土石坝漫顶溃决模型

通过土石坝溃决的特征,建立了土石坝体溃决模型,并预测溃口流量过程和溃口拓展过程。模型溃口假定为梯形,且在坝体溃决过程中底宽和溃口边坡不发生变化。根据水量平衡和坝体物质守恒,模拟溃口的拓展过程;溃口水力过程用宽顶堰流表示,并使用经验公式计算溃口不同水头条件下的冲蚀量。针对这些差分方程为隐性格式,难以直接求解,提出了特殊的迭代解法,且较少的迭代次数即可收敛。通过模型对四川北川唐家山堰塞湖的溃决过程进行了反演,结果显示:坝体溃决相关参数包括溃决持续时间,溃决峰值流量和出现时刻、坝体最终几何形状以及堰塞湖溃决排出总水量,当计算参数选择合理时,预测值与实测值比较吻合。从模拟计算结果来看,模型预测具有一定的可行性,可以为堰塞坝的安全预警和应急处置提供数据支持。     

Real time earthquake forecasting in Italy

We have applied an earthquake clustering epidemic model to real time data at the Italian Earthquake Data Center operated by the Istituto Nazionale di Geofisica e Vulcanologia (INGV) for short-term forecasting of moderate and large earthquakes in Italy. In this epidemic-type model every earthquake is regarded, at the same time, as being triggered by previous events and triggering following earthquakes. The model uses earthquake data only, with no explicit use of tectonic, geologic, or geodetic information. The forecasts are displayed as time-dependent maps showing both the expected rate density of M_l ≥ 4.0 earthquakes and the probability of ground shaking exceeding Modified Mercalli Intensity VI (PGA ≥ 0.01 g) in an area of 100 × 100 km² around the zone of maximum expected rate density in the following 24 h. For testing purposes, the overall probability of occurrence of an M_l ≥ 4.5 earthquake in the same area of 100 × 100 km² is also estimated. The whole procedure is tested in real time, for internal use only, at the INGV Earthquake Data Center.Forecast verification procedures have been carried out in forward-retrospective way on the 2006–2007 INGV data set, making use of statistical tools as the Relative Operating Characteristics (ROC) diagrams. These procedures show that the clustering epidemic model performs up to several hundred times better than a simple random forecasting hypothesis. The seismic hazard modeling approach so developed, after a suitable period of testing and refinement, is expected to provide a useful contribution to real time earthquake hazard assessment, even with a possible practical application for decision making and public information.     

Characteristics of wet-snow avalanche activity: 20 years of observations from a high alpine valley (Dischma,Switzerland)

The occurrence of wet-snow avalanches is, in general, poorly understood. For 20 years (winters of 1975–1976 to 1994–1995), the avalanche activity has been observed in the Dischma valley near Davos (Eastern Swiss Alps). The study area comprises a large starting zone of north-easterly aspect (2,300 m a.s.l.) with several avalanche paths. We have analyzed the occurrence data in combination with meteorological and snowpack data collected at an elevation of 2,090 m a.s.l. During the 20-year observation period, almost 800 wet-snow avalanches were observed, about 4.5 times more loose snow avalanches than slab avalanches. Considering both types of avalanches jointly, snow depth, precipitation and air temperature showed the highest correlation with avalanche activity. Most loose snow avalanches occurred when air temperature was high and/or after a precipitation period. Slab avalanches occurrence was primarily related to warm air temperatures and snowpack properties such as the isothermal state and the existence of capillary barriers. Radiation did not show up as a significant variable. The results suggest that in a transitional snow climate wet-snow avalanches are, as dry snow avalanches, often related to precipitation events, and that wet slab instability strongly depends on snowpack properties in relation to warming of the snowpack and melt water production.     

Sand and gravel mining: effects on ground water resources in Hancock county,Maine, USA

Based on this preliminary study, existing sand and gravel mining regulations (in Maine, USA) can be inferred to provide some protection to water resources. Sand and gravel deposits are important natural resources that have dual uses: mining for construction material and pumping for drinking water. How the mining of sand and gravel affects aquifers and change aquifer vulnerability to contamination is not well documented. Mining regulations vary greatly by state and local jurisdiction. This study test metrics to measure the effectiveness of mining regulations. The sand and gravel aquifer system studied is covered with former and active gravel pits to nearly 25% of its areal extent. Data from homeowner interviews and field measurements found scant evidence of changes in water quantity. Water quality analyses collected from springs, streams, ponds and wells indicate that the aquifer was vulnerable to contamination by chloride and nitrate. However, water quality changes can not be related directly to mining activities.     

西藏驱龙斑岩型铜(钼) 矿床矿化特征及远景预测

驱龙矿床位于西藏冈底斯成矿带东段, 成矿条件优越。该矿床为一典型斑岩型矿床, 其铜 (钼) 矿化与喜山期黑云母花岗闪长斑岩、石英二长花岗斑岩时空及成因关系密切, 具典型的细脉浸染型矿化特征, 围岩蚀变发育且具面型分带现象。区域构造演化、成岩成矿时代表明, 冈底斯成矿带内的斑岩型矿化发生于印度与亚洲板块的后碰撞阶段挤压向伸展的转换期。综合地质、化探与遥感等找矿信息, 预测驱龙矿床找矿潜力巨大, 驱龙东段是进一步勘查工作的首选靶区, 矿体剥蚀程度低, 具较大埋深( > 1 000 m) 。强调斑岩型矿化的同时, 应注意岩体接触带附近矽卡岩型以及不同围岩裂隙中的脉型多金属矿的找矿工作。     

