AREM模式对"05.7"四川大暴雨的敏感性试验

利用AREM中尺度数值模式,对2005年7月8日四川盆地大暴雨天气过程进行了数值模拟,比较分析了AREM模式提供的不同降水方案、地表通量方案、地表辐射参数化方案对此次降水过程的模拟,以及对该过程中西南低涡活动特征的模拟。试验结果表明:各方案较好地反映了四川盆地西南部强降水,对其东北部的强降水模拟存在较大偏差;各方案模拟的涡度场和流场分布决定了降水区域分布;采用降水的显式云微物理过程和大尺度饱和凝结过程模拟的降水强度、低涡强度和低空急流有一定差异,后者模拟的偏强,与实况更接近;不同地表通量过程和地表辐射过程对降水、低涡和低空急流的模拟无明显差异。     

近海水汽初值和对流影响一次华南前汛期沿海强降水对流系统发展过程的机理研究

本研究在对华南季风降水试验（SCMREX）观测资料分析的基础上,采用数值模拟试验探讨南海北部区域湿度场初值误差和海上对流对2014年5月8日华南沿海地区的一次强降雨过程的中尺度对流系统（MCS）的发展和移动的影响。加密探空和风廓线观测分析表明在珠江口地区有西南风和偏东风急流形成的辐合区,为对流在该地区增强发展提供了条件。增加和减少近海湿度以及关闭积云和微物理过程潜热释放,所造成的温度场以及风场的变化对广东沿海地区的对流的强度和移动路径都有明显的影响。特别是增加海上关键区的湿度,由于海上对流的发展改变了整个区域的环流,抑制了陆地上对流的发展。关闭海上关键区对流过程潜热的释放,导致低空急流到达更加偏北的位置,对流系统在模拟的后期向东北移动。通过这些试验表明,海上的湿度等要素场和对流活动对沿海地区的降雨预报有着十分重要的影响,需要进一步加强海上观测及其资料同化方法。     

华北平原参考作物蒸散量对气候因子的季节响应分析

为探讨华北平原作物需水量在不同季节随气候变化的变化规律,对华北平原参考作物蒸散量(Evapotranspiration,简称ET_o)在不同季节对气候因子的响应情况进行了分析研究。首先利用FAO-56Penman-Monteith公式计算了华北平原48个气象站点1960~2012年的ET_o,其次分析了ET_o及温度(T)、日照时数(n)、风速(u)和相对湿度(RH)这4个主要气候因子在各个季节的年际变化规律,然后使用敏感性分析法分析了ET_o对气候因子变化的敏感程度,最后结合ET_o对气候因子的敏感性及气候因子的多年相对变化率分析得出气候因子的变化对ET_o变化的贡献。结果表明:1960~2012年,华北平原ET_o在四季的年际变化均呈下降趋势。气候因子除T呈上升趋势外,n、u和RH均呈下降趋势。ET_o对T、n和u的变化正敏感,对RH的变化负敏感。ET_o对T和n最敏感的季节为夏季,对u和RH最敏感的季节为冬季。ET_o在春季、秋季和冬季的下降主要受u下降的影响,ET_o在夏季的下降则主要归因于n的下降。     

大地电磁非线性共轭梯度拟三维反演

提出了非线性共轭梯度法大地电磁拟三维反演.该方法选取共轭梯度反演算法为拟三维反演的核心.在计算灵敏度（Jacobian）矩阵时,吸取近似灵敏度矩阵思想,采用一维灵敏度矩阵来代替三维灵敏度矩阵,并对非测点的灵敏度元素提出一种近似方法.在第一次反演之后,采用拟牛顿法更新灵敏度矩阵.拟三维反演法在很大程度上节省了计算时间,并且理论模型和实际资料的反演试算结果表明大地电磁拟三维反演法具有一定的实用价值.     

Two‐dimensional joint inversions of cross‐hole resistivity data and resolution analysis of combined arrays

In this study, a new two‐dimensional inversion algorithm was developed for the inversion of cross‐hole direct current resistivity measurements. In the last decades, various array optimisation methods were suggested for resistivity tomography. However, researchers have still collected data by using classical electrode arrays in most cross‐hole applications. Therefore, we investigated the accuracy of both the individual and the joint inversion of the classical cross‐hole arrays by using both synthetic and field data with the developed algorithm. We showed that the joint inversion of bipole–bipole, pole–bipole, bipole–pole, and pole–tripole electrode arrays gives inverse solutions that are closer to the real model than the individual inversions of the electrode array datasets for the synthetic data inversion. The model resolution matrix of the suggested arrays was used to analyse the inversion results. This model resolution analysis also showed the advantage of the joint inversion of bipole–bipole, pole–bipole, bipole–pole, and pole–tripole arrays. We also used sensitivity sections from each of the arrays and their superpositions to explain why joint inversion gives better resolution than the any individual inversion result.     

