On the estimation of a multi-resolution representation of the gravity field based on spherical harmonics and wavelets

The gravitational potential of the Earth is usually modeled by means of a series expansion in terms of spherical harmonics. However, the computation of the series coefficients requires preferably homogeneous distributed global data sets. Since one of the most important features of wavelet functions is the ability to localize both in the spatial and in the frequency domain, regional and local structures may be modeled by means of a spherical wavelet expansion. In general, applying wavelet theory a given input data set is decomposed into a certain number of frequency-dependent detail signals, which can be interpreted as the building blocks of a multi-resolution representation. On the other hand, there is no doubt that the low-frequency part of the geopotential can be modeled appropriately by means of spherical harmonics. Hence, the main idea of this paper is to derive a combined model consisting of an expansion in spherical harmonics for the low-frequency part and an expansion in spherical wavelets for the remaining medium and high-frequency parts of the gravity field. Furthermore, an appropriate parameter estimation procedure is outlined to solve for the unknown model coefficients.     

不同赋存态油裂解条件及油裂解型气源灶的正演和反演研究

油裂解型气源灶是一种特殊的气源灶,是优质生烃母质在成气过程中派生出的.烃源岩生成的油主要有3种赋存形式源内分散状液态烃、源外分散状液态烃和源外富集型液态烃.3种赋存状态液态烃的数量及分配比例受内因和外因多种因素控制,就排油率而言,有机碳含量分别为0.67%、0.62%和10.6%的泥岩、灰岩和油页岩,最大排油率分别为45%、55%和80%.原油与不同介质配样的生气动力学实验表明,不同介质条件下甲烷的生成活化能分布有差异,碳酸盐岩对油裂解条件影响最大,可大大降低其活化能,导致原油裂解热学条件降低,体现为油裂解温度的降低;泥岩次之,砂岩影响最小.碳酸盐岩、泥岩和砂岩对油的催化裂解作用依次减弱,不同介质条件下主生气期对应的Ro值纯原油1.5%～3.8%;碳酸盐岩中的分散原油1.2%～3.2%;泥岩中的分散原油1.3%～3.4%;砂岩中的分散原油1.4%～3.6%.油裂解型气源灶是一种中间体,可以直观看到的是原生气源灶和由此形成的气藏,而对油裂解型气源灶的赋存形式、分布范围、成气数量和储量规模等问题,只能通过正演和反演的研究去确定且相互映证.正演研究以塔里木盆地中下寒武统为例,原始生油量2 232.24×108t,剩余油量806.21×108t,油裂解气量106.95×1012m3.反演研究以川东北飞仙关组白云岩中油裂解气为例,圈定的古油藏面积约735 km2,古油藏原油数量45×108t,油裂解气量及油裂解气资源量分别为2.72×1012 m3和1.36×1012 m3.     

Bayesian analysis of marked stress release models for time-dependent hazard assessment in the western Gulf of Corinth

We consider point processes defined on the space–time domain which model physical processes characterized qualitatively by the gradual increase over time in some energy until a threshold is reached, after which, an event causing the loss of energy occurs. The risk function will, therefore, increase piecewise with sudden drops in correspondence to each event. This kind of behaviour is described by Reid's theory of elastic rebound in the earthquake generating process where the quantity that is accumulated is the strain energy or stress due to the relative movement of tectonic plates. The complexity and the intrinsic randomness of the phenomenon call for probabilistic models; in particular the stochastic translation of Reid's theory is given by stress release models. In this article we use such models to assess the time-dependent seismic hazard of the seismogenic zone of the Corinthos Gulf. For each event we consider the occurrence time and the magnitude, which is modelled by a probability distribution depending on the stress level present in the region at any instant. Hence we are dealing here with a marked point process. We perform the Bayesian analysis of this model by applying the stochastic simulation methods based on the generation of Markov chains, the so called Markov chain Monte Carlo (MCMC) methods, which allow one to reconcile the model's complexity with the computational burden of the inferential procedure. Stress release and Poisson models are compared on the basis of the Bayes factor.     

非线性模糊识别及其在海温异常检测中的应用

基于模糊推理和非线性模糊识别原理，讨论了从实际信号中检测识别主要影响因子，进而滤除干扰的方法，进行了相应的去噪试验。试验结果表明：由于模糊系统具有非线性、容错性和自适应学习等特性，因此，能够较为有效地辨识和检测出复杂非线性信号中的主要影响因子及其贡献大小。作为应用，研究了从观测资料中辩识El Niño/La Niña主要影响因子的诊断检测过程，并对 20世纪70年代以来出现的典型El Niño/La Niña事件中信风因子的影响作用进行了诊断检测和模糊识别，分析发现，70年代的几次ElNiño事件主要是由赤道西太平洋西风异常所触发，而80年代的几次El Niño事件（尤其是1982/1983年暖水事件）则主要是由赤道西太平洋与赤道东太平洋的信风活动异常共同所致，前者触发激励在先，后者巩固加强在后。     

Model selection in Bayesian framework to identify the best WorldView-2 based vegetation index in predicting green biomass of salt marshes in the northern Gulf of Mexico

