Advancements and best practices for analysis and correlation of tephra and cryptotephra in ice

Geochemical analysis of fine grained (<20 μm) tephra found in ice cores is inherently difficult, due to the typically low number and small size of available particles. Ice core tephra samples require specialized sample preparation techniques to maximize the amount of information that can be gained from these logistically limited samples that may provide important chronology to an ice record, as well as linking glacial, marine and terrestrial sediments. We have developed a flexible workflow for preparation of tephra and cryptotephra samples to allow accurate and robust geochemical fingerprinting, which is fundamental to tephrochronology. The samples can be prepared so that secondary electron imagery can be obtained for morphological characterization of the samples to ensure that the sample is tephra-bearing and then the sample can be further prepared for quantitative electron microprobe analysis using wavelength dispersive techniques (EMP-WDS), scanning electron microscopy with energy dispersive spectrometry (SEM-EDS), laser ablation inductively coupled mass spectrometry (LA-ICP-MS) or secondary ion mass spectrometry (SIMS). Some samples may be too small for typical instrumentation conditions to be used (i.e. 20 μm beam on the EMP) to analyze for geochemistry and we present other techniques that can be employed to obtain accurate, although less precise, geochemistry. Methods include analyzing unpolished tephra shards less than 5 μm in diameter with a 1 μm beam on an SEM; using the “broad beam overlap” EMP method on irregular particles less than 20 μm in diameter, and analyzing microlitic shards as well as aphyric shards using EMP to increase the number of analyzed shards in low abundance tephra layers. The methods presented are flexible enough to be employed in other geological environments (terrestrial, marine and glacial) which will help maximize and integrate multiple environments into the overall tephra framework.     

基于FFT-MA的随机介质建模方法

随机介质是描述地球介质小尺度非均匀性的有效模型,在地震散射波场分析、储层描述等领域具有广泛应用,快速准确的随机介质建模方法是开展相关研究的基础与前提.本文首次将FFT-MA（Fast Fourier Transform Moving Average）算法引入到随机介质建模研究中,分析了该方法与传统随机介质建模方法相比具有的优势,并提出了基于FFT-MA的非平稳随机介质建模方法.建模实验表明,与传统的基于谱分解定理的随机介质建模方法相比,基于FFT-MA的方法在空间域产生随机数序列,使随机数序列与结构参数分离,因此,随机数序列与所建模型存在空间上的对应关系,可以分区域建模和局部修改模型.在非平稳随机介质模型建模时,滑动窗口的大小可以根据自相关长度变化而变化,避免了每个采样点都建立一次完整大小的模型,提高了建模效率.因此,FFT-MA随机介质建模方法能准确构建满足自相关函数要求的平稳及非平稳随机介质模型,具有建模效率高、灵活、实用的优点.     

基于改进型BP网络的空间电场分类研究

空间电场信号异常识别是研究地震引起电离层扰动的重要内容。 将空间超低频电场电位数据看作随机数字信号, 以均值、 均方差、 偏度和峰度等四个指标进行描述, 采用“5·12”汶川大地震前空间超低频电场电位数据作为原始数据, 训练改进型BP神经网络, 建立了空间电场信号异常分类识别模型, 并以SOM神经网络进行验证。 计算结果显示, 空间超低频电场电位异常信号主要集中在5°~25°N, 88°~120°E之间的区域, 汶川大地震影响范围内的电离层扰动, 可能是汶川地震发生前引起的, 这与前人研究一致, 说明采用改进型BP神经网络异常分类识别模型研究地震引起的电离层扰动是可行的。     

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。     

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.     

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.     

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.