应急演练中基于G-R关系的主余型地震余震震级模拟

在现有的日常地震演练过程中,与应急救援密切相关的地震余震信息产品较为缺乏,直接影响发震构造的判断以及影响场修正等关键环节。本文从计算机系统提供的均匀分布随机数出发,运用反函数法模拟生成余震序列,并进行系统检验,证实该方法产生的余震序列满足G-R频次关系。模拟生成的余震震级数据既有助于增强地震应急救援演练的现实性,也有助于丰富地震应急宣传产品,提升地震部门的履职能力。     

双稀疏字典和FISTA的地震数据去噪

地震数据的随机噪声去除是地震数据处理中的一项重要步骤,双稀疏字典提供了两层稀疏模型,比单层稀疏模型可以更好地去除噪声.该方法首先利用contourlet变换对地震数据进行稀疏表示,然后在contourlet域中使用快速迭代收缩阈值算法（fast iterative shrinkage-thresholding algorithm,FISTA）对初始字典系数进行更新,接着采用数据驱动紧标架（data-driven tight frame,DDTF）在contourlet域中得到DDTF字典并通过FISTA得到更新后的字典系数,最后通过DDTF字典和更新后的字典系数获得新的contourlet系数,并对新的contourlet系数进行硬阈值和contourlet反变换得到去噪后的数据.通过模拟数据和实际数据的实验证明：与固定基变换去噪方法相比,该方法可以自适应地对地震数据进行稀疏表示,在地震数据较为复杂时得到更高的信噪比;与字典学习去噪方法相比,该方法不仅拥有较快的去噪速度,而且克服了字典学习因为缺少先验约束造成瑕疵的缺点.     

一种基于高斯马尔可夫随机场的异常目标探测方法

利用高斯马尔可夫随机场模型描述像元的邻域相关性信息,并将这种邻域信息引入到局域异常探测器中,提出了一种顾及邻域信息的高光谱遥感影像局域异常目标探测算法。实验证明,该方法克服了传统异常探测方法仅仅利用光谱信息的不足,比经典的RX算法的探测效果更好,并且可以更有效地探测出大于一个像元的异常目标。     

基于随机森林模型的农业电子商务浅析——洛川苹果网络销售问题与对策

随着互联网产业的飞速发展,电子商务开始进入农业领域。以电子商务起步较早的"洛川苹果"作为研究对象进行调研。基于随机森林模型的决策树集成算法,对农业网络销售体系整体进行数据分析,模型构建,从问题表象出发挖掘其在不同部分的影响因子,最终基于影响因素解决问题,提出合理化建议:加强农村基础设施建设、健全农村公共服务体系以及完善农村电子商务培训制度等,因地制宜,推进农业电子商务的健康发展。     

浅析广安华蓥山区山水林田湖综合治理措施——以高顶山矿区为例

高顶山矿区位于广安华蓥市城区东南约5km处。长期的采矿活动,导致区内矿山地质环境问题突出,严重影响华蓥山地区人民的生命财产安全。矿山地质环境问题亟待解决。本文通过分析区内主要存在的矿山地质环境问题,提出通过矿山地质灾害、矿山土地恢复、矿山地形地貌景观恢复治理,河道综合整治、道路修复、生态保育、产业提升等措施;消除安全隐患,保障区内人民生命财产安全;改善生态环境,实现华蓥山地区生态环境全面恢复,生态环境质量提升,提高环境承载力,实现区内"山青、水秀、林美、田良"的目标。并对区内的产业转型升级进行了探讨,提出将高顶山矿区建设成具有科普和教育价值的旅游景观目的地;利用矿区独具特色工业人文景观和别致的自然景观,将高顶山矿区建设成集"科普、休闲、康养、户外、探秘"五大功能于一体的矿山公园,推动矿业经济转型升级,促进产业结构转型和经济社会可持续发展。     

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.     

Gully erosion spatial modelling: Role of machine learning algorithms in selection of the best controlling factors and modelling process

