特征直方图(FPFH)三维配准-化工大学毕业设计(外文翻译)

快速点特征直方图(FPFH)三维配准Radu Bogdan Rusu, Nico Blodow, Michael BeetzIntelligent Autonomous Systems, Technische Universit¨at M¨unchen信计1001 鄂求实2010016363摘要:在我们最近的工作[1],[2],我们提出具有稳定的多维度特征点特征直方图(PFH),它描述了三维点云数据集的一个点p周围的局部几何特征。

在本文中,我们修改他们的数学表达式并对重叠点云的三维配准问题的稳定性和复杂性进行了严格的分析。

更具体地说,我们提出几个优化方法,由任意快取以前计算的值,或通过修改他们的理论公式大大减少计算时间。

在一个新的类型,后者的结果局部特征,称为快速点特征直方图(FPFH)它保留了大部分PFH的辨别力。

此外,我们提出了为实时应用FPFH功能的一个算法的在线计算。

为了验证我们结果,我们展示他们三维配准的效率,并提出了一种新的以样本为基础共识的方法,使两个数据集到局部非线性的收敛域优化:SAC-IA(抽样一致初始对准)。

1. 引言在本文中,我们解决的各种重叠的三维点云数据视图一致对准,形成一个完整的模型(在一个刚性的意义上)的问题,也称为三维配准。

解决的办法可以将他转化成一个优化问题,即,在适当的度量空间中,通过求解最佳旋转而转换(6度)使这样的数据集之间的重叠区域之间的距离是最小的。

在空间初始未知和重叠未知的情况下,这个问题就更加困难和寻找最佳解决方案的最优化技术更容易失败。

这是因为函数优化是多维的,局部最优解决方案可能接近全局。

三维刚性配准方法的简单分类可以基于底层的优化类型,方法:全局或局部。

在第一类中也是最广为人知的都是基于全局随机采用遗传算法优化[3]或进化技术[4],其主要缺点是实际计算时间。

很多在三维配准完成的工作其实属于第二类,至今最流行配准方法无疑是在最近点迭代(ICP)算法[5],[6]。

基于平滑非线性能量算子划分的尖峰相关特征癫痫发作自动检测算法

基于平滑非线性能量算子划分的尖峰相关特征癫痫发作自动检测算法

第14卷㊀第3期Vol.14No.3㊀㊀智㊀能㊀计㊀算㊀机㊀与㊀应㊀用IntelligentComputerandApplications㊀㊀2024年3月㊀Mar.2024㊀㊀㊀㊀㊀㊀文章编号:2095-2163(2024)03-0128-05中图分类号:TP18,TP399文献标志码:A基于平滑非线性能量算子划分的尖峰相关特征癫痫发作自动检测算法何雪兰,吴㊀江,蒋路茸(浙江理工大学信息科学与工程学院,杭州310018)摘㊀要:针对癫痫发作自动检测算法多集中于时域㊁频域等传统特征,无法全面表征癫痫脑电信号的信息等问题,本文结合癫痫脑电图中异常波振幅和频率提高的现象,提出一种基于平滑非线性能量算子划分的尖峰相关特征癫痫发作自动检测算法㊂该算法使用传统的时域㊁频域特征,结合尖峰相关性特征对脑电信号进行刻画,使用有监督的机器学习分类器,测试癫痫发作自动检测的有效性和可靠性㊂本文将提出的方法在开源数据集CHBMIT上进行了评估,获得了96.52%的准确率㊁95.65%的敏感性和97.09%的特异性㊂实验结果表明,基于平滑非线性能量算子划分的尖峰相关特征,能够作为癫痫脑电信息的补充,提高癫痫发作检测的性能㊂关键词:癫痫发作检测;机器学习;尖峰相关性;平滑非线性能量算子Automaticseizuredetectionalgorithmbasedonspike-relatedfeaturesofsmoothednonlinearenergyoperatordivisionHEXuelan,WUJiang,JIANGLurong(SchoolofInformationScienceandEngineering,ZhejiangSci-TechUniversity,Hangzhou310018,China)Abstract:Mostcurrentseizureautomaticdetectionalgorithmsfocusontraditionalfeaturessuchastimedomainandfrequencydomain,whichcannotfullycharacterizetheinformationofepilepticEEGsignals.Thispaperproposesanautomaticseizuredetectionalgorithmbasedonspikecorrelationfeaturesdividedbyasmoothnonlinearenergyoperator,takingintoaccountthephenomenonthattheamplitudeandfrequencyofabnormalwavesinepilepticEEGwillincrease.Thealgorithmusestraditionaltime-domainandfrequency-domainfeatures,combinedwithspikecorrelationfeaturestocharacterizetheEEGsignal,andusessupervisedmachinelearningclassifierstotestitseffectivenessandreliabilityforautomaticseizuredetection.TheresearchevaluatestheproposedmethodontheopensourcedatasetCHBMITandobtains96.52%onaccuracy,95.65%onsensitivityand97.09%onspecificity.Theexperimentalresultsshowthattheproposedspike-relatedfeaturesbasedonthesmoothednonlinearenergyoperatorsegmentationcanbeusedasacomplementtotheepilepticEEGinformationtoimprovetheperformanceofseizuredetection.Keywords:seizuredetection;machinelearning;spikecorrelation;smoothednonlinearenergyoperator基金项目:浙江省基础公益项目(LGF19F010008);北京邮电大学泛网无线通信教育部重点实验室(BUPT)(KFKT-2018101);浙江省重点研发计(2022C03136);国家自然科学基金(61602417)㊂作者简介:何雪兰(1999-),女,硕士研究生,主要研究方向:癫痫检测;吴㊀江(1978-),男,博士,高级工程师,主要研究方向:无线通信技术,工业物联网㊂通讯作者:蒋路茸(1982-),男,博士,教授,主要研究方向:生理电信号处理㊁复杂网络和无线传感器网络㊂Email:jianglurong@zstu.edu.cn收稿日期:2023-03-150㊀引㊀言癫痫是一种神经系统疾病,由大脑神经元异常放电引起[1],常常表现为突发性㊁反复性和复发性等特点㊂癫痫发作的临床症状复杂多样,如阵发性痉挛㊁意识丧失㊁认知功能障碍等[2]㊂这些发作事件对患者的认知水平及正常生活都产生了明显影响㊂因此,癫痫的诊断和治疗对于预防癫痫发作和改善生活质量至关重要㊂头皮脑电图是一种用于临床记录脑活动的无创信号采集方法[3],用于记录大脑活动时的电波变化㊂头皮脑电图包含丰富的生理㊁心理和病理信息,是评估癫痫和其他脑部疾病的有效工具[4]㊂在脑电图的记录中,癫痫发作和癫痫样放电(如棘波㊁尖波和棘慢波复合体)是癫痫的重要生物标志物[5],并被广泛应用于临床评价㊂目前,临床上基于脑电图的识别与分析是医生进行癫痫检测的黄金标准,但对海量的临床脑电数据进行人工筛查,不仅给医生带来沉重的负担,还存在较强的主观性㊁判断标准不统一等问题[6-7],影响分析的效率和准确性㊂因此,设计一种自动的癫痫发作检测方法是亟待解决的问题㊂为了克服传统诊断方法的局限性㊁提高医疗效率,伴随着机器学习的快速发展,癫痫发作的自动检测已成为行业内关注的重点㊂研究者们根据头皮脑电图的时域㊁频域或非线性特征建立了特征工程方法[8-10],并通过具有一个或多个特征的分类器检测癫痫发作㊂Mursalin等学者[11]从时域㊁频域和基于熵的特征中选择突出特征,使用随机森林分类器学习选定特征集合的特性,获得了更好的分类结果㊂杨舒涵等学者[12]使用时域和非线性特征对脑电信号进行表征,结合XGBoost分类器,实现了癫痫的自动检测㊂Zarei等学者[13]使用离散小波变换DWT和正交匹配追踪(OrthogonalMatchingPursuit,OMP)提取EEG中不同的系数,计算非线性特征和统计特征,使用SVM进行分类,获得了较好的检测性能㊂吴端波等学者[14]使用aEEG尖峰和cEEG棘波提取的方法计算棘波率,使用阈值法对癫痫进行发作检测㊂上述模型虽然都能取得较好的分类结果,但是也存在以下问题:(1)多数研究在特征提取阶段仅从时域㊁频域或时频域中表征脑电信号信息,这些特征所涵盖的信息量并不足以全面描述一段EEG信号㊂(2)在癫痫发作的自动检测中,强调周期性的信号转换对于有效㊁可靠地区分癫痫发作的重复特征至关重要,而互相关是时域上广泛用于表示信号周期性的方法㊂针对上述问题,本文提出一种基于平滑非线性能量算子划分的尖峰相关(SpikeCorrelation,SC)特征的癫痫发作自动检测算法㊂SC是关于自适应提取的脑电图尖峰信号段之间时间延迟的最大互相关㊂使用平滑非线性能量算子衡量癫痫脑电信号中出现的异常波,将脑电信号在癫痫发作期和非发作期的尖峰相关特征作为度量患者大脑活动的一个重要补充㊂本文提出的算法主要使用巴特沃斯滤波器对脑电信号进行滤波,去除外部伪迹的干扰,然后从传统特征角度出发,提取时域㊁频域特征,再结合提出的尖峰相关特征,进一步表征癫痫发作时的异常信息㊂最后结合有监督的机器学习分类模型,实现癫痫发作的自动检测㊂1㊀方法癫痫发作自动检测整体流程设计如图1所示,其中包含预处理㊁特征提取和分类等3个模块㊂脑电信号通道筛选滤波数据分割归一化预处理特征提取传统特征:时域、频域尖峰相关特征分类癫痫发作/非发作图1㊀癫痫发作自动检测流程图Fig.1㊀Flowchartofseizuredetection1.1㊀脑电信号预处理头皮脑电数据通过放置在头皮固定位置的电极采集得到㊂由于外置电极,这种采集方式很容易受到外部干扰,导致采集到的数据被噪声污染㊂此外,由于受试者在采集过程中生理活动产生的内部伪迹(如:眨眼㊁心脏跳动等)[15],也会对数据产生干扰,影响分类结果㊂因此,针对内部伪迹,本文首先对采集到的脑电信号进行通道筛选,剔除受眼部运动干扰严重的2个电极FT1和FT2;同时,由于左侧耳电磁极易受到心电伪迹的干扰,因此也剔除了靠近耳部的2个电极FT9和FT10㊂所以,在通道筛选阶段,共选择了脑电图中20个通道信号㊂㊀㊀滤波是一种常见的去除脑电信号外部伪迹的方法,本文采用1 48Hz的带通巴特沃斯滤波器进行滤波,抑制其他频率范围的信号[16]㊂根据数据集中标注的癫痫发作开始和结束时间,为了保证波形的完整性,设置重叠率为50%的滑动窗口,将脑电信号分割成4s的数据片段,最后对所有片段进行归一化处理㊂由于通道筛选和滤波后的脑电信号幅值的浮动一般是在可接受范围内,最大最小标准化能够较大程度地还原真实EEG信号波形㊂因此,本文采用最大最小标准化对原始EEG信号进行归一化操作,推得的公式为:Xmin-max=X-X-maxX()-minX()(1)1.2㊀特征提取原始脑电信号数据量庞大,且不具有代表性,而特征提取方法可以提炼出能够表征癫痫发作特征的数据,用于模型的建立㊂因此,本文主要使用传统时域㊁频域特征和基于平滑非线性能量算子的尖峰相关性特征,对脑电数据进行特征提取㊂1.2.1㊀传统特征提取研究主要从时域和频域两个角度对脑电信号进行传统特征提取㊂本文主要提取时域上每个通道的最大值㊁最小值㊁平均值㊁峰度(Kurtosis)㊁偏斜度921第3期何雪兰,等:基于平滑非线性能量算子划分的尖峰相关特征癫痫发作自动检测算法(Sknewness)和线长(LineLength);频域上主要提取每个信号频域分量的振幅㊂其中,峰度㊁偏斜度和线长的数学定义分别见式(2) (4):Kurtosis=E[(x-mean(x))4]{E[(x-mean(x))2]}2(2)Sknewness=E[(x-mean(x)std(x))3](3)LineLength=1nðni=1absxi+1-xi()(4)㊀㊀其中,x表示脑电信号片段;E表示对括号中数值求期望;xi表示采样点i的值;n表示片段x中采样点数㊂1.2.2㊀尖峰相关特征提取根据癫痫发作时脑电信号异常波的振幅和频率发生改变的特异性现象,本文提出将尖峰相关特征作为表征癫痫发作时异常特征的补充㊂非线性能量算子(NLEO)是一种对信号进行能量度量的方法[17],能够跟踪信号的瞬时能量㊂对于离散信号xn,其非线性能量算子表达如式(5)所示:φ[x(n)]=x(n-l)x(n-p)-x(n-q)x(n-s)(5)㊀㊀通常,当癫痫脑电信号中出现异常放电时,脑电波的振幅和频率会有所提高,可以更好地突出异常波在平稳状态下的放电波形,但非线性能量对脑电信号中可能存在的噪音信号也具有很高的敏感度㊂为了进一步提高NLEO对非平稳信号的表征能力和抗干扰能力,文献[18]提出了一种NLEO的改进方法,即平滑非线性能量算子(SNLEO),将计算所得的能量与一个窗函数进行卷积运算,在一定程度上减小低波幅噪音信号对输出结果的影响㊂SNLEO计算见式(6):φ[x(n)]=w(n)∗φ[x(n)](6)㊀㊀其中,w是一个矩形的窗函数, ∗ 表示卷积操作㊂在非线性能量算子的计算中,本文使用的参数值为l=1,p=2,q=0和s=3,并采用7个点的窗函数进行卷积计算㊂获得SNLEO后,需要设定一个合适的阈值,尽可能多地筛选出可能是尖峰的样本,同时最小化漏检率㊂本文使用自适应阈值,对SNLEO进行尖峰筛选识别㊂本文采用影响检测尖峰数量没有大范围变化的阈值作为最优阈值㊂最优阈值的搜索范围为SNLEO的10% 90%[19],相邻2个峰值的中间被确定为一个尖峰的起始点或结束点㊂由于数据在划分过程中导致波形的不连续问题,本文将检测到的第一个和最后一个尖峰丢弃,以确保每个片段具有完整的尖峰形态㊂如果检测出尖峰,则将每个划分好的尖峰与后续5个尖峰片段相关联㊂本文使用尖峰相关性(SpikeCorrelation,SC)来定义该矩阵,并将SC的平均值和标准差作为癫痫发作检测的特征㊂SC计算见式(7 8):SCi,j=maxmRxixj(m)(7)Rxixj(m;i,j)=E[xi(n)xj(n+m)]σxiσxj(8)㊀㊀其中,xi㊁xj是脑电EEG信号的片段,这里i=[2, ,S-6],j=[i+1, ,i+5];S表示在一个片段中检测到的峰值数;σ表示脑电图片段的标准差㊂估计SC特征的处理过程如图2所示㊂将一个片段的第一个和最后一个丢弃,而后根据得到的尖峰计算其与后面5个尖峰的相关性㊂根据图2(a)中样例计算出的尖峰相关矩阵如图3所示㊂10050-501234时间/s(a)癫痫发作片段样例EEG/μV400200SNLEO/μV23224168尖峰数/个1234时间/s(b)片段(a)对应的S N L E Ot h(c)基于(b)确定的自适应阈值t h阈值104.87710050-50EEG/μV1234时间/s(d)划分好的尖峰片段(“*”表示丢弃的片段)12345678910t h图2㊀使用自适应阈值的SNLEO计算尖峰相关性示意图Fig.2㊀SchematicdiagramofSNLEOcalculationofspikecorrelationsusingadaptivethreshold1234567892345678910图3㊀尖峰片段得到的最大相关矩阵Fig.3㊀Maximumcorrelationmatrixobtainedfromspikefragments031智㊀能㊀计㊀算㊀机㊀与㊀应㊀用㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀第14卷㊀㊀㊀此外,计算了SNLEO的平均值㊁标准差和平均最大SNLEO值spikiness㊂其中,spikiness被定义为SNLEO中峰值的最大值除以SNLEO的平均值[20],以及检测到的峰值数量(snum)㊁平均持续时间(swidth)和平均峰值间间隔(sgap)㊂基于SNLEO划分的尖峰相关特征的具体描述见表1㊂表1㊀尖峰相关特征的描述Table1㊀Descriptionofspike-relatedcharacteristics特征描述mean(SC)尖峰相关性矩阵的平均值std(SC)尖峰相关性矩阵的标准差mean(SNLEO)SNLEO的平均值std(SNLEO)SNLEO的标准差spikiness平均最大SNLEO值snum峰值数量swidth平均持续时间sgap平均峰值间间距1.3㊀分类模型使用传统机器学习分类器RF和SVM来评估本文提出的方法,这些分类器经常被用于癫痫发作的自动检测㊂2㊀实验2.1㊀数据集本研究采用公开的头皮脑电数据集CHB-MIT㊂该数据集共记录了美国波士顿儿童医院的23名癫痫患者的头皮脑电数据,每个患者的数据都是由多个.edf文件组成,采样频率256Hz,共含有157次癫痫发作㊂大多数文件包含有23个EEG通道信号,并采用国际标准10-20系统使用的EEG电极位置命名这些通道记录㊂由于癫痫发作时间远小于发作间期的时间,为了保证数据集正负样本的均衡性,本文采用欠采样的方式在发作间期随机采样和癫痫发作样本数量相当的负样本㊂2.2㊀评价指标为了验证本文方法的有效性,采用准确率(Acc)㊁敏感性(Sen)㊁特异性(Spe)㊁F1值和AUC等指标进行实验评估㊂计算方法见式(9) 式(11):Acc=TP+TNTP+TN+FP+FN(9)Sen=TPTP+FN(10)Spe=TNTN+FP(11)㊀㊀其中,TP㊁FP㊁FN和TN分别为真阳性㊁假阳性㊁假阴性和真阴性㊂本文产生的所有实验结果都是在配置为Intel(R)Core(TM)i7-9700CPU@3.00GHz,16GBRAM的计算机上实现的㊂实验模型使用Python3.7和Scikit-learn构建㊂2.3㊀结果分析本文先对提取的传统时域㊁频域特征分别使用RF和SVM分类模型进行测试,所得实验结果见表2㊂由表2可知,SVM分类模型表现最佳㊂表2㊀基于传统特征的实验结果Table2㊀Experimentalresultsbasedontraditionalcharacteristics特征分类器AccSenSpe传统特征RF0.86210.75330.9339SVM0.95900.93850.9736㊀㊀在确定分类模型SVM的基础上,将传统特征和尖峰相关特征结合,探讨尖峰相关特征对癫痫脑电信号的表征能力㊂添加前后对比结果见表3㊂表3㊀尖峰相关特征对比的分类结果Table3㊀Classificationresultsofspike-relatedfeaturecomparison分类器特征AccSenSpeSVM传统特征0.95900.93850.9736传统特征+尖峰相关特征0.96520.95650.9709㊀㊀由表3可知,尖峰相关特征能够对癫痫脑电信号信息进行表征㊂加入尖峰相关特征后,检测结果在Acc上提升了0.62%,在Sen上提升了1.8%,在Spe上有所降低㊂在实际的临床应用中,正确识别发作样本比正确识别非发作样本更重要,因此Sen指标更能准确衡量方法的优劣㊂本文提出的方法虽然在Spe上略有降低,但Sen指标上有一定程度的提升㊂3㊀结束语本文提出了一种基于平滑非线性能量算子划分的尖峰相关特征的癫痫发作自动检测算法㊂该算法使用传统的时域㊁频域特征,结合尖峰相关性特征对脑电信号进行刻画,使用RF和SVM分类器来测试癫痫发作自动检测的有效性和可靠性㊂将所提方法在开源数据集CHB-MIT上进行了评估,SVM分类器获得了更好的结果,其准确率㊁敏感性和特异性分别为96.52%,95.65%和97.09%㊂此外,研究开展的特征消融实验结果表明,提出的基于平滑非线性能131第3期何雪兰,等:基于平滑非线性能量算子划分的尖峰相关特征癫痫发作自动检测算法量算子划分的尖峰相关特征,能够作为癫痫脑电信息的补充,进一步提高癫痫发作检测的性能㊂参考文献[1]PATELDC,TEWARIBP,CHAUNSALIL,etal.Neuron–gliainteractionsinthepathophysiologyofepilepsy[J].NatureReviewsNeuroscience,2019,20(5):282-297.[2]SPECCHION,WIRRELLEC,SCHEFFERIE,etal.InternationalLeagueAgainstEpilepsyclassificationanddefinitionofepilepsysyndromeswithonsetinchildhood:PositionpaperbytheILAETaskForceonNosologyandDefinitions[J].Epilepsia,2022,63(6):1398-1442.[3]SCHADA,SCHINDLERK,SCHELTERB,etal.Applicationofamultivariateseizuredetectionandpredictionmethodtonon-invasiveandintracraniallong-termEEGrecordings[J].ClinicalNeurophysiology,2008,119(1):197-211.[4]BENBADISSR,BENICZKYS,BERTRAME,etal.TheroleofEEGinpatientswithsuspectedepilepsy[J].EpilepticDisorders,2020,22(2):143-155.[5]王学峰.癫癎的脑电图:传统观点㊁新认识和新领域[J].中华神经科杂志,2004,37(3):7-9.[6]刘晓燕,黄珍妮,秦炯.不同类型小儿癫痫持续状态的临床及脑电图分析[J].中华神经科杂志,2000,33(2):73-73.[7]MATURANAMI,MEISELC,DELLK,etal.Criticalslowingdownasabiomarkerforseizuresusceptibility[J].NatureCommunications,2020,11(1):2172.[8]彭睿旻,江军,匡光涛,等.基于EEG的癫痫自动检测:综述与展望[J].自动化学报,2022,48(2):335-350.[9]HOSSEINIMP,HOSSEINIA,AHIK.AreviewonmachinelearningforEEGsignalprocessinginbioengineering[J].IEEEReviewsinBiomedicalEngineering,2020,14:204-218.[10]ACHARYAUR,HAGIWARAY,DESHPANDESN,etal.CharacterizationoffocalEEGsignals:Areview[J].FutureGenerationComputerSystems,2019,91:290-299.[11]MURSALINM,ZHANGY,CHENY,etal.Automatedepilepticseizuredetectionusingimprovedcorrelation-basedfeatureselectionwithrandomforestclassifier[J].Neurocomputing,2017,241:204-214.[12]杨舒涵,李博,周丰丰.基于机器学习的跨患者癫痫自动检测算法[J].吉林大学学报(理学版),2021,59(1):101-106.[13]ZAREIA,ASLBM.Automaticseizuredetectionusingorthogonalmatchingpursuit,discretewavelettransform,andentropybasedfeaturesofEEGsignals[J].ComputersinBiologyandMedicine,2021,131:104250.[14]吴端坡,王紫萌,董芳,等.基于aEEG尖峰和cEEG棘波提取的癫痫发作检测算法[J].实验技术与管理,2020,37(12):57-62.[15]骆睿鹏,冯铭科,黄鑫,等.脑电信号预处理方法研究综述[J].电子科技,2023,36(4):36-43.[16]OCBAGABIRHT,ABOALAYONKAI,FAEZIPOURM.EfficientEEGanalysisforseizuremonitoringinepilepticpatients[C]//2013IEEELongIslandSystems,ApplicationsandTechnologyConference(LISAT).Farmingdate,USA:IEEE,2013:1-6.[17]BOONYAKITANONTP,LEK-UTHAIA,CHOMTHOK,etal.AreviewoffeatureextractionandperformanceevaluationinepilepticseizuredetectionusingEEG[J].BiomedicalSignalProcessingandControl,2020,57:101702.[18]MUKHOPADHYAYS,RAYGC.Anewinterpretationofnonlinearenergyoperatoranditsefficacyinspikedetection[J].IEEETransactionsonBiomedicalEngineering,1998,45(2):180-187.[19]TAPANIKT,VANHATALOS,STEVENSONNJ.IncorporatingspikecorrelationsintoanSVM-basedneonatalseizuredetector[C]//EMBEC&NBC2017:JointConferenceoftheEuropeanMedicalandBiologicalEngineeringConference(EMBEC)andtheNordic-BalticConferenceonBiomedicalEngineeringandMedicalPhysics(NBC).Singapore:Springer,2018:322-325.[20]TAPANIKT,VANHATALOS,STEVENSONNJ.Time-varyingEEGcorrelationsimproveautomatedneonatalseizuredetection[J].InternationalJournalofNeuralSystems,2019,29(4):1850030.231智㊀能㊀计㊀算㊀机㊀与㊀应㊀用㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀㊀第14卷㊀。

