成矿多过程耦合模型数值模拟研究态势

成矿作用是构造变形-热量传递-流体流动-质量转移等四种性质完全不同过程的耦合。数值模拟是通过建立多过程耦合模型来刻画其物理和化学规律。由于这些耦合模型过于复杂而难以得到其解析解，因而数值模拟被用来讨论这些复杂动力学过程。针对成矿预测领域主要的耦合模型：形变-流动模型、形变-热-流动模型、热-流动，质模型、变形-热-流动-质模型，通过这些耦合模型应用实例分析得出，印证了高差和温度变化是流体对流、矿物沉淀发生的重要机制；详释了构造控制流体输运的方式及过程，高渗透率断裂是流体汇聚的有利场所，从而渗透率等控制成矿的关键参数成为成矿预测的主要指标。     

三层BP神经网络地震灾害人员伤亡预测模型

选择地震发生时刻、震级、震中烈度、建筑物倒塌和严重破坏率、抗震设防水准、人口密度、地震预报等7个评价指标,以20次严重地震灾害为示例（其中,17个作训练样本,3个作验证样本）,建立了三层BP神经网络地震灾害人员伤亡预测模型。基于MATLAB6,5BP神经网络训练,得出的预测结果与各个示例的实际数值比较吻合。验证样本的训练结果表明,该模型适用于地震灾害人员伤亡评估。通过对评价指标的权重计算,确认人口密度、建筑物倒塌与严重破坏率、震中烈度是影响地震灾害人员伤亡的主要因素,地震预报、抗震设防水准、地震发生时刻和震级次之。作为人为可控预测指标,减少人口密度特别是城市人口密度,提高建（构）筑物抗震能力及预测预报水平,对于减少地震灾害人员伤亡起更重要的作用。     

Where are the limits of model predictive capabilities?

Distributed hydrological models can make predictions with much finer spatial resolution than the supporting field data. They will, however, usually not have a predictive capability at model grid scale due to limitations of data availability and uncertainty of model conceptualizations. In previous publications, we have introduced the Representative Elementary Scale (RES) concept as the theoretically minimum scale at which a model with a given conceptualization has a potential for obtaining a predictive accuracy corresponding to a given acceptable accuracy. The new RES concept has similarities to the 25‐year‐old Representative Elementary Area concept, but it differs in the sense that while Representative Elementary Area addresses similarity between subcatchments by sampling within the catchment, RES focuses on effects of data or conceptualization uncertainty by Monte Carlo simulations followed by a scale analysis. In the present paper, we extend and generalize the RES concept to a framework for assessing the minimum scale of potential predictability of a distributed model applicable also for analyses of different model structures and data availabilities. We present three examples with RES analyses and discuss our findings in relation to Beven's alternative blueprint and environmental modeling philosophy from 2002. While Beven here addresses model structural and parameter uncertainties, he does not provide a thorough methodology for assessing to which extent model predictions for variables that are not measured possess opportunities to have meaningful predictive accuracies, or whether this is impossible due to limitations in data and models. This shortcoming is addressed by the RES framework through its analysis of the relationship between aggregation scale of model results and prediction uncertainties and for considering how alternative model structures and alternative data availability affects the results. We suggest that RES analysis should be applied in all modeling studies that aim to use simulation results at spatial scales smaller than the support scale of the calibration data.     

利用Burg反褶积提高地震资料处理质量

从反褶积原理出发,深入研究了Burg反摺积理论,详细推导了Burg反摺积公式。利用该方法对实际地震数据进行了试处理,并与谱模拟反褶积方法处理所得结果进行了对比分析。结果表明:该方法能在一定程度上提高地震资料的信噪比,改善反射同相轴的连续性,同时还能起到压制剖面上线性干扰的作用,可以在较大程度上提高地震资料的处理质量。     

山东区域汛期旱涝预测概论

对1951－2000年期间汛期（6－8月）山东区域旱涝与全国主要多雨带的8个分布类型的逐年对应关系进行了具体的对比和论述。并对烟台、青岛、潍坊、济南、临沂、菏泽等6个地区汛期旱涝的天文、海洋、大气环流和气象要素等方面的预报物理因子进行了全面的普查、筛选、精选和综合分析，并分别建立了汛期降水量预报物理方程。并以青岛地区为例，对汛期降水理的多种交叉学科的预报物理因子进行了最优集成，为青岛地区和水量和旱涝建立了分多级的可操作的预报物理模型。该文优选出来的汛期降水的多学科物理因子对短期气候预测有重要的学术意义，其中所建立的预报物理方程和预报物理模型对山东省各区汛期旱涝的季度和年度预报有重要的应用价值。     

