松散体边坡灾害自组织临界性及工程应用研究

我国的松散体边坡灾害频繁。为了解松散体边坡灾害的共性，从整体上认识其规律性信息，文章采用多种形式的非均匀沙堆作为松散体边坡灾害的概化模型，通过多组实验研究其演化行为规律。自组织临界性是解释具有无序的、非线性复杂系统行为特征的理论之一，分形和闪烁噪声是其空间与时间结构的特征。实验发现几种概化模型表现出自组织临界性。分析滑坡、崩塌及泥石流等松散体边坡灾害实例，也发现其呈现自组织临界性特征。研究表明幂律特征及自组织临界性是许多松散体边坡灾害的共性。进一步模拟真实的松散体边坡的组成特征和某些防治工程措施，进行对比实验，表明不同工程措施的防治作用。最后结合挡、锚、喷、排等工程防治措施，探讨了自组织临界理论对松散体边坡灾害防治的指导作用。     

RESEARCH ON SELF-MEDIA INFORMATION MINING MODEL FOR EARTHQUAKE EMERGENCY RESPONSE

From the events of catastrophic natural disasters that have occurred in recent years, it can be found that social media platforms are increasingly becoming the most important and most convenient way for the general public to timely release and obtain information on disasters. The information obtained from such platforms contains a large amount of information in the form of texts, pictures, etc. that record the current situation of the disaster. And it also has characteristics of high efficiency and high spatial distribution to serve the rapid emergency after the earthquake. In this paper, we firstly make a statistical analysis of 32 689 pieces of historical disaster data acquired from 5 earthquakes with obvious characteristics, such as post-earthquake disaster events, user's expression habits and so on, and adopts cross-validation method. Then information classification system which includes seven first-level categories and more than 50 second-level categories is constructed. The information classification system and evaluation system of crisis degree for post-earthquake emergency response are constructed both using cross-validation method. The former is referred to the thought of existing classification basis and the experience knowledge of several emergency experts. Based on the five indicators of subject word, action word, degree word, time and position measurement, an evaluation system of critically with four levels of severity, moderate intensity, mildness and others was constructed. Considering the sparse features of self-media information and the large difference in the number of training sets, a naive Bayes model for information classification is trained based on the classification system and evaluation system. Its accuracy rate is 73.6%. At the same time, the classification method of feature fusion of machine learning model and semantic calculation model is used to evaluate the criticality of the disaster information. The accuracy rate of the evaluation model is 89.2%, higher than 85.2% of the semantic computing model and 77% of the naive Bayesian model. The evaluation model has combined the advantages of semantic computing method which can evaluate all index features with machine learning method which has high classification efficiency and accuracy. The thresholds for classification between mild and moderate intensity, moderate intensity and severe intensity were 15.2 and 27.39. The model realized in this paper can crawl, classify and evaluate the disaster information in the media in real time after an earthquake, and realizes mining of a small amount of critical and important information from the massive self-media information, thus, to assist in earthquake intensity rapid reporting and accurate rescue. Finally, taking the Jiuzhaigou earthquake on August 8, 2017 as an example, 17 432 pieces of data were crawled in real time within 48 hours after the earthquake. At the same time, based on ArcGIS, the mining information is visualized in time and space, and the availability of the data is evaluated from two perspectives of earthquake intensity quick reporting and accurate rescue after the earthquake. The disaster information of Jiuzhaigou County in the earthquake area is obviously more than that of the non-earthquake area in terms of quantity and emergency degree. The results show that the self-media information with high spatial distribution can effectively find the severer disaster grade area after the earthquake, shorten the time of earthquake intensity prediction, effectively classify and extract information, provide real-time information for precise rescue, and improve the efficiency of emergency response after the earthquake.     

Constraints on the frequency–magnitude relation and maximum magnitudes in the UK from observed seismicity and glacio-isostatic recovery rates

Earthquake populations have recently been shown to have many similarities with critical-point phenomena, with fractal scaling of source sizes (energy or seismic moment) corresponding to the observed Gutenberg–Richter (G–R) frequency–magnitude law holding at low magnitudes. At high magnitudes, the form of the distribution depends on the seismic moment release rate M˙ and the maximum magnitude m _max . The G–R law requires a sharp truncation at an absolute maximum magnitude for finite M˙ . In contrast, the gamma distribution has an exponential tail which allows a soft or 'credible' maximum to be determined by negligible contribution to the total seismic moment release. Here we apply both distributions to seismic hazard in the mainland UK and its immediate continental shelf, constrained by a mixture of instrumental, historical and neotectonic data. Tectonic moment release rates for the seismogenic part of the lithosphere are calculated from a flexural-plate model for glacio-isostatic recovery, constrained by vertical deformation rates from tide-gauge and geomorphological data. Earthquake focal mechanisms in the UK show near-vertical strike-slip faulting, with implied directions of maximum compressive stress approximately in the NNW–SSE direction, consistent with the tectonic model. Maximum magnitudes are found to be in the range 6.3–7.5 for the G–R law, or 7.0–8.2 m _L for the gamma distribution, which compare with a maximum observed in the time period of interest of 6.1 m _L . The upper bounds are conservative estimates, based on 100 per cent seismic release of the observed vertical neotectonic deformation. Glacio-isostatic recovery is predominantly an elastic rather than a seismic process, so the true value of m _max is likely to be nearer the lower end of the quoted range.     

