煤炭地质勘查产业转型发展的思考与探讨

煤炭地质勘查工作关系国家能源安全,在国民经济发展中具有重要的战略地位。伴随煤炭行业绿色转型和新能源革命新形势,及时调整产业结构,向大地质、大资源、大生态方向拓展服务领域,为国家生态文明建设和煤炭产业绿色安全高效发展提供坚实的地质技术保障,成为煤炭地质勘查产业转型发展必然趋势和未来发展方向。结合煤田地质勘查工作发展历程及现状、发展趋势,对煤炭地质勘查产业转型发展方向进行深入剖析和探讨。     

Predicting rock size distribution in mine blasting using various novel soft computing models based on meta-heuristics and machine learning algorithms

Blasting is well-known as an effective method for fragmenting or moving rock in open-pit mines. To evaluate the quality of blasting, the size of rock distribution is used as a critical criterion in blasting operations. A high percentage of oversized rocks generated by blasting operations can lead to economic and environmental damage. Therefore, this study proposed four novel intelligent models to predict the size of rock distribution in mine blasting in order to optimize blasting parameters, as well as the efficiency of blasting operation in open mines. Accordingly, a nature-inspired algorithm (i.e., firefly algorithm – FFA) and different machine learning algorithms (i.e., gradient boosting machine (GBM), support vector machine (SVM), Gaussian process (GP), and artificial neural network (ANN)) were combined for this aim, abbreviated as FFA-GBM, FFA-SVM, FFA-GP, and FFA-ANN, respectively. Subsequently, predicted results from the abovementioned models were compared with each other using three statistical indicators (e.g., mean absolute error, root-mean-squared error, and correlation coefficient) and color intensity method. For developing and simulating the size of rock in blasting operations, 136 blasting events with their images were collected and analyzed by the Split-Desktop software. In which, 111 events were randomly selected for the development and optimization of the models. Subsequently, the remaining 25 blasting events were applied to confirm the accuracy of the proposed models. Herein, blast design parameters were regarded as input variables to predict the size of rock in blasting operations. Finally, the obtained results revealed that the FFA is a robust optimization algorithm for estimating rock fragmentation in bench blasting. Among the models developed in this study, FFA-GBM provided the highest accuracy in predicting the size of fragmented rocks. The other techniques (i.e., FFA-SVM, FFA-GP, and FFA-ANN) yielded lower computational stability and efficiency. Hence, the FFA-GBM model can be used as a powerful and precise soft computing tool that can be applied to practical engineering cases aiming to improve the quality of blasting and rock fragmentation.     

一次极端寒潮天气过程等熵位涡分析

利用2016年1月1日至31日的FNL资料,对一次极端寒潮天气过程进行了等熵位涡分析。结果表明:高位涡主体由极涡分裂而来,前面低位涡区的阻挡与后侧低位涡大气的北上加强了位涡的经向交换,高位涡空气不断由极地向南输送,使得高位涡主体不断加强维持。高位涡在由北向南移动的同时,也由对流层顶向下输送。此次寒潮过程主要有3股冷空气由上而下发展,位置均在高空急流轴的北侧,最南端的一股下沉气流最旺盛,这是其与高空急流相互作用的结果。强盛的冷空气下沉使得寒潮影响范围触及我国华南地区。随着高位涡的向南向下传输,一方面引起对流层中高层低涡系统迅速发展,当它移到中国东部地区时,东亚大槽迅速加深,使槽后强冷空气迅速向南爆发;另一方面,在高位涡输送的过程中,其后侧有强烈的下沉运动,使得地面冷高压快速发展,导致强寒潮天气的爆发。     

太湖蓝藻水华形成的适宜气象指标

根据2005—2017年卫星遥感反演的太湖蓝藻水华信息,利用区域气象观测数据分析各类气象因子对太湖蓝藻深水华形成的影响,结果表明:①在日平均气温为0～35℃区间内会出现蓝藻聚集现象,其中最适宜气温区间为15.1～35℃,该区间内累计出现蓝藻水华的面积占比达90%、出现大面积蓝藻水华占比达93%;②在卫星观测到蓝藻前6h,平均风速为0.2～4.8m/s区间内能观测到蓝藻水华,其中最适宜的平均风速区间为0.5～3.4m/s,该区间内蓝藻水华累计出现次数占比达94.7%,大面积蓝藻水华主要出现在平均风速2.0m/s的情况下,占比89%;③降水总体上呈负效应,但在观测到有大面积蓝藻水华的情况下,前24h有小雨(10mm)的情形与总降水次数之比达88%,说明小雨的适度扰动有利于形成大面积蓝藻水华;④日照充足有利于蓝藻生长,但并非蓝藻水华形成的必要条件。在此基础上建立的多元回归综合气象指数模型,拟合结果较好,通过了0.001的显著性检验。     

