共查询到20条相似文献，搜索用时 15 毫秒
1.
基于偏最小二乘法的玉米FPAR高光谱反演模型研究 总被引：1，自引：0，他引：1
以ASD FR便携式光谱仪与LI-191SA光量子仪对吉林中西部的玉米田进行多次观测,采集到123组有效数据,基于偏最小二乘法(PLS)对玉米FPAR进行高光谱反演。对可见光与近红外光谱(400～1 500nm)进行分析并建立反演模型,对FPAR预测效果进行验证,验证模型的R2为0.785,RMSE为0.117;同时进行了玉米FPAR与光谱反射率、反射率一阶导数之间的关系分析及植被指数与玉米FPAR之间的回归分析。研究结果表明,PLS方法建立的模型可有效地从玉米高光谱反射率数据反演出FPAR含量,反演结果精度较植被指数高。 相似文献
2.
海岸带浅海水深高光谱遥感反演方法研究 总被引：3，自引：1，他引：2
近红外波段(760~900 nm)反射率对水深最为敏感,通过波段比值方法可以提高与水深的相关性,而711nm处反射率一阶微分值与水深的相关系数高达-0.87。对于近岸混浊度高的样本,单波段和比值模型反演效果不好,平均相对误差均高于30%;而光谱微分模型的精度较好,平均相对误差为17%。研究结果证明:水体反射率的一阶微分可以有效地削弱水质变化给水深反演带来的误差。 相似文献
3.
洪河自然保护区乌拉苔草生物量高光谱遥感估算模型 总被引：6，自引：0，他引：6
尝试用不同方法构建洪河自然保护区湿地植被乌拉苔草(Carex meyeriana)的高光谱植被指数,建立水上鲜/干生物量高光谱估算模型,并比较了不同模型的反演精度。通过实测不同覆盖度和水深状况下乌拉苔草的冠层高光谱反射率与水上生物量的数据,采用高光谱可见光—近红外波段及其微分光谱波段(350~1 050 nm)逐波段构建FNDVI、FRVI、FDVI、FDNDVI、FDRVI、FDDVI植被指数,分别找出与水上鲜生物量和干生物量具有最佳相关性波段组合的植被指数,建立乌拉苔草水上生物量的最佳估算模型,并对比分析了反射率光谱植被指数(FNDVI、FRVI、FDVI)模型和微分光谱植被指数(FDNDVI、FDRVI、FDDVI)模型的反演精度。结果显示,微分光谱与乌拉苔草水上生物量的相关性比反射率光谱好;微分光谱植被指数与乌拉苔草水上生物量的相关性比反射率光谱植被指数好,尤其以微分光谱植被指数FDRVI与FDNDVI建立的二次函数模型反演乌拉苔草的水上鲜生物量和干生物量的效果最好,精度分别达74.9%、71.4%,其均方根误差分别为0.074 4和0.026 2,通过了p<0.01极显著验证。这表明,采用微分光谱植被指数FDRVI、FDNDVI对乌拉苔草水上鲜生物量和干生物量的估算可以取得较高的预测精度。 相似文献
4.
Lu Xiang Hasanipanah Mahdi Brindhadevi Kathirvel Bakhshandeh Amnieh Hassan Khalafi Seyedamirhesam 《Natural Resources Research》2020,29(2):641-654
Blast-induced flyrock is a hazardous and undesirable phenomenon that may occur in surface mines, especially when blasting takes place near residential areas. Therefore, accurate prediction of flyrock distance is of high significance in the determination of the statutory danger area. To this end, there is a practical need to propose an accurate model to predict flyrock. Aiming at this topic, this study presents two machine learning models, including extreme learning machine (ELM) and outlier robust ELM (ORELM), for predicting flyrock. To the best of our knowledge, this is the first work that investigates the use of ORELM model in the field of flyrock prediction. To construct and verify the proposed ELM and ORELM models, a database including 82 datasets has been collected from the three granite quarry sites in Malaysia. Additionally, artificial neural network (ANN) and multiple regression models were used for comparison. According to the results, both ELM and ORELM models performed satisfactorily, and their performances were far better compared to the performances of ANN and multiple regression models.
相似文献5.