geophysical research letters的产生过程

geophysical research letters的产生过程

geophysical research letters的产生过程
Geophysical Research Letters (GRL) 是一本国际性的地球物理
学研究期刊,由美国地球物理学会(AGU)于1974年创办。

以下是GRL的产生过程:
1. 初始阶段:AGU于20世纪70年代初注意到地球物理学领
域的快速发展和跨学科研究的需求。

AGU决定创建一本封面
短小、时间迅速、发表高质量研究结果的期刊,以满足地球物理学领域学者对快速传播研究成果的需求。

2. 议案讨论:AGU将该想法提交给其出版委员会,并在地球
物理学界内进行了广泛的讨论。

乔治教曾在1973年年会上提
议创办GRL,并得到了广泛支持。

3. 正式创建:1974年,AGU正式创办了Geophysical Research Letters。

该期刊的首席编辑是Edward C. Bullard,他对期刊的
目标进行了明确规定:尽早发表快速传播的高质量地球物理学研究结果。

4. 后续发展:从创刊开始,GRL的影响力和重要性逐渐增加。

期刊开始涵盖更广泛的地球物理学领域,包括大气科学、地球化学、地球物理学和生物地球化学等。

此外,为了适应跨学科研究的发展趋势,GRL开始接受更多与地球物理学相关的研
究领域的投稿。

5. 电子化与开放获取:随着科技的进步,GRL逐渐实施了电
子化出版,并于1997年开始提供在线访问。

2012年,GRL成
为AGU旗下的开放获取期刊之一,所有论文都可以免费获取。

至今,GRL已成为地球物理学领域的重要期刊之一,为学术
界提供了一个快速传播、高质量地球物理学研究成果的平台。

应用地球化学元素丰度数据手册-原版

应用地球化学元素丰度数据手册-原版

应用地球化学元素丰度数据手册迟清华鄢明才编著地质出版社·北京·1内容提要本书汇编了国内外不同研究者提出的火成岩、沉积岩、变质岩、土壤、水系沉积物、泛滥平原沉积物、浅海沉积物和大陆地壳的化学组成与元素丰度,同时列出了勘查地球化学和环境地球化学研究中常用的中国主要地球化学标准物质的标准值,所提供内容均为地球化学工作者所必须了解的各种重要地质介质的地球化学基础数据。

本书供从事地球化学、岩石学、勘查地球化学、生态环境与农业地球化学、地质样品分析测试、矿产勘查、基础地质等领域的研究者阅读,也可供地球科学其它领域的研究者使用。

图书在版编目(CIP)数据应用地球化学元素丰度数据手册/迟清华,鄢明才编著. -北京:地质出版社,2007.12ISBN 978-7-116-05536-0Ⅰ. 应… Ⅱ. ①迟…②鄢…Ⅲ. 地球化学丰度-化学元素-数据-手册Ⅳ. P595-62中国版本图书馆CIP数据核字(2007)第185917号责任编辑:王永奉陈军中责任校对:李玫出版发行:地质出版社社址邮编:北京市海淀区学院路31号,100083电话:(010)82324508(邮购部)网址:电子邮箱:zbs@传真:(010)82310759印刷:北京地大彩印厂开本:889mm×1194mm 1/16印张:10.25字数:260千字印数:1-3000册版次:2007年12月北京第1版•第1次印刷定价:28.00元书号:ISBN 978-7-116-05536-0(如对本书有建议或意见,敬请致电本社;如本社有印装问题,本社负责调换)2关于应用地球化学元素丰度数据手册(代序)地球化学元素丰度数据,即地壳五个圈内多种元素在各种介质、各种尺度内含量的统计数据。

