基于神经网络的页岩微纳米孔隙微结构分析的正则化和最优化方法

页岩气成藏机理与页岩内部孔隙结构紧密相关,对页岩孔隙结构的研究成为页岩气勘探开发技术中至关重要的一环。页岩内部不同结构体组分对X-射线的吸收能谱不一样,这样就导致观测数据是由不同页岩组分衰减不同波段的X-射线构成的。经过对CT图像分割,能够获得页岩微孔结构的图像,尤其是获得有机质中孔隙类别、形状、尺寸、空间分布、连通特性。本文利用同步辐射X射线扫描重构的页岩CT数据,研究并设计基于多能CT图像的神经网络图像分割技术和算法,以期得到页岩体三维结构特征及空间分布,可以为建立有机质种类和无机矿物组成与微纳孔隙特征的联系以及最终实现页岩气的资源储量评估和勘探开发提供技术支持。     

Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery

For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). We applied our approach to a study area in Minnesota.     

Late Quaternary chironomid community structure shaped by rate and magnitude of climate change

Much is known about how climate change impacts ecosystem richness and turnover, but we have less understanding of its influence on ecosystem structures. Here, we use ecological metrics (beta diversity, compositional disorder and network skewness) to quantify the community structural responses of temperature-sensitive chironomids (Diptera: Chironomidae) during the Late Glacial (14 700–11 700 cal a bp ) and Holocene (11 700 cal a bp to present). Analyses demonstrate high turnover (beta diversity) of chironomid composition across both epochs; however, structural metrics stayed relatively intact. Compositional disorder and skewness show greatest structural change in the Younger Dryas, following the rapid, high-magnitude climate change at the Bølling–Allerød to Younger Dryas transition. There were fewer climate-related structural changes across the early to mid–late Holocene, where climate change was more gradual and lower in magnitude. The reduced impact on structural metrics could be due to greater functional resilience provided by the wider chironomid community, or to the replacement of same functional-type taxa in the network structure. These results provide insight into how future rapid climate change may alter chironomid communities and could suggest that while turnover may remain high under a rapidly warming climate, community structural dynamics retain some resilience.     

成层软土地基预固结处理后桩基水平承载力估算方法

目前通过对软土地基预加固处理来提高桩基水平承载力已被工程界认可,但如何在工程前期设计过程中估算软土地基预处理后桩基水平承载力提高值仍是技术难点。基于此,参考Bowles[1]的地基土水平抗力计算式,同时考虑成层软土地基预排水固结处理影响,通过数学推导,推求出根据原状软土室内土工试验抗剪强度指标及预加固处理时间,估算软土地基预处理后桩基水平承载力提高值的实用计算方法。考虑桩侧土弹塑性屈服影响,推导出成层软土中水平受荷桩弹塑性解析解及塑性区深度的计算式,给出了桩顶水平位移、桩身最大弯矩的无量纲计算式及相关计算源代码。依托于浙江省某水闸桩基工程案例,根据提出的计算方法对桩基水平承载力、桩顶水平位移及桩身最大弯矩等性状进行预估计算,并与地基预处理前、后现场试桩检测值进行验证对比,认为桩基水平承载力、桩顶水平位移及桩身最大弯矩等预估计算成果与工程现场试桩的检测值较接近,对类似工程设计具有较好的参考价值。     

物探技术在布敦乌拉隐伏矿产勘查中的应用效果

内蒙古西乌珠穆沁旗布敦乌拉矿区多为第四系覆盖。在该矿区多金属勘查中,依据土壤地球化学圈定的矿化异常布置激电中梯测量,圈定1处激发极化率异常区,长度约890m,宽度约520m,位于已知断层F 1与推断断层F 2之间。采用可控源音频大地电磁测深剖面对该激电异常进行探测,解释视充电率多峰异常与反演电阻率高低跳跃对应区域为矿致异常区。通过钻探验证,发现银多金属矿床资源储量523.93万t,取得了良好的地质找矿效果。     

Spatial landslide susceptibility assessment using machine learning techniques assisted by additional data created with generative adversarial networks

In recent years,landslide susceptibility mapping has substantially improved with advances in machine learning.However,there are still challenges remain in landslide mapping due to the availability of limited inventory data.In this paper,a novel method that improves the performance of machine learning techniques is presented.The proposed method creates synthetic inventory data using Generative Adversarial Networks(GANs)for improving the prediction of landslides.In this research,landslide inventory data of 156 landslide locations were identified in Cameron Highlands,Malaysia,taken from previous projects the authors worked on.Elevation,slope,aspect,plan curvature,profile curvature,total curvature,lithology,land use and land cover(LULC),distance to the road,distance to the river,stream power index(SPI),sediment transport index(STI),terrain roughness index(TRI),topographic wetness index(TWI)and vegetation density are geo-environmental factors considered in this study based on suggestions from previous works on Cameron Highlands.To show the capability of GANs in improving landslide prediction models,this study tests the proposed GAN model with benchmark models namely Artificial Neural Network(ANN),Support Vector Machine(SVM),Decision Trees(DT),Random Forest(RF)and Bagging ensemble models with ANN and SVM models.These models were validated using the area under the receiver operating characteristic curve(AUROC).The DT,RF,SVM,ANN and Bagging ensemble could achieve the AUROC values of(0.90,0.94,0.86,0.69 and 0.82)for the training;and the AUROC of(0.76,0.81,0.85,0.72 and 0.75)for the test,subsequently.When using additional samples,the same models achieved the AUROC values of(0.92,0.94,0.88,0.75 and 0.84)for the training and(0.78,0.82,0.82,0.78 and 0.80)for the test,respectively.Using the additional samples improved the test accuracy of all the models except SVM.As a result,in data-scarce environments,this research showed that utilizing GANs to generate supplementary samples is promising because it can improve the predictive capability of common landslide prediction models.     