赤柏松矿田含镍基性岩侵入体的成因矿物学研究

本文从成因矿物学角度对所述岩体中主要造岩矿物橄榄石、辉石与斜长石的颜色、形态、晶胞参数、化学成分、微量元素、稀土及氧、硫、锶稳定同位素等方面进行了研究,从而揭示了岩体的成岩成矿物质来源、形成温度、压力,fo_2,Eh、pH值等物理-化学条件与成岩成矿作用特征。同时,概括出岩体的成矿特点与找矿准则。     

Modelling future lahars controlled by different volcanic eruption scenarios at Cotopaxi (Ecuador) calibrated with the massively destructive 1877 lahar

Lahars are among the most hazardous mass flow processes on earth and have caused up to 23 000 casualties in single events in the recent past. The Cotopaxi volcano, 60 km southeast of Quito, has a well-documented history of massively destructive lahars and is a hotspot for future lahars due to (i) its ~10 km² glacier cap, (ii) its 117–147-year return period of (Sub)-Plinian eruptions, and (iii) the densely populated potential inundation zones (300 000 inhabitants). Previous mechanical lahar models often do not (i) capture the steep initial lahar trajectory, (ii) reproduce multiple flow paths including bifurcation and confluence, and (iii) generate appropriate key parameters like flow speed and pressure at the base as a measure of erosion capacity. Here, we back-calculate the well-documented 1877 lahar using the RAMMS debris flow model with an implemented entrainment algorithm, covering the entire lahar path from the volcano edifice to an extent of ~70 km from the source. To evaluate the sensitivity and to constrain the model input range, we systematically explore input parameter values, especially the Voellmy–Salm friction coefficients μ and ξ. Objective selection of the most likely parameter combinations enables a realistic and robust lahar hazard representation. Detailed historic records for flow height, flow velocity, peak discharge, travel time and inundation limits match best with a very low Coulomb-type friction μ (0.0025–0.005) and a high turbulent friction ξ (1000–1400 m/s²). Finally, we apply the calibrated model to future eruption scenarios (Volcanic Explosivity Index = 2–3, 3–4, >4) at Cotopaxi and accordingly scaled lahars. For the first time, we anticipate a potential volume growth of 50–400% due to lahar erosivity on steep volcano flanks. Here we develop a generic Voellmy–Salm approach across different scales of high-magnitude lahars and show how it can be used to anticipate future syneruptive lahars.     

The habitation Model Trend Calculation (MTC): A new effective tool for predictive modeling in archaeology

ABSTRACT

The aim of this paper is to create and present a new archaeological predictive model via GIS, incorporating what archaeologists consider the most important criterion absent of similar past models, that of critical thinking. The new model suggested in this paper is named habitation Model Trend Calculation (MTC) and is not only integrates the archaeological questions with a critical view, but it can be easily adjusted, according to the conditions or the questions concerning the archaeological community. Furthermore, it uses new topographical and geomorphological indexes such as Topographical Index (TPI), Hillslope and Landform Classification that give a new sense of the topographical and geomorphological characteristics of the examined area; therefore this model is a more powerful tool compared to older models that did not use new topographical and geomorphological indexes. The success of the created model is checked as a case study in the region of Messenia, Greece during the Mycenaean era. The region of Messenia is considered as one of the most important Mycenaean regions of Greece due to the great number and the importance of Mycenaean sites identified. For the present paper, 140 habitation sites were divided into four hierarchical categories (centers, large villages, villages, and farms) based on the extent and the plurality of the tholos tombs that exist in the broader region and according to the hierarchical categorization used by the archaeologists who have studied the area. The new predictive model presented in this work can assist in solving a series of criticisms that have been expressed in the previous studies regarding such models. Additionally, in the case of Mycenaean Messenia, the model shows excellent results in relation to the habitats of the time.     

Variation in low food access areas due to data source inaccuracies

Several spatial measures of community food access identifying so called “food deserts” have been developed based on geospatial information and commercially-available, secondary data listings of food retail outlets. It is not known how data inaccuracies influence the designation of Census tracts as areas of low access. This study replicated the U.S. Department of Agriculture Economic Research Service (USDA ERS) food desert measure and the Centers for Disease Control and Prevention (CDC) non-healthier food retail tract measure in two secondary data sources (InfoUSA and Dun & Bradstreet) and reference data from an eight-county field census covering 169 Census tracts in South Carolina. For the USDA ERS food deserts measure accuracy statistics for secondary data sources were 94% concordance, 50–65% sensitivity, and 60–64% positive predictive value (PPV). Based on the CDC non-healthier food retail tracts both secondary data demonstrated 88–91% concordance, 80–86% sensitivity and 78–82% PPV. While inaccuracies in secondary data sources used to identify low food access areas may be acceptable for large-scale surveillance, verification with field work is advisable for local community efforts aimed at identifying and improving food access.