平凉台井下地电阻率观测影响系数分析

根据地电阻率影响系数理论,以平凉台4层电性结构为例,分析了井下对称四极地电阻率观测影响系数随深度和极距的变化。结果表明:对于固定的观测极距,影响系数与电极埋深之间的关系复杂;通过计算各层影响系数的大小,认为平凉台井下观测对地表及浅层干扰有较好的抑制作用,其分析结果可为在类似台址电性结构中实施井下地电阻率观测时选择电极埋深和供电极距提供参考。     

基于一阶速度-应力方程的VTI介质最小二乘逆时偏移

地下地层普遍存在各向异性,忽略介质各向异性会导致速度估计不准确,成像精度下降.基于二阶声波方程的最小二乘逆时偏移忽略了介质各向异性及密度变化的影响,致使模拟地震数据与实际观测数据不匹配,影响收敛速度和反演成像质量.VTI介质一阶速度-应力方程能较好适应各向异性变密度情况,为此,本文首先从VTI介质一阶速度-应力方程出发,进行波动方程线性化;其次推导了相应的扰动方程和伴随方程,并通过伴随状态法得到梯度更新公式;最终形成基于一阶方程的LSRTM算法理论及实现流程.在实现算法的基础上,通过数值试算及成像结果对比,验证了本文算法在处理变密度和VTI介质时的有效性和优越性.偏移速度以及各向异性Thomsen参数误差的敏感性测试及误差收敛曲线对比结果进一步表明:速度及Thomsen参数对成像结果存在明显影响,其中速度敏感性最强,参数epsilon次之,参数delta的敏感性最弱.     

用于石英水平摆倾斜仪的静电力反馈技术

石英水平摆倾斜仪在前兆地震观测中得到了广泛的应用, 但是仍然存在现场安装前灵敏度未知的问题。 为解决该问题, 引入静电力反馈技术, 理论推导了仪器的传递函数和灵敏度等参数, 提出了在平行金属板结构中同时实现微位移测量和静电力反馈的方法, 并进行了具体设计和调试。 实验结果显示, 样机的灵敏度测试结果与反馈模型理论计算结果一致, 表明引入静电力反馈后, 能够实现石英水平摆倾斜仪灵敏度参数的常数化。     

A sensitivity analysis of factors affecting in geologic CO2 storage in the Ordos Basin and its contribution to carbon neutrality

To accelerate the achievement of China’s carbon neutrality goal and to study the factors affecting the geologic CO₂ storage in the Ordos Basin, China’s National Key R&D Programs propose to select the Chang 6 oil reservoir of the Yanchang Formation in the Ordos Basin as the target reservoir to conduct the geologic carbon capture and storage (CCS) of 100000 t per year. By applying the basic theories of disciplines such as seepage mechanics, multiphase fluid mechanics, and computational fluid mechanics and quantifying the amounts of CO₂ captured in gas and dissolved forms, this study investigated the effects of seven factors that influence the CO₂ storage capacity of reservoirs, namely reservoir porosity, horizontal permeability, temperature, formation stress, the ratio of vertical to horizontal permeability, capillary pressure, and residual gas saturation. The results show that the sensitivity of the factors affecting the gas capture capacity of CO₂ decreases in the order of formation stress, temperature, residual gas saturation, horizontal permeability, and porosity. Meanwhile, the sensitivity of the factors affecting the dissolution capture capacity of CO₂ decreases in the order of formation stress, residual gas saturation, temperature, horizontal permeability, and porosity. The sensitivity of the influencing factors can serve as the basis for carrying out a reasonable assessment of sites for future CO₂ storage areas and for optimizing the design of existing CO₂ storage areas. The sensitivity analysis of the influencing factors will provide basic data and technical support for implementing geologic CO₂ storage and will assist in improving geologic CO₂ storage technologies to achieve China’s carbon neutralization goal.©2022 China Geology Editorial Office.     

Hybrid meta-heuristic machine learning methods applied to landslide susceptibility mapping in the Sahel-Algiers

Landslides are considered as one among many phenomena jeopardizing human beings as well as their constructions. To prevent this disastrous problem, researchers have used several approaches for landslide susceptibility modeling, for the purpose of preparing accurate maps marking landslide prone areas. Among the most frequently used approaches for landslide susceptibility mapping is the Artificial Neural Network (ANN) method. However, the effectiveness of ANN methods could be enhanced by using hybrid metaheuristic algorithms, which are scarcely applied in landslide mapping. In the current study, nine hybrid metaheuristic algorithms, genetic algorithm (GA)-ANN, evolutionary strategy (ES)-ANN, ant colony optimization (ACO)-ANN, particle swarm optimization (PSO)-ANN, biogeography based optimization (BBO)-ANN, gravitational search algorithm (GHA)-ANN, particle swarm optimization and gravitational search algorithm (PSOGSA)-ANN, grey wolves optimization (GWO)-ANN, and probability based incremental learning (PBIL)-ANN have been used to spatially predict landslide susceptibility in Algiers’ Sahel, Algeria. The modeling phase was done using a database of 78 landslides collected utilizing Google Earth images, field surveys, and six conditioning factors (lithology, elevation, slope, land cover, distance to stream, and distance to road). Initially, a gamma test was used to decrease the input variable numbers. Furthermore, the optimal inputs have been modeled by the mean of hybrid metaheuristic ANN techniques and their performance was assessed through seven statistical indicators. The comparative study proves the effectiveness of the co-evolutionary PSOGSA-ANN model, which yielded higher performance in predicting landslide susceptibility compared to the other models. Sensitivity analysis using the step-by-step technique was done afterward, which revealed that the distance to the stream is the most influential factor on landslide susceptibility, followed by the slope factor which ranked second. Lithology and the distance to road have demonstrated a moderate effect on landslide susceptibility. Based on these findings, an accurate map has been designed to help land-use managers and decision-makers to mitigate landslide hazards.