基于树结构的数字遥感图像存储方法与应用

数字遥感图像一般是采用栅格结构来存储。图像中地物的结构特征是隐含其中的。该文从图像识别角度，重点研究了基于树结构的数字遥感图像存储方法。这种方法与遥感中常用的四又树等存储方法不同，它注重图像中地物本身的结构特征。通过树结构使像元按地物目标形成一个个的组合体。该文以机场跑道识别为例，介绍了基于树结构的数字遥感图像存储方法的应用以及将图像变为树的算法。     

三种地统计学图像纹理用于遥感图像分类的比较

该文运用LANDSAT TM数据对三种地统计学纹理量测方法用于图像分类的性能进行了比较和分析。研究发现，基于绝对值变差函数的纹理具有更好的性能。经典的变差函数和基于方根的变差函数的性能依赖于提取纹理时所用的窗口大小。     

华南地区小流域水污染控制规划初探

随着经济和城市化进程的快速发展，华南地区小流域水环境污染日趋严重，因此以规划的方法控制该区小流域水污染成为了一种重要的途径。从水环境污染控制规划的基本定义出发，重点分析了小流域水环境污染控制规划的一般规范及其在实施中存在的一些问题，并就这些问题提出了相应的对策，指出小流域进行水环境污染控制时必须加强环境意识，提高环境工作者素质，结合自身实际，走出一条既符合中国国情更符合华南地区小流域的水污染控制规划道路，为华南地区的持续，快速发展创造条件。     

图像处理技术在地质灾害监测中的应用

三维激光微位移监测技术是一种对地质灾害进行长期监测的新方法。该技术用激光作为光源，用CCD摄像器作为镜头，通过图象采集卡把视频信号采入计算机中，用与该仪器配套的专门软件对采集到的光斑图像进行处理，计算出光斑的三维中心坐标值，把该值和原点坐标值进行比较，就可以算出灾害体滑动的距离。该系统可以对灾害体进行长期、非接触监测，监测时间、监测频率可以任意设定。文章概括介绍该监测的原理，各个组成部分，重点介绍软件部分，即采集的视频信号进入计算机后的图像处理过程和数据库结构。该监测系统已在三块地区试运行了3个月，效果较好。     

声波扫描成像在灾害防治及环境保护中的应用

利用超声波扫描成像测井技术，在地质灾害的预防与环境保护中可以检测由于采矿而形成的冒落带、裂隙带、离层带、洞穴及软弱层的存在状况及部位，测定基岩风化蚀变程度及深度，在地质灾害与环境保护的治理时可以监测治理的质量，提供了直观精确可靠的基础资料。     

MATLAB及其在重力基点网平差中的应用

MATLAB作为最优秀的数学软件之一，在国内外工程技术界产生愈来愈大的影响，其强大的数据计算和图形显示功能尤为突出。复杂的物探重力基点网平差计算通过用MATLAB的矩阵计算功能，变得简单易懂。而编制的MATLAB的M文件程序，计算快速准确。相信MATLAB在物探数据处理工作中将发挥重要的作用。     

岳阳及周边地区地震构造与地震活动性研究

陈述了岳阳市及周边地区的主要断裂带及其地震活动，归纳了发震部位。认为利用小震条带预报本区地震具有积极意义，并运用数理统计法预测了本区今后50及100年内的地震活动水平。     

Statistical tests for regional seismic phase characterizations

In seismic analysis some assumptions are often made aboutthe data, e.g. stationarity and Gaussianity. This is not obvious for all realseismic data. Here, we use statistical tests for characterization of regionalseismic data. We apply tests for stationarity, symmetry, linearity, andtime-reversibility. In the analysis we use twelve regional seismic events inFennoscandia recorded with the seismic small-aperture arrays NORESS,ARCESS and FINESA at hypocentral distances in the range from 160 to1580 km. For the tests we use the regional phases Pn, secondary P, Sn and Lg-phases and the preceding noise. Two of the eventsare explosions, two are rockbursts and eight are earthquakes. Theperformance and possibilities of using statistical tests based on bispectra, asa complementary tool for conventional analysis of seismic phases isdemonstrated. The preceding noise recorded before the first onset of theP-wave for the twelve events is tested to be spatially stationarybetween each channel within each array and temporal stationary in 21consecutive time windows of 3.2 sec each. Also, the preceding noise issymmetric and linear. The seismic phases defined by the symmetry test asnon-Gaussian (not symmetric) are all linear. This means a linear model canbe used to characterize both the noise and the phases. The first P-phase for the two explosions is characterized as non-Gaussian at thethree arrays. For all 36 possibly first P-phase arrivals at the three arraystations, 23 are non-Gaussian. The second P-phase is non-Gaussian at13 of 36 data records, the S-phase at eleven of 36 and the Lg-phase at nine of 36. For all the four phases more than 32 of possible36 are time-reversible.     

Identification of soil degradation during earthquake excitations by Bayesian inference

A Bayesian inference approach is introduced to identify soil degradation behaviours at four downhole array sites. The approach of inference is based on a parametric time‐varying infinite impulse response filter model. The approach is shown to be adaptive to the changes of filter parameters and noise amplitudes. Four sites, including the Lotung (Taiwan), Chiba (Japan), Garner Valley (California), and Treasure Island (California) sites with downhole seismic arrays are analysed. Our results show two major types of soil degradation behaviour: the well‐known strain‐dependent softening, and reduction in stiffness that is not instantaneously recoverable. It is also found that both types of soil degradation are more pronounced in sandy soils than in clayey soils. The mechanism for the second type of soil degradation is not yet clear to the authors and suggested to be further studied. Copyright © 2003 John Wiley & Sons, Ltd.     

大庆徐家围子地区深部致密砂砾岩气层识别

大庆徐家圈子地区深部致密砂砾岩储层的骨架成分除了含有石英、长石外，还含有许多酸性喷发岩，而且各种骨架成分的含量随深度变化比较大；复杂的岩性使得气的影响被岩性的影响所淹没，从而造成常规气层识别方法失效。针对这一问题，应用神经网络方法准确求取地层颗粒密度和泥质含量，然后对中子和密度测井值进行岩性影响校正和泥质影响校正，使得求出的中子和密度孔隙度仅受地层气的影响，最后将得到的中子和密度孔隙度进行对比，应用中子和密度孔隙度的差异识别气层。