Stability of Kerogen Classification with Regard to Image Segmentation

This paper investigates the stability of an automatic system for classifying kerogen material from images of sieved rock samples. The system comprises four stages: image acquisition, background removal, segmentation, and classification of the segmented kerogen pieces as either inertinite or vitrinite. Depending upon a segmentation parameter d, called “overlap”, touching pieces of kerogen may be split differently. The aim of this study is to establish how robust the classification result is to variations of the segmentation parameter. There are two issues that pose difficulties in carrying out an experiment. First, even a trained professional may be uncertain when distinguishing between isolated pieces of inertinite and vitrinite, extracted from transmitted-light microscope images. Second, because manual labelling of large amount of data for training the system is an arduous task, we acquired the true labels (ground truth) only for the pieces obtained at overlap d=0.5. To construct ground truth for various values of d we propose here label-inheritance trees. With thus estimated ground truth, an experiment was carried out to evaluate the robustness of the system to changes in the segmentation through varying the overlap value d. The average system accuracy across values of d spanning the range from 0 to 1 was 86.5%, which is only slightly lower than the accuracy of the system at the design value of d=0.5 (89.07%).     

Classify-normalize-classify

Changing atmospheric conditions often result in a data distribution shift in remote sensing images for different dates and locations making it difficult to discriminate between various classes of interest. To alleviate this data shift issue, we introduce a novel supervised classification framework, called Classify-Normalize-Classify (CNC). The proposed scheme uses a two classifier approach where the first classifier performs a rough segmentation of the class of interest (COI) in the input image. Then, the median signal of the estimated COI regions is subtracted from all image pixels values to normalize them. Finally, the second classifier is applied to the normalized image to produce the refined COI segmentation. The proposed methodology was tested to detect deforestation using bitemporal Landsat 8 OLI images over the Amazon rainforest. The CNC framework compared favorably to benchmark masks of the PRODES program and state-of-the-art classifiers run on surface reflectance images provided by USGS.     

Using reflectance crossplots and rotational polarization for determining first-cycle vitrinite for maturation studies

Vitrinite reflectance on dispersed sedimentary organic matter is traditionally determined from histograms of vitrinite and inertinite reflectance measured on either whole rock samples or kerogen concentrates, or from particles usually in whole rock preparations chosen by morphology and relative reflectance to be first-cycle vitrinite. This paper discusses an alternative method: discrimination of first-cycle vitrinite using apparent maximum and minimum reflectance data plotted on reflectance crossplots. The use of crossplots increases the confidence that the macerals identified as first-cycle vitrinite are optically similar. The maximum–minimum data, in this study, were collected on a quantitative reflectance microscope outfitted with rotational polarization, a technique that eliminates the usual difficulty in collecting such data on very small macerals by stage rotation.     

面向对象的遥感图像分类方法研究

影响遥感图像分类效果的主要因素之一是空间分辨率。通过融合多分辨率遥感图像,引入面向对象的思想,有效地克服了多光谱图像空间分辨率低的问题。该方法由图像分割和分类等一系列技术组成,首先用基于区域分割法则对正射校正SPOT图像进行分割,然后把它作为参考用最大似然法分类器和其他一些经验规则对TM图像进行分类。对土地覆盖图分类进行精度测试,取得了良好的应用效果。     

Strength of ensemble learning in multiclass classification of rockburst intensity

Rockbust is a violent expulsion of rock due to the extreme release of strain energy stored in surrounding rock mass, leading to considerable damages to underground strucures and equipment, and threatening workers' safety. As the operational depth of engineering projects increases, a larger number of factors influence the mechanism of rockburst. Therefore, accurate classification of rockburst intensity cannot be achieved based on conventional criteria. It is urgent to develop new models with high accuracy and ease to implement in practice. This study proposed an ensemble machine learning method by aggregating seven individual classifiers including back propagation neural network, support vector machine, decision tree, k-nearest neighbours, logistic regression, multiple linear regression and Naïve Bayes. In addition, we proposed nine data imputation methods to replace the missing values in the compiled database including 188 rockburst instances. Five-fold cross validation and the beetle antennae search algorithm are used to tune hyperparameters and voting weights of the individual classifiers. The results show that the rockburst classification accuracy obtained by the classifier ensemble has increased by 15.4% compared with the best individual classifier on the test set. The predictor importance obtained by the classifier ensemble shows that the elastic energy index is the most sensitive input variable for rockburst intensity classification. This robust ensemble method can be extended to solve other classification problems in underground engineering projects.     

