国产磁通门磁力仪稳定性分析

国产磁通门磁力仪(GM-3磁通门磁力仪)在全国各地磁台站推广应用,两年来,该仪器在武汉地震台工作比较稳定。本文对该仪器的基线值进行了多层线性回归分析。结果表明仪器性能比较稳定。     

大陆岩石圈有效弹性厚度的计算及其地质意义

大陆范围内 T e值有很大的范围,不同构造单元有不同的 T e值.T e值的大小与岩石圈的热结构(热年龄)、壳幔耦合等因素有关.同时 T e值和地壳厚度、地表有关矿产的分布、岩石圈地幔的物质组成等也有一定关系.     

Efficient collection of training data for sub-pixel land cover classification using neural networks

Artificial neural networks (ANNs) are a popular class of techniques for performing soft classifications of satellite images. They have successfully been applied for estimating crop areas through sub-pixel classification of medium to low resolution images. Before a network can be used for classification and estimation, however, it has to be trained. The collection of the reference area fractions needed to train an ANN is often both time-consuming and expensive. This study focuses on strategies for decreasing the efforts needed to collect the necessary reference data, without compromising the accuracy of the resulting area estimates. Two aspects were studied: the spatial sampling scheme (i) and the possibility for reusing trained networks in multiple consecutive seasons (ii). Belgium was chosen as the study area because of the vast amount of reference data available. Time series of monthly NDVI composites for both SPOT-VGT and MODIS were used as the network inputs. The results showed that accurate regional crop area estimation (R² > 80%) is possible using only 1% of the entire area for network training, provided that the training samples used are representative for the land use variability present in the study area. Limiting the training samples to a specific subset of the population, either geographically or thematically, significantly decreased the accuracy of the estimates. The results also indicate that the use of ANNs trained with data from one season to estimate area fractions in another season is not to be recommended. The interannual variability observed in the endmembers’ spectral signatures underlines the importance of using up-to-date training samples. It can thus be concluded that the representativeness of the training samples, both regarding the spatial and the temporal aspects, is an important issue in crop area estimation using ANNs that should not easily be ignored.     

都匀市河床地区岩土层工程地质特征

本文对都匀河床地区各岩土层工程地质特征进行了较为详细的阐述,为该区拟建物基础设计提供了较为可靠的工程地质资料。     

浅谈高分辨率卫星影像数据在城市污水行业的应用

随着遥感技术的发展和卫星影像分辨率的不断提高,高分辨率卫星影像广泛应用于各个行业。本文介绍了对卫星影像进行DOM(数字正射影像)制作、河涌排放口定位、污水等级监督分类及变化监测等在城市污水行业中的应用。     

基于AHP-信息量法的珠海市地质灾害风险评价

在充分分析珠海市地质灾害发育特征及分布规律的基础上,采用AHP-信息量结合的方法对珠海市地质灾害进行风险评价。首先选取7 类崩塌、滑坡评价因子和2 类地面沉降评价因子,利用ArcGIS 软件进行叠加运算形成易发性评价图,其次叠加降雨量、沉降速率形成危险性评价图,最后在危险性评价图的基础上叠加承灾体易损性形成地质灾害风险分区。结果表明,地质灾害风险评价过程中针对不同地质灾害类型采用不同评价模型和方法行之有效,且评价结果与实际相符。该成果可提高珠海市地质灾害群测群防效率。     

IMPACT OF DIABATIC PROCESSES IN AGCM ON 4-DIMENSIONAL VARIATIONAL DATA ASSIMILATION*

The impact of diabatic processes on 4-dimensional variational data assimilation (4D-Var) was studied using the 1995 version of NCEP's global spectral model with and without full physics.The adjoint was coded manually.A cost function measuring spectral errors of 6-hour forecasts to "observation" (the NCEP reanalysis data) was minimized using the L-BFGS (the limited memory quasi-Newton algorithm developed by Broyden,Fletcher,Goldfard and Shanno) for optimizing parameters and initial conditions.Minimization of the cost function constrained by an adiabatic version of the NCEP global model converged to a minimum with a significant amount of decrease in the value of the cost function.Minimization of the cost function using the diabatic model, however,failed after a few iterations due to discontinuities introduced by physical parameterizations.Examination of the convergence of the cost function in different spectral domains reveals that the large-scale flow is adjusted during the first 10 iterations,in which discontinuous diabatic parameterizations play very little role.The adjustment produced by the minimization gradually moves to relatively smaller scales between 10-20th iterations.During this transition period,discontinuities in the cost function produced by "on-off" switches in the physical parameterizations caused the cost function to stay in a shallow local minimum instead of continuously decreasing toward a deeper minimum. Next,a mixed 4D-Var scheme is tested in which large-scale flows are first adiabatically adjusted to a sufficient level,followed by a diabatic adjustment introduced after 10 to 20 iterations. The mixed 4D-Var produced a closer fit of analysis to observations,with 38% and 41% more decrease in the values of the cost function and the norm of gradient,respectively,than the standard diabatic 4D-Var,while the CPU time is reduced by 21%.The resulting optimal initial conditions improve the short-range forecast skills of 48-hour statistics.The detrimental effect of parameterization discontinuities on minimization was also reduced.     

北京大灰厂试验场重力变化特征及其解释

介绍了北京大灰厂试验场（简称京西试验场）的概况，对１９９３年３月以来重力重复观测资料进行了处理。计算了各期重力观测精度，结果显示１９９３年３月～１９９５年５月京西试验场存在一定重力变化。采用给定的模型反演了八宝山断层介质的密度变化，认为断层破碎带介质密度的变化可能是引起地表重力变化的主要因素     

Developing constitutive models from EPR‐based self‐learning finite element analysis

A constitutive model that captures the material behavior under a wide range of loading conditions is essential for simulating complex boundary value problems. In recent years, some attempts have been made to develop constitutive models for finite element analysis using self‐learning simulation (SelfSim). Self‐learning simulation is an inverse analysis technique that extracts material behavior from some boundary measurements (eg, load and displacement). In the heart of the self‐learning framework is a neural network which is used to train and develop a constitutive model that represents the material behavior. It is generally known that neural networks suffer from a number of drawbacks. This paper utilizes evolutionary polynomial regression (EPR) in the framework of SelfSim within an automation process which is coded in Matlab environment. EPR is a hybrid data mining technique that uses a combination of a genetic algorithm and the least square method to search for mathematical equations to represent the behavior of a system. Two strategies of material modeling have been considered in the SelfSim‐based finite element analysis. These include a total stress‐strain strategy applied to analysis of a truss structure using synthetic measurement data and an incremental stress‐strain strategy applied to simulation of triaxial tests using experimental data. The results show that effective and accurate constitutive models can be developed from the proposed EPR‐based self‐learning finite element method. The EPR‐based self‐learning FEM can provide accurate predictions to engineering problems. The main advantages of using EPR over neural network are highlighted.