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.     

FEASIBLE STUDY ON THE INTEGRATION SYSTEM FOR THE SPACE MONITORING OF MAJOR EARTHQUAKES AND VOLCANOES IN TERRESTRIAL LAND

With the rapid development of space technology, earth observation technology and sky observatory technolo-gy, they have played a more and more important part in monitoring and predicting of earthquakes and volcanoes in the terres-trial land. In recent years, the related agencies have done the experiments and researches on monitoring and predicting ofearthquakes and volcanoes in the forewarning period by means of many approaches, such as satellite thermal infrared re-mote sensing (TIRS), Global Positioning System (GPS), differential interferometric synthesis aperture radar (D-INSAR),astronomical time-latitude residual anomaly, and Geographic Information Systems (GIS), etc. A quite large number of re-search foundation has been built in the fundamental theories and application methods. The experiments and researcheshave shown that these technology is efficient methods for high frequency crust movement. If the existed separate scientificforces and results are possibly assembled together to form a more complete integration monitoring system with the combina-tion of space, sky observation, ground, deep geology and macro anomaly, it will come into a new stage of monitoring andpredicting of earthquakes and volcanic eruptions.     

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.     

GPS/LEO掩星观测的变分同化技术

在简单介绍GPS/LEO掩星探测大气的发展历史和科学意义之后,详细阐述了反演的基本原理;分析了标准反演中存在的问题,并说明一维变分同化(1DVAR)在反演方法中的重要性;给出了一维变分同化中价值函数的求解,以及各种同化因子;简单介绍了对当前气象学中普遍使用的四维变分同化(4DVAR);重点讨论了各种同化方法,以及使用各种同化因子的优缺点。最后,通过CHAMP卫星的观测实例分析,验证了GPS数据在数值天气预报(NWP)中的作用,以及相对于标准反演法一维变分对气象要素的改进。     

Approach to Mountain Hazards in Tibet, China

Tibet is located at the southwest boundary of China. It is the main body of the Qinghai-Tibet Plateau, the highest and the youngest plateau in the world. Owing to complicated geology, Neo-tectonic movements, geomorphology, climate and plateau environment, various mountain hazards, such as debris flow, flash flood, landslide, collapse, snow avalanche and snow drifts, are widely distributed along the Jinsha River (the upper reaches of the Yangtze River), the Nu River and the Lancang River in the east, and the Yarlungzangbo River, the Pumqu River and the Poiqu River in the south and southeast of Tibet. The distribution area of mountain hazards in Tibet is about 589,000 km^2, 49.3% of its total territory. In comparison to other mountain regions in China, mountain hazards in Tibet break out unexpectedly with tremendously large scale and endanger the traffic lines, cities and towns, farmland, grassland, mountain environment, and make more dangers to the neighboring countries, such as Nepal, India, Myanmar and Bhutan. To mitigate mountain hazards, some suggestions are proposed in this paper, such as strengthening scientific research, enhancing joint studies, hazards mitigation planning, hazards warning and forecasting, controlling the most disastrous hazards and forbidding unreasonable human exploring activities in mountain areas.     

星风吸积引起的钡星重元素超丰

首先指出目前星风吸积模型中理论上的不自洽，考虑到δｒ≠０，重新推导轨道参量变化方程，消去了理论上的不自洽．提出一个新模型：首次将星风吸积同内禀ＡＧＢ星核合成模型结合起来计算钡（Ｂａ）星的重元素超丰，并将计算结果与观测值进行了比较．各参量按标准情况取值时，计算结果不太理想．取Ｂｏｎｄｉｈｏｙｌｅ吸积率的０．５倍或取较大的星风速率时，对于较长轨道周期（Ｐ＞１０００天）的Ｂａ星，计算结果与观测值基本符合；而较短轨道周期（Ｐ＜６００天）的Ｂａ星，其重元素超丰机制可能是盘吸积     

Optical monitoring of the quasar 4C 38.41

The results of a photometric monitoring of the quasar 4C 38.41, performed at the optical R and B bands in 2002 February–March, are presented. With a 60/90 cm Schmidt telescope at the Xinglong station of the National Astronomical Observatories of China, we observed the source exhibiting amplitude variations of up to 0.78 mag in both bands during the whole campaign. Intraday and even intranight variations are detected as well. A typical variability time-scale of about 36 d is derived from our 2-month observations at the optical bands, which is identical to that found at a radio wavelength of 92 cm, suggesting a common origin for the variations in 4C 38.41 from optical to radio bands.     

SIMULATION AND PREDICTION OF DEBRIS FLOW USING ARTIFICIAL NEURAL NETWORK

Debris flow is one of the most destructive phenomena of natural hazards. Recently, major natural haz-ard, claiming human lives and assets, is due to debris flow in the world. Several practical methods for forecasting de-bris flow have been proposed, however, the accuracy of these methods is not high enough for practical use because of the stochastic and non-linear characteristics of debris flow. Artificial neural network has proven to be feasible and use-fill in developing models for nonlinear systems. On the other hand, predicting the future behavior based on a time se-ries of collected historical data is also an important tool in many scientific applications. In this study we present a three-layer feed-forward neural network model to forecast surge of debris flow according to the time series data collect-ed in the Jiangjia Ravine, situated in north part of Yunnan Province of China. The simulation and prediction of debris flow using the proposed approach shows this model is feasible, however, further studies are needed.     

The FAST/SKA site selection in Guizhou province

Many karst depressions with diameters of 300 m to 500 m, suitable for constructing Arecibo-style radio telescopes, were identified in the south of Guizhou Province by Remote Sensing (RS) and Geographic Information System (GIS) technologies together with field investigations. Fundamental topography and landform databases were established for 391candidate depressions, and using GIS the3-dimensional images of depressions, at a scale of 1:10000, were then simulated to fit a spherical antenna. This revised version was published online in July 2006 with corrections to the Cover Date.