它是应用地球化学研究解决资源与环境问题上重要的资料。

将这些数据资料汇编在一起将使研究人员节省不少查找文献的劳动与时间。

这本小册子就是按照这样的想法编汇的。

滇东岩溶高原矿泉水类型及地质控制

滇东岩溶高原矿泉水类型及地质控制

2021年5月地 球 学 报 May 2021第42卷 第3期: 333-340Acta Geoscientica SinicaVol.42No.3: 333-340本文由国家重点研发计划专项(编号: 2016YFC0502502)和中国地质调查局地质调查项目“滇西北地区生态地质调查”(编号: DD2019)联合资助。

收稿日期: 2020-06-08; 改回日期: 2020-07-24; 网络首发日期: 2020-08-04。

责任编辑: 闫立娟。

第一作者简介: 张贵, 男, 1964年生。

教授级高级工程师。

长期从事水工环地质调查研究工作。

通讯地址: 650216, 昆明市人民东路王大桥云南省地质环境监测院。

E-mail:****************。

滇东岩溶高原矿泉水类型及地质控制张 贵, 张 华, 王 波, 张文鋆, 高 瑜, 何绕生, 周翠琼, 彭淑惠云南省地质环境监测院, 云南昆明 650216摘 要: 滇东岩溶高原区地层构造复杂, 出露岩类齐全, 北部属于上扬子地台、南部属于华南褶皱带, 地球化学过程和岩溶作用过程, 导致矿泉类型多样和水资源丰富, 已发现各类矿泉水点363个。

通过调查、梳理总结, 区内矿泉水分为6类: 偏硅酸、盐类、微量元素、碳酸、复合型、温矿水; 据形成条件主控因素差异, 成因类型可分3类: 断裂带对流型、深循环层控型、潜流溶滤型。

岩溶作用对滇东矿泉水的形成具有影响作用, 并可形成特色锶矿泉水。

总结了开发利用存在的问题, 提出了矿泉水优化利用的对策建议。

关键词: 滇东; 岩溶高原; 矿泉水; 分类特征; 成因类型; 对策建议 中图分类号: P641.5 文献标志码: A doi: 10.3975/cagsb.2020.073101Mineral Water Types and Geological Control in Karst Plateau ofEastern YunnanZHANG Gui, ZHANG Hua, WANG Bo, ZHANG Wen-jun, GAO Yu, HE Rao-sheng,ZHOU Cui-qiong, PENG Shu-huiYunnan Institute of Geo-Environment Monitoring, Kunming, Yunnan 650216Abstract: The karst plateau area in eastern Yunnan has a complex stratigraphic structure and a complete range of outcropping rocks. The north belongs to the Upper Yangtze Platform and the south belongs to the South China Fold Belt. Geochemical processes and karstification processes have led to diverse types of mineral springs and abundant water resources. 363 water points consisting of various types of mineral springs have been discovered. Through investigation and summary, the mineral water in the area is classified into 6 types: metasilicic acid, salt, trace element, carbonic acid, compound type, and warm mineral water; according to the main controlling factors of formation conditions, the genetic types can be divided into 3 types: fault zone convection type, deep circulation stratification control type, and underflow leaching type. Karst has an impact on the formation of mineral water in eastern Yunnan, and can form characteristic strontium mineral water. The problems in development and utilization are summarized, and countermeasures and suggestions for optimal utilization of mineral water are put forward.Key words: eastern Yunnan; karst plateau; mineral water; classification characteristics; genetic types; countermeasures and suggestions本文所指矿泉水包括天然饮用矿泉水、医疗矿泉水。

《珍稀濒危植物四合木Genic-SSR标记的开发及种群遗传学研究》范文

《珍稀濒危植物四合木Genic-SSR标记的开发及种群遗传学研究》范文

《珍稀濒危植物四合木Genic-SSR标记的开发及种群遗传学研究》篇一一、引言四合木作为一种珍稀濒危植物,其保护与遗传学研究对于生物多样性的维护和生态系统的平衡具有重要意义。

随着分子生物学技术的不断发展,Genic-SSR(简单序列重复)标记作为一种有效的分子标记技术,被广泛应用于植物遗传学和种群遗传学研究中。

本文旨在开发四合木的Genic-SSR标记,并对其种群遗传学进行深入研究,以期为四合木的保护与利用提供理论依据。

二、材料与方法1. 实验材料选取四合木的不同地理种群作为实验材料,采集新鲜叶片用于基因组DNA的提取。

2. Genic-SSR标记的开发利用生物信息学方法,对四合木的基因组进行序列分析,设计并筛选出多态性高、重复性好的Genic-SSR引物。

3. 种群遗传学研究采用PCR技术对各地理种群的四合木进行Genic-SSR标记扩增,通过数据统计与分析,揭示四合木的种群遗传结构、遗传多样性和遗传变异等特征。

三、结果与分析1. Genic-SSR标记的开发结果通过生物信息学分析,成功设计并筛选出多态性高、重复性好的Genic-SSR引物XX余对。

这些引物在四合木基因组中表现出较高的多态性,适用于后续的种群遗传学研究。

2. 种群遗传学研究结果(1)遗传结构:通过Genic-SSR标记扩增,我们揭示了四合木不同地理种群的遗传结构。

各地理种群间存在一定的遗传差异,表明四合木具有较复杂的种群遗传结构。

(2)遗传多样性:四合木的遗传多样性较高,表现为多个等位基因的存在。

不同地理种群间的遗传多样性存在一定差异,可能与地理位置、生态环境等因素有关。

(3)遗传变异:通过Genic-SSR标记数据,我们发现四合木种群内存在一定程度的遗传变异。

这些变异可能受到自然选择、基因流、突变等因素的影响。

3. 数据分析与讨论通过对Genic-SSR标记数据的统计分析,我们发现四合木的种群遗传结构、遗传多样性和遗传变异等特征与地理位置、生态环境等因素密切相关。

TechniquesforProteinSequenceAlignmentandDatabase

TechniquesforProteinSequenceAlignmentandDatabase
Identity Scoring
Simplest Scoring scheme Score 1 for Identical pairs Score 0 for Non-Identical pairs Unable to detect similarity Percent Identity
For any alignment one need scoring scheme and weight matrix
Important Point
All algorithms to compare protein sequences rely on some scheme to score the equivalencing of each 210 possible pairs. 190 different pairs + 20 identical pairs Higher scores for identical/similar amino acids (e.g. A,A or I, L) Lower scores to different character (e.g. I, D)
The Scoring Schemes or Weight Matrices
Matrices Derived from Structure
Structure alignment is true/reference alignment Allow to compare distant proteins Risler 1988, derived from 32 protein structures
Which Matrix one should use
Matrices derived from Observed substitutions are better BLOSUM and Dayhoff (PAM) BLOSUM62 or PAM250

the rising sea 代数几何 发表

the rising sea 代数几何 发表

the rising sea 代数几何发表The Rising Sea: Algebraic Geometry in Modern ApplicationsIn the field of mathematics, algebraic geometry is a vibrant and rapidly growing discipline. It merges geometry and algebra, creating a powerful toolset for understanding and manipulating complex mathematical structures. The Rising Sea: Algebraic Geometry in Modern Applications is a groundbreaking textbook that delves into the depth and breadth of this fascinating field.The book opens with a discussion of the fundamental principles and building blocks of algebraic geometry. It introduces the reader to the basic concepts of polynomial rings, ideals, and varieties. The explanations are clear and accessible, laying a solid foundation for the more advanced topics that follow.The Rising Sea then delves into more advanced topics, such as abstract algebra, commutative algebra, and scheme theory. The text carefully explains these concepts, providing numerous examples and exercises to aid comprehension. It also introduces the reader to the powerful tools of homological algebra and algebraic geometry, further enriching their understanding of the subject.The book’s scope extends beyond the theoretical aspects of algebraic geometry. It also includes chapters on the applications of algebraic geometry in various fields, such as computer science, physics, and economics. These applications demonstrate the practical relevance and impact of algebraic geometry in modern society.The Rising Sea is an essential resource for students and researchers in mathematics, computer science, and related fields. Its clear and comprehensive approach makes it an excellent textbook for undergraduate and graduate courses on algebraic geometry. It will also serve as a valuable reference for professionals in industry and academia who seek a deeper understanding of this rapidly developing field.。

碳骨架气质联用法测定纺织品中短链氯化石蜡

碳骨架气质联用法测定纺织品中短链氯化石蜡

碳骨架气质联用法测定纺织品中短链氯化石蜡戴宏翔1沈群1匡伟伟2"1杭州市质量技术监督检测院,杭州310019$.杭州市余杭区质量计量监测中心,杭州311100)摘要:针对组成复杂的短链氯化石蜡(SCCPs),目前尚未有完善测定纺织品中SCCPs的方法,本实验把从样品中提取的SCCPs经钯催化脱氯氢化反应,采用GC-MS检测生成的C10〜C13直链烷烃,外标法定量,通过优化氢气流速和进样口温度,建立了碳骨架GC-MS测定纺织品中SCCPs的方法。

该方法加标回收率在84.5%〜106.65%,精密度(RSD)为3.65〜7.64%,检出限(LOD)为2.5mg/kg#可用于纺织品中SCCPs的定性定量分析。

关键词:短链氯化石蜡钯催化气质联用法碳骨架DOI:10.3969/j.issn.1001—232x.2021.02.004Determination of short-chain chlorinated paraffins in textiles by carbon skeleton-GC/MS.Dai Hongx-i a ng1#Shen Qun'#Ku a ng Weiwei2(1.Ha ngzhou Institute of Test a nd Ca libra tion for Qu a lily a nd Technology Supervision#Hangzhou310019#China;2.Hangzhou Yuhang District Quality Measurement MonitoringCenter#H)ngzhou311100#Chin))Abstract:The SCCPs eJtracted from samples were dechlorinated and hydrogenated with pa l adium as catalyzer.The generated straight-chain paraffins of C w—C:3were detected by gas chromatography-mass spectrometry(GC-MS)and quantified by external standard method.The flow rate of hydrogen and the temperatureofinjectionportwereoptimized.Therecoveriesofthis methodforSCCPsinteJtilesamples varied from84.5%to106.65%#the precisions(RSD)ranged from3.65%to7.64%#and the limit of de-tection(LOD)was2.5mg/kg.This method is suitable for qualitative and quantitative analysis of SCCPs in textiles.Key words:Short-chain chlorinated para f ins(SCCPs)$Pa l adium catalyze$Gas chromatography-mass spectrometry(GC-MS)$Carbonskeleton1引言短链氯化石蜡(SCCPs)是由10〜13个碳原子长度的直链烷烃经氯化而成的复杂混合物,广泛用于金属加工润滑剂、表面处理剂、油漆、皮革增塑剂、防霉剂、纺织粘合剂、阻燃剂等[13]。

基于拉普拉斯算子迭代法的点云骨架提取

基于拉普拉斯算子迭代法的点云骨架提取

基于拉普拉斯算子迭代法的点云骨架提取点云骨架提取是计算机视觉领域中一项重要的任务,它可以将点云中的主要形状特征提取出来,为后续的物体识别、形状分析等任务打下基础。

本文将重点介绍一种基于拉普拉斯算子迭代法的点云骨架提取方法。

点云是由大量的三维点坐标组成的数据集,广泛应用于三维重建、场景分析、虚拟现实等领域。

然而,点云数据通常包含大量的细节和噪声,导致形状特征难以直接提取。

因此,对点云进行骨架提取能够将其简化为一系列骨架点,保留主要形状特征,提高后续处理的效率和准确性。

拉普拉斯算子迭代法是一种基于局部特征的骨架提取方法,其基本思想是通过迭代优化的方式,逐步确定点云中的骨架点。

具体步骤如下:首先,将点云数据进行三角化处理,得到点之间的邻接关系。

然后,计算每个点的法向量,用于后续的相关计算。

接着,进行初始化,选择一部分点作为初始的骨架点,并计算其质心。

然后,对每个点进行迭代优化。

首先,计算每个点到其邻接点的平均距离。

然后,根据平均距离,计算每个点的拉普拉斯能量,作为点的重要性度量。

通过比较每个点的拉普拉斯能量,选择能量最小的点作为新的骨架点,并更新骨架点集合和质心。

接下来,更新每个点的邻接关系和法向量。

对于新的骨架点,将其添加到邻接关系中,并重新计算其他点到它的平均距离。

同时,对于与新的骨架点相邻的点,更新其法向量。

最后,根据骨架点的质心和邻接关系,生成点云的骨架。

该方法的优势在于能够充分利用点云中的局部特征,准确地提取出主要形状特征。

同时,该方法可以根据应用需要进行参数调整,灵活性较高。

然而,该方法的实际应用中仍然存在一些挑战。

首先,点云数据通常非常庞大,导致计算量大、耗时长。

其次,点云数据中可能存在噪声和异常点,对骨架提取结果产生干扰。

因此,如何进一步优化算法的效率和鲁棒性仍然是一个值得研究的问题。

综上所述,基于拉普拉斯算子迭代法的点云骨架提取方法在点云处理中具有广泛的应用前景。

通过对点云进行骨架提取,可以提取出主要的形状特征,为后续的形状分析和物体识别任务提供有力支持。

液晶弹性体

液晶弹性体

3. Actuators based on LCEs
3.1. Actuators based on thermally actuated LCEs
Fig 3. Micrometer-sized nematic LCE actuators consisting of a pillar array. (a) Experimental setup used to prepare the responsive pillars. (b) Top view (under an optical microscope) of the pillar pattern obtained by the imprint in the nematic liquid crystal elastomer. (Inset) Zoom on the structure (pillar diameter=20mm)[1]. [1 ]Buguin A, Li M H, Silberzan P, et al. Journal of the American Chemical Society, 2006, 128(4): 1088-1089.
4. Summary
1.Introduction
Smart materials:
There is a group of materials capable of responding to external stimuli with mechanical deformation.
Fig 1. The diferent kinds of actuator materials both in natural and synthetic systems
3. Actuators based on LCEs

某轻型车用户道路与试验场道路当量关系计算

某轻型车用户道路与试验场道路当量关系计算

2023年第6期77doi:10.3969/j.issn.1005-2550.2023.06.015 收稿日期:2023-09-12李安民毕业于郑州大学,研究生学历,现就职于东风汽车股份有限公司商品研发院工程验证中心,任工程师,主要研究方向为整车道路性能试验技术和车辆路谱数据处理研究。

某轻型车用户道路与试验场道路当量关系计算李安民,周志明,林文干(东风汽车股份有限公司商品研发院,襄阳 430000)摘 要:根据客户调研结果,实测用户道路载荷谱和试验场道路载荷谱,用nCode GlyphWorks软件进行数据处理和分析。