利用干酪根热解动力学模拟实验研究塔里木盆地下古生界古地温

通过热解动力学模拟实验，对塔里木盆地塔参1井奥陶系干酪根的热演化过程进行了模拟，以此基础上利用KINETICS软件求取干酪根镜质体反射率（R0）的生成动力学参数，并结合塔参1井的沉积埋藏史，计算塔参1井寒武－奥陶系的古地温，这对于研究塔里木盆地下古生界高过成熟烃源岩的古地温是一种新方法。     

一种基于光谱角和光谱距离自动加权融合分类方法

高分辨率遥感影像分类一直是业内研究的热点之一,考虑到影像地物光谱角和光谱距离在分类中具有较好的互补性,提出了一种基于光谱角和光谱距离自动加权融合的分类方法,对传统多分类器分类的融合策略进行改进,能够在训练阶段根据样本自动地调整好各分类器对各类别进行分类的权重系数,使得融合后的分类结果更加科学和准确。QuickBird影像的分类实验表明,方法的分类精度明显优于单纯的光谱角或距离法,可广泛用于各种高分辨率影像的分类识别。     

甘肃西成铅—锌矿田干酪根的特征

本文对西成矿田中泥盆统内含有机碳岩石中干酪根分析，认为该区内的干酪根主要由镜质体，壳质体，藻质体，惰质体，惰质组和矿物质组成，属于腐植型干酪根，颜色从淡黄到黑色，从西向东颜色加深，Ｒ０值也有同样的趋势，说明在矿区内成熟度不一致，热解分析发现Ｓ２峰有分叉现象，经过氯仿抽提后分叉消失，从可溶有机质的色谱分析中发现有大量氮硫氧化合物，证明分叉现象可能是由氮硫氧化合物引起。Ｒ０值越大，反映出干酪根的再改造     

Comparison of source rock geochemistry of selected rocks from the Schei Point group and Ringnes formation, Sverdrup basin, arctic Canada

Organic-rich from the Schei Point group (middle to late Triassic in age) and the Ringnes formation (late Jurassic) from the Sverdrup basin of the Canadian arctic archipelago have been geochemically evaluated for source rock characterization. Most samples from the Schei Point group are organic-rich (> 2% TOC and are considered as immature to mature oil-prone source rocks [kerogen types I, I–II (IIA) and II (IIA)]. These kerogen types contain abundant AOM1, AOM2 and alginite (Tasmanales, Nostocopsis, Leiosphaeridia, acritarch and dinoflagellate) with variable amounts of vitrinite, inertinite and exinite. Samples from the Ringnes formation contain dominant vitrinite and inertinite with partially oxidized AOM2, alginite and exinite forming mostly immature to mature condensate- and gas-prone source rocks [kerogen type II–III (IIB), III and a few II (IIA)]. Schei Point samples contain higher bitumen extract, saturate hydrocarbons and saturate/ aromatic ratio than the Ringnes samples. Triterpane and sterane (dominant C₃₀) distribution patterns and stable carbon isotope of bitumen and kerogen suggest that the analyzed samples from the Schei Point group are at the onset of oil generation and contain a mixture of sapropelic (algal) and minor terrestrial humic organic matter. Sterane carbon number distributions in the Ringnes formation also suggest a mixed algal and terrestrial organic matter type. There are some variations in hopane carbon number distributions, but these are apparently a function of thermal maturity rather than significant genetic differences among samples. Pyrolysis-gas chromatography/mass spectrometry of the two samples with similar maturity shows that the Schei Point sample generates three times more pyrolyzate than the Ringnes sample. Both samples have a dominant aliphatic character, although the Ringnes sample contains phenol and an aromaticity that is higher than that of the Schei Point sample.     

封闭体系有机质与有机碳氢氮恢复动力学研究

在封闭体系的条件下,对典型的Ⅰ、Ⅱ、Ⅲ型干酪根在热演化过程中的损失进行生烃动力学研究,获得了Ⅰ、Ⅱ、Ⅲ型干酪根的总量、有机碳、氢以及氮质量损失动力学参数。用Kinetics软件计算了封闭体系干酪根有机碳丰度、氢碳原子比和氮碳原子比的恢复系数。认为在对高成熟—过成熟干酪根进行生烃评价时,Ⅰ、Ⅲ型干酪根残余有机碳丰度需要进行恢复,而Ⅱ型干酪根残余有机碳丰度不需要恢复。三种类型干酪根的氢碳原子比均需要进行恢复。     

Comparative studies of organic matter petrography of the late palaeozoic black shales from Southwestern Poland

