Holocene permafrost history and cryostratigraphy in the High‐Arctic Adventdalen Valley,central Svalbard

This paper presents the history and cryostratigraphy of the upper permafrost in the High‐Arctic Adventdalen Valley, central Svalbard. Nineteen frozen sediment cores, up to 10.7 m long, obtained at five periglacial landforms, were analysed for cryostructures, ice, carbon and solute contents, and grain‐size distribution, and were ¹⁴C‐ and OSL‐dated. Spatial variability in ice and carbon contents is closely related to the sedimentary history and mode of permafrost aggradation. In the valley bottom, saline epigenetic permafrost with pore ice down to depths of 10.7 m depth formed in deltaic sediments since the mid‐Holocene; cryopegs were encountered below 6 m. In the top 1 to 5 m, syngenetic and quasi‐syngenetic permafrost with microlenticular, lenticular, suspended and organic‐matrix cryostructures developed due to loess and alluvial sedimentation since the colder late Holocene, which resulted in the burial of organic material. At the transition between deltaic sediments and loess, massive ice bodies occurred. A pingo developed where the deltaic sediments reached the surface. On hillslopes, suspended cryostructure on solifluction sheets indicates quasi‐syngenetic permafrost aggradation; lobes, in contrast, were ice‐poor. Suspended cryostructure in eluvial deposits reflects epigenetic or quasi‐syngenetic permafrost formation on a weathered bedrock plateau. Landform‐scale spatial variations in ground ice and carbon relate to variations in slope, sedimentation rate, moisture conditions and stratigraphy. Although the study reveals close links between Holocene landscape evolution and permafrost history, our results emphasize a large uncertainty in using terrain surface indicators to infer ground‐ice contents and upscale from core to landform scale in mountainous permafrost landscapes.     

A machine learning approach for predicting computational intensity and domain decomposition in parallel geoprocessing

ABSTRACT

High performance computing is required for fast geoprocessing of geospatial big data. Using spatial domains to represent computational intensity (CIT) and domain decomposition for parallelism are prominent strategies when designing parallel geoprocessing applications. Traditional domain decomposition is limited in evaluating the computational intensity, which often results in load imbalance and poor parallel performance. From the data science perspective, machine learning from Artificial Intelligence (AI) shows promise for better CIT evaluation. This paper proposes a machine learning approach for predicting computational intensity, followed by an optimized domain decomposition, which divides the spatial domain into balanced subdivisions based on the predicted CIT to achieve better parallel performance. The approach provides a reference framework on how various machine learning methods including feature selection and model training can be used in predicting computational intensity and optimizing parallel geoprocessing against different cases. Some comparative experiments between the approach and traditional methods were performed using the two cases, DEM generation from point clouds and spatial intersection on vector data. The results not only demonstrate the advantage of the approach, but also provide hints on how traditional GIS computation can be improved by the AI machine learning.     

An approximate method for evaluating the shear band thickness in saturated soils

A formula for the thickness of a shear band formed in saturated soils under a simple shear or a combined stress state has been proposed. It is shown that the shear band thickness is dependent on the pore pressure properties of the material and the dilatancy rate, but is independent of the details of the combined stress state. This is in accordance with some separate experimental observations. Copyright © 2004 John Wiley & Sons, Ltd.     

成都市冬季重污染日气象条件成因分析及预报

本文通过对成都10个重污染日进行天气学分析,将污染浓度与气象要素进行聚类、研究了重污染日形成原因及污染浓度与气象要素的关系。在此基础上,建立了SO_2、TSP日平均浓度分级预报方程。     

The comparison between allozyme and RAPD makers for the population genetic structure analysis of scallop Chlamys farreri

1INTRODUCTION Since the first use in human genetic research in1966,allozyme has become the most popular genetic marker for population genetics studies for almost all groups of animals and plants(Lewontin and Hubby,1966;Murphy et al.,1996).In the past seve…     

Neutrino mass,luminosity variation,and spectrum of SN1987A

In this paper, the neutrino mass has been determined from SN1987a observation in a manner that the simultaneity of neutrino emission is not regarded as the starting point, but is itself defined through the analysis by Monte-Carlo simulation. The result is that the neutrino mass lies in 3–4 eV, possiblym _v 3.6 eV. Neutrino luminosity variation and neutrino spectrum are also obtained. Comparison with theories gives further support to the mass determination, and also predicts the mass of progenitor star of SN1987a to be in the range of 12–25M .The project supported by the National Natural Science Foundation of China.     

Possible chemical structures and biological precursors of different vitrinites in coal measure in Northwest China

Chemical structure of Jurassic vitrinites isolated from the coals in basins in NW China have been checked using solid state ¹³C NMR and flash pyrolysis-GC/MS. Study shows some Jurassic collodetrinites are rich in aliphatic products in pyrolysates, consisting with the high amount of methylene carbon in ¹³C NMR spectra. In contrast, pyrolysates of Jurassic collotelinites are rich in phenols and alkylbenzenes. Also one Pennsylvanian and one Permian vitrinite selected from the Ordos basin, NW China have been checked for comparison. The proportion of aliphatics is low in pyrolysates, and aliphatic carbon peak in ¹³C NMR spectrum of Permian vitrinite is mostly composed of gas-prone carbons compared with collodetrinites in those Jurassic basins. But both pyrolysis and ¹³C NMR data shows the Pennsylvanian vitrinite is not only gas-prone but also oil-prone. Relatively high proportion of long chain aliphatic structure of some Jurassic vitrinite in Junggar, Turpan-Hami basins may be due to the contribution of liptodetrinites, which may be included during the formation of vitrinites. And it seems that suberinite is the most possible precursor of long chain aliphatics in the structure of Jurassic collodetrinite.     

火试金法测定铜精矿中金含量结果的不确定度评定

对火试金法测定铜精矿中金含量的结果进行不确定度评定。分析了铜精矿样品称量、铜精矿样品的不均匀性和配料处理,以及金粒称量等因素对金含量测量结果不确定度的影响,并得出火试金法测量铜精矿中金含量的扩展不确定度。     

高、低煤阶煤层气藏地质特征及控气作用差异性研究

高、低煤阶煤层气地质特征及控气作用差异性是研究煤层气富集成藏的重要组成部分,是煤层气勘探开发理论研究过程中重要的基础性研究领域之一。本文以中国沁水、阜新盆地和美国粉河盆地等典型的含气盆地为例,探讨了高、低煤阶煤层气的储层物性差异,分析了构造控气和水文地质控气作用的差异性。研究表明,高煤阶气藏含气量高,CH4百分含量高,δ13C1值大于-38.75‰,储层渗透率变化小,储层改造难,构造热事件对煤层气的生成、富集贡献大,持续的水动力使气藏遭到破坏,且破坏幅度大,现今地下水格局对气藏的形成具有一定的影响;低煤阶气藏含气量低,CH4百分含量低,δ13C1值大于-49.11‰,储层渗透率变化大,储层易改造,煤热演化史及煤阶影响着煤层气的生成、富集,在煤层气生成过程中活跃的水动力是甲烷生成的主要的水文地质条件之一,但持续的水动力使气藏遭到破坏,且破坏幅度小,而合适的地层水矿化度则是低煤阶煤层气生成的重要条件,地下水格局对气藏的调整和改造起到决定性的影响。