Benthic megafauna on steep slopes at the Northern Mid‐Atlantic Ridge          下载免费PDF全文

James B. Bell  Claudia H. S. Alt  Daniel O. B. Jones 《Marine Ecology》2016,37(6):1290-1302

The role of small‐scale (<10 km) habitat availability in structuring deep‐sea hard substratum assemblages is poorly understood. Epibenthic megafauna and substratum availability were studied on steep slopes at the Mid‐Atlantic Ridge from May to July 2010 northwest, northeast, southwest and southeast of the Charlie‐Gibbs Fracture Zone (CGFZ; 48–54°N) at between 2095 and 2601 m depth. Megafauna were six times denser north of the CGFZ compared with the south and differences in density were almost entirely driven by sessile fauna. There was no significant difference in habitat availability amongst sites. Rocky substratum made up 48% of the total area surveyed, with individual transects having between 0% and 82% rock. Assemblage structures were different amongst all superstations. The north was dominated by demospongids and hexactinellids, whereas the southern superstations were dominated by anthozoans and hexactinellids. Differences in megafaunal assemblages north and south of the CGFZ primarily reflected variations in demospongid and anthozoan species composition. With 213–1825 individuals·ha^?1, and 7–24 species per superstation, hexactinellids were the most species‐rich (36 species) and cosmopolitan taxa at the study site, supporting observations elsewhere along the ridge and in the CGFZ. The absence of significant differences in substrata availability suggested alternative drivers for density or percentage cover. The amount of hard substratum available only limited sessile megafauna density at one transect that was entirely covered with sediments. Species richness was highest for areas with intermediate values of substratum coverage (35–43% rock).  相似文献   

A method for examining the geospatial distribution of CO2 storage resources applied to the Pre-Punta Gorda Composite and Dollar Bay reservoirs of the South Florida Basin,U.S.A.     

《Marine and Petroleum Geology》2016

This paper demonstrates geospatial modification of the USGS methodology for assessing geologic CO₂ storage resources, and was applied to the Pre-Punta Gorda Composite and Dollar Bay reservoirs of the South Florida Basin. The study provides detailed evaluation of porous intervals within these reservoirs and utilizes GIS to evaluate the potential spatial distribution of reservoir parameters and volume of CO₂ that can be stored. This study also shows that incorporating spatial variation of parameters using detailed and robust datasets may improve estimates of storage resources when compared to applying uniform values across the study area derived from small datasets, like many assessment methodologies. Geospatially derived estimates of storage resources presented here (Pre-Punta Gorda Composite = 105,570 MtCO₂; Dollar Bay = 24,760 MtCO₂) were greater than previous assessments, which was largely attributed to the fact that detailed evaluation of these reservoirs resulted in higher estimates of porosity and net-porous thickness, and areas of high porosity and thick net-porous intervals were incorporated into the model, likely increasing the calculated volume of storage space available for CO₂ sequestration. The geospatial method for evaluating CO₂ storage resources also provides the ability to identify areas that potentially contain higher volumes of storage resources, as well as areas that might be less favorable.  相似文献   

Deep-lacustrine sandy debrites and turbidites in the lower Triassic Yanchang Formation,southeast Ordos Basin,central China: Facies distribution and reservoir quality     

《Marine and Petroleum Geology》2016

The deep lacustrine gravity-flow deposits are widely developed in the lower Triassic Yanchang Formation, southeast Ordos Basin, central China. Three lithofacies include massive fine-grained sandstone, banded sandstone, and massive oil shale and mudstone. The massive fine-grained sandstones have sharp upper contacts, mud clasts, boxed-shaped Gamma Ray (GR) log, but no grading and Bouma sequences. In contrast, the banded sandstones display different bedding characteristics, gradational upper contacts, and fine-upward. The massive, fine-grained sandstones recognized in this study are sandy debrites deposited by sandy debris flows, while the banded sandstones are turbidites deposited by turbidity currents not bottom currents. The sediment source for these deep gravity-flow sediments is a sand-rich delta system prograding at the basin margin. Fabric of the debrites in the sandy debris fields indicates initial formation from slope failure caused by the tectonic movement. As the sandy debris flows became diluted by water and clay, they became turbidity currents. The deep lacustrine depositional model is different from the traditional marine fan or turbidite fan models. There are no channels or wide lobate sand bodies. In the lower Triassic Yanchang Formation, layers within the sandy debrites have higher porosity (8–14%) and permeability (0.1–4 mD) than the turbidites with lower porosity (3–8%) and permeability (0.04–1 mD). Consequently, only the sandy debrites constitute potential petroleum reservoir intervals. Results of this study may serve as a model for hydrocarbon exploration and production for deep-lacustrine reservoirs from gravity-flow systems in similar lacustrine depositional environments.  相似文献   