There are at least two sapropelic units associated with Late Palaeozoic black shales in Central Europe. The older unit, of Late Carboniferous age, is the lower part of the Anthracosia Shales in the Intrasudetic Basin, and the younger one is the well-known Zechstein Kupferschiefer in both the Foresudetic Monocline and the Northsudetic Basin. The first unit is of lacustrine origin, while the second one represents deposition in a shallow marine depositional environment. Both units contain high amounts of organic matter, thus being typical black shales.The organic matter dispersed in these shales was studied petrographically. In general, the vitrinite reflectance of the shales studied indicates variable, but moderate organic matter maturity (0.68–1.25%), equivalent to the oil window. Detailed microscopic studies of the organic material dispersed in the lower unit of the Anthracosia Shales showed that liptinite, especially alginite is the most abundant component. Secondary altered organic matter, i.e. solid hydrocarbons, rarely occurs. Organic components together with mineral matter constitute a lacustrine sapropelic association, a humic (terrestrial) association and an intermediary association. The character and predominance of alginite and lacustrine sapropelic association are indicative of an open-lacustrine depositional environment. In general, this organic composition is typical of type I kerogen.Microscopic analysis of the Kupferschiefer revealed a mixture of liptinite, vitrinite and inertinite macerals, and other organic components such as amorphous sapropelic mass (ASM) and solid bitumens. The most common organic components are liptinite macerals. Bituminite and alginite predominate, and are diagnostic macerals of this unit. The amount of bituminite locally exceeds 85 vol.%. Other liptinite macerals such as sporinite and liptodetrinite, are present in significantly lower amounts, one exception being ASM, which may be present in higher amounts. Humic constituents (vitrinite and inertinite) are rare, present in small amounts in the Kupferschiefer beds. The organic matter composition points to type II kerogen for this unit.     

A comparison of different land-use classification techniques for accurate monitoring of degraded coal-mining areas

Classification of different land features with similar spectral response is an enigmatical task for pixel-based classifiers, as most of these algorithms rely only on the spectral information of the satellite data. This study evaluated the performance of six major pixel-based land-use classification techniques (both common and advanced) for accurate classification of the heterogeneous land-use pattern of Jharia coalfield, India. WorldView-2 satellite data was used in the present study. The land-use classification results revealed that Maximum Likelihood classifier algorithm performed best out of the four common algorithms with an overall accuracy of about 84%. The advanced classifiers used in the study were Neural-Net and Support Vector Machine both of which gave excellent results with an overall accuracy of 91% and 95%, respectively. It was observed that use of very high-resolution data is not sufficient for obtaining high classification accuracy, selection of an appropriate classification algorithm is equally important to get better classification results. Advanced classifiers gave higher accuracy with minimal errors, hence, for critical planning and monitoring tasks these classifiers should be preferred.     

The properties of macerals in Czechoslovak coals rich in inertinite

Czechoslovak bituminous coals rich in inertinite contain a considerable amount of inertinite with a reflectance range displaced towards and partly overlapping that of the vitrinite reflectance. Together with the existence of the transitional maceral group of semivitrinite, this causes difficulties in maceral analysis as well as in the technological evaluation of these coals. The relationship between the volatile matter of vitrinite and its reflectance is very close for both vitrinite- and inertinite-rich coals. The analogous relationship between the vitrinite reflectance and the volatile matter of inertinite displays a considerable scatter due to the effects of some higher values of the volatile matter of inertinite — related to the presence of inertinite with relatively low reflectance. The results of investigations into the coking properties of coals rich in inertinite, however, do not supply any proof of a higher fusibility of these coals.     

Fine classification of construction land using high-resolution remote sensing images: a case study in planning restricted zone of nuclear power plant

Detailed construction land information plays a significant role in monitoring planning restricted zone of nuclear power plant and ecological environment protection. This study focuses on developing fine classifying method of construction land in planning restricted zone of nuclear power plant using high spatial resolution GF(GaoFen)-1 remote sensing images. The object-oriented classification method is used in this study; the important process of which is image segmentation and classification. Multi-scale segmentation method, rule-based decision tree, and the nearest neighbor classifier are used in classifying construction land classes, i.e., road, industrial, and residential. An optimal segmentation scale is crucial to image segmentation in object-oriented classification. Instead of laborious trial-and-error experiments for optimal image segmentation, the change rates of the local variance in the homogeneous region are calculated to get the optimal segmentation scales. Multi-level classification strategy is used in the following classification. Rule-based decision tree is used to classify road and water, vegetation and non-vegetation, and industrial and residential. And the nearest neighbor classifier is used to classify cropland and forest within the vegetation land use type. The accuracy assessment result shows that the overall accuracy is 89.67% and Kappa coefficient is 0.85 for object-oriented classification, which is much higher than pixel-based maximum likelihood classifier (overall accuracy is 79.17% and Kappa coefficient is 0.74) and support vector machine classifier (overall accuracy is 74.16% and Kappa coefficient is 0.68).     