通过对轴头加速度、轴头位移以及板簧应变的损伤计算,确定出基于轴头位移进行试验场路面与用户路面的当量关系计算。

最后通过对数据的雨流计数分布以及频域损伤谱的分析,进一步验证了所计算当量关系的准确性以及适用性,为在试验场进行整车路面负荷可靠性快速验证提供了一定的理论依据。

关键词:路面载荷谱;当量关系;整车可靠性试验;频域损伤谱中图分类号:U467.5 文献标志码:A 文章编号:1005-2550(2023)06-0077-05Calculation of Equivalent Relationship Between Proving Groundand Real RoadLI An-min, ZHOU Zhi-ming, LIN Wen-gan(Commercial Product R&D Institute, Dongfeng AutomobileCo., Ltd., Xiangyang 430000, China)Abstract: According to the customer survey results, loading data were measured in proving ground and real road respectively, and the data were processed and analyzed with nCode GlyphWorks. Through the damage calculation of the axle head acceleration, displacement and leaf spring strain, the equivalent relationship between proving ground and real road is determined based on the axle head displacement. Finally, the accuracy and applicability of the calculated equivalence relationship are further verified by analyzing the rain flow counting distribution and fatigue damage spectrum of the data, which provides a theoretical basis for the rapid verification of vehicle road load reliability in proving ground.Key Words: Loading Spectrum; Equivalent Relation; Vehicle Reliability T est; Fatigue Damage Spectrum1 引言在汽车开发阶段,试验场验证作为最后一道考核和检验手段具有举足轻重的地位。

人工智能辅助的药物设计

人工智能辅助的药物设计

内存峰值 (GB)
3000
TB
2500
2000
1500
1000
GB
500
0
0
2000 4000 6000 8000
序列长度 (aa)
越低越好
w/o TPP w/ TPP 多项式 (w/o TPP) 线性 (w/ TPP)
预测的蛋白结构
内存占用量级
预测的蛋白结构示例
单卡GPU是最优选择
~20GB
~40GB
ICX6330
ICX6330
SPR9462
谢谢!
人工智能辅助的药物设计
内容总览
1. 市场挑战及突破口 2. 大分子药物设计代表场景的优化 3. 小分子药物设计代表场景的优化
Al药物设计的场景和挑战
大分子药物设计
A. Anishchico et al. Nature 2021
基础研究的工具分子
Hallucination AfDesign trRosetta
基线 + icc

使用英特尔 C++ 编译器 配置 jemalloc
基线 + icc + AVX512

使用英特尔 AVX-512 指令集 优化MSA热点函数
基线 + icc + AVX512 + 并 行MSA
Parallel MSA

并行处理 MSA 搜索
1.97
1.00 Baseline
1.25 Intel C++ Compiler + jemalloc
xB
64GB
HBM2e
up to
112.5MB

基于句法依存分析的图网络生物医学命名实体识别

基于句法依存分析的图网络生物医学命名实体识别

2021⁃02⁃10计算机应用,Journal of Computer Applications 2021,41(2):357-362ISSN 1001⁃9081CODEN JYIIDU http ://基于句法依存分析的图网络生物医学命名实体识别许力,李建华*(华东理工大学信息科学与工程学院,上海200237)(∗通信作者电子邮箱jhli@ )摘要:现有的生物医学命名实体识别方法没有利用语料中的句法信息,准确率不高。

针对这一问题,提出基于句法依存分析的图网络生物医学命名实体识别模型。

首先利用卷积神经网络(CNN )生成字符向量并将其与词向量拼接,然后将其送入双向长短期记忆(BiLSTM )网络进行训练;其次以句子为单位对语料进行句法依存分析,并构建邻接矩阵;最后将BiLSTM 的输出和通过句法依存分析构建的邻接矩阵送入图卷积网络(GCN )进行训练,并引入图注意力机制优化邻接节点的特征权重得到模型输出。

所提模型在JNLPBA 和NCBI -disease 数据集上的F1值分别达到了76.91%和87.80%,相比基准模型分别提升了2.62和1.66个百分点。

实验结果证明,提出的方法能有效提升模型在生物医学命名实体识别任务上的表现。

关键词:生物医学;命名实体识别;双向长短期记忆网络;图卷积网络;句法依存分析;图注意力机制中图分类号:TP391.1文献标志码:ABiomedical named entity recognition with graph network based onsyntactic dependency parsingXU Li ,LI Jianhua *(School of Information Science and Engineering ,East University of Science and Technology ,Shanghai 200237,China )Abstract:The existing biomedical named entity recognition methods do not use the syntactic information in the corpus ,resulting in low precision.To solve this problem ,a biomedical named entity recognition model with graph network based onsyntactic dependency parsing was proposed.Firstly ,the Convolutional Nerual Network (CNN )was used to generate character vectors which were concatenated with word vectors ,then they were sent to Bidirectional Long Short -Term Memory (BiLSTM )network for training.Secondly ,syntactic dependency parsing to the corpus was conducted with a sentence as a unit ,and the adjacency matrix was constructed.Finally ,the output of BiLSTM and the adjacency matrix constructed bysyntactic dependency parsing were sent to Graph Convolutional Network (GCN )for training ,and the graph attention mechanism was introduced to optimize the feature weights of adjacency nodes to obtain the model output.On JNLPBAdataset and NCBI -disease dataset ,the proposed model reached F1score of 76.91%and 87.80%respectively ,which were2.62and 1.66percentage points higher than those of the baseline model respectively.Experimental results prove that the proposed method can effectively improve the performance of the model in the biomedical named entity recognition task.Key words:biomedicine;named entity recognition;Bidirectional Long Short -Term Memory (BiLSTM)network;GraphConvolutional Network (GCN);syntactic dependency parsing;graph attention mechanism引言在生物医学领域,每年都会新增大量的专利、期刊和报告等文献。

基于骨架的玉米植株三维点云果穗分割与表型参数提取

基于骨架的玉米植株三维点云果穗分割与表型参数提取

第37卷第6期农业工程学报 V ol.37 No.62021年3月Transactions of the Chinese Society of Agricultural Engineering Mar. 2021 295 基于骨架的玉米植株三维点云果穗分割与表型参数提取朱超,苗腾※,许童羽,李娜,邓寒冰,周云成(1. 沈阳农业大学信息与电气工程学院,沈阳 110866;2. 辽宁省农业信息化工程技术研究中心,沈阳 110866)摘要:当前三维点云处理技术难以在玉米植株点云上对果穗进行识别和表型参数提取。

针对该问题,该研究采用基于骨架的玉米植株器官分割流程对植株三维点云的果穗器官进行分割和表型参数提取。

首先,优化基于骨架的玉米植株茎叶分割方法,在成熟期植株点云上实现植株骨架的提取、器官子骨架的分解以及器官点云的分割;再根据器官高度、子骨架长度、圆柱特征和点云数量4个约束条件从器官点云中识别出果穗点云;最后提取果穗相关的表型参数。

试验结果表明,该研究方法对玉米果穗的识别率为91.3%;果穗点云分割的平均F1分数、精确度、召回率分别为0.73、0.82和0.70;穗位高、穗长、穗粗、株高穗位高比4个表型参数的提取值与人工实测值线性关系显著,决定系数分别为0.97、0.78、0.85和0.96,均方根误差分别为3.23 、4.98、 0.73 cm和0.07。

该研究方法具备提取果穗器官点云和表型参数的能力,可为玉米高通量表型检测、玉米三维重建等研究和应用提供技术支持。

关键词:植物;表型;机器视觉;玉米果穗;点云分割;骨架提取doi:10.11975/j.issn.1002-6819.2021.06.036中图分类号:TP391.4 文献标志码:A 文章编号:1002-6819(2021)-06-0295-07朱超,苗腾,许童羽,等. 基于骨架的玉米植株三维点云果穗分割与表型参数提取[J]. 农业工程学报,2021,37(6):295-301. doi:10.11975/j.issn.1002-6819.2021.06.036 Zhu Chao, Miao Teng, Xu Tongyu, et al. Ear segmentation and phenotypic trait extraction of maize based on three-dimensional point cloud skeleton[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2021, 37(6): 295-301. (in Chinese with English abstract) doi:10.11975/j.issn.1002-6819.2021.06.036 0 引 言玉米是世界上最重要的粮食作物之一,其产量对保障全球粮食供应至关重要。

基于转录组测序的紫薇SSR_特征分析

基于转录组测序的紫薇SSR_特征分析

引用格式:秦 波,韦广绥,覃 杰,等. 基于转录组测序的紫薇SSR特征分析[J]. 湖南农业科学,2023(4):1-4. DOI:DOI:10.16498/ki.hnnykx.2023.004.001紫薇(Lagerstroemia indica)属千屈菜科紫薇属落叶灌木或小乔木,具有花期长、花量大、抗逆性强等特点,是重要的夏季观赏木本花卉,被广泛应用于世界各地的园林景观中[1-2]。

紫薇栽培历史悠久,最早的栽培记录可追溯至我国东晋王嘉所著的《拾遗记》。

经过长期选育,紫薇形成了丰富多样的品种,目前全世界的紫薇品种已超过500个[3]。

SSR(Simple Sequence Repeats)又称为微卫星DNA(Microsatellite DNA),是由1~6个核苷酸组成的短串联重复序列,具有可变的重复次数和保守的侧翼序列,因而可通过设计特异引物进行PCR以扩增串联重复序列的多态性[4]。

SSR具有共显性遗传、多态性丰富、重复性高等特点,被广泛应用于物种亲缘关系分析、遗传多样性分析、品种鉴定、杂种鉴定等研究中。

董俊美等[5]基于山药转录组数据挖掘了大量的SSR位点,并通过开发分子标记对来自河北省、河南省、山东省及福建省等山药主产区的43份山药种质资源进行了遗传多样性和亲缘关系分析;胡光明等[6]基于猕猴桃全基因组数据筛选得到一批多态性高、通用性强的SSR引物,并对种质资源进行了基因分型及亲缘聚类分析;张成才等[7]通过筛选薄壳山核桃核心SSR引物,构建了36个常见品种的特异分子指纹图谱和分子身份证;谷艳鹏等[8]以花楸树为母本、少叶花楸为父本进行种间杂交,利用筛选出的9对扩增结果稳定且具多态性的 基于转录组测序的紫薇SSR特征分析 秦 波1,韦广绥2,覃 杰1,孙开道1,秦赐梅3,刘 昊1,黄 欣1(1.广西壮族自治区林业科学研究院,广西南宁 530002;2.广西壮族自治区国有高峰林场,广西南宁 530025;3.广西壮族自治区国有黄冕林场,广西柳州 545600)摘 要:以紫薇叶片为材料,通过Illumina Hiseq X Ten测序平台进行高通量测序,利用MISA软件分析转录组的SSR相关信息。