Coastal wetlands are among the most productive ecosystems globally but have experienced dramatic degradation and loss within the past several decades. Vegetation biomass of coastal wetlands is not only the key component of blue carbon storage but also plays an important role in vertical accretion, important for maintaining these habitats under relative sea-level rise. Remote sensing offers a cost-effective approach to study vegetation biomass at a broad spatial scale. We developed statistical models to predict peak aboveground green biomass of Spartina alterniflora and Juncus roemerianus, two dominant species of salt marshes using WorldView-2 satellite imagery at the Grand Bay National Estuarine Research Reserve (NERR) on the Mississippi coast in the northern Gulf of Mexico. The model accounted for nested data structures in the sampled biomass, assimilated uncertainties from data, parameters and model structures, and helped determine the best vegetation index among a variety of commonly-used indices to predict aboveground green biomass. We developed a series of mixed-effects models, which included different combinations of fixed effect(s), random intercept, and random slope(s). The fixed effects were species and one of the 60 vegetation indices derived from a WorldView-2 image obtained on 6 October 2012. The random effect used was site. We implemented the models in a Bayesian framework and selected the best model structure and vegetation index based on minimum posterior predictive loss and deviance information criterion. The results showed that the best vegetation index to predict peak green biomass was the green chlorophyll index derived from the reflectance values of band 8 (near-infrared) and band 3 (green), and its effect on biomass prediction varied among sites. The inclusion of species as a fixed effect improved the model prediction. The study demonstrated the need to account for spatial dependence of data in developing a robust model, and the importance of the second WorldView-2 near-infrared band (860–1040 nm) in predicting aboveground green biomass for the Grand Bay NERR. The analysis using mixed-effects modeling in Bayesian inference which coherently combined field and WorldView-2 data with uncertainties accounted for provides a robust and nondestructive tool for resource managers to monitor the status of coastal wetlands at a high spatial resolution in a timely manner. Through this study, we hope to emphasize the importance of appropriately accounting for nested data structures using mixed-effects models and promote wider application of Bayesian inference to facilitate assimilation of uncertainties in remote sensing applications.     

Meteorological drought analysis using data-driven models for the Lakes District,Turkey

Abstract

Droughts may be classified as meteorological, hydrological or agricultural. When meteorological drought appears in a region, agricultural and hydrological droughts follow. In this study, the standardized precipitation index (SPI) was applied for meteorological drought analysis at nine stations located around the Lakes District, Turkey. Analyses were performed on 3-, 6-, 9- and 12-month-long data sets. The SPI drought classifications were modelled by Adaptive Neural-Based Fuzzy Inference System (ANFIS) and Fuzzy Logic, which has the advantage that, in contrast to most of the time series modelling techniques, it does not require the model structure to be known a priori. Comparison of the observed values and the modelling results shows a better agreement with SPI-12 and ANFIS models than with fuzzy logic models.     

井间地震在济南市小冶睦里煤矿采空区勘查中的应用

以济南市章丘区小冶睦里地区石炭-二叠纪含煤地层采空区为研究对象,采取资料收集、井间地震、钻探验证等方法手段,分析井间地震层析成像技术对采空区解译推断的有效性。结果表明,井间地震层析成像速度反演结果体现为高速分布的区域,推断为基岩完整区域;速度反演结果体现为低速分布的区域,推断为采煤采空区域。经与钻探工程比对,井间地震层析成像技术推断的采空区位置与钻探揭露深度较为吻合,表明井间地震层析成像技术在济南市章丘区小冶睦里煤矿采空区勘查中具有较高的可行性和实用性,为该地区同类型的采空区勘查提供了参考依据。     

Uncertainty quantization of meteorological input and model parameters for hydrological modelling using a Bayesian-based integrated approach

The traditional treatment of uncertainty in hydrological modelling primarily attributes it to model parameters, but rarely systematically considers meteorological input errors, especially in quantifying the impact of meteorological input errors on parameter uncertainty. This study developed a Bayesian-based integrated approach to quantitatively investigate uncertainties in meteorological inputs (precipitation and temperature) and model parameters as well as the variation in parameter uncertainty due to meteorological input errors. Additionally, we analysed the propagation from these uncertainties to runoff response in snowmelt and non-snowmelt periods. The applicability and advantages of this approach were presented by applying of the Soil and Water Assessment Tool to the Shitoukoumen Reservoir Catchment. Differential Evolution Adaptive Metropolis-Markov Chain Monte Carlo was applied for the straightforward Bayesian inference the uncertainties of meteorological inputs and model parameters. On this basis, multilevel factorial analysis technology was used to quantitatively investigate the specific impact of the model parameters' individual and interactive effects due to meteorological input errors. Finally, the impact of meteorological input errors and model parameter uncertainty on the model performance were analysed and quantified systematically. The results showed that the meteorological input errors could affect the random characteristics of multiple model parameters. Moreover, meteorological input errors could further affect the model parameters' effects on annual average runoff. Overall, the above results have significant implications in enhancing hydrological model to simulate/predict runoff and understanding hydrological processes during different periods.     

贝叶斯同化重力反演方法构建龙门山地壳密度模型

重力异常对地壳横向密度变化敏感,而无约束重力反演得到的密度模型其垂向分辨能力往往不理想.为了改善反演结果的垂向分辨率,本文参考已有先验分层模型,基于贝叶斯原理,提出了一种重震联合反演的新策略,可实现多种参考模型和复杂加权参数条件下的最大后验概率估计.理论模型测试结果表明,对于深度加权、多参考模型约束等多种问题,本文提出的新方法都可以稳健地获得最优化的模型参数.本文同时以中国地震科学台阵在龙门山地区及周边的一维接收函数分层模型和地震层析成像结果为参考,通过此方法对该区的重力异常进行反演,获得了该区的高精度三维密度结构,其水平分辨率优于10 km,垂直分辨率优于5 km.结合四条通过汶川和芦山地震震中的剖面进行分析后发现,反演得到的密度结构模型在过强震震源区位置横向变形显著,其揭示的分层地壳结构和变形模式与地表已知断裂构造具有相关性.本文提出的重震联合反演新策略,可为研究潜在强震风险源区的地壳结构和物性特征提供有效的科技方法支撑.