岩石加速破裂行为的物理自相似律

掌握岩石变形破坏过程中体积膨胀点至峰值强度点之间加速破裂行为的演化规律,是实现地质灾害物理预测的关键.本文考虑裂纹张开和闭合两种情况,基于断裂力学建立了三轴应力作用下裂纹扩展临界尺度与等效应力的关系.对微元体破坏概率,分别采用以等效应力表达的Weibull分布函数和裂纹尺度分形函数,通过对比导出了形状参数m与裂纹分布分维D_f关系的表达式.一个有趣的发现是,岩石峰值强度点与体积膨胀点应变比仅与m或D_f有关.对岩石蠕变或准蠕变破坏,合理的m值范围为[1.0,4.0],在此范围内应变比近似为常数1.48,该常数是描述不同尺度岩石加速破裂规律的物理自相似常数.实例分析表明,基于岩石加速破裂规律构建的多锁固段脆性破裂理论,其适用性广,尤其在崩滑和大地震预测领域,具有良好应用前景.此外,本文给出了b值与m值定量关系,以解释b值的物理意义,并探讨将其用于地震预测的可行性.     

Data-based modelling approach for variable density flow and solute transport simulation in a coastal aquifer

Data-based models, namely artificial neural network (ANN), support vector machine (SVM), genetic programming (GP) and extreme learning machine (ELM), were developed to approximate three-dimensional, density-dependent flow and transport processes in a coastal aquifer. A simulation model, SEAWAT, was used to generate data required for the training and testing of the data-based models. Statistical analysis of the simulation results obtained by the four models show that the data-based models could simulate the complex salt water intrusion process successfully. The selected models were also compared based on their computational ability, and the results show that the ELM is the fastest technique, taking just 0.5 s to simulate the dataset; however, the SVM is the most accurate, with a Nash-Sutcliffe efficiency (NSE) ≥ 0.95 and correlation coefficient R ≥ 0.92 for all the wells. The root mean square error (RMSE) for the SVM is also significantly less, ranging from 12.28 to 77.61 mg/L.     

面向地震应急的山区人口百米格网分布——以麟游县为例

破坏性地震发生后,山区易发生滑坡、泥石流等地质灾害。精细化人口空间分布数据能够有效提升灾情评判精度,为应急救援决策提供参考依据。本文以陕西省麟游县为例,利用"天地图"高分辨率遥感影像解译大比例尺居民地数据,结合人口、行政区划等数据,利用GIS（地理信息系统）技术制作100 m×100 m格网人口分布数据。通过与陕西省地震局现有人口空间数据进行对比,发现百米格网人口分布数据可明显提升山区次生灾害影响人口计算结果的可靠性。     

面向目标的起伏地表组合震源延时参数计算方法

组合震源技术通过调节延迟激发时间和震源埋藏深度能够实现地震波场的定向传播,提高地震波的照明能量和地震数据信噪比.然而,目前国内外对基于特定目标的组合震源最佳聚焦方向的选取以及起伏地表组合震源定向理论的研究相对较少.在聚焦方向选取方面,目前基本是利用多种不同延时参数的组合震源分别进行试验,从而确定较好的组合震源延时参数.本文则利用爆炸反射面原理,在目的层界面上均匀布置震源同时激发地震波,能够产生垂直于地层界面传播的平面波,当初至波传播至地表时进行地表方向统计.根据互易性原理,以初至波传播方向的反方向为聚焦方向,激发产生的地震波主波束方向将垂直入射到目的层界面上,从而精确的计算出组合震源的最佳聚焦方向.在组合震源定向方面,目前主要是利用组合震源方向因子公式进行计算,但该方法要求震源布置在同一条直线上,并不能满足起伏地表的应用要求.本文以惠更斯-菲涅尔原理为理论基础,提出通过炮点向量在聚焦方向上的投影来确定组合震源传播至虚拟波前的走时,从而确定任意起伏地表组合震源的延迟激发时间.该方法不仅能够计算出沿给定地质模型的目的层界面垂直入射的组合震源聚焦方向,也能够确定任意起伏地表情况下形成沿该聚焦方向定向传播地震波场的组合震源延迟激发时间.因此,本文提出的面向目标的起伏地表组合震源延时参数计算方法对组合震源数据采集具有理论与实践意义.     

