Preliminary sensitivity study of eruption source parameters for operational volcanic ash cloud transport and dispersion models — A case study of the August 1992 eruption of the Crater Peak vent, Mount Spurr, Alaska

Ash clouds are one of the major hazards that result from volcanic eruptions. Once an eruption is reported, volcanic ash transport and dispersion (VATD) models are used to forecast the location of the ash cloud. These models require source parameters to describe the ash column for initialization. These parameters include: eruption cloud height and vertical distribution, particle size distribution, and start and end time of the eruption. Further, if downwind concentrations are needed, the eruption mass rate and/or volume of ash need to be known. Upon notification of an eruption, few constraints are typically available on many of these source parameters. Recently, scientists have defined classes of eruption types, each with a set of pre-defined eruption source parameters (ESP). We analyze the August 18, 1992 eruption of the Crater Peak vent at Mount Spurr, Alaska, which is the example case for the Medium Silicic eruption type. We have evaluated the sensitivity of two of the ESP – the grain size distribution (GSD) and the vertical distribution of ash – on the modeled ash cloud. HYSPLIT and Puff VATD models are used to simulate the ash clouds from the different sets of source parameters. We use satellite data, processed through the reverse absorption method, as reference for computing statistics that describe the modeled-to-observed comparison. With the grain size distribution, the three options chosen, (1) an estimated distribution based on past eruption studies, (2) a distribution with finer particles and (3) the National Oceanic and Atmospheric Administration HYSPLIT GSD, have little effect on the modeled ash cloud. For the initial vertical distribution, both linear (uniform concentration throughout the vertical column) and umbrella shapes were chosen. For HYSPLIT, the defined umbrella distribution (no ash below the umbrella), apparently underestimates the lower altitude portions of the ash cloud and as a result has a worse agreement with the satellite detected ash cloud compared to that with the linear vertical distribution for this particular eruption. The Puff model, with a Poisson function to represent the umbrella cloud, gave similar results as for a linear distribution, both having reasonable agreement with the satellite detected cloud. Further sensitivity studies of this eruption, as well as studies using the other source parameters, are needed.     

Time and space nonlocalities underlying fractional-derivative models: Distinction and literature review of field applications

We investigate the spatiotemporal nonlocality underlying fractional-derivative models as a possible explanation for regional-scale anomalous dispersion with heavy tails. Properties of four fractional-order advection–dispersion equation (fADE) models were analyzed and compared systematically, including the space fADEs with either maximally positive or negative skewness, the time fADE with a temporal fractional-derivative 0<γ<1$0<\gamma <1$, and the extension of the time fADE with 1<γ<2$1<\gamma <2$. Space fADEs describe the dependence of local concentration change on a wide range of spatial zones (i.e., the space nonlocality), while time fADEs describe dynamic mass exchange between mobile and multiple immobile phases and therefore record the temporal history of concentration “loading” (i.e., the time-nonlocality). We then applied the fADEs as models of anomalous dispersion to four extensively-studied, regional-scale, natural systems, including a hillslope composed of fractured soils, a river with simultaneous active flow zones and various dead-zones, a relatively homogeneous glaciofluvial aquifer dominated by stratified sand and gravel, and a highly heterogeneous alluvial aquifer containing both preferential flowpaths and abundant aquitards. We find that the anomalous dispersion observed at each site might not be characterized reasonably or sufficiently by previous studies. In particular, the use of the space fADE with less than maximally positive skewness implies a spatial dependence on downstream concentrations that may not be physically realistic for solute transport in watershed catchments and rivers (where the influence of dead-zones on solute transport can be described by a temporal, not spatial, fractional model). Field-scale transport studies show that large ranges of solute displacement can be described by a space nonlocal, fractional-derivative model, and long waiting times can be described efficiently by a time-nonlocal, fractional model. The unknown quantitative relationship between the nonlocal parameters and the heterogeneity, and the similarity in concentration profiles that are solutions to the different nonlocal transport models, all demonstrate the importance of distinguishing the representative nonlocality (time and/or space) for any given regional-scale anomalous dispersion process.     

基于ERDAS二次开发的震害图像信息提取方法研究

通过ERDAS IMAGINE软件二次开发,建立了一些实用的震害信息提取方法,利用这些方法开发的震害遥感信息提取的程序模块对1976年唐山地震影像、1999年台湾集集地震影像及Quickbid影像进行了处理,将得到的结果与目视解译结果进行对比,结果表明研究中提出的震害信息提取方法具有较好的实用性.     