基于GCN-LSTM_的频谱预测算法

基于GCN-LSTM_的频谱预测算法

doi:10.3969/j.issn.1003-3114.2023.02.001引用格式:薛文举,付宁,高玉龙.基于GCN-LSTM 的频谱预测算法[J].无线电通信技术,2023,49(2):203-208.[XUE Wenju,FU Ning,GAO Yulong.Spectrum Prediction Algorithm Based on GCN-LSTM[J].Radio Communications Technology,2023,49(2):203-208.]基于GCN-LSTM 的频谱预测算法薛文举,付㊀宁,高玉龙(哈尔滨工业大学通信技术研究所,黑龙江哈尔滨150001)摘㊀要:无线频谱是一项重要的㊁难以再生的自然资源㊂在频谱数据中随着信道的动态变化,各个信道不能建模成规则的结构㊂由于卷积神经网络提取的是规则数据结构的相关性,没有考虑信道动态变化以及各个信道节点之间的相关性影响,基于此研究了基于图卷积神经网络(Graph Convolutional Network,GCN)和长短期记忆(Long Short-TermMemory,LSTM)网络结合的GCN-LSTM 频谱预测模型,并且引入了注意力机制,仿真得到了GCN-LSTM 在正确数据集和有一定错误数据的数据集上的预测性能和算法运行时间㊂结果表明在引入注意力机制后,GCN-LSTM 预测模型的准确性和实时性都得到了提高㊂关键词:频谱预测;图神经网络;LSTM;注意力机制中图分类号:TN919.23㊀㊀㊀文献标志码:A㊀㊀㊀开放科学(资源服务)标识码(OSID):文章编号:1003-3114(2023)02-0203-06Spectrum Prediction Algorithm Based on GCN-LSTMXUE Wenju,FU Ning,GAO Yulong(Communication Research Center,Harbin Institute of Technology,Harbin 150001,China)Abstract :Wireless spectrum is an important and hard-to-regenerate natural resource.Since convolutional neural network extractscorrelation of regular data structure,dynamic changes of channel and the correlation between each channel node are not considered.Therefore,this paper studies a GCN-LSTM spectrum prediction model based on the combination of graph convolution neural network GCN and LSTM network,and introduces an attention mechanism.Simulation results show that the prediction performance and algorithm running time of GCN-LSTM on the correct dataset and the dataset with certain error data.Results show that the accuracy and real-timeperformance of GCN-LSTM prediction model are improved after introducing the attention mechanism.Keywords :spectrum prediction;graph neural network;LSTM;attention mechanism收稿日期:2022-12-29基金项目:国家自然科学基金(62171163)Foundation Item :National Natural Science Foundation of China(62171163)0 引言随着无线通信事业的蓬勃发展,各种接入无线网的智能设备数量迅速增长[1],频谱资源趋于紧缺㊂传统的静态频谱分配方式不适配于需求日渐多样化的频谱环境,出现了大量的 频谱空洞 ,造成了频谱资源浪费㊂为解决频谱利用不足的问题,Mitola 在1999年提出了认知无线电(Cognitive Radio,CR)的概念[2]㊂频谱预测的核心就是挖掘并利用历史频谱数据的相关性特征㊂频谱预测可以分为预测信道的占用情况或者是预测用户的位置和传输功率两大类㊂本文主要针对第一类,即预测信道的占用情况㊂早期研究主要采用例如自回归模型[3]㊁隐马尔可夫模型[4]㊁模式挖掘等传统方法㊂随着神经网络的发展,人们开始将神经网络,比如循环神经网络(Recurrent Neural Network,RNN)[5]和长短期记忆网络(Long Short-Term Memory,LSTM)[6]用于预测,LSTM 网络有效缓解了梯度消失和梯度爆炸现象㊂此外,有很多学者对时频联合域频谱预测展开了研究㊂文献[7]利用频谱的这种相关性提出一种二维频繁模式挖掘算法㊂由于不同地点频谱的使用情况也会有很大不同,因此也有研究将频谱预测的维度扩展到时频空域上㊂文献[8]利用神经网络来进行多维频谱预测的方法研究,提出了LSTM网络和其他神经网络结合的方法进行时频空三维的预测,然而只是提出了想法,并没有实现,算法仍处于仿真阶段㊂图神经网络最早由Gori等人[9]提出㊂GCN广泛用于提取图结构的特征信息,从理论上可以将GCN分为基于谱域和空域两类㊂Bruna等人在2014年提出了第一代GCN[10],定义了图上的卷积方法图结构㊂基于空域的图卷积则没有借助谱图理论,可以直接在空域上操作,非常灵活㊂Petar等人在2018年提出了图注意力网络(Graph Attention Network, GAT)[11],在图卷积网络中使用注意力机制,为图结构中不同的节点赋以不同的权重也就是注意力系数,解决了图卷积神经网络(Graph Convolutional Network,GCN)必须提前知道完整图结构的不足㊂把数据处理成图结构之后,利用图神经网络来学习图结构形式的数据可以更有效地挖掘发现其内部特征和模式,与频谱预测的核心不谋而合,因此可以使用图神经网络来进行频谱预测㊂本文首先分析了频谱预测的特点和发展趋势,说明了频谱预测的重要性和可行性㊂其次,针对频谱预测问题提出了GCN-LSTM模型进行二维时频频谱的预测,采用GCN提取频谱数据的拓扑特征,提取得到频谱数据中的频率相关性之后㊂然后利用LSTM网络进行时间维度动态性特征的提取㊂最后,通过引入注意力机制对GCN-LSTM频谱预测算法进行了改进研究㊂1 基于GCN-LSTM网络的频谱预测问题建模㊀㊀图神经网络可以通过分析研究各个节点的空间特征信息得到既包含内容也包含结构的特征表示,因此在本文中处理频谱数据时,不再是建模成规则的图片,而是建模成如图1所示的图结构㊂图结构中的每个节点代表频谱中的各个信道,信道之间是存在关联的,用图中的边表示,时间维度上的各个信道状态即是各个节点的特征㊂图1㊀频谱建模成图结构Fig.1㊀Spectrum modeling and mapping structure为了提取非欧式拓扑图的空间特征,研究人员利用GCN通过图结构的信息和图中节点的信息提取图的结构特征[12],如图2所示㊂GCN如今已经广泛应用于图数据的研究处理领域[13]㊂图2㊀图神经网络的结构示意图Fig.2㊀Structure diagram of graph neural network对于给定的图G=(V,E),V表示图中的节点集合,假设其长度为N㊂可以用图中的节点V和边E来对图进行定义㊂第二代图卷积GCN公式可以简化成:x G∗gθʈðK k=0θk T k(L~)x㊂(1)㊀㊀由式(1)可以看出,图上的卷积不需要整个图都参与运算,只需捕捉到图上的局部特征,减少了需要训练学习的参数量;并且不再需要对图进行特征分解,避免了特征分解的高昂代价㊂但是由于进行矩阵相乘操作,计算的时间复杂度仍然比较高㊂为了对问题进行简化,Kipf等人在文献[14]中设置K=1,只考虑节点的一阶邻居节点㊂如图3所示,当K=1时,对每个节点的特征进行更新时,不但会考虑各个节点本身的输入特征,还会将各个节点的一阶邻域的邻居节点的输入特征也考虑在内㊂取λmax =2,K =1,得到多层传播的图卷积计算公式:H (l +1)=σD ~-12A ~D ~-12H (l )W (l )(),(2)式中,σ(㊃)为非线性激活函数,A ~=A +I N ,A ~为加上自身属性后的邻接矩阵,D ~=ðjA ~ij 表示邻接矩阵A ~的度矩阵,H (l )为第l 层中图节点特征,H (0)=χ,即输入的特征矩阵,W (l )为第l 层的权重,即可训练的卷积滤波参数㊂图3㊀图卷积计算的简单示意图Fig.3㊀Simple diagram of convolution calculation2㊀增加注意力机制的GCN-LSTM 频谱预测算法2.1㊀GCN-LSTM 网络模型利用信道占用模型产生频谱数据,然后将频谱建模成图,频谱中的各个信道建模成图中的各个节点,在频率上提取信道之间的相关性即是提取节点之间的相关性,用GCN 进行提取,时间上的相关性则由LSTM 进行提取㊂GCN-LSTM 频谱预测算法示意如图4所示,内部结构如图5所示㊂图4㊀GCN-LSTM 模型示意图Fig.4㊀GCN-LSTM modeldiagram图5㊀GCN-LSTM 模型内部结构Fig.5㊀Internal structure diagram of GCN-LSTM model图4中,先将图结构形式的频谱输入GCN,提取其拓扑结构特征(即频率相关性),GCN 的输出Z N t 是已经提取了频率相关性的序列数据;然后将提取频率相关性的Z N t 序列输入进LSTM 网络,提取序列数据的时序相关性;最终通过激活函数的激活得到输出,并与真实的频谱数据利用损失函数衡量比较得到误差㊂Z N t 代表输入数据χt 经过图卷积网络后的数据特征㊂i t ㊁f t ㊁o t 分别代表了输入门(Input Gate)㊁遗忘门(Forget Gate)和输出门(Output Gate)㊂图5所示的χt 代表输入的处理成图结构的频谱数据,节点之间的关联强弱代表信道相关性的强弱㊂GCN-LSTM 预测模型公式如下:i t=σ(W iχ㊃Z Nt +W ih ㊃h t -1+b t )f t =σ(W f χ㊃Z N t +W fh ㊃h t -1+b f )o t =σ(W o χ㊃Z N t +W o h ㊃h t -1+b o )c ~t =g (W c χ㊃Z N t +W ch ㊃h t -1+b c )c t =i t☉c ~t +f t ☉c t -1h t =o t☉h -(c t )ìîíïïïïïïïï㊂(3)2.2㊀增加注意力机制的GCN-LSTM 预测模型注意力机制[15]是关注更重点的信息而忽略一些无关的信息,在GCN-LSTM 模型基础上,加入注意力机制,就是对不同时间步的特征赋予不同的权重㊂Soft Attention 注意力机制示意如图6所示,可以分成三步:一是信息输入h j ;二是注意力系数e ij 的计算,e ij 利用神经网络计算,再利用softmax 函数对e ij 进行归一化得到注意力的分布a ij ;三是利用注意力分布αij 与输入的信息进行加权平均得到输出c i㊂αij =exp(e ij )ðN k =1exp(eik)㊂(4)㊀㊀输出c i 为权重与输入的加权平均:c i =ðN j =1αijh j㊂(5)图6㊀Soft attention 注意力机制示意F i g.6㊀Schematic diagram of Soft Attention mechanism㊀㊀增加了注意力机制的GCN-LSTM 模型网络,如图7所示㊂将GCN-LSTM 的输出作为注意力层的输入,通过一个全连接层,再经过softmax 归一化,计算对时间步的权重即注意力分配矩阵,将注意力分配矩阵和输入数据进行逐元素的相乘即得到注意力的输出㊂图7㊀增加注意力机制的GCN-LSTM 模型示意图Fig.7㊀Schematic diagram of GCN-LSTM model forincreasing attention mechanism3 仿真结果利用信道占用模型,产生了5个信道的频谱数据,时间长度为10000,损失函数选择二分类交叉熵损失函数㊂在实验中,设置GCN 的模型参数为:图卷积网络层数为1,初始学习率为0.001,评价GCN-LSTM 预测算法的性能指标为准确率㊂预测窗口长度为10,隐藏单元数hidden_units 为128,batch_size 为64,迭代次数epoch 为20㊂基于GCN-LSTM 预测算法预测的准确率如图8和图9所示㊂图8㊀GCN-LSTM 模型准确率Fig.8㊀GCN-LSTM modelaccuracy图9㊀增加注意力机制的GCN-LSTM 模型精确率Fig.9㊀Increase the accuracy of GCN-LSTMmodel of attention mechanism二分类交叉熵binary_cross entropy 公式为:loss (y ,y ^)=-1nðni(y i lb(y^i )+(1-y i )lb(1-y ^i )),(6)式中,y i 为真实的值,y^i 为预测的值㊂在基础的GCN-LSTM 模型上增加了注意力机制之后,同样训练20轮之后,准确率从96.89%增长到97.86%,准确率得到了提升,训练时间从10.23s 变为12.69s,网络输出时间从0.13s 变为0.15s,时间基本为原来的1.19倍㊂这是因为增加注意力机制后,训练的参数数量从70020增长为78120,数量增多㊂增加注意力机制确实可以提高GCN-LSTM 模型整体的预测性能,而且性能略平稳一些㊂同时对比在频谱数据出现错误情况下的GCN-LSTM 和增加了注意力机制之后的预测模型的预测性能㊂图10为错误概率为0.05的情况,图11为错误概率为0.1的情况㊂比较无错误㊁错误概率为0.05和0.1时,随着错误概率的增加,准确率会略有下降㊂增加注意力机制后的预测算法比没有增加注意力机制的GCN-LSTM 算法指标提高一点,预测性能更好㊂图10㊀GCN-LSTM 模型错误率为0.05时的准确率Fig.10㊀Accuracy when GCN-LSTM model error rate is 0.05图11㊀GCN-LSTM 模型错误率为0.1时的准确率Fig.11㊀Accuracy when GCN-LSTM model error rate is 0.14 结论本文主要研究了基于GCN-LSTM 的频谱预测算法,采用GCN 和LSTM 复合网络GCN-LSTM 预测模型进行时频频谱预测㊂为了考量不同时间步的重要程度,在GCN-LSTM 预测模型基础上增加了注意力机制来提高预测效果㊂此外,实际数据可能存在错误的情况,对无错误数据和错误数据的情况分别进行了仿真㊂仿真结果表明,GCN-LSTM 方法预测准确率较高,且训练时间和预测时间更短,实时性大大提升㊂另外,增加注意力机制后,预测性能也得到一些提高,时间约是没增加注意力机制时的1.2倍㊂对比数据出现错误的情况下,使用GCN-LSTM 算法的预测性能也在可以接受的范围内㊂参考文献[1]㊀DEHOS C,GONZÁLEZ J L,DOMENICO A D,et -limeter-wave Access Andbackhauling:The Solution to the Exponential Data Traffic Increase in 5G Mobilecommuni-cations Systems [J ].IEEE Communications Magazine,2014,52(9):88-95.[2]㊀MITOLA J,MAGUIRE G Q.Cognitive Radio:MakingSoftware 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Networks on Graphs [J /OL].arXiv:1312.6203[2022-12-20].https:ʊ /abs /1312.6203.[11]VELIC㊅KOVIC'P,CUCURULL G,CASANOVA A,et al.Graph Attention Networks[J/OL].arXiv:1710.10903[2022-12-20].https:ʊ/abs/1710.10903.[12]魏金泽.基于时空图网络的交通流预测方法研究[D].大连:大连理工大学,2021.[13]SCHLICHTKRULL M,KIPF T N,BLOEM P,et al.Mod-eling Relational Data with Graph Convolutional Networks[C]ʊEuropean Semantic Web Conference.Heraklion:Springer,2018:593-607.[14]KIPF T N,WELLING M.Semi-supervised Classificationwith Graph Convolutional Networks[J/OL].arXiv:1609.02907[2022-12-20].https:ʊ/abs/1609.02907.[15]UNGERLEIDER L G,KASTNER S.Mechanisms of VisualAttention in the Human Cortex[J].Annual Review ofNeuroscience,2003,23(1):315-341.作者简介:㊀㊀薛文举㊀哈尔滨工业大学硕士研究生㊂主要研究方向:频谱预测㊂㊀㊀付㊀宁㊀哈尔滨工业大学硕士研究生㊂主要研究方向:频谱预测㊂㊀㊀高玉龙㊀哈尔滨工业大学教授,博士生导师㊂主要研究方向:智能通信㊁频谱态势认知㊁智能信息融合㊂。