百色气候变化特征及城市适应策略建议

利用气候观测数据分析表明,近60a来,百色出现了与全球气候变化背景一致的增暖变化,主要变化特征有:气温明显升高,年降水日数减少,冬季降水量增多,年日照时数减少;高温和干旱频率高,20世纪90年代以来暴雨日数偏多。与广西平均气候变化状况相比,百色的气候变化程度风险低于广西平均水平。预估到本世纪中期,百色气温仍将缓慢升高,干旱和强降水的强度可能加剧。建议科学规划,统筹协调,科学评估城市气候承载力,开展城市适应气候变化风险管理,提升生态气候环境监测及自然灾害预警应急能力,建设生态气候宜居城市。     

新疆乌什6.2级地震的中期预测

介绍了利用地倾斜资料和中国地壳运动网络工程GPS复测资料对新疆地区震情进行跟踪研究的基本依据、方法和过程,对2005年2月15日发生的乌什Ms6.2地震给出了较好的年度预测意见。     

北京市蔬菜和菜地土壤砷含量及其健康风险分析

通过对北京市蔬菜和菜地土壤砷含量状况进行大规模调查,研究了蔬菜和土壤砷含量及其健康风险,并筛选出抗砷污染能力强的蔬菜品种。根据蔬菜的消费量并兼顾品种多样性的原则,在北京市规模化蔬菜栽培基地 (采集蔬菜及土壤样品) 和蔬菜批发市场 (蔬菜样品) 共采集93种蔬菜400多份蔬菜样品和39个土壤样品系统地分析其砷含量。研究发现：北京市菜地土壤砷含量范围和平均含量分别为4.44~25.3和9.40 mg/kg,明显超过北京市土壤砷背景值;北京市蔬菜砷含量最高达0.479 mg/kg,平均含量为0.028 mg/kg,均未超过《食品中砷限量卫生标准》(GB4810-94) 的限量值,但其中有2个蔬菜样品 (萝卜和大蒜) 砷含量超过WHO/FAO制订的限量标准。北京市蔬菜砷含量在东偏北方向 (偏转11^o-30^o) 存在明显的"U"型趋势分布。裸露地蔬菜砷含量显著高于设施蔬菜。油菜、萝卜、小白菜、大葱、芥菜、黄瓜、大白菜和甘蓝抗砷污染能力较弱,而辣椒、云架豆、冬瓜、茄子、菠菜、西红柿、芹菜等抗砷污染能力则较强。北京市居民从蔬菜中摄入砷平均值为0.016 mg/(人·d),蔬菜砷对北京市部分人群存在一定的健康风险。     

Design of high precision horizontal control network for large-scale hydropower project

IntroductionThe design of high precision horizontal controlnetwork for large-scale hydropower project toconstruct control network and measure deforma-tion of monitoring network.It can be also divid-ed into horizontal control network and verticalcontrol ne…     

Kinematic source model for simulation of near-fault ground motion field using explicit finite element method

1 Introduction The prediction of broadband near-fault ground motion is an important aspect of seismic risk analysis, and is essential for the evaluation of urban seismic safety. Many methodologies are used to model the near-fault ground motions, such as the Green function method, ?nite element (FE) / ?nite difference (FD) method and a hybrid method (Aagaard, 1999; Oprsal and Zahradnik, 2002; Oprsal et al., 2003 a and b). In the hybrid method, the FE / FD method is used to model the low fr…     

基于兴趣点（POI）大数据的东北城市空间结构分析

以东北三省36个城市为研究区,利用 400余万条兴趣点行业分类大数据,采用核密度估计、标准差椭圆、区位熵等方法分析城市空间结构及其行业构成机制。研究发现,东北城市要素显著集聚,城市内部空间结构呈现集中团块型、分散组合型、线型、放射型特征以及多元复合类型;第二产业和房地产业呈现郊区化特征,各行业发展方向与东北大区域的经济轴线一致,多数行业尚未形成专业化功能区;第二产业与房地产业的离心发展对集中团块型城市的塑造作用不强,但对分散组合型城市空间结构的贡献较大。线型城市通常兼具多中心特征,放射型城市各行业仍偏向于向心聚集。