陆表定量遥感反演方法的发展新动态   总被引：2，自引：0，他引：2  

梁顺林  程洁  贾坤  江波  刘强  刘素红  肖志强  谢先红  姚云军  袁文平  张晓通  赵祥 《遥感学报》2016,20(5):875-898

随着获取的遥感数据越来越多,定量遥感正处于一个飞速发展的时期。本文从反演方法和遥感数据产品生成两个主要方面对近期陆表定量遥感的发展进行评述。由于大气—陆表系统的环境变量数远远超过遥感观测数,定量遥感反演的本质是个病态反演问题。在评述机器学习方法(包括人工神经网络、支持向量回归、多元自适应回归样条函数等)的应用基础上,重点关注克服病态反演的7种正则化方法:多源数据、先验知识、最优化反演的求解约束、时空约束、多反演算法集成、数据同化和尺度转换。定量遥感发展的另外一个显著特征是由数据提供者(比如数据中心)将观测的遥感数据转换成不同的地球生物物理化学参数产品,即遥感高级产品,并服务于数据使用者。概括介绍了北京师范大学牵头研发的GLASS(Global LAnd Surface Satellite)产品的新进展与全球气候数据集的研发情况。  相似文献   

高职院校摄影测量与遥感课程教学改革实践探索     

王涛  谢忠俍  张亚 《测绘与空间地理信息》2016,(7)

摄影测量与遥感是高职高专工程测量技术专业的一门专业必修课。本文针对摄影测量与遥感课程理论抽象、实践性强、入门难等特点,分析了该课程在教学过程中存在的问题。结合摄影测量与遥感课程特点和高职学生特点,从教学目标、教学改革的思路和方法、课程定位、实践教学等方面,对摄影测量与遥感课程的教学内容、教学方式、实践教学环节等进行分析和探讨,以期对其他课程教学改革起到借鉴作用。  相似文献   

A novel deep neural network model approach to predict Indian Ocean dipole and Equatorial Indian Ocean oscillation indices     

《Dynamics of Atmospheres and Oceans》2021

Indian Ocean Dipole (IOD) and Equatorial Indian Ocean oscillation (EQUINOO) are important climatic system oscillation events in the Indian Ocean region that affects the Indian summer monsoon rainfall (ISMR). The prime focus of this study is to deliberate the influence of these events on ISMR and an attempt has been made to predict these events for future time scales using a Long short term memory (LSTM) deep learning model. LSTM is a special kind of recurrent neural network (RNN) which specializes in learning long-term dependencies and extracting important features. The features learnt by the model is then ranked using correlational analysis (linear and nonlinear). This approach helps in selecting decisive and imperative set of relevant predictors, which can be employed to predict IOD and EQUINOO. Nonlinear correlational identified predictors are found to forecast with greater precision as to their linear counterparts. The model-calibrated correlation coefficient for IOD and for EQUNIOO was 0.90 and 0.88 respectively at a lead of 5 months. Our proposed model was observed to work at par with the other existing models in terms of various statistical evaluation measures.  相似文献   