This investigation assessed the efficacy of 10 widely used machine learning algorithms (MLA) comprising the least absolute shrinkage and selection operator (LASSO), generalized linear model (GLM), stepwise generalized linear model (SGLM), elastic net (ENET), partial least square (PLS), ridge regression, support vector machine (SVM), classification and regression trees (CART), bagged CART, and random forest (RF) for gully erosion susceptibility mapping (GESM) in Iran. The location of 462 previously existing gully erosion sites were mapped through widespread field investigations, of which 70% (323) and 30% (139) of observations were arbitrarily divided for algorithm calibration and validation. Twelve controlling factors for gully erosion, namely, soil texture, annual mean rainfall, digital elevation model (DEM), drainage density, slope, lithology, topographic wetness index (TWI), distance from rivers, aspect, distance from roads, plan curvature, and profile curvature were ranked in terms of their importance using each MLA. The MLA were compared using a training dataset for gully erosion and statistical measures such as RMSE (root mean square error), MAE (mean absolute error), and R-squared. Based on the comparisons among MLA, the RF algorithm exhibited the minimum RMSE and MAE and the maximum value of R-squared, and was therefore selected as the best model. The variable importance evaluation using the RF model revealed that distance from rivers had the highest significance in influencing the occurrence of gully erosion whereas plan curvature had the least importance. According to the GESM generated using RF, most of the study area is predicted to have a low (53.72%) or moderate (29.65%) susceptibility to gully erosion, whereas only a small area is identified to have a high (12.56%) or very high (4.07%) susceptibility. The outcome generated by RF model is validated using the ROC (Receiver Operating Characteristics) curve approach, which returned an area under the curve (AUC) of 0.985, proving the excellent forecasting ability of the model. The GESM prepared using the RF algorithm can aid decision-makers in targeting remedial actions for minimizing the damage caused by gully erosion.     

Application of a cascading filter implemented using morphological filtering and time–frequency peak filtering for seismic signal enhancement

Inspired by the idea of the iterative time–frequency peak filtering, which applies time–frequency peak filtering several times to improve the ability of random noise reduction, this article proposes a new cascading filter implemented using mathematic morphological filtering and the time–frequency peak filtering, which we call here morphological time–frequency peak filtering for convenience. This new method will be used mainly for seismic signal enhancement and random noise reduction in which the advantages of the morphological algorithm in processing nonlinear signals and the time–frequency peak filtering in processing nonstationary signals are utilized. Structurally, the scheme of the proposed method adopts mathematic morphological operation to first preprocess the signal and then applies the time–frequency peak filtering method to ultimately extract the valid signal. Through experiments on synthetic seismic signals and field seismic data, this paper demonstrates that the morphological time–frequency peak filtering method is superior to the time–frequency peak filtering method and its iterative form in terms of valid signal enhancement and random noise reduction.     

复杂山地随机介质GMM-ULTI法射线追踪

对复杂山地介质的非均质性以及介质中地震波运动学特征进行深入研究,对于提高复杂山地区域地震勘探的效果有着重要的理论意义和实际价值.为了研究复杂山地非均质性和该介质中地震波的一些运动特性,提出了一种复杂山地随机介质的建模方法和一种新的射线追踪算法.与常规算法相比,复杂山地随机介质的生成方法采用更贴近实际介质特点的梯度介质作为背景介质,并在模型生成过程中加入地形修正步骤;新提出的GMM-ULTI射线追踪算法,充分融合群推进法、迎风思想、走时插值法的优势,采用先计算走时后追踪射线路径的两步策略完成射线追踪.算法分析与计算实例表明:复杂山地随机介质的生成方法能灵活、精细且更贴近实际地刻画复杂山地介质的非均质特点;新射线追踪算法兼顾精度和效率、能无条件稳定且灵活地适应复杂山地随机介质的特点;同时基于对几个模型试算结果的分析也得出了复杂山地随机介质中的地震波的一些传播规律.     

多相离散随机介质模型及其探地雷达波场特征研究

沥青混凝土是由骨料、沥青胶浆、空气按照一定的体积百分比混合而成的多相非匀质混合物,其骨料、沥青胶浆和空气的体积不等、形状各异、介电特性不同、空间位置随机分布,具有明显的多相、离散、随机介质特征.本文基于随机介质模型理论,(1)测量与统计了介电常数在典型沥青混凝土芯样空间上的随机分布统计特征;(2)估算了沥青混凝土介质的自相关函数及其特征参数(自相关长度、自相关角度等),确定其随机介质类型;(3)提出了量化约束下的多相离散随机介质建模算法,以混合型椭圆自相关函数为基础,构建了不同粗糙度因子的多相离散随机介质模型;(4)构建了不同空隙率的多相离散随机介质模型,正演模拟与对比分析了探地雷达波在均匀介质、连续型随机介质和多相离散随机介质中的传播特征.结果表明:多相离散随机介质模型不仅描述了沥青混凝土的多相、离散与空间随机分布统计特征,而且进一步描述了其各组成物质体积百分比,能更全面、准确地描述沥青混凝土的介质特征,同时也为描述其他类似材料或介质提供了新的方法和途径;在多相离散随机介质模型中,探地雷达波散射强烈,随机、无序传播的散射波相互叠加干涉,形成了明显的随机扰动和"噪声",致使异常体反射波扭曲变形、不连续,降低了探地雷达回波的信噪比和分辨率.研究探地雷达波的随机扰动特征与多相离散随机介质模型参数之间的关系,将为定量评价多相离散随机介质的属性参数提供参考和帮助.