光伏发电英文文献Ultra-High Efficiency Photovoltaic

光伏发电英文文献Ultra-High Efficiency Photovoltaic

Ultra-High Efficiency Photovoltaic Cells for Large Scale Solar Power GenerationYoshiaki NakanoAbstract The primary targets of our project are to dras-tically improve the photovoltaic conversion efficiency and to develop new energy storage and delivery technologies. Our approach to obtain an efficiency over40%starts from the improvement of III–V multi-junction solar cells by introducing a novel material for each cell realizing an ideal combination of bandgaps and lattice-matching.Further improvement incorporates quantum structures such as stacked quantum wells and quantum dots,which allow higher degree of freedom in the design of the bandgap and the lattice strain.Highly controlled arrangement of either quantum dots or quantum wells permits the coupling of the wavefunctions,and thus forms intermediate bands in the bandgap of a host material,which allows multiple photon absorption theoretically leading to a conversion efficiency exceeding50%.In addition to such improvements, microfabrication technology for the integrated high-effi-ciency cells and the development of novel material systems that realizes high efficiency and low cost at the same time are investigated.Keywords Multi-junctionÁQuantum wellÁConcentratorÁPhotovoltaicINTRODUCTIONLarge-scale photovoltaic(PV)power generation systems, that achieve an ultra-high efficiency of40%or higher under high concentration,are in the spotlight as a new technology to ease drastically the energy problems.Mul-tiple junction(or tandem)solar cells that use epitaxial crystals of III–V compound semiconductors take on the active role for photoelectric energy conversion in such PV power generation systems.Because these solar cells operate under a sunlight concentration of5009to10009, the cost of cells that use the epitaxial crystal does not pose much of a problem.In concentrator PV,the increased cost for a cell is compensated by less costly focusing optics. The photons shining down on earth from the sun have a wide range of energy distribution,from the visible region to the infrared region,as shown in Fig.1.Multi-junction solar cells,which are laminated with multilayers of p–n junctions configured by using materials with different band gaps,show promise in absorbing as much of these photons as possible,and converting the photon energy into elec-tricity with minimum loss to obtain high voltage.Among the various types of multi-junction solar cells,indium gallium phosphide(InGaP)/gallium arsenide(GaAs)/ger-manium(Ge)triple-junction cells that make full use of the relationship between band gaps and diverse lattice con-stants offered by compound semiconductors have the advantage of high conversion efficiency because of their high-quality single crystal with a uniform-size crystal lat-tice.So far,a conversion efficiency exceeding41%under conditions where sunlight is concentrated to an intensity of approximately5009has been reported.The tunnel junction with a function equivalent to elec-trodes is inserted between different materials.The positive holes accumulated in the p layer and the electrons in the adjacent n layer will be recombined and eliminated in the tunnel junction.Therefore,three p–n junctions consisting of InGaP,GaAs,and Ge will become connected in series. The upper limit of the electric current is set by the mini-mum value of photonflux absorbed by a single cell.On the other hand,the sum of voltages of three cells make up the voltage.As shown in Fig.1,photons that can be captured in the GaAs middle cell have a smallflux because of the band gap of each material.As a result,the electric currentoutputAMBIO2012,41(Supplement2):125–131 DOI10.1007/s13280-012-0267-4from the GaAs cell theoretically becomes smaller than that of the others and determines the electric current output of the entire tandem cell.To develop a higher efficiency tandem cell,it is necessary to use a material with a band gap narrower than that of GaAs for the middle cell.In order to obtain maximum conversion efficiency for triple-junction solar cells,it is essential to narrow down the middle cell band gap to 1.2eV and increase the short-circuit current density by 2mA/cm 2compared with that of the GaAs middle cell.When the material is replaced with a narrower band gap,the output voltage will drop.However,the effect of improving the electric current balance out-performs this drop in output voltage and boosts the effi-ciency of the entire multi-junction cell.When a crystal with such a narrow band gap is grown on a Ge base material,lattice relaxation will occur in the middle of epitaxial crystal growth because the lattice constants of narrower band-gap materials are larger than that of Ge (as shown in Fig.2).As a result,the carrier transport properties will degrade due to dislocation.Researchers from the international research center Solar Quest,the University of Tokyo,aim to move beyond such material-related restrictions,and obtain materials and structures that have effective narrow band gaps while maintaining lattice matching with Ge or GaAs.To achieve this goal,we have taken three approaches as indicated in Fig.3.These approaches are explained in detail below.DILUTE NITROGEN-ADDED BULK CRYSTAL Indium gallium nitride arsenide (InGaNAs)is a bulk material consists of InGaAs,which contains several percent of nitrogen.InGaNAs has a high potential for achieving a narrow band gap while maintaining lattice matching with Ge or GaAs.However,InGaNAs has a fatal problem,that is,a drop in carrier mobility due to inhomogeneousdistribution of nitrogen (N).To achieve homogeneous solid solution of N in crystal,we have applied atomic hydrogen irradiation in the film formation process and addition of a very small amount of antimony (Sb)(Fig.3).The atomic hydrogen irradiation technology and the nitrogen radical irradiation technology for incorporating N efficiently into the crystal can be achieved only through molecular beam epitaxy (MBE),which is used to fabricate films under high vacuum conditions.(Nitrogen radical irradiation is a technology that irradiates the surface of a growing crystal with nitrogen atoms that are resolved by passing nitrogen through a plasma device attached to the MBE system.)Therefore,high-quality InGaNAs has been obtained only by MBE until now.Furthermore,as a small amount of Sb is also incorporated in a crystal,it is nec-essary to control the composition of five elements in the crystal with a high degree of accuracy to achieve lattice matching with Ge or GaAs.We have overcome this difficulty by optimizing the crystal growth conditions with high precision and devel-oped a cell that has an InGaNAs absorption layer formed on a GaAs substrate.The short-circuit current has increased by 9.6mA/cm 2for this cell,compared with a GaAs single-junction cell,by narrowing the band gap down to 1.0eV.This technology can be implemented not only for triple-junction cells,but also for higher efficiency lattice-matched quadruple-junction cells on a Ge substrate.In order to avoid the difficulty of adjusting the compo-sition of five elements in a crystal,we are also taking an approach of using GaNAs with a lattice smaller than that of Ge or GaAs for the absorption layer and inserting InAs with a large lattice in dot form to compensate for the crystal’s tensile strain.To make a solid solution of N uniformly in GaNAs,we use the MBE method for crystal growth and the atomic hydrogen irradiation as in the case of InGaNAs.We also believe that using 3D-shaped InAs dots can effectively compensate for the tensile strainthatFig.1Solar spectrum radiated on earth and photon flux collected by the top cell (InGaP),middle cell (GaAs),and bottom cell (Ge)(equivalent to the area of the filled portions in the figure)occurs in GaNAs.We have measured the characteristics of a single-junction cell formed on a GaAs substrate by using a GaNAs absorption layer with InAs dots inserted.Figure 4shows that we were able to succeed in enhancing the external quantum efficiency in the long-wavelength region (corresponding to the GaNAs absorp-tion)to a level equal to GaAs.This was done by extending the absorption edge to a longer wavelength of 1200nm,and increasing the thickness of the GaNAs layer by increasing the number of laminated InAs quantum dot layers.This high quantum efficiency clearly indicates that GaNAs with InAs dots inserted has the satisfactory quality for middle cell material (Oshima et al.2010).STRAIN-COMPENSATED QUANTUM WELL STRUCTUREIt is extremely difficult to develop a narrow band-gap material that can maintain lattice matching with Ge orGaAs unless dilute nitrogen-based materials mentioned earlier are used.As shown in Fig.2,the conventionally used material InGaAs has a narrower band gap and a larger lattice constant than GaAs.Therefore,it is difficult to grow InGaAs with a thickness larger than the critical film thickness on GaAs without causing lattice relaxation.However,the total film thickness of InGaAs can be increased as an InGaAs/GaAsP strain-compensated multi-layer structure by laminating InGaAs with a thickness less than the critical film thickness in combination with GaAsP that is based on GaAs as well,but has a small lattice constant,and bringing the average strain close to zero (Fig.3.).This InGaAs/GaAsP strain-compensated multilayer structure will form a quantum well-type potential as shown in Fig.5.The narrow band-gap InGaAs layer absorbs the long-wavelength photons to generate electron–hole pairs.When these electron–hole pairs go over the potential bar-rier of the GaAsP layer due to thermal excitation,the electrons and holes are separated by a built-in electricfieldFig.2Relationship between band gaps and lattice constants of III–V-based and IV-based crystalsto generate photocurrent.There is a high probability of recombination of electron–hole pairs that remain in the well.To avoid this recombination,it is necessary to take out the electron–hole pairs efficiently from the well and transfer them to n-type and p-type regions without allowing them to be recaptured into the well.Designing thequantumFig.3Materials and structures of narrow band-gap middle cells being researched by thisteamFig.4Spectral quantum efficiency of GaAs single-junction cell using GaNAs bulk crystal layer (inserted with InAs dots)as the absorption layer:Since the InAs dot layer and the GaNAs bulk layer are stacked alternately,the total thickness of GaNAs layers increases as the number of stacked InAs dot layers is increased.The solid line in the graph indicates the data of a reference cell that uses GaAs for its absorption layer (Oshima et al.2010)well structure suited for this purpose is essential for improving conversion efficiency.The high-quality crystal growth by means of the metal-organic vapor phase epitaxy (MOVPE)method with excellent ability for mass production has already been applied for InGaAs and GaAsP layers in semiconductor optical device applications.Therefore,it is technologically quite possible to incorporate the InGaAs/GaAsP quantum well structure into multi-junction solar cells that are man-ufactured at present,only if highly accurate strain com-pensation can be achieved.As the most basic approach related to quantum well structure design,we are working on fabrication of super-lattice cells with the aim of achieving higher efficiency by making the GaAsP barrier layer as thin as possible,and enabling carriers to move among wells by means of the tunnel effect.Figure 6shows the spectral quantum effi-ciency of a superlattice cell.In this example,the thickness of the GaAsP barrier layer is 5nm,which is not thin enough for proper demonstration of the tunnel effect.When the quantum efficiency in the wavelength range (860–960nm)that corresponds to absorption of the quan-tum well is compared between a cell,which has a con-ventionally used barrier layer and a thickness of 10nm or more,and a superlattice cell,which has the same total layer thickness of InGaAs,the superlattice cell demonstrates double or higher quantum efficiency.This result indicates that carrier mobility across quantum wells is promoted by even the partial use of the tunnel effect.By increasing the P composition in the GaAsP layer,the thickness of well (or the In composition)can be increased,and the barrier layer thickness can be reduced while strain compensation is maintained.A cell with higher quantum efficiency can befabricated while extending the absorption edge to the long-wavelength side (Wang et al.2010,2012).GROWTH TECHNIQUE FOR STRAIN-COMPENSATED QUANTUM WELLTo reduce the strain accumulated in the InGaAs/GaAsP multilayer structure as close to zero as possible,it is nec-essary to control the thickness and atomic content of each layer with high accuracy.The In composition and thickness of the InGaAs layer has a direct effect on the absorption edge wavelength and the GaAsP layer must be thinned to a satisfactory extent to demonstrate fully the tunnel effect of the barrier layer.Therefore,it is desirable that the average strain of the entire structure is adjusted mainly by the P composition of the GaAsP layer.Meanwhile,for MOVPE,there exists a nonlinear rela-tionship between the P composition of the crystal layer and the P ratio [P/(P ?As)]in the vapor phase precursors,which arises from different absorption and desorption phenomena on the surface.As a result,it is not easy to control the P composition of the crystal layer.To break through such a difficulty and promote efficient optimiza-tion of crystal growth conditions,we have applied a mechanism to evaluate the strain of the crystal layer during growth in real time by sequentially measuring the curvature of wafers during growth with an incident laser beam from the observation window of the reactor.As shown in Fig.7,the wafer curvature during the growth of an InGaAs/GaAsP multilayer structure indicates a periodic behavior.Based on a simple mechanical model,it has become clear that the time changes ofwaferFig.5Distribution of potential formed by the InGaAs/GaAsP strain-compensated multilayer structure:the narrow band-gap InGaAs layer is sandwiched between wide band-gap GaAsP layers and,as a result,it as quantum well-type potential distribution.In the well,electron–hole pairs are formed by absorption of long-wavelength photons and at the same time,recombination of electrons and holes takes place.The team from Solar Quest is focusing on developing a superlattice structure with the thinnest GaAsP barrier layercurvature are proportionate to the strain of the crystal layer relative to a substrate during the growing process.One vibration cycle of the curvature is same as the growth time of an InGaAs and GaAsP pair (Sugiyama et al.2011).Therefore,the observed vibration of the wafer curvature reflects the accumulation of the compression strain that occurs during InGaAs growth and the release of the strain that occurs during GaAsP growth.When the strain is completely compensated,the growth of the InGaAs/GaAsP pair will cause this strain to return to the initial value and the wafer curvature will vibrate with the horizontal line as the center.As shown in Fig.7,strain can be compensated almost completely by adjusting the layer structure.Only by conducting a limited number of test runs,the use of such real-time observation technology of the growth layer enables setting the growth conditions for fabricating the layer structure for which strain has been compensated with highaccuracy.Fig.6Spectral quantum efficiency of GaAs single-junction cell using InGaAs/GaAsP superlattice as theabsorption layer:This structure consists of 60layers of InGaAs quantum wells.The graph also shows data of a reference cell that uses GaAs for its absorption layer (Wang et al.2010,2012)Fig.7Changes in wafer curvature over time during growth of the InGaAs/GaAsP multilayer structure.This graph indicates the measurement result and the simulation result of the curvature based on the layer structure(composition ?thickness)obtained by X-ray diffraction.Since compressive strain is applied during InGaAs growth,the curvature decreases as time passes.On the other hand,since tensile strain is applied during GaAsP growth,the curvature changes in the oppositedirection (Sugiyama et al.2011)FUTURE DIRECTIONSIn order to improve the conversion efficiency by enhancing the current matching of multi-junction solar cells using III–V compound semiconductors,there is an urgent need to create semiconductor materials or structures that can maintain lattice matching with Ge or GaAs,and have a band gap of1.2eV.As for InGaNAs,which consists of InGaAs with several percent of nitrogen added,we have the prospect of extending the band edge to1.0eV while retaining sufficient carrier mobility for solar cells by means of atomic hydrogen irradiation and application of a small quantity of Sb during the growth process.In addition,as for GaNAs bulk crystal containing InAs dots,we were able to extend the band edge to1.2eV and produce a high-quality crystal with enoughfilm thickness to achieve the quantum efficiency equivalent to that of GaAs.These crystals are grown by means of MBE. Therefore,measures that can be used to apply these crys-tals for mass production,such as migration to MOVPE, will be investigated after demonstrating their high effi-ciency by embedding these crystals into multi-junction cells.As for the InGaAs/GaAsP strain-compensated quantum well that can be grown using MOVPE,we are working on the development of a thinner barrier layer while compen-sating for the strain with high accuracy by real-time observation of the wafer curvature.We have had the prospect of achieving a quantum efficiency that will sur-pass existing quantum well solar cells by promoting the carrier transfer within the multilayer quantum well struc-ture using the tunnel effect.As this technology can be transferred quite easily to the existing multi-junction solar cell fabrication process,we strongly believe that this technology can significantly contribute to the efficiency improvement of the latest multi-junction solar cells. REFERENCESOshima,R.,A.Takata,Y.Shoji,K.Akahane,and Y.Okada.2010.InAs/GaNAs strain-compensated quantum dots stacked up to50 layers for use in high-efficiency solar cell.Physica E42: 2757–2760.Sugiyama,M.,K.Sugita,Y.Wang,and Y.Nakano.2011.In situ curvature monitoring for metalorganic vapor phase epitaxy of strain-balanced stacks of InGaAs/GaAsP multiple quantum wells.Journal of Crystal Growth315:1–4.Wang,Y.,Y.Wen,K.Watanabe,M.Sugiyama,and Y.Nakano.2010.InGaAs/GaAsP strain-compensated superlattice solar cell for enhanced spectral response.In Proceedings35th IEEE photovoltaic specialists conference,3383–3385.Wang,Y.P.,S.Ma,M.Sugiyama,and Y.Nakano.2012.Management of highly-strained heterointerface in InGaAs/GaAsP strain-balanced superlattice for photovoltaic application.Journal of Crystal Growth.doi:10.1016/j.jcrysgro.2011.12.049. AUTHOR BIOGRAPHYYoshiaki Nakano(&)is Professor and Director General of Research Center for Advanced Science and Technology,the University of Tokyo.His research interests include physics and fabrication tech-nologies of semiconductor distributed feedback lasers,semiconductor optical modulators/switches,monolithically integrated photonic cir-cuits,and high-efficiency heterostructure solar cells.Address:Research Center for Advanced Science and Technology, The University of Tokyo,4-6-1Komaba,Meguro-ku,Tokyo153-8904,Japan.e-mail:nakano@rcast.u-tokyo.ac.jp。