地质灾害与降雨雨型的关系研究

地质灾害的发生与降雨历时、降雨量及降雨雨型有密切的关系,不同雨型的降雨诱发地质灾害的诱发机制具有明显的差异性。本文通过收集的降雨实况与地质灾害反馈资料,将诱发地质灾害的降雨雨型分为3种类型:台风降雨、持续强降雨和局地暴雨。研究表明,台风降雨型降雨诱发地质灾害具有即雨即滑的特点,即地质灾害的发生与降雨在时间上具有较好的对应关系;地质灾害发生空间位置与台风运移轨迹也基本一致。多为群发型地质灾害,灾害规模较小,一般为表层或浅层滑坡、崩塌。持续强降雨型降雨诱发地质灾害,在强降雨过程中地质灾害具有同步发生的特点;在降雨强度不大但连续降雨过程中,地质灾害具有一定的滞后效应;在地质灾害大规模发生后,诱发新的地质灾害雨量阈值提高。局地暴雨型降雨诱发的地质灾害往往集中发生在出现局地暴雨的当日当地。     

中国钼矿资源供需预测

本文在对我国钼矿成矿地质条件、资源储量、开发利用现状、产业与市场进行分析的基础上,分析了我国钼矿资源的特点及产业发展趋势。利用矿产品部门消费法和回归分析法,对我国2020年、2025年和2030年钼矿资源性产品的需求量(金属量,下同)和产量(能)进行了预测。预测结果表明:2020年、2025年和2030年我国钼矿消费量分别为:8.26~8.3万吨、8.77~9.0万吨、9.22~9.5万吨;产量(能)分别为:12万吨、10万吨和9.5万吨。研究表明:我国钼矿资源储量大,以斑岩型矿床为主,品位低(0.056%),高品位(Mo≥0.12%)的保有资源储量占比不足1/5,单矿石可选性好;在一段时间内仍将维持供大于求的格局,钼精矿产能利用率持续降低。为此,本文提出通过实施严格控制钼矿开采总量、提高环保准入门槛,以及去产能化等对策建议,解决我国钼矿资源产业中存在的问题。本文研究结论对我国钼矿资源产业可持续发展战略制定等具有重要的参考价值。     

海湾水环境影响的约瑟夫-新德那模式预测

对应用约瑟夫-新德那模式进行海湾水环境影响预测中存在的问题作了初步分析,海域水流、混合深度值的选取及特殊污染物的存在等是影响预测结果准确性的主要因素。潮流憩流状态时,污染物的迁移扩散主要取决于污染物的混合速度,污染物基本上以近似于半圆形呈π弧度的角度向水体混合扩散,预测结果与污染物实际迁移扩散状况大致吻合;潮流非憩流或大部分时间为非憩流状态时,海域水流对污染物的迁移扩散影响较大,混合角度与海流流速成反比,污染物向水体扩散的实际混合角度要小于π弧度,预测的结果与污染物实际迁移扩散状况存在较大差距。海域水流对污染物迁移距离产生很大影响,水流愈大,污染物迁移的距离就愈远。混合深度的取值也直接关系到预测结果的准确性,若按可能受影响范围海域的平均水深值作为混合深度取值,其预测结果要比水深取2m时的预测结果更接近真实状况。对已达标排放的废水进行影响预测时,结果与实际情况接近,而对高浓度石油类的废水含量的影响预测却与实际情况差异极大。     

Computer model of wind, waves, and longshore currents during a coastal storm

A mathematical model has been developed to forecast or hindcast wind, waves, and longshore currents during the passage of a coastal storm. Storm intensity is a function of the barometric pressure gradient which is modeled by rotating an inverted normal curve around the center of an ellipse. The length and orientation of the major and minor axes of the ellipse control the size and shape of the storm. The path of the storm is determined by a sequence of storm positions for the hindcast mode, and by interpolated positions assuming constant speed and direction for the forecast mode. The site location, shoreline orientation, and nearshore bottom slope provide input data for the shore position. The geostrophic wind speed and direction at the shore site are computed from the latitude and barometric pressure gradient. The geostrophic wind is converted into surface wind speed and direction by applying corrections for frictional effects over land and sea. The surface wind speed and direction, effective fetch, and wind duration are used to compute wave period, breaker height, and breaker angle at the shore site. The longshore current velocity is computed as a function of wave period, breaker height and angle, and nearshore slope. The model was tested by comparing observed data for several coastal locations with predicted values for wind speed, wave period and height, and longshore current velocity. Forecasts were made for actual storms and for hypothetical circular and elliptical storms.     

Univariate and multivariate stochastic modeling of the chemical composition of iron ores in Northern Goa, India

Chemical components such as SiO ₂,TiO ₂,MnO, P ₂ O ₅,and especially Fe ₂ O ₃ of the iron ores of Bicholim Mine, Northern Goa, have been determined for lateral and vertical sections of the mine at equal intervals of 3 and 1 m, respectively, so as to form the spatial (time) series. Univariate stationary models of the type Autoregressive moving average—ARMA (p, q)—were established for each series on the basis of statistical analyses of their auto (acf) and partial auto (pacf) correlation functions. These models were used for forecasting assay values at different lead distances from any pivot. Principles of parsimony simplified all of the candidate ARMA (p, q) models to pure AR (p) models, and the univariate forecasts were significantly improved by multivariate stochastic forecasts.