扎龙湿地克钦湖富营养化状态的高光谱遥感评价 总被引：2，自引：1，他引：1
通过分析扎龙湿地克钦湖水体高光谱反射率与水质参数的相关关系,采用单波段、波段比值等算法分别选取特征波长建立水质参数的高光谱定量模型,并且结合修正营养状态指数(TSIM)和综合营养状态指数法,对水体的富营养化程度进行了监测和评价。结果表明,单波段归一化反射率对叶绿素a估测模型效果较为理想;利用高光谱一阶微分反射率,诊断各水质参数的敏感波段,建立线性模型,确定了TN、TP、SD、CODMn的敏感波段分别为733 nm、765 nm、782 nm、680 nm。单因素水质参数评价水体富营养化水平具有一定的局限性。综合考虑多个水质指标,对水质的富营养化程度进行了评价,结果显示,克钦湖水体呈现出中营养化状态,需要采取一定的措施,防范于未然。 相似文献
6.
The combination of vitrinite reflectance, apatite fission track and present‐day borehole temperature data is very useful when performing tectonic and thermal reconstructions in sedimentary basins which, in turn, are essential for assessing risk in hydrocarbon exploration and for testing hypotheses of basin evolution. Releasing the full potential of the combined data set requires that the predictive models are accurate in themselves. Here, we calibrate a new kinetic vitrinite reflectance model ‘basin%Ro’ using borehole data from a number of sedimentary basins and vitrinite reflectance data from laboratory maturation experiments. The entire data set is inverted for the kinetic parameters of the reflectance model under consideration of uncertainty in the temperature histories of the calibration samples. The method is not sensitive to inconsistent calibration data, which are revealed by significant corrections to the temperature histories. The performance of the model is tested on independent well data from the East China Sea and the Nova Scotian Shelf. The widely used easy%Ro‐model overestimates vitrinite reflectance in the interval 0.5–1.7% Ro by up to 0.35%. Delimiting of oil generating intervals by prediction of vitrinite reflectance may lead to significant underestimation of the generative potential, which may call for a revision of some petroleum systems. The overestimation by easy%Ro may have fuelled the idea of pressure retardation of vitrinite reflectance evolution under sedimentary basin conditions, where pressures in fact are too low for this to be important. 相似文献
7.
水分胁迫条件下棉花生理变化及其高光谱响应分析 总被引：19，自引：4，他引：15
利用ASD地物光谱仪,测定水分胁迫条件下棉花不同生育时期内叶片的光谱反射率,应用微分技术处理棉花的反射光谱,并结合棉花叶面积指数(LAI)、叶绿素(a b)含量(Chlt)、叶片全氮(TN)含量等生物参数进行分析,研究棉花水分胁迫情况下的高光谱特征,结果表明,一阶微分光谱720nm波段的数值与LAI的正相关(R=0．7656);750nm处一阶微分值与叶绿素含量呈显著正相关关系(R=0．7774);微分光谱690nm～740nm数值积分面积与TN含量呈正相关(R=0．7669),采用比值反射率对反射光谱1300nm～1500nm波段范围内最小值与棉花叶片的含水量作相关分析,达到极显著水平(R^2=0．8298),验证了一阶微分光谱数据与棉花的生理参数有很好的相关性,可见光和近红外波段光谱反射率能够反映出棉花生长发育的动态特征;证明了棉花的花铃期是高光谱遥感对棉花长势和生理参数定量诊断的最佳时期。本研究通过建立一系列线性光谱模型对棉花生理参数进行估测,为基于高光谱数据的棉花生长模型和棉花长势的遥感监测提供了理论依据。 相似文献
8.
Recent upward trends in acres irrigated have been linked to increasing near-surface moisture. Unfortunately, stations with dew point data for monitoring near-surface moisture are sparse. Thus, models that estimate dew points from more readily observed data sources are useful. Daily average dew temperatures were estimated and evaluated at 14 stations in Southwest Georgia using linear regression models and artificial neural networks (ANN). Estimation methods were drawn from simple and readily available meteorological observations, therefore only temperature and precipitation were considered as input variables. In total, three linear regression models and 27 ANN were analyzed. The two methods were evaluated using root mean square error (RMSE), mean absolute error (MAE), and other model evaluation techniques to assess the skill of the estimation methods. Both methods produced adequate estimates of daily averaged dew point temperatures, with the ANN displaying the best overall skill. The optimal performance of both models was during the warm season. Both methods had higher error associated with colder dew points, potentially due to the lack of observed values at those ranges. On average, the ANN reduced RMSE by 6.86% and MAE by 8.30% when compared to the best performing linear regression model. 相似文献
9.
