Channelized and hillslope sediment transport and the geomorphology of mountain belts

This paper uses the results of landscape evolution models and morphometric data from the Andes of northern Peru and the eastern Swiss Alps to illustrate how the ratio between sediment transport on hillslopes and in channels influences landscape and channel network morphologies and dynamics. The headwaters of fluvial- and debris-flow-dominated systems (channelized processes) are characterized by rough, high-relief, highly incised surfaces which contain a dense and hence a closely spaced channel network. Also, these systems tend to respond rapidly to modifications in external forcing (e.g., rock uplift and/or precipitation). This is the case because the high channel density results in a high bulk diffusivity. In contrast, headwaters where landsliding is an important sediment source are characterized by a low channel density and by rather straight and unstable channels. In addition, the topographies are generally smooth. The low channel density then results in a relatively low bulk diffusivity. As a consequence, response times are greater in headwaters of landslide-dominated systems than in highly dissected drainages. The Peruvian and Swiss case studies show how regional differences in climate and the litho-tectonic architecture potentially exert contrasting controls on the relative importance of channelized versus hillslope processes and thus on the overall geomorphometry. Specifically, the Peruvian example illustrates to what extent the storminess of climate has influenced production and transport of sediment on hillslopes and in channels, and how these differences are seen in the morphometry of the landscape. The Swiss example shows how the bedding orientation of the bedrock drives channelized and hillslope processes to contrasting extents, and how these differences are mirrored in the landscape. An erratum to this article can be found at     

裂隙网络模拟与REV尺度研究

研究了利用平面四边形模拟节理岩体三维裂隙网络的方法。在生成裂隙网络时,同时考虑结构面几何参数和力学参数的随机性。利用裂隙网络研究了确定岩体REV尺度的指标。在此基础上,编制了岩体裂隙网络模拟程序、裂隙网络图形输出程序和岩体REV尺度指标分析程序。通过算例分析,验证了研究成果及程序的合理性,并得出了岩体REV尺度约为各组裂隙中最大迹长期望值3~4倍的结论。研究成果为后续计算岩体等效力学参数奠定了基础。     

并行网络电法在煤层覆岩破坏监测中的应用

并行网络电法勘探现场观测数据表明,随着开采煤层上覆岩体破坏程度的加剧,其电阻率值呈上升趋势:当煤层上覆岩层视电阻率值相对于背景电阻率值增大1~2倍时为裂隙发育带;当增大3倍以上时为离层冒落带。对于全空间电阻率观测,其视电阻率值的变化主要是以突出异常为主。应用结果表明,利用并行网络电法进行煤层顶板视电阻率动态监测,可以直观反映“三带”的发育形态以及开采产生矿山压力的显现规律,为矿井安全提供有效的探测手段。     

基于模糊神经网络的义县萤石成矿预测

成矿预测的模糊综合评判方法中评价因素权重确定时具有较强的人为因素,模糊神经网络方法可以建立在无模型基础上,自动获取输入与输出的关系,在专家确定权重初始值的情况下,通过网络训练确定出最优权重值,这在一定程度上减少了人为因素的影响.应用该方法对义县萤石成矿远景区进行了评价,其评价结果与实际一致.表明该方法可推广到其他区域、其他矿种的预测,但应根据预测对象的实际情况建立控矿因素集和相应的隶属度函数.     

Neural network prediction of nitrate in groundwater of Harran Plain, Turkey

Monitoring groundwater quality by cost-effective techniques is important as the aquifers are vulnerable to contamination from the uncontrolled discharge of sewage, agricultural and industrial activities. Faulty planning and mismanagement of irrigation schemes are the principle reasons of groundwater quality deterioration. This study presents an artificial neural network (ANN) model predicting concentration of nitrate, the most common pollutant in shallow aquifers, in groundwater of Harran Plain. The samples from 24 observation wells were monthly analysed for 1 year. Nitrate was found in almost all groundwater samples to be significantly above the maximum allowable concentration of 50 mg/L, probably due to the excessive use of artificial fertilizers in intensive agricultural activities. Easily measurable parameters such as temperature, electrical conductivity, groundwater level and pH were used as input parameters in the ANN-based nitrate prediction. The best back-propagation (BP) algorithm and neuron numbers were determined for optimization of the model architecture. The Levenberg–Marquardt algorithm was selected as the best of 12 BP algorithms and optimal neuron number was determined as 25. The model tracked the experimental data very closely (R = 0.93). Hence, it is possible to manage groundwater resources in a more cost-effective and easier way with the proposed model application.     

三维地震属性参数在煤层厚度预测中的应用

依托“西部煤炭资源高精度三维地震勘探技术”项目工程,对晋城某矿南翼大巷东南区5m×5m×1ms的三维地震数据体,采用三维地震属性参数预测煤层厚度及其变化规律：沿3煤层、15煤层10ms时窗提取地震属性42种,根据钻孔资料,计算出煤厚与地震属性相关系数;从中优选出相关系数大于0．35的地震属性,其中3煤层9个、15煤层10个;然后进行地震属性互相关分析,优选出与3煤、15煤层厚度相关系数较大的4种属性,建立预测煤厚的BP神经网络模型,分别选取3煤层12个、15煤层4个实测数据作为学习训练和测试样本,以钻孔地震属性作为学习样本,对网络进行训练,最终获得全区煤层厚度。经与预留钻孔成果资料对比,预测精度较高,结果可用。     

VPN技术在煤炭地质档案网络服务中的应用

网络服务是煤炭地质档案业务发展的必由之路,为了解决在公共网络传输过程中的安全问题,本文介绍了一种虚拟专用网络技术,通过分析其应用于煤炭地质档案网络服务中的优势,为行业内外部的信息共享提供了一个新的思路。     

高位瓦斯抽放钻孔在降低综采工作面瓦斯浓度中的应用

林南仓属于低瓦斯矿井,但存在高瓦斯区域。煤层和采空区是瓦斯的主要来源,尤以采空涌出量大,给煤矿生产和安全带来了极大隐患。通过在1129综采工作面风道施工高位瓦斯孔,把钻孔打到采空区一侧煤层顶板以上冒落裂隙带内,用钻孔进行瓦斯抽放,使采空内的瓦斯通过裂隙带沿钻孔抽出,有效降低综采工作面瓦斯浓度,保证综采工作面正常回采和安全生产。     

改进BP神经网络在岩质边坡稳定性分析中的应用

使用MATLAB软件中的NNtool工具箱建立了一个通过添加动量改进的BP神经网络模型,用以分析岩质边坡的稳定性。通过对天津某工程的边坡进行分析来检验这个模型的可靠度。根据现场调查的实际情况分析,该边坡岩体比较破碎,因此文中选择了几个有代表性的岩石完整性参数作为输入层参数,通过收集的一些类似边坡的参数对所建立的神经网络模型进行训练,以保证此模型适合被用来分析该工程。结果证明,所建立的模型是可靠的,此种方法在分析岩质边坡稳定性的的实践中具有广泛的使用性。     

一种新的可靠度计算方法及其在工程中的应用

利用神经网络拟合极限状态方程、几何法计算结构可靠度,并推导了基于神经网络的几何法计算公式。最后比较了该法与其他可靠度计算方法的精度以及计算效率,并将该法应用于实际工程当中。