基于遥感特征指数的地表水体自动提取技术研究

为了从海量遥感数据中有效地提取地表水体信息,并提高自动化提取效率,提出了一种基于遥感特征指数的地表水体自动提取方法.该方法选取归一化植被指数(NDVI)、归一化建筑指数(NDBI)和修正归一化水体指数(MNDWI)作为遥感特征指数集,并根据这些指数构建了遥感特征指数曲线.通过分析,发现地表水体在特征曲线中单调上升,植被在特征曲线中单调下降,而其它地物并无此特征.因此,根据这一规律,利用ERDAS IMAGINE软件建立了自动化提取模型.通过与其他方法对比,表明所建立的模型在精度和自动化方面都明显优于其他方法,可用于海量数据地表水体的自动提取.最后,在ARCGIS环境下,并通过决策树模型初步实现了地表水体的自动分类.     

利用自然电位与自然伽马测井曲线划分沉积相带及储层分布

自然电位、自然伽马测井曲线形态和特征与沉积相带及其储集砂体关系密切,它们对不同岩性地层特征响应十分敏感.利用自然伽马、自然电位同步减小的较大幅度评价识别渗砂层,指示划分水下分流河道主体沉积微相带;利用自然伽马、自然电位减小的幅度差评价识别低渗砂、致密砂层,指示划分水下分流河道堤泛(侧翼)沉积微相带;并以自然电位比自然伽马减小幅度的相对细小差异识别油水层.在安塞油田沿河湾地区长6自然伽马、自然电位曲线划分沉积相带及储层的应用中,建立了自然伽马、自然电位划分储层的下限标准,有效地评价了特低渗透储层沉积微相带及储层类型,提高和扩大了测井曲线的应用及效果.     

阵列声波处理技术的研究与应用

本文将根据在声学解释处理技术方面的研究成果,有侧重地介绍高分辨率阵列声波提取技术、频散校正技术、利用斯通利波同时反演地层渗透性和各向异性的阵列声波处理技术,同时还将详细的阐述这些特殊处理技术的基本原理和具体的实现办法,并通过一些实际应用的例子来证明这些特殊处理技术的有效性和先进性.     

微动勘查方法及其研究进展

本文简要回顾了微动的发展历史,并着重介绍了微动勘查方法的原理、方法及其最新进展,当今,城市物探中传统的地球物理勘探方法越来越受到限制,微动勘查方法以施工简单、成本低、费时短、对环境没有任何影响等优点,越发显现出其优势,由于横波速度对煤层陷落柱、采空区等速度异常区反映敏感,微动方法对探测陷落柱及解决村庄覆盖区的煤层构造问题,具有得天独厚的技术优势和应用前景.     

Bayesian methods for estimating multi-segment discharge rating curves

This study explores Bayesian methods for handling compound stage–discharge relationships, a problem which arises in many natural rivers. It is assumed: (1) the stage–discharge relationship in each rating curve segment is a power-law with a location parameter, or zero-plane displacement; (2) the segment transitions are abrupt and continuous; and (3) multiplicative measurement errors are of equal variance. The rating curve fitting procedure is then formulated as a piecewise regression problem where the number of segments and the associated changepoints are assumed unknown. Procedures are developed for describing both global and site-specific prior distributions for all rating curve parameters, including the changepoints. Estimation and uncertainty analysis is evaluated using Markov chain Monte Carlo simulation (MCMC) techniques. The first model explored accounts for parameter and model uncertainties in the interpolated area, i.e. within the range of available stage–discharge measurements. A second model is constructed in an attempt to include the uncertainty in extrapolation, which is necessary when the rating curve is used to estimate discharges beyond the highest or lowest measurement. This is done by assuming that the rate of changepoints both inside and outside the measured area follows a Poisson process. The theory is applied to actual data from Norwegian gauging stations. The MCMC solutions give results that appear sensible and useful for inferential purposes, though the latter model needs further efforts in order to obtain a more efficient simulation scheme.     

基于RAGA的指数曲线模型预测基桩承载力

预测挤扩支盘桩单桩极限承载力指数曲线模型的函数表达式是一个超定非线性方程。采用传统的最小二乘法对该模型参数进行回归处理时,往往由于计算复杂和人为因素的影响使预测结果存在较大的误差。为此,用实数编码加速遗传算法（RAGA）,对指数曲线模型的参数和理论极限承载力进行优化,并基于MATLAB环境下编写了模型的计算程序。结合工程实例,应用指数曲线模型对挤扩支盘桩静载荷试验实测数据进行了拟合分析。结果表明,基于RAGA的指数曲线优化模型能够更好地拟合实测数据和有效地预测单桩极限承载力,且基于RAGA的指数曲线优化模型具有求解速度快、计算精度和自动化程度高、人为干扰因素小、通用性强等优点。