塔城盆地地下水氟分布特征及富集机理

塔城盆地位于新疆维吾尔自治区西北部,干旱少雨,蒸发强烈。但相对于新疆其他盆地,塔城盆地地下水水质相对较好,溶解性总固体和F^-含量相对较低。为解译这种差异及盆地内高氟地下水的成因,本文在对盆地地下水样品水化学组分系统分析的基础上,结合多种水文地质调查数据,利用数理统计、离子比及主成分分析等手段,研究高氟水的成因及其分布规律。结果表明:受气候以及地质等因素控制,研究区地下水氟浓度总体较低,高氟水主要分布于扇前洼地及盆地中部的低洼地带;受承压含水层的顶托补给,地下水氟浓度呈现出上高下低的垂向分带特征。研究区地下水径流途径短,水循环快,水岩相互作用时间较短,且山区地下水以深径流形式循环补给平原区深层承压含水层,再顶托补给潜水,避免了强烈的蒸发浓缩作用。山前洪积扇地下水氟富集主要受控于沉积地层中含氟矿物的风化溶解,而岩石风化、蒸发浓缩、阳离子交换、竞争吸附为平原区地下水氟浓度的主要影响因素。     

Landslide susceptibility zonation method based on C5.0 decision tree and K-means cluster algorithms to improve the efficiency of risk management

Machine learning algorithms are an important measure with which to perform landslide susceptibility assessments,but most studies use GIS-based classification methods to conduct susceptibility zonation.This study presents a machine learning approach based on the C5.0 decision tree(DT)model and the K-means cluster algorithm to produce a regional landslide susceptibility map.Yanchang County,a typical landslide-prone area located in northwestern China,was taken as the area of interest to introduce the proposed application procedure.A landslide inventory containing 82 landslides was prepared and subse-quently randomly partitioned into two subsets:training data(70％landslide pixels)and validation data(30％landslide pixels).Fourteen landslide influencing factors were considered in the input dataset and were used to calculate the landslide occurrence probability based on the C5.0 decision tree model.Susceptibility zonation was implemented according to the cut-off values calculated by the K-means clus-ter algorithm.The validation results of the model performance analysis showed that the AUC(area under the receiver operating characteristic(ROC)curve)of the proposed model was the highest,reaching 0.88,compared with traditional models(support vector machine(SVM)=0.85,Bayesian network(BN)=0.81,frequency ratio(FR)=0.75,weight of evidence(WOE)=0.76).The landslide frequency ratio and fre-quency density of the high susceptibility zones were 6.76/km2 and 0.88/km2,respectively,which were much higher than those of the low susceptibility zones.The top 20％interval of landslide occurrence probability contained 89％of the historical landslides but only accounted for 10.3％of the total area.Our results indicate that the distribution of high susceptibility zones was more focused without contain-ing more"stable"pixels.Therefore,the obtained susceptibility map is suitable for application to landslide risk management practices.     

降水对江浙沪PM_(2.5)的清除效率研究

为了解不同程度的降水对江浙沪地区大气PM_(2.5)的清除作用,搜集了2014—2016年该地区41个城市的降水和PM_(2.5)观测数据,通过对比2 a非降水和全时段PM_(2.5)平均浓度的差异,发现前者显著高于后者,说明降水对该地区PM_(2.5)具有清除作用。利用降水前与降水期间PM_(2.5)的浓度差异作为降水对PM_(2.5)的清除率,降水后与降水期间的浓度差异作为雨后浓度回升的增加率,分别研究了目标区域不同时期、不同降雨量以及不同降雨时长对PM_(2.5)的清除效果。结果显示:(1)与江浙沪南部地区不同的是,北部地区降水清除率与降水前浓度存在正相关,降水后浓度的增加与当地的排放量呈正相关。(2)当降水量为30 mm或者降水时长为36 h时,清除率增幅减缓,说明降水对PM_(2.5)的清除效率存在着阈值。     

