声频应力波在锚杆锚固状态检测中的应用

研究了声频应力波法快速普查检测锚杆锚固状态的原理及评价分级标准,并将它成功地应用于潘一矿锚杆施工质量的检测,得出了利用声频应力波快速普查检测锚杆锚固质量的方法是切实可行和有效的。     

On the forward-in-time upstream advection scheme for non-uniform and time-dependent flow

Summary The classical forward-in-time upstream advection scheme for uniform flow field has been extended to include non-uniform and time-dependent advective flow. This generalised scheme is described in one dimension for an advective flow which varies both in time and in space. The classical upstream advection scheme is only first-order accurate both in time and in space if the advective flow is not uniform. Higherorder accuracy in both time and space, however, can be easily obtained in the generalised scheme.This generalised scheme with third-order accuracy is applied to the one-dimensional inviscid Burgers equation (socalled self-advection problem), two-dimensional steady flow, and to a time-split shallow water equation model. The results are compared with those obtained from the Takacs' (1985) scheme and from a standard third-order semi-Lagrangian scheme, and also with those obtained from the fourth-order Lax-Wendroff scheme of Crowley (1968) in the time-split shallow water equation model. It is shown that the generalised scheme performs as well as, but is more efficient than, the standard semi-Lagrangian scheme with same order. It is much more accurate than the Takacs' scheme which has large dissipation errors, especially for the flow with strong deformation. In contrast, the generalised scheme has very weak dissipation and has much better dispersion and shapeconserving properties. Although the fourth-order Lax-Wendroff scheme has higher accuracy and can give more accurate numerical solutions for uniform advective flow or solid rotational flow (Crowley, 1968), it is inferior to the generalised third-order scheme for non-uniform flow with strong deformation or large spatial gradients. This generalised scheme, therefore, has considerable application potential in different numerical models, especially for the models using time-split algorithms.With 8 Figures     

Bottom Simulating Reflector and Gas Seepage in Okinawa Trough： Evidence of Gas Hydrate in an Active Back-Arc Basin

To look for gas hydrate, 22 multi-channel and 3 single-channel seismic lines on the East China Sea (ECS) shelf slope and at the bottom of the Okinawa Trough were examined. It was found that there was indeed bottom simulating reflector (BSR) occurrence, but it is very rare. Besides several BSRs, a gas seepage was also found. As shown by the data, both the BSR and gas seepage are all related with local geological structures, such as mud diapir, anticline, and fault-controlled graben-like structure. However, similar structural "anomalies" are quite common in the tectonically very active Okinawa Trough region, but very few of them have developed BSR or gas seepage. The article points out that the main reason is probably the low concentration of organic carbon of the sediment in this area. It was speculated that the rare occurrence of gas hydrates in this region is governed by structure-controlled fluid flow. Numerous faults and fractures form a network of high-permeability channels in the sediment and highly fractured igneous basement to allow fluid circulation and ventilation. Fluid flow in this tectonic environment is driven primarily by thermal buoyancy and takes place on a wide range of spatial scales. The fluid flow may play two roles to facilitate hydrate formation:to help gather enough methane into a small area and to modulate the thermal regime.     

Determination of kolmogorov entropy of chaotic attractor included in one—dimensional time series of meteorological data

The 1970-1985 day to day averaged pressure dataset of Shanghai and the extension method in phase space are used to calculate the correlation dimension D and the second-order Renyi entropy K2 of the approximation of Kolmogorov’s entropy, the fractional dimension D = 7.7~7.9 and the positive value K2 ≈ 0.1 are obtained. This shows that the attractor for the short-term weather evolution in the monsoon region of China exhibits a chaotic mo-tion. The estimate of K2 yields a predictable time scale of about ten days. This result is in agreement with that ob-tained earlier by the dynamic-statistical approach.The effects of the lag time τ on the estimate of D and K2 are investigated. The results show that D and K2 are convergent with respect to τ. The day to day averaged pressure series used in this paper are treated for the extensive phase space with τ = 5, the coordinate components are independent of each other; therefore, the dynamical character quantities of the system are stable and reliable.     

气候变化背景下珠江流域极端洪水事件的变化趋势

根据1951-2010年珠江流域23个典型断面流量资料,用P-III型分布曲线拟合洪水系列进行频率计算,分析了珠江流域极端洪水事件的变化趋势。结果表明：1980年以来,珠江流域极端洪水事件发生的频次明显增加,尤其是自1990年以来增加趋势显著;1981-2010年较1951-1980年珠江流域约70%典型断面极端洪水事件呈增加趋势,主要分布在西江、北江、粤西;而近30%的典型断面呈减少趋势,主要分布在东江和桂南。     

人力资本的空间迁移模式与影响因素分析——以南京市高校毕业生为例

知识经济时代人才是决定区域经济竞争力的重要资源,各地政府发起的人才争夺战日益白热化,中央政府也一再强调应当引导合理、公正、畅通、有序的人才社会性流动.基于南京市高校《毕业生就业质量报告》和一手问卷调查数据,进行毕业生择校迁移与择业迁移模式并采用多项逻辑斯蒂回归模型探究其影响因素研究.研究表明:①南京市高校毕业生中近84...     

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.     

Automatic mapping of lunar landforms using DEM-derived geomorphometric parameters

Developing approaches to automate the analysis of the massive amounts of data sent back from the Moon will generate significant benefits for the field of lunar geomorphology. In this paper, we outline an automated method for mapping lunar landforms that is based on digital terrain analysis. An iterative self-organizing (ISO) cluster unsupervised classification enables the automatic mapping of landforms via a series of input raster bands that utilize six geomorphometric parameters. These parameters divide landforms into a number of spatially extended, topographically homogeneous segments that exhibit similar terrain attributes and neighborhood properties. To illustrate the applicability of our approach, we apply it to three representative test sites on the Moon, automatically presenting our results as a thematic landform map. We also quantitatively evaluated this approach using a series of confusion matrices, achieving overall accuracies as high as 83.34% and Kappa coefficients (K) as high as 0.77. An immediate version of our algorithm can also be applied for automatically mapping large-scale lunar landforms and for the quantitative comparison of lunar surface morphologies.     

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.