基于EC细网格产品在乌鲁木齐机场低能见度预测中的释用

利用2015—2018年乌鲁木齐机场航空例行天气报告(METAR报)、ECMWF(European Centre for Medium-Range Weather Forecasting)细网格数值预报产品对影响能见度的主要因子进行分析,提取与低能见度相关性高的物理量作为预报因子,采用SVM方法,分别基于Poly、RBF核函数建立乌鲁木齐机场未来21 h能见度预报模型。结果表明:(1)基于预报因子区间分类的SVM模型物理意义明确,试验结果较好;以RBF为函数建立的SVM模型(SVM-RBF)预报能力更好,其训练样本预测的TS评分0.84,准确率89.20%。(2)SVM-RBF模型的检验样本中,预报准确样本的预报误差整体偏小;在漏报样本中则有能见度越低、预报误差越大的特点,模型的振荡性明显。(3)结合NCEP/NCAR再分析资料研究SVM-RBF模型对天气过程的预报表现,发现模型对于特定天气形势下引发的低能见度天气,预报误差较小且预报提前量较大。     

将微粒群和支持向量机用于耕地驱动因子选择的研究

结合微粒群算法(PSO)具有执行速度快、受问题维数变化影响小的优点及支持向量机算法(SVM)结构风险最小化原理,构建了基于离散二进制微粒群(BPSO)与支持向量机的耕地驱动力因子选择方法,使用特征子集中确定的特征来训练支持向量回归机,用适应度函数来评价回归机的性能,指导BPSO的搜索。实验表明,该方法能有效地提取出耕地驱动因子的特征子集,从而降低了指标的维数,保留了关键信息,以获得知识的最小表达。     

中国地区水平地壳形变模型的建立

我国大陆板块是欧亚大陆板块的重要组成部分,其板块运动剧烈,地质构造复杂,是全球研究板块运动的重要地区。本文通过对各个块体分别建模和解算,得出结论:中国地区除东北块体相对于欧亚板块不动外,其他块体均有不同程度的形变运动,东部形变较小,西部形变较大;华北块体总体上以大约6mm/a的速率向东运动;华南块体以约5~8mm/a的速率向东南方向运动;新疆块体以约10~15mm/a的速率向北东方向运动;西藏块体也向北东方向运动,但运动速率从南到北逐步减少。     

Assessing model of highway slope stability based on optimized SVM

Considering the geological hazards attributed to the highway slope, using a common simple model cannot accurately assess the stability of the slope. First, principal component analysis (PCA) was conducted to extract the principal components of six factors (namely, bulk density, cohesion, internal friction angle, slope angle, slope height, and pore water pressure ratio) affecting the slope stability. Second, four principal components were adopted as input variables of the support vector machine (SVM) model optimized by genetic algorithm (GA). The output variable was slope stability. Lastly, the assessing model of highway slope stability based on PCA-GA-SVM is established. The maximal absolute error of the model is 0.0921 and the maximal relative error is 9.21% by comparing the assessment value and the practical value of the test sample. The above studies are conducive to enrich the assessing model of highway slope stability and provide some reference for highway slope engineering treatment.     

水井测温在地热普查中的应用

利用水井测温方法确定恒温层温度,以恒温层的温度值作为湖顶地热异常区的下限值,用内插法勾绘温度等值线图,高于该下限值范围的即为地热异常区。曹县庄寨地区的地热普查证明该方法是快速有效的。     

How do machine learning techniques help in increasing accuracy of landslide susceptibility maps?

Landslides are abundant in mountainous regions.They are responsible for substantial damages and losses in those areas.The A1 Highway,which is an important road in Algeria,was sometimes constructed in mountainous and/or semi-mountainous areas.Previous studies of landslide susceptibility mapping conducted near this road using statistical and expert methods have yielded ordinary results.In this research,we are interested in how do machine learning techniques help in increasing accuracy of landslide susceptibility maps in the vicinity of the A1 Highway corridor.To do this,an important section at Ain Bouziane(NE,Algeria) is chosen as a case study to evaluate the landslide susceptibility using three different machine learning methods,namely,random forest(RF),support vector machine(SVM),and boosted regression tree(BRT).First,an inventory map and nine input factors were prepared for landslide susceptibility mapping(LSM) analyses.The three models were constructed to find the most susceptible areas to this phenomenon.The results were assessed by calculating the receiver operating characteristic(ROC) curve,the standard error(Std.error),and the confidence interval(CI) at 95%.The RF model reached the highest predictive accuracy(AUC=97.2%) comparatively to the other models.The outcomes of this research proved that the obtained machine learning models had the ability to predict future landslide locations in this important road section.In addition,their application gives an improvement of the accuracy of LSMs near the road corridor.The machine learning models may become an important prediction tool that will identify landslide alleviation actions.