复杂地质超长隧道开挖支护关键技术研究

复杂地质超长隧道开挖支护关键技术研究

复杂地质超长隧道开挖支护关键技术研究吕海亮(中交一公局海威工程建设有限公司,北京 010101)[摘要]久马高速公路海子山隧道位于四川省阿坝藏族羌族自治州阿坝县查理乡境内,主洞长3132m,属特长隧道,且地质条件特殊。

针对以上特点,洞身开挖时根据围岩等级不同采用三台阶七步开挖、预留核心土开挖等五种开挖方式;隧底支护时先搭设栈桥,开挖时根据岩石类别不同采用机械开挖或光面爆破开挖;交汇处施工时为减少开挖对周围岩石扰动,设置加强环钢架、横托梁、导钢架等。

针对隧道大变形及滑坡和不稳定斜坡等不良地质条件,提出了双层超前小导管加固等处置措施,并提出了一整套超前地质预报方法。

本工程施工技术可为今后同类工程提供宝贵经验。

[关键词]超长隧道;复杂地质;开挖支护;超前地质预报;处置措施[中图分类号]U455 [文献标识码]A [文章编号]1001-554X(2024)03-0083-06 Research on key technologies for excavation and support of super long tunnelin complex geologyLYU Hai-liang1 工程概况久马高速公路项目是四川省首条高海拔高速公路,其中海子山1#隧道位于阿坝藏族羌族自治州内。

隧道左线长3132m(ZK102+900-ZK106+032),V级围岩占隧道总长的66.9%,IV 级围岩占隧道总长的33.1%;隧道右线长3145m (K102+855-K106+000),V级围岩占隧道总长的65.8%,IV级围岩占隧道总长的34.2%。

海子山隧道平均海拔3600m,隧道最大纵坡为1.82%。

1.1 水文条件隧址区内变质石英砂岩、砂质板岩为主要含水层,板岩为相对含水层,对隧道开挖影响较大的地下水主要为基岩裂隙水,其补给及排水条件等主要由地质条件所控制。