Constraining basin thermal history and petroleum generation using palaeoclimate data in the Piceance Basin,Colorado 
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下载免费PDF全文 Yao Tong Daniel E. Ibarra Jeremy K. Caves Tapan Mukerji Stephan A. Graham 《Basin Research》2017,29(4):542-553
Careful assessment of basin thermal history is critical to modelling petroleum generation in sedimentary basins. In this paper, we propose a novel approach to constraining basin thermal history using palaeoclimate temperature reconstructions and study its impact on estimating source rock maturation and hydrocarbon generation in a terrestrial sedimentary basin. We compile mean annual temperature (MAT) estimates from macroflora assemblage data to capture past surface temperature variation for the Piceance Basin, a high‐elevation, intermontane, sedimentary basin in Colorado, USA. We use macroflora assemblage data to constrain the temporal evolution of the upper thermal boundary condition and to capture the temperature change with basin uplift. We compare these results with the case where the upper thermal boundary condition is based solely upon a simplified latitudinal temperature estimate with no elevation effect. For illustrative purposes, 2 one‐dimensional (1‐D) basin models are constructed using these two different upper thermal boundary condition scenarios and additional geological and geochemical input data in order to investigate the impact of the upper thermal boundary condition on petroleum source rock maturation and kerogen transformation processes. The basin model predictions indicate that the source rock maturation is very sensitive to the upper thermal boundary condition for terrestrial basins with variable elevation histories. The models show substantial differences in source rock maturation histories and kerogen transformation ratio over geologic time. Vitrinite reflectance decreases by 0.21%Ro, source rock transformation ratio decreases 10.5% and hydrocarbon mass generation decreases by 16% using the macroflora assemblage data. In addition, we find that by using the macroflora assemblage data, the modelled depth profiles of vitrinite reflectance better matches present‐day measurements. These differences demonstrate the importance of constraining thermal boundary conditions, which can be addressed by palaeotemperature reconstructions from palaeoclimate and palaeo‐elevation data for many terrestrial basins. Although the palaeotemperature reconstruction compiled for this study is region specific, the approach presented here is generally applicable for other terrestrial basin settings, particularly basins which have undergone substantial subaerial elevation change over time. 相似文献
10.
Alessandra Borghi Daniela Carrion Giovanna Sona 《Geophysical Journal International》2007,171(2):539-549
An up to date determination of a high-resolution geoid requires the use of best available databases concerning digital terrain model (DTM), bathymetry, global geopotential model and gravity field. The occasion to revisit methods to validate and merge different data sets has been created by a new project for the determination of a new European Geoid.
Since the computation of the latest European geoid and quasi-geoid model (EGG97), significant new or improved data sets have become available, such as new global geopotential models from CHAMP and GRACE missions, new national and global DTMs and new or upgraded gravity data sets.
In the context of the new European Gravity and Geoid Project (EGGP), within the IAG Commission 2, some data validation tests have been performed in the Italian zone.
In the area 19°× 17° wide, covering Italy, three kinds of tests have been performed: comparison among different DTMs in order to choose the best one to be used; comparisons in terms of geoid computation in some coastal areas, to evaluate bathymetry effects, and the validation of the EIGEN-CG01C and EIGEN-CG03C new global models up to degree and order 360.
These preliminary tests lead to the choice of SRTM DTM (integrated in no-data holes), with an added bathymetry derived by the Italian 1:25 000 official cartography near the coasts and the NOAA bathymetry in high seas. The validation of the new global models and the comparison with EGM96 model show that, in terms of geoid computation, the EGM96 yields better results. Moreover, the validation of new available land gravity data and the cross-validation of two sets of gravity data on sea have been completed. 相似文献
Since the computation of the latest European geoid and quasi-geoid model (EGG97), significant new or improved data sets have become available, such as new global geopotential models from CHAMP and GRACE missions, new national and global DTMs and new or upgraded gravity data sets.
In the context of the new European Gravity and Geoid Project (EGGP), within the IAG Commission 2, some data validation tests have been performed in the Italian zone.
