松辽盆地徐深1井气藏气源的氢、碳同位素特征

选取松辽盆地内泥岩样品和煤样进行热模拟实验,建屯了两个样品成甲烷的氢、碳同位素分馏动力学模型并标定了动力学参数.分别以徐深1井区、沉降中心地质资料为例进行研究,表明两处源岩均有短期内大量生气的特点,气源岩生气期分别为距今95.5～73 Ma和距今0～73 Ma.计算得到两处源岩沙河子组暗色泥岩和煤、火石岭组暗色泥岩和煤所生天然气单独运聚成藏（自开始生烃到现今累积成藏）所对应的δDCH4 和δ13C1,进而定量计算出徐深1井区源岩所生甲烷的δDCH4 为-237.3‰,δ13C1为-28.8‰,沉降中心气源岩所生甲烷的δDCH4 为-2.5‰,δ13C1为-24.8‰.以各区域天然气混合后的δDCH4 作为来源气体的端元同位素值,根据物质平衡原理计算得到：徐深1井区源岩对该区气藏的贡献比例约占72%,沉降中心源岩的贡献比例约为28%.同理以δ13C1.方法得到徐深1井区源岩对该区气藏的贡献比例约占66%,沉降中心源岩的贡献比例约为34%.氢、碳同位素分馏的化学动力学地质应用结果存在的差异与同位素分馏模型标定所用热模拟实验为不加水实验有关.     

胶东地区蓬莱群沉积岩碎屑锆石年龄和物源

分布于鲁东胶北地区蓬莱群为一套浅变质沉积岩，角度不整合覆盖在太古宙胶东群、元古宙粉子山群上。关于蓬莱群的沉积时代尚存较大的争议，认为是震旦纪或古生代沉积形成的。蓬莱群和五莲杂岩、北淮阳变质岩带都是位于大别-苏鲁超高压变质造山带北部的重要岩石构造单元。本文报道蓬莱群沉积岩碎屑锆石年龄和始同位素分析资料，并探讨其可能的物质来源。 在栖霞地区采集了14个蓬莱群样品，主要岩石为石英岩、千枚岩、片岩、页岩，从其中4个挑出了锆石样品。分选出的碎屑锆石均为浅棕色，浑圆状，反映锆石经历了搬运作用。CL图像显示绝大多数锆石颗粒具有清晰的韵律环带内部结构，具有岩浆成因特征。在中国科学院地质与地球物理所固体同位素地球化学实验室IsoProbe-T质谱计上，采用即蒸即测方法，测得锆石207Pb/206Pb比值，其对应的年龄值主要分布在1000~1800 Ma,峰值为1200 Ma和1600 Ma左右（图1)。应用中国科学院地质与地球物理所多通道等离子质谱计Neptune MC-ICP-MS测定碎屑锆石 同位素组成。根据所获得的同位素组成，计算得到比同位素模式年龄TDM(Hf）分布在1300~3200 Ma之间，峰值为1700 Ma左右；对应的初始εHf(1200 Ma）和εHf (1600 Ma）平均值分别为-5.8和2.9（图2)。 碎屑锆石1350~1800 Ma年龄段，特别是峰期的1600 Ma，可能对应全球广泛分布的非造山事件，可能和Columbia超大陆的裂解有一定的联系。大部分锆石初始εHf (1600 Ma）值大于0，可以说明岩浆来源为幔源。而1050~1300 Ma的锆石则可能与Rodinia超大陆的汇聚形成阶段或Grenville造山事件有较密切的联系。华北克拉通大量地出露太古宙末期（2500 Ma左右）和早元古代末期(1800 Ma左右）的岩石，但在所分析的蓬莱群沉积岩几乎没有显示，可能可以排除华北克拉通为主要的沉积物源。所获得的碎屑锆石年龄虽在扬子板块有一定的出露，但缺少与Rodinia裂解有关的晚元古代700~800 Ma的岩浆锆石信息。上述资料可能暗示沉积时代为1000~800 Ma之间，但与目前的古生物证据不吻合。因此，这些碎屑锆石年龄和铅同位素数据尚不能肯定蓬莱群变质沉积岩是来源于扬子陆块，还是游离于华北、扬子的一个微陆块。如果是一个微陆块，该微陆块与Columbia裂解作用和Grenville造山作用有关，但可能未遭受到Rodinia超大陆裂解事件的影响。     

Delineation of Integrated Anomaly with Generative Adversarial Networks and Deep Neural Networks in the Zhaojikou Pb-Zn Ore District,Southeast China

Geochemical maps are of great value in mineral exploration. Integrated geochemical anomaly maps provide comprehensive information about mapping assemblages of element concentrations to possible types of mineralization/ore, but vary depending on expert’s knowledge and experience. This paper aims to test the capability of deep neural networks to delineate integrated anomaly based on a case study of the Zhaojikou Pb-Zn deposit, Southeast China. Three hundred fifty two samples were collected, and ea...     

Shale Oil Occurrence and Reservoir Characteristics of the Qijia-Gulong Depression in Songliao Basin

<正>1 Introduction The technology breakthrough in the exploration of shale gas and tight oil has greatly extended the global fossil fuel resources(Jia et al.,2012;Zou et al.,2012;Qiu et al.,2013).Although shale oil has been the global hot topic in the study of unconventional resources,there are varied definitions with respect to shale oil by different researchers.     