Reducing wind power curtailment in China: comparing the roles of coal power flexibility and improved dispatch

Renewable energy curtailment is a critical issue in China, impeding the country’s transition to clean energy and its ability to meet its climate goals. This paper analyzes the impacts of more flexible coal-fired power generation and improved power dispatch towards reducing wind power curtailment. A unit commitment model for power dispatch is used to conduct the analysis, with different scenarios demonstrating the relative impacts of more flexible coal-fired generation and improved power dispatch. Overall, while we find both options are effective in reducing wind power curtailment, we find that improved power dispatch is more effective: (1) the effect of ramping down coal-fired generators to reduce wind power curtailment lessens as the minimum output of coal-fired generation is decreased; and (2) as a result, at higher wind capacity levels, wind curtailment is much more significantly reduced with improved power dispatch than with decreased minimum output of coal-fired generation.

Key policy insights

• China should emphasize both coal power flexibility and dispatch in its policies to minimize renewable power curtailment and promote clean energy transition.

• China should accelerate the process of implementing spot market and marginal cost-based economic dispatch, while making incremental improvements to the existing equal share dispatch in places not ready for spot market.

• A key step in improving of dispatch is incorporating renewable power forecasts into the unit commitment process and updating the daily unit commitment based on the latest forecast result.

• China should expand the coal power flexibility retrofit programme and promote the further development of the ancillary service market to encourage more flexibility from coal-fired generation.

     

汕头市PM2.5的气象要素影响分析及预报研究

利用2014—2017年汕头市PM_2.5的日浓度资料、以及汕头市国家基准气象观测站的同期地面气象资料,重点分析了汕头市PM_2.5浓度的变化特征以及风、混合层厚度、降水等气象条件对PM_2.5浓度的影响,同时探讨了污染物浓度变化的成因。在此基础上,根据汕头市的气候特点,采用BP （Back-Propagation）人工神经网络方法针对汛期和非汛期分别建立了PM_2.5质量浓度预报模型。结果表明：与多数内陆城市不同,汕头市PM_2.5浓度日变化为单峰型,这与汕头地处沿海受海陆风影响有关;PM_2.5浓度日峰值出现在08时左右,除早高峰污染物排放增加的因素外,与早晨时段的低风速环境有关;PM_2.5日均浓度随着风速的增大呈现减小趋势,PM_2.5日均浓度与08时混合层厚度显著相关（相关系数为-0.143）;汕头市非汛期PM_2.5浓度比汛期高,这与汕头市的亚热带季风气候特征有关,汛期各量级降水（暴雨以上除外）对PM_2.5的清除效果无明显差别,而非汛期降水对PM_2.5浓度有明显清除作用;BP人工神经网络模型的预报效果表明,汛期和非汛期的PM_2.5级别命中率TS分别为100%和90.3%,准确指数分别为87.7%和89.9%,总体预报效果良好。不同时期预报模型出现正误差的数量和程度均大于负误差,汛期预报模型在有强降水发生时误差较大,而非汛期预报模型在有冷空气入侵时误差较大。     

广西气象数据中心机房供电系统设计与保障

针对供配电系统容易发生的安全隐患,优化设计配电系统结构、采用一体化运维监控平台和采用高质量、高可靠性的设备,消除单点故障瓶颈,确保在停电、设备故障、维修维护等各种情况下提供不中断或快速恢复供电,避免停电对业务正常运行造成影响,确保机房供电安全。     

Measuring,mapping and quantifying the effects of trust and informal communication on transboundary collaboration in the Great Lakes fisheries policy network

Ecosystem-based management of fisheries and other transboundary natural resources require a number of organizations across jurisdictions to exchange knowledge, coordinate policy goals and engage in collaborative activities. Trust, as part of social capital, is considered a key mechanism facilitating the coordination of such inter-organizational policy networks. However, our understanding of multi-dimensional trust as a theoretical construct and an operational variable in environmental and natural resource management has remained largely untested. This paper presents an empirical assessment of trust and communication measures applied to the North American Great Lakes fisheries policy network. Using a scale-based method developed for this purpose, we quantify the prevalence of different dimensions of trust and in/formal communication in the network and their differentiated impacts on decision-making and goal consensus. Our analysis reveals that calculation-based ‘rational trust’ is important for aligning mutual goals, but relationship-based ‘affinitive trust’ is most significant for influencing decision-making. Informal communication was also found to be a strong predictor of how effectively formal communication will influence decision-making, confirming the “priming” role of informal interactions in formal inter-agency dealings. The results also show the buffering and interactive functions of these components in strengthening institutional resilience, with procedural trust undergirding the system to compensate for a lack of well-developed relationships. Overall, this study provides evidence to suggest that informal communication and multi-dimensional trust constitute a crucial element for improving collaboration and reducing conflict in the networked governance of transboundary natural resource systems.