In the area 19°× 17° wide, covering Italy, three kinds of tests have been performed: comparison among different DTMs in order to choose the best one to be used; comparisons in terms of geoid computation in some coastal areas, to evaluate bathymetry effects, and the validation of the EIGEN-CG01C and EIGEN-CG03C new global models up to degree and order 360.
These preliminary tests lead to the choice of SRTM DTM (integrated in no-data holes), with an added bathymetry derived by the Italian 1:25 000 official cartography near the coasts and the NOAA bathymetry in high seas. The validation of the new global models and the comparison with EGM96 model show that, in terms of geoid computation, the EGM96 yields better results. Moreover, the validation of new available land gravity data and the cross-validation of two sets of gravity data on sea have been completed. 相似文献
11.
Application of Artificial Neural Networks to Complex Groundwater Management Problems 总被引：4，自引：0，他引：4
Coppola Emery Poulton Mary Charles Emmanuel Dustman John Szidarovszky Ferenc 《Natural Resources Research》2003,12(4):303-320
As water quantity and quality problems become increasingly severe, accurate prediction and effective management of scarcer water resources will become critical. In this paper, the successful application of artificial neural network (ANN) technology is described for three types of groundwater prediction and management problems. In the first example, an ANN was trained with simulation data from a physically based numerical model to predict head (groundwater elevation) at locations of interest under variable pumping and climate conditions. The ANN achieved a high degree of predictive accuracy, and its derived state-transition equations were embedded into a multiobjective optimization formulation and solved to generate a trade-off curve depicting water supply in relation to contamination risk. In the second and third examples, ANNs were developed with real-world hydrologic and climate data for different hydrogeologic environments. For the second problem, an ANN was developed using data collected for a 5-year, 8-month period to predict heads in a multilayered surficial and limestone aquifer system under variable pumping, state, and climate conditions. Using weekly stress periods, the ANN substantially outperformed a well-calibrated numerical flow model for the 71-day validation period, and provided insights into the effects of climate and pumping on water levels. For the third problem, an ANN was developed with data collected automatically over a 6-week period to predict hourly heads in 11 high-capacity public supply wells tapping a semiconfined bedrock aquifer and subject to large well-interference effects. Using hourly stress periods, the ANN accurately predicted heads for 24-hour periods in all public supply wells. These test cases demonstrate that the ANN technology can solve a variety of complex groundwater management problems and overcome many of the problems and limitations associated with traditional physically based flow models. 相似文献
12.
Landslide susceptibility mapping using geological data, a DEM from ASTER images and an Artificial Neural Network (ANN) 总被引：7，自引：0，他引：7
An efficient and accurate method of generating landslide susceptibility maps is very important to mitigate the loss of properties and lives caused by this type of geological hazard. This study focuses on the development of an accurate and efficient method of data integration, processing and generation of a landslide susceptibility map using an ANN and data from ASTER images. The method contains two major phases. The first phase is the data integration and analysis, and the second is the Artificial Neural Network training and mapping. The data integration and analysis phase involve GIS based statistical analysis relating landslide occurrence to geological and DEM (digital elevation model) derived geomorphological parameters. The parameters include slope, aspect, elevation, geology, density of geological boundaries and distance to the boundaries. This phase determines the geological and geomorphological factors that are significantly correlated with landslide occurrence. The second phase further relates the landslide susceptibility index to the important geological and geomorphological parameters identified in the first phase through ANN training. The trained ANN is then used to generate a landslide susceptibility map. Landslide data from the 2004 Niigata earthquake and a DEM derived from ASTER images were used. The area provided enough landslide data to check the efficiency and accuracy of the developed method. Based on the initial results of the experiment, the developed method is more than 90% accurate in determining the probability of landslide occurrence in a particular area. 相似文献
13.