煤和干酪根纳米结构的研究进展

对煤和干酪根结构的认识得益于技术和方法的不断创新。基于各种方法,先后建立了多个煤和干酪根的结构模型,但至今没有得到普遍认可。原子力显微镜(AFM)可以实时、实空间、原位成像,可以观察单个原子层的局部表面结构,直接观察表面缺陷、表面重构、表面吸附体的形态和位置以及表面扩散等动态过程。在对图像的分析中,AFM超越了传统仪器单纯平面成像的功能,可提供样品表面动态三维图像和用于分析的定量化信息。通过纳米技术,实现了原子级的分辨率的观察,揭示了煤和干酪根聚集态分子和纳米级孔隙的形态、大小、结构及相互间的空间排列特征,显示出在煤和干酪根结构研究中的巨大潜力。纳米技术为煤和干酪根结构的基础研究工作拓展了新的途径,也为非常规油气的勘探开发和煤炭的二次转化研究提供了科学依据。     

Methane adsorption comparison of different thermal maturity kerogens in shale gas system

To determine the effect of thermal maturity on the methane sorption in shale gas system, two different thermal maturity kerogens of type II isolated from Barnett shale of Fort Worth Basin were used to measure the methane adsorption amount under the pressure ranging from 0 to 14 MPa at constant temperatures. One kerogen was called Lee C-5-1 with 0.58% of vitrinite reflectance; the other was called Blakely#1 kerogen with 2.01% of vitrinite reflectance. The results suggested that the methane sorption capacity of kerogen Blakely#1 was higher than the immature kerogen Lee C-5-1, and its Langmuir constant and Langmuir maximum sorption amount, which were reached by fitting the measured data for at least square method, greater than the immature kerogen Lee C-5-1. This may be associated with that nanopores opened up during the degradation of organic matter, and which increased the specific surface area of kerogen. Therefore, the over mature kerogen has greater methane adsorption capacity.     

Notes on the origin of inertinite macerals in coals: Observations on the importance of fungi in the origin of macrinite

Macrinite is a, generally, rare inertinite maceral, often incorporating remnants and fragments of other macerals, including vitrinite, liptinite, and other inertinite. The associated inertinites include multiple forms of funginite. Funginite is also commonly found in association with vitrinite of slightly elevated reflectance and with degraded varieties of vitrinite. Together with the highly degraded macrinite, the latter two associations are here inferred to be part of a continuum of fungal and microbial degradation of peat. In any case, the origin of some macrinite is potentially distinct from that of inertinite generated by fire.     

新疆中生代煤中半镜质组特征及其研究意义

通过分析显微图像的光学性质和测定煤的反射率等方法,研究了新疆中生代煤中半镜质组特征。结果表明:新疆中生代煤中含有一定量的半镜质组;均质半镜质体占总体(均质镜质体与均质半镜质体之和)的49%~64%,均质半镜质体的最大反射率(R_max)比均质镜质体的高0.08%~0.13%;均质半镜质体的R_max介于均质镜质体和结构镜质体之间,结构半镜质体的R_max比结构镜质体的高0.28%~0.34%。由此可以得出:在三大组分划分方案中,应将均质半镜质体归入镜质组的均质体中,同时认为,按显微组分三大组分划分方案中,将结构半镜质体归入惰质组的半丝质体中更合理;在测定煤的反射率时,不能将均质半镜质体作为反射率的测定对象。本项研究结果对于指导本地区煤炭的炼焦配煤、煤炭液化和深加工利用等具有重要意义。      

灵武煤田煤中镜质组与丝质组的甾萜类化合物对比研究

本文对灵武煤田含煤岩系延安组煤岩显微组分的甾萜类化合物作了对比研究．结果表明,同一沉积环境中镜质组与丝质组的甾萜类化合物特征及不同沉积环境中同一显微组分（镜质组或丝质组）的甾萜类化合物特征有着规律性的变化．     

广西百色盆地干酪根的研究--兼探干酪根的演化与粘土矿物的关系

本文用X射线、微区分析等方法对广西百色盆地那读三段泥岩中的干酪根进行了详细研究,该盆地干酪根的芳香度（Fa）为0.28至0.37,芳香核厚度（Le）为16.60A到20.76A,芳香片层数（n）为5到6层,证明属Ⅱ型干酪根,在同一剖面,fa随深度增加而变大,表明了干酪根向成熟方向转化。在干酪根中碳元素的分布是不均匀的,一般中间含量高,向边部则含量变低,铁及硅元素也向中间聚集,多数情况下形成FeS₂核,在核的周围分布有粘土矿物。干酪根成熟程度的变化趋势与粘土矿物的演化趋势在宏观上是一致的,即与蒙脱石转化为伊/蒙混层粘土矿物的趋势一致。并从化学健和能量方面对这种过程进行了一些探讨。