长春市地下水开发环境问题研究

本文根据近期地下水动态监测资料，结合地下水赋存的环境水文地质条件，分析论述了长春市由于不合理开发地下水资源而产生地下水水位下降，水量削减、水质污染、地面沉降等环境地质问题，提出了防治对策及建议。     

Karst Collapse Mechanism and Criterion for Its Stability

Karst collapse, caused by natural or artificial abstraction of groundwater, has been a focus of environmentalgeological problems for its ever-increasing hazardousness. The potential erosion theory and vacuum suction erosion theory, which reveal the origin of karst collapse macroscopically, are popularly accepted. However, a mathematic prediction criterion for karst collapse cannot be established only by these two theories. From a new perspective, this paper attempts to explain the microcosmic mechanism of karst collapse on the basis of these two theories. When the shear stress surpasses the shear strength of soil, a certain point or a certain plane in the unconsolidated soil covering karst caves will fail under the mechanical effects of water and air as well as its load-pressure, and with the increase of damaged points, a breaking plane appears and the soil on karst caves is completely damaged; as a result, the karst ground collapses. On the basis of the Mohr-Coulomb failure theory and previous studies,     

前馈神经网络隐性单调与泛化能力

应用前馈人工神经网络对广域单调的两组样本进行了模拟反演，引入单调前馈网络的概念对其权值和阈值定解问题和泛化能力进行了较诉研究。表明前馈人工神经网络是一个表达形式简单的复杂系统，其单调特征是隐性的，而且训练网络的成熟性对样本数量和样本内在规律性有一定依赖。强调了前馈人工神经网络的应用效果，指出单调与复合问题还需进一步深入研究。     

反演二维弹性波动方程组多参数的渐近展开法

采用摄动法对二维弹性波动方程组的系数反问题进行渐近近似,给出了反演系数的具体计算方法。用摄动法解微分方程组的系数反问题还是首次。此外,还采用了地球物理分层模型,这种分层模型便于更精细地反映地球不同圈层的物理特征,更接近于实际情况。同时对进一步提高反演分辨率、增加反演参数、对病态方程组如何克服不适定性等问题,作了具体阐述,具有重要的理论意义与应用价值。     

On the global solution of the N-body problem

In connection with the publication (Wang Qiu-Dong, 1991) the Poincaré type methods of obtaining the maximal solution of differential equations are discussed. In particular, it is shown that the Wang Qiu-Dong'sglobal solution of the N-body problem has been obtained in Babadzanjanz (1979). First the more general results on differential equations have been published in Babadzanjanz (1978).     

Conditioning mean steady state flow on hydraulic head and conductivity through geostatistical inversion

Nonlocal moment equations allow one to render deterministically optimum predictions of flow in randomly heterogeneous media and to assess predictive uncertainty conditional on measured values of medium properties. We present a geostatistical inverse algorithm for steady-state flow that makes it possible to further condition such predictions and assessments on measured values of hydraulic head (and/or flux). Our algorithm is based on recursive finite-element approximations of exact first and second conditional moment equations. Hydraulic conductivity is parameterized via universal kriging based on unknown values at pilot points and (optionally) measured values at other discrete locations. Optimum unbiased inverse estimates of natural log hydraulic conductivity, head and flux are obtained by minimizing a residual criterion using the Levenberg-Marquardt algorithm. We illustrate the method for superimposed mean uniform and convergent flows in a bounded two-dimensional domain. Our examples illustrate how conductivity and head data act separately or jointly to reduce parameter estimation errors and model predictive uncertainty.This work is supported in part by NSF/ITR Grant EAR-0110289. The first author was additionally supported by scholarships from CONACYT and Instituto de Investigaciones Electricas of Mexico. Additional support was provided by the European Commission under Contract EVK1-CT-1999-00041 (W-SAHaRA-Stochastic Analysis of Well Head Protection and Risk Assessment).     

A BME solution of the inverse problem for saturated groundwater flow

In most real-world hydrogeologic situations, natural heterogeneity and measurement errors introduce major sources of uncertainty in the solution of the inverse problem. The Bayesian Maximum Entropy (BME) method of modern geostatistics offers an efficient solution to the inverse problem by first assimilating various physical knowledge bases (hydrologic laws, water table elevation data, uncertain hydraulic resistivity measurements, etc.) and then producing robust estimates of the subsurface variables across space. We present specific methods for implementing the BME conceptual framework to solve an inverse problem involving Darcys law for subsurface flow. We illustrate one of these methods in the case of a synthetic one-dimensional case study concerned with the estimation of hydraulic resistivity conditioned on soft data and hydraulic head measurements. The BME framework processes the physical knowledge contained in Darcys law and generates accurate estimates of hydraulic resistivity across space. The optimal distribution of hard and soft data needed to minimize the associated estimation error at a specified sampling cost is determined. This work was supported by grants from the National Institute of Environmental Health Sciences (Grant no. 5 P42 ES05948 and P30ES10126), the National Aeronautics and Space Administration (Grant no. 60-00RFQ041), the Army Research Office (Grant no. DAAG55-98-1-0289), and the National Science Foundation under Agreement No. DMS-0112069.