Innovation efforts in developing soft computing models (SCMs) of researchers and scholars are significant in recent years, especially for problems in the mining industry. So far, many SCMs have been proposed and applied to practical engineering to predict ground vibration intensity (BIGV) induced by mine blasting with high accuracy and reliability. These models significantly contributed to mitigate the adverse effects of blasting operations in mines. Despite the fact that many SCMs have been introduced with promising results, but ambitious goals of researchers are still novel SCMs with the accuracy improved. They aim to prevent the damages caused by blasting operations to the surrounding environment. This study, therefore, proposed a novel SCM based on a robust meta-heuristic algorithm, namely Hunger Games Search (HGS) and artificial neural network (ANN), abbreviated as HGS–ANN model, for predicting BIGV. Three benchmark models based on three other meta-heuristic algorithms (i.e., particle swarm optimization (PSO), firefly algorithm (FFA), and grasshopper optimization algorithm (GOA)) and ANN, named as PSO–ANN, FFA–ANN, and GOA–ANN, were also examined to have a comprehensive evaluation of the HGS–ANN model. A set of data with 252 blasting operations was collected to evaluate the effects of BIGV through the mentioned models. The data were then preprocessed and normalized before splitting into individual parts for training and validating the models. In the training phase, the HGS algorithm with the optimal parameters was fine-tuned to train the ANN model to optimize the ANN model's weights. Based on the statistical criteria, the HGS–ANN model showed its best performance with an MAE of 1.153, RMSE of 1.761, R2 of 0.922, and MAPE of 0.156, followed by the GOA–ANN, FFA–ANN and PSO–ANN models with the lower performances (i.e., MAE?=?1.186, 1.528, 1.505; RMSE?=?1.772, 2.085, 2.153; R2?=?0.921, 0.899, 0.893; MAPE?=?0.231, 0.215, 0.225, respectively). Based on the outstanding performance, the HGS–ANN model should be applied broadly and across a swath of open-pit mines to predict BIGV, aiming to optimize blast patterns and reduce the environmental effects.
相似文献14.
开展干旱预测是有效应对干旱风险的前提基础,根据1960-2016年三江平原7个站点逐日降水和气温数据,利用ARIMA和ANN模型对不同时间尺度标准化降水蒸散指数（SPEI）序列进行分析建模预测。借助相关系数R、纳什效率系数NSE、Kendall秩相关系数τ、均方误差MSE和Kolmogorov-Smirnov （K-S）检验对模型的有效性进行了判定,然后分别用ARIMA和ANN模型进行12步预测,并将预测值与实际值进行比较。结果表明：（1） ARIMA模型和ANN模型对SPEI的预测能力都随时间尺度的增加而逐渐提高。（2）两种模型对3、6个月尺度SPEI的预测精度偏低,9、12、24个月的SPEI的预测精度在70%以上;（3）SPEI-9、SPEI-12、SPEI-24三个时间尺度ANN模型的预测精度优于ARIMA模型。 相似文献
15.
土壤盐渍化遥感应用研究进展 总被引：31，自引：4，他引：31
文章从地面数据的调查、盐渍土影象的目视判读特征、光谱特征和土壤盐渍化区域的植被特征以及多光谱、高光谱遥感技术等方面综述国内外应用遥感数据探测土壤盐渍化程度及其制图的研究。利用数字图象并结合野外调查数据进行目视解译和计算机自动解译、图象变换提取盐渍土信息;结合G IS方法在分类中加入非遥感数据来提高分类精度;在研究盐渍土的光谱特征的基础上应用高光谱技术定量或半定量地提取盐渍土信息。这都是制定综合治理措施、决定土地利用方向的关键,也是进行区域土壤盐渍化动态预报的重要依据。 相似文献
16.
基于SAIL模型的多角度多光谱遥感叶面积指数反演 总被引：1，自引：0，他引：1
随着多角度传感器的陆续出现及植被遥感传输机理研究的深入,多角度遥感逐渐成为地表信息反演的热点问题.以SAIL冠层反射率模型为基础,通过联合多角度和多光谱数据,可以从物理机理角度进行植被叶面积指数(LAI)反演的应用研究.首先通过计算得到多角度多光谱遥感影像的角度信息,并经6S模型纠正后得到多光谱多角度植被冠层反射率数据.然后将PROSPECT模型模拟出的植被叶片反射率和透过率,以及多角度观测数据、LAI和其它实测数据输入SAIL模型,模拟得到了多角度多光谱冠层反射率,进而建立多角度多光谱冠层反射率与LAI的查找表.最后,将影像的多角度多光谱冠层反射率与查找表进行匹配,实现植被LAI的反演.最后对反演结果进行了验证和分析,结果表明反演精度较高,误差均在合理范围之内. 相似文献
17.
