Relative source time functions of seismic events at the Rudna copper mine, Poland: estimation of inversion uncertainties

The relative source time function (RSTF) inversion uncertainty assessment was performed for two small, mining-induced seismic events (M _W =2.9 and 3.0) that occurred at Rudna copper mine in Poland. The seismograms of selected events were recorded by the seismic net work composed of over 60, short-period, vertical seismometers, recording ground velocity, located in the distance ranging from 400 m up to 8 km from their hypocenters. The RSTFs were calculated for each seismic station independently, using the empirical Green’s function technique. The pseudospectral approximation of the sought RSTF by a finite sum of Gaussian kernel functions was used and the inverse problem was solved with the adaptive simulated annealing algorithm. Both methods improved the stability of the deconvolution procedure and physical correctness of the final solution in comparison to the classical deconvolution methods. To estimate the inversion uncertainties, classical Markov-chain Monte-Carlo techniques were used. The uncertainty analysis allows for improved selection of a priori data to the following inversion for kinematic rupture process.     

一种新的融合空间信息的半监督变化监测方法

基于改进的半监督FCM算法和马尔科夫随机场,提出了一种新的融合空间信息的半监督变化监测方法。首先将两幅遥感图像相减得到差值图像,并通过第4波段的差值给出了一种新的样本标记方法;然后,通过标记样本对差值图像利用半监督FCM算法进行聚类;最后,为了提高监测精度和去除聚类噪音点,利用像元点之间的空间邻接关系和马尔科夫随机场,通过更新后的隶属度矩阵得到了监测结果。为了验证本文方法的有效性,选取了两组TM遥感图像,监测了森林的变化。试验结果表明,改进的半监督FCM算法可以减少监测的漏检率,马尔科夫随机场方法可以很好地去除聚类过程中形成的噪声点,减少监测的虚检率。     

1955-2011年兰州沙尘暴、浮尘天气事件发生概率的Markov模型研究

依据甘肃兰州市1955-2010年沙尘暴、 浮尘天气事件年变化资料, 划分年强度分级, 应用Markov模型对其不同强度发生概率进行分析, 在此基础上预测和检验了2011年兰州沙尘暴浮尘天气事件的发生概率.结果显示: 2011年沙尘暴发生的特强年、 强烈年、 强发生年、 一般发生年和少发年的概率为,而事实上2011年发生沙尘暴0次,属于少发年, 符合预测结果; 2011年浮尘天气发生的特强年、 强烈年、 强发生年、 一般发生年和少发年的概率为,而事实上2011年发生浮尘9次,介于平常年和少发年范围, 符合预测结果.随着时间序列的延长, Markov模型可成为短期沙尘暴浮尘天气变化预测的有效途径.     

Modelling streamflow trends for a watershed with limited data: case of the Litani basin,Lebanon

Abstract

Streamflow variability in the Upper and Lower Litani basin, Lebanon was modelled as there is a lack of long-term measured runoff data. To simulate runoff and streamflow, daily rainfall was derived using a stochastic rainfall generation model and monthly rainfall data. Two distinct synthetic rainfall models were developed based on a two-part probabilistic distribution approach. The rainfall occurrence was described by a Markov chain process, while the rainfall distribution on wet days was represented by two different distributions (i.e. gamma and mixed exponential distributions). Both distributions yielded similar results. The rainfall data were then processed using water balance and routing models to generate daily and monthly streamflow. Compared with measured data, the model results were generally reasonable (mean errors ranging from 0.1 to 0.8?m³/s at select locations). Finally, the simulated monthly streamflow data were used to investigate discharge trends in the Litani basin during the 20th century using the Mann-Kendall and Sen slope nonparametric trend detection methods. A significant drying trend of the basin was detected, reaching a streamflow reduction of 0.8 and 0.7 m³/s per decade in January for the Upper and Lower basin, respectively.

Editor D. Koutsoyiannis; Associate editor Sheng Yue

Citation Ramadan, H.H., Beighley, R.E., and Ramamurthy, A.S., 2012. Modelling streamflow trends for a watershed with limited data: case of the Litani basin, Lebanon. Hydrological Sciences Journal, 57 (8), 1516–1529.     

Unsupervised change detection in multitemporal SAR images using MRF models

An unsupervised change-detection method that considers the spatial contextual information in a log-ratio difference image generated from multitemporal SAR images is proposed. A Markov random filed (MRF) model is particularly employed to exploit statistical spatial correlation of intensity levels among neighboring pixels. Under the assumption of the independency of pixels and mixed Gaussian distribution in the log-ratio difference image, a stochastic and iterative EM-MPM change-detection algorithm based on an MRF model is developed. The EM-MPM algorithm is based on a maximiser of posterior marginals (MPM) algorithm for image segmentation and an expectation-maximum (EM) algorithm for parameter estimation in a completely automatic way. The experiment results obtained on multitemporal ERS-2 SAR images show the effectiveness of the proposed method.     