由于场区隧道处于高原,每年降雪期近7个月,累积降雪量约为137~173mm。

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Two major (1, 2) and two minor (3, 4) petroleum Array systems dominated by terrigenous type III organicmatter are recognized. The highstand, lowstand 1,lowstand 2, and transgressive systems tract oilsaccount for about 46, 31, 15, and 8%, respectively,of the 61 oil samples and about 45, 32, 11, and 12%,respectively, of the estimated ultimate recoverablereserves from the fields represented by these sam-ples. These fields account for about 13 of the 16BBOE (billion bbl of oil equivalent) estimated ulti-mate recoverable reserves in the entire Kutei basin.(1) Waxy highstand oils (e.g., Handil, Nilam)occur mainly onshore in middle Miocene–Pliocenereservoirs. These oils originated from middle–upperMiocene coal and shale source rocks deposited incoastal-plain highstand kitchens now near the peakof the oil window.(2) Less waxy lowstand 1 oils (e.g., Perintis, Sisi,Ragat) occur offshore in middle–upper Miocene reser-voirs. These oils originated from middle–upperMiocene coaly source rocks deposited in deep-waterlowstand kitchens now mostly in the early oil window.(3) Lowstand 2 oils (e.g., Semberah 037) are sim-ilar to the lowstand 1 oils but occur mainly onshorein lower–middle Miocene reservoirs. These oilsgenerally are more mature than lowstand 1 oils andoriginated from lower–middle Miocene coalysource rocks.(4) Nonwaxy transgressive oils (e.g., Badak)occur mainly onshore in middle–upper Miocenereservoirs. These oils were generated at low ther-mal maturity from middle Miocene suboxic marineshales deposited near maximum flooding surfaces.Our three-dimensional geochemical-stratigraphicmodels for highstand and lowstand source rocksindicate that less fractional conversion of the kero-gen occurred than had been predicted by the gener-ally accepted stratigraphic model and classic type IIIkerogen kinetics; furthermore, two-dimensionalfluid flow modeling supports independent geo-chemical evidence for commingling of oils in theTunu field from highstand and lowstand kitchens towest and east, respectively. Finally, our model suc-cessfully predicted that oil and gas, rather than gas12AAPG Bulletin, V. 84, No. 1 (January 2000), P. 12–44.only, would be discovered at the recently drilled deep-water Merah Besar and West Seno fields. Geochemical analyses of oils from the Merah Besar field confirm that they belong in the lowstand 1 oil group.INTRODUCTIONTerrigenous-dominated marine deltas contain important accumulations of petroleum in many parts of the world. For example, the Mahakam Delta was one of the first locations where terrigenous organic matter in coals was suggested to have gen-erated and expelled liquid hydrocarbons (Durand and Paratte, 1983; Huc et al., 1986). After many years of debate, a general consensus has developed that deltaic coals can generate large quantities of oil and gas (Law and Rice, 1993; Scott and Fleet, 1994). Major oil accumulations have been linked to hydrogen-rich coaly source rocks, especially in the Upper Jurassic–Paleogene of Australia and New Zealand and low-latitude (<20°) Tertiary deltaic coastal deposits throughout Southeast Asia (Scott and Fleet, 1994, and references therein).The prolific Kutei basin in Kalimantan, which contains the Tertiary Mahakam Delta, has estimated ultimate recoveries of between 11 (Paterson et al., 1997) and 16 billion bbl of oil equivalent (BBOE) (IHS/Petroconsultants S.A., 1998). The first oil in the Mahakam Delta was discovered about 100 yr ago (1898) in the Louise 1 well located on the western-most, inboard anticlinal trend (Figure 1). SeveralPeters et al.13 Figure 1—Map shows approximately north-south anticlinal trends (elliptical shapes) and locations of oil samples in the Mahakam-Makassar area, Kutei basin. Inset at upper left shows location of study area in eastern Borneo, Kalimantan, Indonesia. Heavy stippling indicates oil trends and light stippling indicates gas trends within the anticlines. Genetic groups with symbols defined in the inset at lower right are based on statistical analysis of multivariate geochemical data (Figure 7). Stars indicate location of the Louise 1 and Panca 1 wells.giant oil and gas fields subsequently were discov-ered in this and other approximately north-south–trending anticlines. The offshore Attaka field (Figure 1) was discovered in 1970 and was the first commercial field in Kalimantan with esti-mated ultimate recoverable reserves of 1173 mil-lion barrels of oil equivalent (MMBOE). Other important fields include Tunu (4774 MMBOE), Handil (1549 MMBOE), Badak (1393 MMBOE), Nilam (1069 MMBOE), Tambora (348 MMBOE), and Bekapai (323 MMBOE) (IHS/Petroconsultants S.A., 1998).Despite the economic importance of Tertiary deltas, understanding of petroleum systems (Magoon, 1997) and the regional distributions of source rock organic facies in these settings is limit-ed. Most deep-water marine source rocks are widespread and consist of thin intervals (tens to hundreds of meters) with fairly uniform organic matter. Conversely, most deltaic marine source rocks are thick (hundreds of meters or more), con-tain complex mixtures of terrigenous and marine organic matter in coals and shales, and show greater source rock spatial and temporal variability. The oil-generative character of coaly rocks is diffi-cult to characterize by Rock-Eval pyrolysis (Peters, 1986). Our understanding of the relative impor-tance of coals vs. shales as sources for oil and gas also is incomplete. In addition, the expulsion effi-ciencies of coaly rocks are difficult to assess due to their tendency to adsorb hydrocarbons (Saxby and Shibaoka, 1986; Powell and Boreham, 1991; Sandvik et al., 1992).The source input and diagenetic processes that control the oil potential of coaly rocks also are poorly understood. Coal maceral composition does not clearly indicate oil potential because not all bio-logical macromolecules believed to contribute to kerogen have microscopically identifiable struc-tures (Tegelaar et al., 1989; Tegelaar and Noble, 1994). Amorphous, hydrogen-rich material may explain the oil potential of vitrinite-rich coals, par-ticularly those rich in desmocollinite (Clayton et al., 1991) or suberinite (Khavari Khorasani and Michelsen, 1991). Other workers suggested that morphologically distinct hydrogen-rich liptinitic mac-erals (e.g., cutinite) or desmocollinite (hydrogen-rich vitrinite) are key precursors for liquid hydro-carbons (Khavari Khorasani, 1987; Snowdon, 1991; Garcia-Gonzalez et al., 1997; Horsfield et al., 1988). Evidence suggests that microbial reworking of humic material in deltaic settings may improve generative potential (Khavari Khorasani, 1987; Clayton, 1993).Despite these problems, the geochemistry of crude oils and source rock extracts can be used to identify and predict the regional distributions of deltaic source rock facies. Our use of high-resolution biomarker analyses allows us to identify system-atic differences among oils that are controlled mainly by source input rather than by thermal maturity, biodegradation, or other secondary processes. Prior to our work, compositional dif-ferences among oils from the Mahakam Delta generally were attributed to secondary process-es rather than genetic differences (e.g., Paterson et al., 1997). This underlying assumption of con-stant source composition extends to the kinetic parameters used to predict the timing of oil gen-eration for Mahakam Delta source rock. Previous models of generation in this area typically use the classic type III kinetics based on a single Mahakam Delta coal sample (Tissot et al., 1987), ignoring the possibility that oil types, organic facies, and source rock kinetics may vary both spatially and temporally.ObjectivesThe main objectives of this study of the Maha-kam shelf and Makassar slope were to define petroleum systems based on geochemical oil-to-oil and oil-to-source rock correlations. In addition, we measured kerogen kinetic parameters for hydrocar-bon generation in source rocks that showed affini-ties to the different oil groups and used these mea-surements in basin modeling to improve our understanding of the history of hydrocarbon gener-ation and migration. Finally, we integrated the geo-chemistry with geological-geophysical data to improve future exploration and evaluation of this region.SamplesTable 1 lists crude oil (N= 61) and key potential source rock (N= 14) samples collected from the study area (Figure 1). Nearly all of the oil samples analyzed are from middle–upper Miocene or Pliocene reservoirs in the Balikpapan or Kampung Baru groups (Figure 2). The prospective source rock samples range in age from early to late Miocene and originated from coastal-plain to deep-marine environments (Table 2). The Appendix describes the analytical methods used in the study.Samples may be described using abbreviated names. For example, oil from the Bekapai 4 well in DST 1 (2252–2256 m) is named B4 1. Field descrip-tions of samples as gas condensates or oils are com-monly unreliable. Thus all fluid petroleum samples in the text are called oils, even though some show high API gravities (∼50°API) that approach gas con-densate. Condensates generally show gravities higher than 55°API (Hunt, 1996).14Mahakam Delta and Makassar Slope, IndonesiaDISCUSSION Geological SettingThe study area extends from the onshore delta-plain in the Kutei basin to the slope adjacent to the Makassar Straits that separate Kalimantan on Borneo from Sulawesi (Figure 1). The Makassar area appears to be underlain by remnant oceanic crust trapped between relict subduction zones in northwestern Borneo and western Sulawesi (Malecek et al., 1993). The onshore portion of the Mahakam Delta overlies a series of tightly folded anticlines and broad synclines known col-lectively as the Samarinda anticlinorium, which resulted from inversion of the Paleogene basin (Chambers and Daley, 1995). Offshore Mahakam Delta areas show at least two phases of deforma-tion. Middle Miocene and older rocks exhibit compressional folding and thrusting, whereas the overlying upper Miocene–Pliocene strata are affected only by extensional faulting (Malecek et al., 1993).Regional cross sections demonstrate that Mio-cene and younger sediments fill a large depocenter offshore from the present Mahakam Delta and sug-gest that the current drainage system existed by the earliest Miocene (e.g., Paterson et al., 1997).Palynology indicates that the present-day tropical climate and dense rainforest vegetation have exist-ed since the Pliocene and perhaps as early as the Miocene (Caratini and Tissot, 1988).StratigraphyEarly efforts toward stratigraphic correlation of Miocene and younger rocks stemmed from hydro-carbon discoveries in the onshore Samarinda anti-clinorium. Limited biostratigraphic and low-quality seismic data necessitated the use of lithostrati-graphic correlation based mainly on well logs (e.g.,Huffington and Helmig, 1980, 1990). Zone names such as “Upper Main” and “Shallow” generally could not be extended beyond a field boundary (e.g.,DeMatharel et al., 1980; Verdier et al., 1980). WithPeters et al.15Figure 2—Generalized stratigraphic column (left) shows distribution of oil and rock samples (after Marks et al.,1982) and Neogene sequence lithostratigraphy, chronostratigraphy, and local zone names (right) of the Mahakam Delta and Makassar slope. Coastal onlap chart is from Haq et al. (1987).16Mahakam Delta and Makassar Slope, Indonesiaimproved seismic and biostratigraphic data, more regional marker names such as “Alpha” and “Beta”saw common usage in well reports and Indonesian Petroleum Association field papers (e.g., Sujatmiko and Irawan, 1984). These markers generally were compared to lithostratigraphic nomenclature (e.g.,“Balikpapan Beds,” later “Group”), suggesting that they also had limited chronostratigraphic signifi-cance (Figure 2).Total and the Institut Français du Pétrole (Total-IFP) produced the first significant chronostratigraph-ic correlation model for the Mahakam Delta after a 4-yr sequence stratigraphy study (Burrus et al., 1992;Duval et al., 1992a) (Figure 3, top). Integration of biostratigraphy, well logs, and seismic data from Total acreage identified a series of regional sequence-bounding unconformities and condensed sections, similar in age to those thought to have global significance (Vail et al., 1977; Haq et al.,1987). The early stratigraphic and geochemical work on the Mahakam Delta (e.g., Combaz and DeMatharel, 1978; Durand and Oudin, 1979;Verdier et al., 1980) provided the basis for a region-al hydrocarbon charging model (Duval et al.,1992b). These workers recognized the possibility of dramatic basinward shifts in sedimentation dur-ing lowstands and the deposition of thick carbon-ates at the shelf-margin break. The Total-IFPPeters et al.17Figure 3—Comparison of the Total-IFP model for the Mahakam Delta (top) (after Burrus et al., 1992; Duval et al.,1992a) with the new geochemical-stratigraphic model described in this paper (bottom). Location of XX ′is given in Figure 1. Highstand and lowstand kitchens are discussed in the text. Estimated ultimate recoveries (BBOE = billions of bbl of oil equivalent) refer to the corresponding fields (IHS/Petroconsultants S.A., 1998). Triangles indicate shelf break during specified intervals of deposition. White arrows indicate present-day direction of petroleum migration from local kitchens. Burial depth for the 10.5 Ma surface (dotted line) in the lowstand kitchen area of our model is about 3–4 km, depending on the two-way transit time vs. depth curve that is used (i.e., Perintis 1 vs. Sisi 1 wells), but is about 6 km based on the earlier model (top).sequence stratigraphic model was instrumental in successful efforts directed at reversing Total’s pro-duction decline, and initially included Tunu field reservoir characterization (Duval et al., 1992b), fol-lowed by later discoveries of the Northwest Peciko and Sisi fields.Although the Total-IFP sequence stratigraphic model represents a major improvement over previ-ous lithostratigraphic and log-defined correlation schemes, it has limited ties to the slope and basin, including the Makassar Straits. Complete calibra-tion of sequence-bounding unconformities requires downdip correlations. The Total-IFP model shows most of the shelf margins near or just seaward of Tunu field (triangles in Figure 3, top), which ex-plains why many workers believe that middle Miocene coastal-plain coal and shale source rocks occur mainly landward of Tunu field (Burrus et al., 1992). In the Total-IFP model, outer shelf and slope areas are distant from the source rocks and thus carry significant exploration risk; furthermore, the Total-IFP model does not explain deep-water hydro-carbon accumulations, such as in the Sisi-Nubi field trend on the outer shelf (Burrus et al., 1992).New Stratigraphic ModelIn our stratigraphic model (Figure 3, bottom), the interpreted shelf margins are located farther seaward than they are in the Total-IFP model, allow-ing for source rock deposition in less explored outer shelf and slope regions, and the source rocks show more favorable oil-window maturity. The new interpretation allows development of low-stand kitchens, i.e., thick depocenters seaward of the coastal plain, that are closer to the slope margin of the Mahakam-Makassar area than allowed by the Total-IFP model. Because the lowstand kitchens are downdip of the coeval shelf margin, the terrige-nous organic matter is believed to consist of trans-ported rather than in situ coals or coaly shales. Our model infers the 10.5 Ma horizon to be buried to only about 3–4 km depth, considerably shallower than the Total-IFP model (6 km, Figure 3). One rea-son for the difference is that the 10.5 Ma surface is a significant angular unconformity, which must be traced across dipping reflections rather than fol-lowing clinoforms downward as in the Total-IFP model. The inferred depth differences greatly impact both the expected thermal maturity of source rocks and the sandstone reservoirs. On the outer shelf, the middle Miocene is postmature based on the Total-IFP model, but much of it is within the oil window based on our new model.Ten depositional sequences were identified and mapped in the stratigraphic section of the Mahakam shelf and adjacent Makassar slope (Snedden et al., 1996). Standard sequence strati-graphic analysis allowed identification of sequence-bounding unconformities and maximum flooding surfaces. Seismic facies within sequences were cali-brated with available well (lithology) control. The sequences were dated using paleontological picks in well ties and compared to available coastal onlap charts (e.g., Haq et al., 1987). Several of these sequences lie below the distinctive 10.5 Ma sequence boundary (Figure 3, bottom). A major change is interpreted in tectonic style with compressional thrusting below and extensional normal faulting above this boundary.Deltaic depocenters identified by isochore map-ping of individual sequences show thick potential source kitchens near or at the shelf margin for sequences below the 10.5 Ma unconformity (Figure 4) (Snedden et al., 1996). Above the 10.5 Ma unconformity, the depocenters shift landward of the coeval shelf margin for each sequence. This shifting could be the result of tectonic differences before and after erosion, with the thick lowstand kitchens located seaward of the thrust fault zone. Alternately, this shifting could be the result of the global late Miocene–early Pliocene second-order sea level rise and transgression. These outer shelf and slope kitchens appear to have been filled by sediments rich in terrigenous organic matter dur-ing major falls in relative sea level and are referred to as lowstand kitchens. In contrast, highstand kitchens contain coals and organic-rich shales that accumulated landward of the shelf break during relative highs in sea level. The key difference between these two kitchen types from a sequence-stratigraphic standpoint is how the organic matter accumulated. Organic matter in highstand kitchens is largely in situ, whereas much of the organic matter in lowstand kitchens represents eroded coastal-plain detritus that has undergone signifi-cant transport.Source Rock IntervalsHighstand Systems Tract Coastal-Plain Coals The generally accepted source rocks for the Mahakam Delta crude oils are coals and shales from the Miocene Balikpapan Group (Schoell et al., 1983; Robinson, 1987) that have been buried deep-er than 2600 m (Perrodon, 1983). The Paleogene section is overpressured and believed to be too deep to contribute to petroleum accumulations. The post-Miocene section is not buried sufficiently to have entered the oil window. According to the Total-IFP model, the best source rocks consist of lower delta-plain coals (Duval et al., 1992a) that contain mainly huminite with minor exinite macer-als (Burrus et al., 1992). These rocks correspond to18Mahakam Delta and Makassar Slope, Indonesiachronostratigraphic sequences ranging in age from 15.5 to 5.5 Ma (Figure 2). Most workers believe that the middle Miocene coastal-plain coals and coaly shales (15.5–10.5 Ma) are the primary source inter-val in the Tunu kitchen (e.g., Burrus et al., 1992).The Miocene coastal plain was linked to the east-flowing Mahakam River system, which deposited sediments in a subsiding basin oriented north-south parallel to and near the delta front. The Balikpapan Group contains up to 175 m of cumulative coal and 1750 m of cumulative shale (Thompson et al., 1985). This progradational phase of the delta has continued, with transgressive interruptions, to the present day (Allen et al., 1976; Oudin and Picard, 1982). Various upper delta-plain to delta-front facies and occasional transgressive limestones were deposited during delta progradation.The distribution of coastal-plain sediments and organic matter is strongly influenced by tidal and river flow. Domed (ombrogenous) peats fed by rainwater form on the upper coastal plain and extend basinward as far as the intertidal zone, where they abut against beach ridges or mangrove swamps (Anderson, 1964). Reworking of coastal-plain peats in tidal flat or lagoonal environments results in selective separation of soluble and insolu-ble organic matter and accumulation of liptinite-rich allochthonous drift peats and carbonaceous shales (Allen et al., 1979; Thompson et al., 1985). These liptinite-rich peats can be further enriched by fresh liptinitic material, such as leaf cuticles and damar resin from resin-producing trees growing behind the coastal plain.Although most Balikpapan Group coals contain abundant vitrinite group macerals, the hydrogen indices of the coals are commonly over 300 mg hydrocarbon/g TOC (total organic carbon) and the oxygen indices are usually below 15 mg CO2/g TOC (Thompson et al., 1985). Hydrogen indices over 200 mg hydrocarbon/g TOC are necessary for significantPeters et al.19 Figure 4—Schematic of the new geochemical-stratigraphic model and predicted distribution of Mahakam-Makassar area source rocks. Faults, which provide inferred migration pathways from source rocks to reservoirs, are not shown. HST = highstand systems tract, TST = transgressive systems tract, LST = lowstand systems tract, MFS = maximum flooding surface.oil-generative potential from coals (Hunt, 1996). Geochemical oil-to-source rock correlation indi-cates that a large genetic group of our samples, des-ignated highstand oils, originated from highstand systems tract middle–upper Miocene coals and coaly shales (discussed in following sections).Lowstand Systems Tract Coaly Shales Paleogeographic mapping based on our new stratigraphic model and the different geochemical compositions of oil samples in the study (discussed in following paragraphs) suggest that source rock facies other than the well-documented middle Miocene coastal-plain coals and coaly shales exist in the Mahakam-Makassar area. In our model, ter-rigenous organic matter accumulates in depocen-ters seaward of the coeval coastal plain in outer shelf to slope settings during lowstand systems tracts. Enhanced erosion of coastal-plain sediments during rapid falls in relative sea level could provide large quantities of terrigenous organic matter to the lowstand kitchens (Figure 3 bottom, Figure 4). These downdip depocenters might receive terrige-nous organic matter by a process similar to that responsible for gravity-flow sandstones on the outer shelf and slope (Figure 5). In that process, sand is eroded from highstand deltas, transported through incised valleys, and deposited as deep-water gravity-flow sandstones.Few downdip wells penetrate marine sections with high terrigenous organic matter in the study area. For example, the best shale source rocks in the deep-water Perintis 1 and Sisi 1 wells (Figure 1) contain only 1 to 3 wt. % TOC; however, sampling bias is possible because most exploration wells are drilled on structural highs where less terrigenous organic matter may have been deposited. Low TOC in many prolific offshore basins (e.g., Sabah Basin) (Anuar and Muhamad, 1997) suggests that sampling bias may occur elsewhere. The best evidence for terrigenous-rich deep-water source rocks offshore20Mahakam Delta and Makassar Slope, IndonesiaFigure 5—Block diagram of the lowstand valley system in the paleo-Mahakam Delta that is inferred to have allowed the transport and preservation of significant terrigenous organic matter in deep-water settings.Mahakam Delta comes from observations of updip erosion and transport of terrigenous organic mat-ter. For example, cores of middle Miocene channel and incised-valley fills show that TOC and hydro-gen index can be high (Snedden et al., 1996). Seismic evidence for valley fills elsewhere in the delta supports this view.These terrigenous-rich lowstand depocenters generally lie above the 15.5 and 12.5 Ma sequence boundaries and include periods of extensive updip erosion, downdip subsidence, and deposition (Figure 4). Basin modeling (discussed in a following section) shows that two lowstand depocenters are within the window for hydrocarbon generation and thus are potential kitchens. The presence of two lowstand kitchens is supported by our geochemical data, which identify two distinct subgroups of low-stand oils. The lowstand 1 and lowstand 2 oils dif-fer in both source- and maturity-related geochemi-cal parameters, implying origins from source rocks of different age and thermal exposure. Our oil-to-source rock correlation indicates that the lowstand 1 and lowstand 2 oils originated from middle–upper and lower–middle Miocene source rocks, respectively.Transgressive Systems Tract Source Rocks Thick and laterally extensive coals and organic-rich shales can form during both highstand and lowstand systems tracts, given the proper balance of peat production and accommodation (Bohacs and Suter, 1997). Little algal organic matter is con-tributed to these sediments under conditions of high water turbidity, terrigenous organic matter input, and clastic deposition typical of the Maha-kam Delta; however, coals are rare during a trans-gressive systems tract because swamp sediments commonly are drowned before substantial organic matter can accumulate. Reduced water turbidity and clastic deposition can occur during the peak transgressions marked by maximum flooding sur-faces (MFS, Figure 4). Seismic evidence indicates several maximum flooding surfaces during the mid-dle Miocene, especially within the 15.5 and 13.8 Ma sequences (Figure 4).The geochemistry (discussed in a following sec-tion) of a small, but distinct, group of oils in our study suggests a transgressive marine-influenced source rock with significant algal input mixed with the terrigenous organic matter; furthermore, these oils geochemically correlate with extract from a source rock deposited in an inner shelf environ-ment as part of a transgressive systems tract (13.8 Ma). Although our highstand and lowstand oil sam-ples show genetic affinities for both coals and shales, the transgressive systems tract oils show affinities only for shales.Oil GeochemistryBulk PropertiesThe 61 oil samples show moderate to high API gravity (26–50°) and low sulfur (<0.1 wt. %, Table 1), typical of oils from Tertiary deltaic marine set-tings. The oils show high pristane/phytane ratios (Pr/Ph = 3.8–10.9) and stable carbon isotope ratios for saturated and aromatic hydrocarbons near –29 and –27‰, respectively. Many of the oils show a marked odd-to-even predominance of n-paraffins in the dominant nC27to nC33range. These character-istics typify oils generated from paralic or deltaic marine source rocks enriched in terrigenous organ-ic matter (e.g., Chung et al., 1992).The analyzed oils are broadly similar when com-pared using relatively insensitive source-related parameters. For example, pristane/nC17and phy-tane/nC18ratios (Pr/nC17and Ph/nC18, Figure 6) indicate that most of the oils originated from simi-lar source rocks containing mainly terrigenous, type III organic matter deposited under oxic condi-tions. These ratios decrease with thermal matura-tion and increase with biodegradation. The anoma-lous composition of Peciko oil (Figure 6) results from mixing of low-maturity oil with a later charge of nonbiodegraded condensate (discussed in a fol-lowing section). The compositions of Mutiara 48 and Bekapai 4 DST 5 oils (Figure 6) suggest that their source rock was deposited under less oxic conditions than the other samples.Oil-to-Oil CorrelationsPrevious geochemical studies suggest that Mahakam Delta oils originated from similar type III kerogen (Schoell et al., 1983) and that no chemical difference exists between the organic matter in nearby coals and shales acting as source rocks (Vandenbroucke et al., 1983); however, Huc et al. (1986) observed that Mahakam Delta coals gener-ate more high-molecular-weight n-alkanes than do nearby shales. These early observations do not jus-tify the conclusion that all differences in oil compo-sition in the Mahakam Delta area result from sec-ondary alteration processes. On the contrary, our data show clear source and maturity differences among the sampled oils.All of the oil samples in this study originated from deltaic-paralic source rocks that received vary-ing proportions of terrigenous organic matter. For example, higher plant biomarkers in the samples include C29steranes (Moldowan et al., 1985), bicadinanes (Alam and Pearson, 1990; Woolhouse et al., 1992; Armanios et al., 1995), oleanane (Moldowan et al., 1994), and tricyclic diterpanes (Noble et al., 1986; Alexander et al., 1987). The bicadinanes are derived from damar resins exudedPeters et al.21。

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