山东月湖的沉积物分布特征及搬运趋势

1998年 11～ 12月和 1999年 8～ 9月各一个月对月湖进行的野外工作 ,共取得表层沉积物样品 131个 ,应用激光粒度仪并结合传统的筛分法对这些样品进行粒度分析 ,进行矩法计算获得粒度参数。采用Gao命名方法 ,将沉积物划分出 5种主要类型。利用Gao -Collins粒径趋势分析模型 ,计算该区沉积物粒径趋势所显示的沉积物净搬运方向 ,结果表明 ,沉积物从四周向湖中心搬运 ,同时显示湾顶的西部和北部、涨潮三角洲以及湖心等地貌单元是沉积的优势区域。     

Groundwater regime and calculation of yield response in North China Plain: a case study of Luancheng County in Hebei Province

The groundwater table has been declining at a rate of 0.65 m/yr in Luancheng County since large scale groundwater extraction carried out in the 1960s. The drop of precipitation, substantial increase in agricultural output, variations of crop planting structure and construction of water conservancy projects in the headwater area all tie up with the decline of the groundwater table. On the basis of analyzing the hydrogeological conditions and the water resources utilization of Luancheng County, a three-dimensional groundwater flow model was developed to simulate the county’s groundwater flow through finite-difference method using Visual Modflow software. We divide the research field into four parts after analyzing the hydrogeological condition. Based on parameter calibration and adjustment using measured data, the hydraulic conductivity and specific yield were simulated. Using the calibrated model, we analyze the agricultural water saving potentiality and its influence on the groundwater. The results are as follows: (1) if we decrease the amount of water extracted by 0.14xl0⁸ m³, the average groundwater table of the five observation wells in December will rise by 0.33 m; (2) if we decrease the water by 0.29x 10⁸m³, the average groundwater table of the five observation wells in December will rise by 0.64 m; and (3) if we increase the water by 0.29 x 10⁸m³, the average groundwater table of the five observation wells in December will decline by 0.45 m. So we can draw a conclusion that controlling the agricultural water use is an important way to prevent the decline of groundwater table.     

Use of Noise to Augment Training Data: A Neural Network Method of Mineral–Potential Mapping in Regions of Limited Known Deposit Examples

One of the main factors that affects the performance of MLP neural networks trained using the backpropagation algorithm in mineral-potential mapping isthe paucity of deposit relative to barren training patterns. To overcome this problem, random noise is added to the original training patterns in order to create additional synthetic deposit training data. Experiments on the effect of the number of deposits available for training in the Kalgoorlie Terrane orogenic gold province show that both the classification performance of a trained network and the quality of the resultant prospectivity map increasesignificantly with increased numbers of deposit patterns. Experiments are conducted to determine the optimum amount of noise using both uniform and normally distributed random noise. Through the addition of noise to the original deposit training data, the number of deposit training patterns is increased from approximately 50 to 1000. The percentage of correct classifications significantly improves for the independent test set as well as for deposit patterns in the test set. For example, using ±40% uniform random noise, the test-set classification performance increases from 67.9% and 68.0% to 72.8% and 77.1% (for test-set overall and test-set deposit patterns, respectively). Indices for the quality of the resultant prospectivity map, (i.e. D/A, D × (D/A), where D is the percentage of deposits and A is the percentage of the total area for the highest prospectivity map-class, and area under an ROC curve) also increase from 8.2, 105, 0.79 to 17.9, 226, 0.87, respectively. Increasing the size of the training-stop data set results in a further increase in classification performance to 73.5%, 77.4%, 14.7, 296, 0.87 for test-set overall and test-set deposit patterns, D/A, D × (D/A), and area under the ROC curve, respectively.     

大兴安岭浅覆盖区生态地质评价方法初探——以1∶25万区调呼中镇幅为例

通过野外调查，在收集统计大量基础地质、经济地质、环境地质、旅游地质、农(林)业地质等数据信息的基础上，在GIS技术指导下，采用定量定性相结合的半定量分析评价方法，对森林浅覆盖区生态地质现状及各类资源的开发利用潜力进行了综合评价。在研究和探讨森林浅覆盖区生态地质综合评价方法的基础上，提出了森林浅覆盖区生态地质评价模型，确定了综合参数，划分了生态地质类型。     

影响新生界"底含"发育的因素及含水层参数修正

分析了“底含”发育的主要影响因素，并以祁东煤矿为例，利用地下水动力学方法重新计算了“底含”的有关参数，对以往抽水试验参数进行修正，并成功地运用到邻近矿井。该研究表明，在富水性较强的古河道发育位置，要合理留设防水煤岩柱，才能保证煤矿安全生产。     

软土大变形固结计算中参数识别的遗传算法

含水量很高的饱和软土在外荷载作用下沉降很大,小变形分析误差太大,必须通过大变形非线性固结计算来模拟。遗传算法是一种全局优化和搜索的仿生算法。近年来随着工程领域中复杂的大规模非线性系统的出现,遗传算法日益得到青睐,目前已经广泛应用到各个领域中。本文对遗传算法做了改进,主要体现在杂交算子的选取和轮盘赌模型的模拟退火拉伸,并将其用于饱和软土的大变形固结分析,解决其中的关键参数识别问题。研究表明该方法是行之有效的,值得进一步研究探索。     

结构随机响应的一种改进的概率谱叠加方法

目前有一些关于多自由度结构体系的地震响应中响应峰值按降序(即由大到小的顺序)排列时的峰值统计性质的研究。本文对这些研究中的概率方法提出一些改进。由于这些改进，概率谱叠加方法在实际应用中就变得更准确和实用。本文以五层的结构模型为例，计算其在埃尔森特罗(1940)地震激励下的响应，并与时程分析结果进行比较。结果表明，改进的概率谱叠加方法能够谁确的计算较高几阶重要响应峰值的幅值。