一种用于海底类型聚类的新方法

By using sonar imaging, this paper presents a new algorithm for the clustering of seabed types based on the self-organizing feature maps （SOFM） neural network. The theory as well as data processing is studied in detail. Some valuable conclusions and suggestions are given     

A Directed-Line-Segment-Based Photogrammetry Method to Survey the Tunneling Working Surface of Mine Roadway

Absolute orientation is a basic technical work in digital image geologic logging of underground coal mine. Traditional control-point-based absolute orientation method requires setting object space control points of the known three-dimensional coordinates, which may lead to low efficiency. Therefore, this paper proposed a point-free close-range photogrammetry absolute orientation algorithm, which utilized direction line segments including plumb line segments and line segments with known directions and lengths to identify the dimensional orientation of a stereoscopic model. Experiment results show that the precision of the orientation results is favorable. σ _X and σ _Y are as high as 0.5 mm, and σ _Z is 0.3 mm. Finally, this paper introduced the application of the proposed algorithm in rapid geological logging of coal mine roadway, which was fast and reliable, convenient and feasible.     

贺兰山群变质杂岩主期变质作用的温压环境研究

分布于贺兰山脉北部的贺兰山群变质杂岩的主期变质作用达角闪麻粒岩相,其矿物组合特征与低压相系麻粒岩相似、利用共生矿物组合和矿物对的温度计、压力计估算主期变质温度740—764(?),压力460—501MPa。温度压力条件与低压相系的地热梯度线较接近,变质作用强度具面型分布的特征,与东部密云、冀东等地的麻粒岩相区相似,但变质压力明显低,压力类型也不同,应属不同构造背景下的变质产物。     

白龙江中上游地区金矿控矿因素及成矿模式探讨

白龙江中上游地区微细浸染型金矿,是受地层和构造双重控制的金矿床。在阐述矿床空间分布规律、控矿因素、区域矿化和地球化学特征等基础上,初步建立了地质地球化学成矿模式。     

我国贫困山区可持续发展的因素与目标研究--以黔江地区为例

以黔江地区为例,分析了我国贫困山区可持续发展面临的主要问题和具备的有利条件,在此基础上把区域看作一个完整的系统,运用系统论的观点建立了区域可持续发展的目的体系,并采用灰色系统等定量分析技术与定性分析相结合的方法,探讨了区域可持续发展目标的制定方法。     

Predicting Sea Surface Salinity Using an Improved Genetic Algorithm Combining Operation Tree Method

The purpose of this study is to demonstrate the use of an improved genetic algorithm combining operation tree method (IGAOT) and apply it to monitor the salinity of the Taiwan Strait by using remote-sensing data. The genetic algorithm combining operation tree (GAOT) is a data mining method used to automatically discover relationships among nonlinear systems. Based on genetic algorithms (GAs), the relationships between input and output can be expressed as parse trees. The GAOT method typically has the disadvantages of premature convergence, which means it cannot produce satisfying solutions and performs satisfactorily when applied to only low-dimensional problems. Therefore, the GAOT method is enhanced using an automatic incremental procedure to improve the search ability of the method and avoid trapping in a local optimum. In this case study, an IGAOT is used to determine the relationship between the in situ data on the salinity of the Taiwan Strait and the data on the spectral parameters, seven wavebands, of a Moderate-Resolution Imaging Spectroradiometer (MODIS) sensor. The results indicate that the IGAOT model performs more favorably than do the GAOT and linear regression (LR₁ and LR₂) models, exhibits higher correlation coefficients, and involves fewer estimating errors. The results of this study indicate that the proposed technique is useful for estimating the Taiwan Strait salinity.     

补偿电流对核旋分量仪的影响及补偿稳流装置

着重讨论补偿电流对核旋分量磁力仪在观测地磁场垂直分量中的影响,介绍了补偿稳流装置和在大连地磁台应用的效果。     

Temporal and spatial changes of dunes in the Changjiang (Yangtze) estuary,China

The response of subaqueous dunes to the variation of riverine sediment supply in an estuary is rarely reported. Five sets of field measurements of bed sediments and topography were made in the Changjiang estuary, China during dry and flood seasons for the past 9 years. Results show that dunes were mainly three-dimensional (3D) during flood seasons and two-dimensional (2D) dunes during dry seasons. Dunes were also much larger in flood seasons. Dunes mainly occurred within the main channels of the estuary although they extended 11.7 km seaward of their normal extent in the South Channel during dry seasons from 2002 to 2006. This extension of the dune field is likely due to a sharp decline of sediment discharge caused by large reservoirs such as Three Gorges.     

Spatiotemporal filtering of regional GNSS network’s position time series with missing data using principle component analysis

The existing spatiotemporal analysis methods suppose that the involved time series are complete and have the same data interval. However missing data inevitably occur in the position time series of Global Navigation Satellite Systems networks for many reasons. In this paper, we develop a modified principal component analysis to extract the Common Mode Error (CME) from the incomplete position time series. The principle of the proposed method is that a time series can be reproduced from its principle components. The method is equivalent to the method of Dong et al. (J Geophys Res 111:3405–3421, 2006) in case of no missing data in the time series and to the extended ‘stacking’ approach under the assumption of a uniformly spatial response. The new method is first applied to extract the CME from the position time series of the Crustal Movement Observation Network of China (CMONOC) over the period of 1999–2009 where the missing data occur in all stations with the different gaps. The results show that the CMEs are significant in CMONOC. The size of the first principle components for the North, East and Up coordinates are as large as 40, 41 and 37 % of total principle components and their spatial responses are not uniform. The minimum amplitudes of the first eigenvectors are only 41, 15 and 29 % for the North, East and Up coordinate components, respectively. The extracted CMEs of our method are close to the data filling method, and the Root Mean Squared error (RMS) values computed from the differences of maximum CMEs between two methods are only 0.31, 0.52 and 1.55 mm for North, East and Up coordinates, respectively. The RMS of the position time series is greatly reduced after filtering out the CMEs. The accuracies of the reconstructed missing data using the two methods are also comparable. To further comprehensively test the efficiency of our method, the repeated experiments are then carried out by randomly deleting different percentages of data at some stations. The results show that the CMEs can be extracted with high accuracy at the non missing-data epochs. And at the missing-data epochs, the accuracy of extracted CMEs has a strong dependence on the number of stations with missing data.     

A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods

Rapid flood mapping is critical for local authorities and emergency responders to identify areas in need of immediate attention. However, traditional data collection practices such as remote sensing and field surveying often fail to offer timely information during or right after a flooding event. Social media such as Twitter have emerged as a new data source for disaster management and flood mapping. Using the 2015 South Carolina floods as the study case, this paper introduces a novel approach to mapping the flood in near real time by leveraging Twitter data in geospatial processes. Specifically, in this study, we first analyzed the spatiotemporal patterns of flood-related tweets using quantitative methods to better understand how Twitter activity is related to flood phenomena. Then, a kernel-based flood mapping model was developed to map the flooding possibility for the study area based on the water height points derived from tweets and stream gauges. The identified patterns of Twitter activity were used to assign the weights of flood model parameters. The feasibility and accuracy of the model was evaluated by comparing the model output with official inundation maps. Results show that the proposed approach could provide a consistent and comparable estimation of the flood situation in near real time, which is essential for improving the situational awareness during a flooding event to support decision-making.