改进后灰色神经网络及短期基坑沉降预测研究

在短期基坑沉降监测中,由于数据量少且呈非线性变化,沉降模型很难准确建立。灰色GM(1,1)对数据少、趋势性强、波动小的数据有较高的预测精度,但不能模拟复杂的非线性函数;BP神经网络可以对非线性数据进行学习训练,具有自学习、自适应能力;通过将GM(1,1)与BP神经网络组合,并优化网络部分的学习率、权值和阈值等,建立一种改进的灰色神经网络模型,该模型具有对非线性数据自学习、自适应能力和预测精度更高等优点。通过某基坑沉降监测分析,验证改进的灰色神经网络模型预测精度更高,适合短期建模,具有很好的实用性。     

叶蛇纹岩脆性-半脆性破裂研究

利用Paterson气体介质高温高压流变仪对纯叶蛇纹岩在100~400MPa围压、25~700℃温度和10-5~1.5×10-6s-1应变速率下进行了三轴压缩变形实验。实验结果表明叶蛇纹石在低压条件下表现为脆性破裂,高压或脱水条件下表现为半脆性破裂。随着温度的增加,叶蛇纹石的强度显示逐渐降低的趋势;尤其在脱水条件下,温度的增加可导致叶蛇纹石强度大幅度地降低,而且此时预热时间对强度的影响比未发生脱水时更加显著。结合前人的研究并对比发现,围压在室温下的增加导致叶蛇纹岩强度增加;但在高温下围压的增加导致试样强度整体上降低,这很可能是试样内聚力的局部损失与韧性增强引起的。围压和温度的升高,以及断层面上流体的增加很可能会增加破裂面的韧性,从而减小摩擦系数。此外,叶蛇纹石并非以往人们所认为的那样具有极低的强度,其强度要比低温蛇纹石(如利蛇纹石和纤蛇纹石)的大得多,即便在高温(大约600℃)下差应力大于约600MPa和中-低温(≤400℃)下差应力大于约1000MPa时仍没有表现出明显屈服的迹象。在脱水条件下,蛇纹岩并没有发生脱水致脆,相反脱水使得试样的断裂行为变得更加温和些。因此,俯冲带蛇纹岩脱水更可能诱发其周围更加脆性的岩石发生地震而不是脱水的蛇纹岩本身发生地震。     

Tectono–Thermal Evolution, Hydrocarbon Filling and Accumulation Phases of the Hari Sag, in the Yingen–Ejinaqi Basin, Inner Mongolia, Northern China

This work restored the erosion thickness of the top surface of each Cretaceous formations penetrated by the typical well in the Hari sag, and simulated the subsidence burial history of this well with software BasinMod. It is firstly pointed out that the tectonic subsidence evolution of the Hari sag since the Cretaceous can be divided into four phases: initial subsidence phase, rapid subsidence phase,uplift and erosion phase, and stable slow subsidence phase. A detailed reconstruction of the tectonothermal evolution and hydrocarbon generation histories of typical well was undertaken using the EASY R_0% model, which is constrained by vitrinite reflectance(R_0) and homogenization temperatures of fluid inclusions. In the rapid subsidence phase, the peak period of hydrocarbon generation was reached at c.a.105.59 Ma with the increasing thermal evolution degree. A concomitant rapid increase in paleotemperatures occurred and reached a maximum geothermal gradient of about 43-45℃/km. The main hydrocarbon generation period ensued around 105.59-80.00 Ma and the greatest buried depth of the Hari sag was reached at c.a. 80.00 Ma, when the maximum paleo-temperature was over 180℃.Subsequently, the sag entered an uplift and erosion phase followed by a stable slow subsidence phase during which the temperature gradient, thermal evolution, and hydrocarbon generation decreased gradually. The hydrocarbon accumulation period was discussed based on homogenization temperatures of inclusions and it is believed that two periods of rapid hydrocarbon accumulation events occurred during the Cretaceous rapid subsidence phase. The first accumulation period observed in the Bayingebi Formation(K_1 b) occurred primarily around 105.59-103.50 Ma with temperatures of 125-150℃. The second accumulation period observed in the Suhongtu Formation(K_1 s) occurred primarily around84.00-80.00 Ma with temperatures of 120-130℃. The second is the major accumulation period, and the accumulation mainly occurred in the Late Cretaceous. The hydrocarbon accumulation process was comprehensively controlled by tectono-thermal evolution and hydrocarbon generation history. During the rapid subsidence phase, the paleo temperature and geothermal gradient increased rapidly and resulted in increasing thermal evolution extending into the peak period of hydrocarbon generation,which is the key reason for hydrocarbon filling and accumulation.     

地质雷达电磁干扰分析及在隧洞岩溶探测中的应用

将地质雷达用于千岛湖配水工程某隧洞岩溶探测,分析了倾斜状电磁干扰异常与溶洞雷达异常差异,通过雷达剖面及疑似异常区单道时间-频率谱的分析,推断了隧底岩溶的性质及空间分布特征,含水溶洞在雷达剖面反映为同相轴错断,强振幅及能量高衰减的特性,高频干扰波振幅幅值较异常区小且能量不满足随时间推移逐渐衰减的规律,雷达成果为隧洞安全施工提供了准确的参考。      