94/220 GHz星载雷达双波长比对非球形冰晶云参数敏感性分析

利用不同形状冰晶的散射特性,获得了非球形冰晶云的94/220 GHz测云雷达双波长比,探讨了非球形冰晶云的双波长比与云内微物理参数的关系,分析了衰减前后的星载雷达反射率因子及双波长比的垂直廓线。结果表明:（1）双波长比可以反映小到0.1 mm中值尺度的冰粒子,对粒子总数、谱的形状参数不敏感,对粒子大小、形状、云衰减较敏感。（2）雷达灵敏度一定时,星载雷达可测云厚与雷达波长、冰含水量（IWC）的垂直分布、云厚及衰减有关;没有进行衰减订正时,双波长比和衰减有关,冰含水量越大,波长越短,衰减越大,双波长比最大值与可探测云厚有关。两部雷达可探测冰含水量为0.001—0.1 g/m3、厚2 km的冰云;当云厚5 km、冰含水量垂直分布在0.001—0.2 g/m3时,云厚的94%基本可以被220 GHz云雷达探测到。（3）如果两部雷达气象方程中用水的介电因子,测量回波强度应进行介电因子的订正后再计算双波长比。      

台风“摩羯”与“温比亚”环流中龙卷小尺度涡旋特征及可预警性分析

利用多普勒天气雷达探测资料,结合常规气象观测资料和天气实况及灾情调查,对2018年8月14日台风“摩羯”（1814）和8月19日台风“温比亚”（1818）产生龙卷的环境物理量及龙卷风暴强度结构特征进行了分析,对诱发龙卷和未诱发龙卷的小尺度气旋性涡旋特征进行了对比。结果表明：两次台风减弱低压东北象限是龙卷发生的关键区,低层高湿,强的低层垂直风切变和大的相对风暴螺旋度是关键物理量;龙卷出现时都伴有ΔV＞20.0 m·s-1的小尺度气旋性涡旋,且基本出现在2.0 km高度以下,但并不是所有这种低层小尺度气旋性涡旋都能诱发龙卷;以ΔV＞20.0 m·s-1为阈值,龙卷识别具有较高的命中率,识别准确率为31.8%,空报率为67.4%,漏报率为6.7%;约35.7%的龙卷没有识别时间提前量,半数龙卷几乎没有预警时间提前量。     

相控震源对地震信号信噪比的改善研究

当测区噪声很强,采用组合震源工作仍然不能满足信噪比要求时,为进一步提高地震勘探信噪比,引入能形成定向地震波的相控震源.针对水平层状介质模型,对组合震源、相控震源地震进行了数值模拟,并定量计算了采用4,8,15,20单元的组合与相控震源地震数据的信噪比,同组合地震相比,相控地震使来自不同反射层的反射波信号的信噪比平均提高了154～990 dB.结果表明,相控震源地震得到的反射波信号信噪比高于组合地震,并且随着激震器数目增加,相控震源合成地震记录中的反射波地震数据信噪比明显提高.     

关于井炮激发参数优化设计的思考

利用正演模拟的手段进行分析,对影响井炮激发的不同影响因素展开分析,结合对炸药特征阻抗与围岩的阻抗匹配系数、炸药几何耦合系数、激发药量、井深等方面进行分析,对井炮激发参数进行优化设计.通过优化设计,寻求有效提高地震子波品质的方法.结合实际的地震地质条件和地震勘探目标,设计最佳的井炮激发参数,提高地震勘探的效益.     

轴压比对中等高度LC结构剪力墙抗震性能的影响

轻骨料混凝土(L ight-we ight concrete,简称LC)剪力墙的轴压比是剪力墙抗震设计时一个重要的控制因素,直接关系到其延性性能。为使设计规程具有足够的理论依据,本文通过对六榀轻骨料混凝土剪力墙试件的试验,研究了在低周反复荷载作用下,着重考虑不同轴压比对中等高度有边框剪力墙的破坏形态和变形性能的影响效应,对其滞回特性进行分析,计算了各剪力墙的刚度退化率与延性系数,并将无竖缝剪力墙与带竖缝剪力墙的抗震性能进行了对比。     

震源机制和场地条件对近场强震地面运动特性的影响

将欧洲数百条地震动记录按震级、震中距、场地类别及震源机制等条件分类,通过对地震动记录加速度峰值比、加速度峰值衰减规律及反应谱比等内容的统计分析,研究了震源机制、场地类别等因素对竖向地震动的影响。结果表明,震源机制和场地类别这两种因素在近场区对竖向地震动特性有显著的影响。     

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.