黄河流域旅游生态安全的动态演变特征及驱动因素

科学测度与审视旅游生态安全对促进黄河流域旅游经济和生态环境协调可持续发展具有重要的理论和实践价值。从流域可持续发展视角,基于DPSIR系统模型构建黄河流域旅游生态安全理论框架和指标体系,采用改进TOPSIS法、核密度曲线、空间自相关、传统和空间马尔可夫链及面板分位数回归方法对其动态演化特征和驱动因素进行探析。结果表明：① 时间上,旅游生态安全均值波动下降后上升,呈收敛趋势,且有向“双核”形态演变的趋势。空间上,旅游生态安全值整体较低（0~0.55）,由2008年以风险级为主（占89.04%）转变为2017年以风险级和恶化级为主（分别占60.27%和31.51%）,且仍存在较大下行转移风险。② 动态演化特征上,不考虑邻域条件下,旅游生态安全风险级、敏感级转移具有“路径依赖”与“自身锁定”效应。而恶化级、临界安全级保持初始状态概率较小,且类型转移通常发生在相邻等级递次转移。邻域背景在其动态变化过程中起着重要作用,且其动态演化特征具有显著时空异质性。③ 影响因素方面,环境污染对低层级单元的旅游生态安全负向抑制作用较为明显,旅游负荷水平对较高层级地区旅游生态安全的正向边际效应略大,而旅游发展水平、经济发展水平等变量则对较低等级单元旅游生态安全的正向边际效应较大。     

Inverse trishear modeling of bedding dip data using Markov chain Monte Carlo methods

We present a method for fitting trishear models to surface profile data, by restoring bedding dip data and inverting for model parameters using a Markov chain Monte Carlo method. Trishear is a widely-used kinematic model for fault-propagation folds. It lacks an analytic solution, but a variety of data inversion techniques can be used to fit trishear models to data. Where the geometry of an entire folded bed is known, models can be tested by restoring the bed to its pre-folding orientation. When data include bedding attitudes, however, previous approaches have relied on computationally-intensive forward modeling. This paper presents an equation for the rate of change of dip in the trishear zone, which can be used to restore dips directly to their pre-folding values. The resulting error can be used to calculate a probability for each model, which allows solution by Markov chain Monte Carlo methods and inversion of datasets that combine dips and contact locations. These methods are tested using synthetic and real datasets. Results are used to approximate multimodal probability density functions and to estimate uncertainty in model parameters. The relative value of dips and contacts in constraining parameters and the effects of uncertainty in the data are investigated.     

1978-2011年间中国区域消费水平的时空演变

区域消费水平是经济发展的重要动力。本文以1978-2011年间我国各省区的人均消费品零售额表征区域消费水平,在我国区域消费水平差异测度的基础上,对区域消费水平差异的时间序列值进行突变点检测,进而将我国区域消费水平变化划分为1978-1986年和1986-2011年2个阶段。通过利用传统马尔科夫链及空间马尔科夫链方法,分别构建人均消费水平的非空间和空间马尔科夫转移概率矩阵,对1978-1986年与1986-2011年2个阶段的区域消费水平的时空格局演变特征进行分析。研究结果表明：（1）区域消费水平在2个研究时段内的变化均存在着“俱乐部趋同”现象,无论是低消费水平与高消费水平区域均沿着最初类型稳步演进。（2）区域消费水平变化受到来自邻域消费水平背景的影响,使得其趋同的过程在空间上不独立。（3）区域间消费水平相互作用呈现出显著的东西分异特征,东部地区多为区域自身消费水平与邻域消费水平同时向上转移的状态,西部地区多为向下转移的状态,而中部地区多为平稳分布,邻域消费水平的状态变化较大。     

遥感时间序列影像变化检测研究进展

同一区域、不同时期大量历史数据的积累,以及同一区域能够方便地获取高时间分辨率遥感数据,使遥感时间序列影像变化检测成为近年来遥感技术与应用的研究热点。本文系统总结和评述了当前遥感时间序列影像变化检测的相关研究进展和应用状况,在阐明遥感时间序列分析的意义,以及时间序列影像在变化检测中的优势的基础上,从非遥感领域时间序列变化检测方法出发,针对遥感时间序列影像变化检测的需求,明确和归纳了遥感时间序列变化检测的问题与类型,并对当前最新研究进行了综述,总结了各种方法的优点与不足,重点介绍了基于经验模态分解的遥感时间序列影像异常信息检测方法和基于隐马尔可夫模型的土地利用/覆盖变化检测方法,以期能够为相关研究提供参考。最后总结了该研究领域的发展趋势和存在问题,并对今后的研究工作和未来发展方向进行了展望。     

Modelling Potential Field Sources in the Gelibolu Peninsula (Western Turkey) Using a Markov Random Field Approach

In this study, a Markov Random Field (MRF) approach is used to locate source boundary positions which are difficult to identify from Bouguer gravity and magnetic maps. As a generalized form of Markov Chains, the MRF approach is an unsupervised statistical model based algorithm and is applied to the analysis of images, particularly in the detection of visual patterns or textures. Here, we present a dynamic programming based on the MRF approach for boundary detection of noisy and super-positioned potential anomalies, which are produced by various geological structures. In the MRF method, gravity and magnetic maps are considered as two-dimensional (2-D) images with a matrix composed of N₁ × N₂ pixels. Each pixel value of the matrix is optimized in real time with no a priori processing by using two parameter sets; average steering vector (θ) and quantization level (M). They carry information about the correlation of neighboring pixels and the locality of their connections. We have chosen MRF as a processing approach for geophysical data since it is an unsupervised, efficient model for image enhancement, border detection and separation of 2-D potential anomalies. The main benefit of MRF is that an average steering vector and a quantization level are enough in evaluation of the potential anomaly maps. We have compared the MRF method to noise implemented synthetic potential field anomalies. After satisfactory results were found, the method has been applied to gravity and magnetic anomaly maps of Gelibolu Peninsula in Western Turkey. Here, we have observed Anafartalar thrust fault and another parallel fault northwest of Anafartalar thrust fault. We have modeled a geological structure including a lateral fault, which results in a higher susceptibility and anomaly amplitude increment. We have shown that the MRF method is effective to detect the broad-scale geological structures in the Gelibolu Peninsula, and thus to delineate the complex tectonic structure of Gelibolu Peninsula.