大型矿床和成矿区(带)在混沌边缘

笔者将复杂性科学和矿床地质学相结合提出了一种新的金属成矿理论———“金属成矿动力系统的复杂性与自组织临界性”,并应用这一理论研究扬子古陆周缘四大成矿区（带）的矿床成因与成矿规律,发现“大型矿床和成矿区（带）在混沌边缘”。文章在概述“金属成矿动力系统的复杂性与自组织临界性”的理论纲要与噪声、混沌和混沌边缘基本概念的基础上,提出地质成矿系统在混沌边缘的四条判定准则：Ⅰ．自组织临界性的标志———广义地质学（地质学、地球物理学、地球化学）场（温度、流速、浓度、压力等场）的场量之时空幂律分布———及其基本属性：（１）长程时空关联与连通性及时空分形结构;（２）崩塌动力学;（３）“元胞自动机”的动力学机制;（４）自组织临界性涌现于“混沌边缘”,并具有最大的复杂性、演化性和创新性。Ⅱ．地质成矿过程向时间混沌及地质成矿系统向时空混沌的演化。Ⅲ．岩浆和热液“孤子”、“孤波”与“相干结构”以及其它弱混沌“拟序结构”。Ⅳ．超临界地质流体参与地质成矿作用。笔者进一步提出金属成矿动力系统的复杂性与自组织临界性的三大基础理论：（１）地质成矿过程的非线性动力学———应用非线性动力学理论研究矿床和矿集区形成的动力学机制;（２）“地质成矿作用?     

天池火山三期浮岩气孔结构局域化特征与喷发动力学初探

本文在长白山天池火山野外地质工作基础上,对天池火山黄色浮岩（50 000年前大喷发）、灰白色浮岩（千年大喷发）和黑色浮岩（八卦庙期喷发）等三期空降浮岩开展了扫描电子显微镜高精度结构分析以及浮岩气孔结构的局域化定量研究。三期浮岩洛伦茨分析表明：黄色浮岩和黑色浮岩气孔均一化程度基本一致,而灰白色浮岩均一化程度更高,反映了千年大喷发在岩浆演化程度和喷发动力学行为上的特殊性,推测可能与幔源岩浆注入地壳岩浆房的动力过程有关。气孔大小幂律分布以及气孔形态分形特征的复杂性分析表明,50 000年前大喷发的浮岩幂指数和分形维数最高,揭示了其自组织临界态程度最高,对外界环境触发最为敏感,而另外两期浮岩幂指数和分形维数呈现出标准的幂律衰减,据此我们推测三期喷发可能均处在天池火山系统逐渐稳态恢复的大周期中。     

海洋溢油合成孔径雷达图像特征提取及其关键度分析

就SAR图像溢油检测的方法论而言,用于识别溢油和疑似现象的定性或定量的统计特征量选择,通常是任意的。对于不同的分类模型,所选用的特征量也不尽相同。主要是进行海洋SAR图像特征提取及其关键度分析。其目的是将"最小距离"判别法应用于海上溢油和疑似溢油的识别研究。首先,针对海洋SAR图像溢油检测常用的特征量,进行冗余处理;然后,引入关键系数,定量地研究特征量的关键度,提取显著特征量;藉以构造一个多维的特征矢量空间,以适于最小距离判别法在特征矢量空间中进行溢油和疑似溢油的识别研究。     

Quantifying fluvial non linearity and finding self organized criticality? Insights from simulations of river basin evolution

A numerical study was undertaken to investigate non linearity and the potential for self-organized criticality (SOC) in the evolution of river basins. Twenty-three simulations were carried out, using the authors' CAESAR landscape evolution model, in which the magnitude of storm events, variability of storm events, sediment heterogeneity, sources of sediment supply, and catchment morphology are systematically varied to evaluate their importance as possible drivers for non linear behavior and SOC.Temporal fluctuations in simulated sediment yield show notable non linear behavior. Storm magnitude and occurrence of landslides appear to have little impact on variability of the sediment yield, when compared to the impacts of sediment heterogeneity, rainfall variability and catchment morphology. Particularly, it appears that the non linearity of sediment yields results from the manner in which the catchment processes the variable rainfall, rather than just the rainfall variability itself.The variations in sediment yield show a power law magnitude–frequency distribution, which is a possible, but inconclusive, indicator of SOC. However, several other, more qualitative arguments can be made to support the case for SOC in these simulations. Specifically, we identify the nature of the critical state and suggest two cascade mechanisms by which the system can organize itself around this critical state. Combined, these arguments indicate that simulated evolution of river basins indeed exhibits SOC, at least with respect to sediment yield. The critical state appears to be an indicator of the connectivity of the drainage network. Thus, the simulations indicate that, unlike traditional SOC systems, the critical state of the system can vary in time, as sudden changes in drainage network connectivity may result in sudden changes in the SOC behavior of the system.     

Long-range connective sandpile models and its implication to seismicity evolution

We propose a new variant of the sandpile model, the long-range connective sandpile model, by means of introducing randomly internal connections between two separated distant cells. The long-range connective sandpile model demonstrates various self-organized critical states with different scaling exponents in the power-law frequency-size distributions. We found that a sandpile with higher degree of randomly internal long-range connections is characterized by a higher value of the scaling exponent for the distribution, whereas the nearest neighbor sandpile is possessed of a lower scaling exponent. Our numerical experiments on the long-range connective sandpile models imply that higher degree of random long-range connections makes the earthquake fault system more relaxant that releases accumulated energy more easily and produces fewer catastrophic events, whereas lower degree of long-range connections possibly caused by fracture healing very likely motivates accelerating seismicity of moderate events.