1980-2017年中国碳强度关键影响因子变化特征     

唐志鹏  梅子傲  刘卫东  夏炎 《地理学报(英文版)》2020,30(5):743-756

The Chinese government ratified the Paris Climate Agreement in 2016.Accordingly,China aims to reduce carbon dioxide emissions per unit of gross domestic product(carbon intensity)to 60%–65%of 2005 levels by 2030.However,since numerous factors influence carbon intensity in China,it is critical to assess their relative importance to determine the most important factors.As traditional methods are inadequate for identifying key factors from a range of factors acting in concert,machine learning was applied in this study.Specifically,random forest algorithm,which is based on decision tree theory,was employed because it is insensitive to multicollinearity,is robust to missing and unbalanced data,and provides reasonable predictive results.We identified the key factors affecting carbon intensity in China using random forest algorithm and analyzed the evolution in the key factors from 1980 to 2017.The dominant factors affecting carbon intensity in China from 1980 to 1991 included the scale and proportion of energy-intensive industry,the proportion of fossil fuel-based energy,and technological progress.The Chinese economy developed rapidly between 1992 and 2007;during this time,the effects of the proportion of service industry,price of fossil fuel,and traditional residential consumption on carbon intensity increased.Subsequently,the Chinese economy entered a period of structural adjustment after the 2008 global financial crisis;during this period,reductions in emissions and the availability of new energy types began to have effects on carbon intensity,and the importance of residential consumption increased.The results suggest that optimizing the energy and industrial structures,promoting technological advancement,increasing green consumption,and reducing emissions are keys to decreasing carbon intensity within China in the future.These approaches will help achieve the goal of reducing carbon intensity to 60%–65%of the 2005 level by 2030.  相似文献   

Spatial interpolation using conditional generative adversarial neural networks   总被引：1，自引：0，他引：1  

Di Zhu  Ximeng Cheng  Fan Zhang  Xin Yao  Yong Gao 《International journal of geographical information science》2020,34(4):735-758

ABSTRACT

Spatial interpolation is a traditional geostatistical operation that aims at predicting the attribute values of unobserved locations given a sample of data defined on point supports. However, the continuity and heterogeneity underlying spatial data are too complex to be approximated by classic statistical models. Deep learning models, especially the idea of conditional generative adversarial networks (CGANs), provide us with a perspective for formalizing spatial interpolation as a conditional generative task. In this article, we design a novel deep learning architecture named conditional encoder-decoder generative adversarial neural networks (CEDGANs) for spatial interpolation, therein combining the encoder-decoder structure with adversarial learning to capture deep representations of sampled spatial data and their interactions with local structural patterns. A case study on elevations in China demonstrates the ability of our model to achieve outstanding interpolation results compared to benchmark methods. Further experiments uncover the learned spatial knowledge in the model’s hidden layers and test the potential to generalize our adversarial interpolation idea across domains. This work is an endeavor to investigate deep spatial knowledge using artificial intelligence. The proposed model can benefit practical scenarios and enlighten future research in various geographical applications related to spatial prediction.  相似文献   

一种基于支持向量机的纹理图像分类法   总被引：7，自引：3，他引：4  

辛宪会  郭建星  解志刚  邱振戈 《海洋测绘》2005,25(2):41-43,47

支持向量机是机器学习领域的研究热点之一,是在统计学习理论基础上发展起来的新的学习算法。传统的分类法用于纹理图像分类效果往往不佳,该文研究了支持向量机的实现方法,并以纹理图像分类为例分析了支持向量机的分类性能。  相似文献   

基于机器学习规则推理的湿地识别研究   总被引：2，自引：0，他引：2  

阮仁宗  冯学智  肖鹏峰  沈渭寿 《地理科学》2005,25(6):731-736

遥感作为人类获取地表信息最重要的手段之一,对于湿地研究具有重要的价值。文章运用机器学习规则推理的方法克服常规遥感研究方法在湿地研究中的弊端,使分类精度得到很大的提高。与传统的最大似然法相比,分类精度提高了13.51%, 达到了83.81%。基于机器学习规则推理分类,不仅利用了光谱信息,而且利用了纹理信息和坐标信息,并使用了掩膜技术。研究发现,这种方法能使湿地类型以及湿地与高地之间的混淆现象得到有效的克服。  相似文献