应用新型固体质谱计IsoProbe-T高精度地测定单颗粒锆石年龄

固体质谱计，以其高精度和可靠性度，在同位素年代学和同位素地球化学的研究领域的应用依然前景广阔。近年来，微量样品测试手段已经成为地质科学和环境科学等领域极其重要的研究方法，促使国际国内地球化学实验室对固体热电离质谱计更新换代。中国科学院地质与地球物理研究所固体同位素地球化学实验室于2004年引进英国GV公司的IsoProbe-T固体热电离质谱计，具有较宽质量谱带，装备多通道离子计数接收器，高灵敏度高精度，易于操作等特点。该类型仪器将是今后的同位素年代学和同位素地球化学研究的主导之一。该质谱计配置了9个法拉第接收器、１个戴利接收器和7个离子计数器（图1)。它的运行将有望大幅度地促进年代学和同位素地球化学在壳幔相互作用与深部物质成分、古大陆形成与演化、流体与成矿等主要领域研究工作的应用。本文主要介绍采用IsoProbe-T质谱计测定单颗粒错石U-Pb和Pb-Pb蒸发年龄方法和应用。传统的锆石Pb-Pb蒸发法定年原理是将锆石单颗粒包裹于铼灯丝中并加热，将锆石中铅蒸发至另一铼灯丝上，之后加热电离沉淀于该灯丝上的铅样品，采用单个离子计数器动态方式测量铅同位素组成，从而获得207Pb/206Pb比值和对应的年龄。这一方法技术简单但极为耗时。采用IsoProbe-T质谱计的多个离子计数器测量，可以克服耗时缺点。方法是将包裹于铼灯丝的锆石颗粒加热，采用静态方式直接测量蒸发出来的铅同位素组成，获得207Pb/206Pb年龄。该方法简便省时，同时可以直观地观测到锆石内部铅同位素组成的变化，如207Pb/206Pb比值，指示锆石中Th/U比值的变化，或207Pb/206Pb比值变化，直接反映出继承锆石的存在与否。离子计数器之间的效率差别可以采用测定铅标准溶液同位素比值来校正。尽管该方法不能与SHRIMP微区U-Pb定年媲美，却可以获得成因单一锆石高精度207Pb/206Pb年龄且节省经费。应用该方法测定内蒙古渣尔泰群沉积岩中碎屑错石，获得的年龄与采用传统错石Pb-Pb蒸发法和U-Pb稀释法获得的年龄一致，集中分布于2.4~2.5 Ga.多个离子计数器测量技术也可以应用于单颖粒错石U-Pb年代学方法，该方法所获得的数据可靠性主要地取决于实验流程U和Pb本底情况。为了降低流程本底，笔者采用低本底的蒸汽法高温高压条件下溶解错石，（图2)。将错石和205Pb-235U混合稀释剂加于小溶样杯内，HF酸置于Teflon焖罐底部，在高温高压条件下加热，完全溶解错石。采用该溶样方法和高纯度水和试剂，获得小于5 pg和10 pg的全流程U和Pb本底。应用该方法测定了苏鲁地区南部海州群单颗粒锆石U-Pb年龄。3个云母石英片岩样品中的错石大部分具有振荡环带内部结构，指示岩浆成因，退晶化程度也较明显。采用铀-铅同位素稀释法和铅-铅蒸发法分析，获得错石年龄范围为801-787Ma。时代上，海州群云母石英片岩的原岩可与大别-苏鲁超高压变质带内超高压和高压变质岩的原岩对比，可能代表与晚元古代Rodima超大陆裂解有关的岩浆作用的产物。     

Application of deep learning in ecological resource research: Theories,methods, and challenges

Ecological resources are an important material foundation for the survival, development, and self-realization of human beings. In-depth and comprehensive research and understanding of ecological resources are beneficial for the sustainable development of human society. Advances in observation technology have improved the ability to acquire long-term, cross-scale,massive, heterogeneous, and multi-source data. Ecological resource research is entering a new era driven by big data. Traditional statistical learning and machine learning algorithms have problems with saturation in dealing with big data. Deep learning is a method for automatically extracting complex high-dimensional nonlinear features, which is increasingly used for scientific and industrial data processing because of its ability to avoid saturation with big data. To promote the application of deep learning in the field of ecological resource research, here, we first introduce the relationship between deep learning theory and research on ecological resources, common tools, and datasets. Second, applications of deep learning in classification and recognition,detection and localization, semantic segmentation, instance segmentation, and graph neural network in typical spatial discrete data are presented through three cases: species classification, crop breeding, and vegetation mapping. Finally, challenges and opportunities for the application of deep learning in ecological resource research in the era of big data are summarized by considering the characteristics of ecological resource data and the development status of deep learning. It is anticipated that the cooperation and training of cross-disciplinary talents may promote the standardization and sharing of ecological resource data,improve the universality and interpretability of algorithms, and enrich applications with the development of hardware.     

北欧海深层水的研究进展简

As a connection region between North Atlantic and Arctic Oceans, the Nordic Sea plays a critical role in global climate system. In the Nordic Seas, surface water converts into intermediate water and deep water after cooling and other effects. These waters transport southward, and enter into North Atlantic as a form of overflow, therefore, they are the main source of the North Atlantic Deep Water(NADW), which play a key role in global ocean conveyor. The causes and processes of the deep water formation in the Nordic Seas are still uncertain. Based on a review of current and historical research results of the deep water in the Nordic Seas, the most important process for deep water formation convection is addressed. Factors and physical processes that may have impact on deep water formation are summarized. The transport of deep water in the Nordic Seas is summed up. Multi year variation of the deep water is described with the aim of giving some instructions and research directions to the readers.