土壤理化性质影响土壤质量,直接决定作物的产量,极易受到灌溉的影响。选择新疆典型绿洲——渭干河-库车河三角洲绿洲作为靶区,利用土壤光谱反射率预测土壤的电导率、pH值。首先,对土壤光谱反射率做变换,得到18种形式的反射率;其次,对18种形式的反射率与土壤电导率、pH值进行相关与回归分析,得到预测方程;最后,验证预测方程的精度,并确定最佳方程。结果显示:可以用土壤的光谱反射率预测土壤电导率、pH值,土壤电导率的预测方程为反射率的一阶导数微分形式,均方根误差为0.184;土壤pH值的预测方程为倒数的二阶导数微分形式,均方根误差为0.278。快速预测土壤电导率、pH值可以为土壤质量的评价提供数据基础,有利于正确有效地指导农业生产。 相似文献
18.
为探索快速提取典型绿洲棉田土壤盐分的有效方法,获取区域尺度的土壤盐渍化特征及空间分布,进而为土壤盐渍化防治提供参考。以新疆兵团农二师31团为研究区域,2019、2021年春季Landsat 8 OLI多光谱影像和野外实测土壤含盐量为数据源,将波段组、光谱指数组和全变量组作为模型输入变量组,采用多元逐步回归（Multiple stepwise regression, MSR）、偏最小二乘回归（Partial least squares regression, PLSR）、极限学习机（Extreme learning machine, ELM）、支持向量机（Support vector machine, SVM）和BP神经网络（Back propagation neural network, BPNN）构建基于3个输入变量组的土壤盐分遥感反演模型,探究输入变量和建模方法对模型精度的影响效果,通过对比确定春季土壤盐分最优反演模型,定量反演地表土壤含盐量。结果表明：（1） 研究区主要为非盐化土和轻度盐化土,总样本变异系数为0.67,呈中等变异性;光谱反射率与土壤盐渍化程度的关系表现为土壤盐渍化越重,光谱反射率越高。（2） 海岸波段（b1）、蓝波段（b2）、绿波段（b3）、红波段（b4）和盐分指数（SI1、SI2、SI3、SI4、S3、S4、S5）均通过显著性检验P<0.01,相关系数均达到0.4以上。（3） 所有模型中,基于全变量组建立的BPNN反演模型精度最高,建模集R2为0.705;验证集R2为0.556。（4） 由反演结果可知,2019、2021年春季耕作区土壤主要为非盐化土,分别占耕作区总面积的55.55%和64.62%,其次为轻度盐化土,分别占44.31%和35.17%;2021年土壤盐渍化程度较2019年有所减轻。 相似文献
19.
长江口水域多光谱遥感水深反演模型研究 总被引：3，自引：0，他引：3
利用Landsat-7 ETM 遥感影像反射率和实测水深值之间的相关性可以探测水深。该文介绍单波段、双波段比值和多波段3种线性回归模型以及动量BP人工神经网络水深反演模型。选择长江口北港河道上段作为研究区,利用上述模型,分两种情况进行水深反演:一是以河道全部历史样本建模;二是将河道按自然水深划分为浅水区和深水区分别建模。结果表明:神经网络模型预测精度高于线性回归模型;水深分区后线性回归和神经网络模型预测误差均有所减小。 相似文献
20.
Measurement of dispersed vitrinite reflectance in organic sediments is one of the few regional data sets used for placing bounds on the thermal history of a sedimentary basin. Reflectance data are important when access to complementary information such as high‐quality seismic data is unavailable to place bounds on subsidence history and in locations where uplift is an important part of the basin history. Attributes which make vitrinite reflectance measurements a useful data set are the relative ease of making the measurement, and the availability of archived well cores and cuttings in state, provincial, and federal facilities. In order to fully utilize vitrinite data for estimating the temperature history in a basin, physically based methods are required to calibrate an equivalent reflectance from a modelled temperature history with measured data. The most common method for calculating a numerical vitrinite reflectance from temperature history is the EASY%Ro method which we show systematically underestimates measured data. We present a new calculated reflectance model and an adjustment to EASY%Ro which makes the correlation between measured vitrinite values and calculated vitrinite values a physical relationship and more useful for constraining thermal models. We then show that calibrating the thermal history to vitrinite on a constant age date surface (e.g., top Cretaceous) instead of calibrating the thermal history in depth removes the heating rate component from the reflectance calculation and makes thermal history calibration easier to understand and more directly related to heat flow. Finally, we use bounds on the vitrinite–temperature relationships on a constant age date surface to show that significant uncertainty exists in the vitrinite data reported in most data sets. 相似文献