微束分析测试技术十年(2011~2020)进展与展望

现代地球科学研究的重大突破在很大程度上取决于观测和分析技术的创新。新世纪以来,我国地球科学领域引进了一批高性能新型微束分析仪器设备,建立了一批高规格的实验室。本文回顾了近十年来微束分析技术与方法的主要进展及其在地球科学研究中的应用实例,包括电子探针、扫描电镜、透射电镜、大型离子探针、纳米离子探针、飞行时间二次离子质谱、激光剥蚀等离子体质谱、激光诱导原子探针、原子探针技术、显微红外光谱、同步辐射等,这些分析技术的进步和广泛应用极大地提高了我们对地球和行星演化历史及许多地质过程的理解。今后,应加快微束分析的新技术、新方法和新标准的开发,特别是高水平人才队伍建设,提高创新能力并在国际学术舞台上发挥重要作用。     

论GIS的模糊不确定性

文章对GIS的模糊不确定性进行了研究,主要目的是系统论证GIS确实舍有模糊不确定性,使人们真正认识到GIS不仅含有随机不确定性而且含有模糊不确定性。文章主要是从GIS构成的四部分出发即：硬件、软件、人员和数据,分别对这四部分论述其模糊不确定性,即GIS存在着GIS数据模糊不确定性、GIS硬件模糊不确定性、GIS软件模糊不确定性和GIS人员的模糊不确定性;最后,指出GIS的模糊不确定性的研究还处于初步阶段,特别是人员的模糊不确定性还有很多因素无法进行研究以及如何减弱GIS各部分的模糊不确定性的理论和方法。     

Rapid Urbanization Induced Extensive Forest Loss to Urban Land in the Guangdong-Hong Kong-Macao Greater Bay Area,China

China has experienced rapid urbanizations with dramatic land cover changes since 1978. Forest loss is one of land cover changes, and it induces various eco-environmental degradation issues. As one of China’s hotspot regions, the Guangdong-Hong KongMacao Greater Bay Area(GBA) has undergone a dramatic urban expansion. To better understand forest dynamics and protect forest ecosystem, revealing the processes, patterns and underlying drivers of forest loss is essential. This study focused on the spatiotemporal evolution and potential driving factors of forest loss in the GBA at regional and city level. The Landsat time-series images from 1987 to2017 were used to derive forest, and landscape metrics and geographic information system(GIS) were applied to implement further spatial analysis. The results showed that: 1) 14.86% of the total urban growth area of the GBA was obtained from the forest loss in1987–2017;meanwhile, the forest loss area of the GBA reached 4040.6 km2, of which 25.60%(1034.42 km2) was converted to urban land;2) the percentages of forest loss to urban land in Dongguan(19.14%), Guangzhou(18.35%) and Shenzhen(15.81%) were higher than those in other cities;3) the forest became increasingly fragmented from 1987–2007, and then the fragmentation decreased from2007 to 2017);4) the landscape responses to forest changes varied with the scale;and 5) some forest loss to urban regions moved from low-elevation and gentle-slope terrains to higher-elevation and steep-slope terrains over time, especially in Shenzhen and Hong Kong.Urbanization and industrialization greatly drove forest loss and fragmentation, and, notably, hillside urban land expansion may have contributed to hillside forest loss. The findings will help policy makers in maintaining the stability of forest ecosystems, and provide some new insights into forest management and conservation.     

重庆市夏季电力气象用户需求分析

【目的】面对日益增加的夏季电力气象服务用户需求和激烈的市场竞争,必须明确用户需求的优先顺序,为有序改善产品提供参考。【方法】利用Kano模型,通过问卷调查的方式收集了受试者对各项需求点的反馈,并计算B系数和W系数,绘制B-W象限图,得到17个需求点的需求类型和优先级排序。【结果】17个需求点可分为基本型（6个,35%）、期望型（4个,24%）、魅力型（4个,24%）和无差异型（3个,18%）4种类型。基本型最多,无差异型最少,各类型间界限较清晰。各需求点优先级顺序较明确,即用户首先要求产品应准时、内容完整和节点透明,其次要求产品应具备较高的准确率和个性化水平,最后要求产品的美观模块化程度便于高效地抽取使用并及时得到专家支持,而产品是否处于商用化阶段和是否有特色的优先级最差。但值得关注的是D16特色产品的需求点被视为魅力型需求的票数高达20票（共50票）,接近归类于魅力型需求（21票）。【结论】应以智能化业务系统建设和提升专家支持服务水平为工作核心来满足用户需求。     

1:10 000基础测绘数字产品的质量控制

从生产实际出发,对1:10000基础测绘数字产品DLG,DEM,DOM,E00质量控制的主要内容和方法进行了较全面的阐述,并提出了一些影响测绘产品质量的问题。     

Time-Domain Full Waveform Inversion Using the Gradient Preconditioning Based on Transmitted Wave Energy

The gradient preconditioning approach based on seismic wave energy can effectively avoid the huge memory consumption of the gradient preconditioning algorithms based on the Hessian matrix. However, the accuracy of this approach is prone to be influenced by the energy of reflected waves. To tackle this problem, the paper proposes a new gradient preconditioning method based on the energy of transmitted waves. The approach scales the gradient through a precondition factor, which is calculated by the ‘approximate transmission wavefield' simulation based on the nonreflecting acoustic wave equation. The method requires no computing nor storing of the Hessian matrix and its inverse matrix. Furthermore, the proposed method can effectively eliminate the effects of geometric spreading and disproportionality in the gradient illumination. The results of model experiments show that the time-domain full waveform inversion(FWI) using the gradient preconditioning based on transmitted wave energy can achieve higher inversion accuracy for deep high-velocity bodies and their underlying strata in comparison with the one using the gradient preconditioning based on seismic wave energy. The field marine seismic data test shows that our proposed method is also highly applicable to the FWI of field marine seismic data.