Predictive landslide susceptibility mapping using spatial information in the Pechabun area of Thailand

For predictive landslide susceptibility mapping, this study applied and verified probability model, the frequency ratio and statistical model, logistic regression at Pechabun, Thailand, using a geographic information system (GIS) and remote sensing. Landslide locations were identified in the study area from interpretation of aerial photographs and field surveys, and maps of the topography, geology and land cover were constructed to spatial database. The factors that influence landslide occurrence, such as slope gradient, slope aspect and curvature of topography and distance from drainage were calculated from the topographic database. Lithology and distance from fault were extracted and calculated from the geology database. Land cover was classified from Landsat TM satellite image. The frequency ratio and logistic regression coefficient were overlaid for landslide susceptibility mapping as each factor’s ratings. Then the landslide susceptibility map was verified and compared using the existing landslide location. As the verification results, the frequency ratio model showed 76.39% and logistic regression model showed 70.42% in prediction accuracy. The method can be used to reduce hazards associated with landslides and to plan land cover.     

典型岩溶槽谷区土壤水和地下水氢氧稳定同位素对隧道建设的响应

隧道建设引起地下水流场改变,对区域水分运移过程造成严重影响.以重庆市中梁山岩溶槽谷为例,于2017年4月～2019年4月收集降水、土壤水、地下水和隧道排水,利用氢氧稳定同位素分析隧道影响区和非隧道影响区的土壤水和地下水运移过程,探索隧道建设对其产生的影响.结果表明:隧道影响区土壤水δ2H和δ18O变化幅度较非隧道影响区...     

地震流动观测防护罩集成优化与效果分析

为提升地震流动观测质量,适应恶劣条件下野外长期工作,研制集传感器、供电、通讯网络三大模块于一体的流动观测仪器整体防护罩。利用2套GL-PS2一体化短周期地震仪进行对比观测,一套安装于南京台室内基岩观测墩,另一套配备防护罩浅层掩埋于户外。结果表明新型研制的观测仪整体集成防护罩具有以下优点:①保温性能好,内部环境日温差小于3℃,月温度变化小于5℃。②防水性好,经历多次暴雨大雪天气后,防护罩内部干燥,无水滴或结露现象。③台基噪声略优于室内观测墩。④2台设备相关系数垂直向优于水平向,夜晚优于白天。⑤地震记录波形相关系数均在0.6以上,震级越大,相关性越高,且信噪比差距较小。     

Field-observation for an anticyclonic mesoscale eddy consisted of twelve gliders and sixty-two expendable probes in the northern South China Sea during summer 2017

An intensive field observation experiment using 12 Chinese gliders equipped with conductivity-temperature-depth (CTD) sensors and 62 expendable CTD probes (XCTDs) was performed to investigate the 3-D structure and time evolution of an anticyclonic eddy in the northern South China Sea (NSCS). The observed results showed that the anticyclonic eddy had a horizontal radius of about 80 km at surface and a vertical depth of impact of more than 1000 m. The largest temperature and salinity anomalies compared with the averaged values of the temperature and salinity profiles were 3.5°C and 0.4 psu at 120 m depth, respectively. Combined analysis of altimeter sea level and water mass properties indicated that the anticyclonic eddy was shed from the Kuroshio loop current. The vertical axis of the anticyclonic eddy tilted from surface to the observed maximum depth (1000 m) along its translation direction against the 2000 m isobath. The center of the anticyclonic eddy remained in the region east of Dongsha Island for more than half a month. During this time, the long axis direction of the eddy changed from across the slope to along the slope. Then, the eddy moved southward along the 2000 m isobaths. Both the geostrophic current and temperature distribution revealed that the eddy intensity weakened during the observation period gradually. These observations indicated strong interaction between the anticyclonic eddy and the slope topography of Dongsha Island.     

Application of deep learning in ecological resource research: Theories,methods, and challenges

Ecological resources are an important material foundation for the survival, development, and self-realization of human beings. In-depth and comprehensive research and understanding of ecological resources are beneficial for the sustainable development of human society. Advances in observation technology have improved the ability to acquire long-term, cross-scale,massive, heterogeneous, and multi-source data. Ecological resource research is entering a new era driven by big data. Traditional statistical learning and machine learning algorithms have problems with saturation in dealing with big data. Deep learning is a method for automatically extracting complex high-dimensional nonlinear features, which is increasingly used for scientific and industrial data processing because of its ability to avoid saturation with big data. To promote the application of deep learning in the field of ecological resource research, here, we first introduce the relationship between deep learning theory and research on ecological resources, common tools, and datasets. Second, applications of deep learning in classification and recognition,detection and localization, semantic segmentation, instance segmentation, and graph neural network in typical spatial discrete data are presented through three cases: species classification, crop breeding, and vegetation mapping. Finally, challenges and opportunities for the application of deep learning in ecological resource research in the era of big data are summarized by considering the characteristics of ecological resource data and the development status of deep learning. It is anticipated that the cooperation and training of cross-disciplinary talents may promote the standardization and sharing of ecological resource data,improve the universality and interpretability of algorithms, and enrich applications with the development of hardware.     

Natural 222Rn as a tracer of mixing and volatilization in a shallow aquifer during a CO2 injection experiment

This study aims to evaluate the application of ²²²Rn in groundwater as a tracer for monitoring CO₂ plume migration in a shallow groundwater system, which is important to detect potential CO₂ leakage in the carbon capture and storage (CCS) project. For this research, an artificial CO₂-infused water injection experiment was performed in a shallow aquifer by monitoring hydrogeochemical parameters, including ²²²Rn. Radon in groundwater can be a useful tracer because of its sensitivity to sudden changes in subsurface environment. To monitor the CO₂ plume migration, the data were analysed based on (a) the influence of mixing processes on the distribution of ²²²Rn induced by the artificial injection experiment and (b) the influence of a carrier gas role by CO₂ on the variation of ²²²Rn. The spatio-temporal distributions of radon concentrations were successfully explained in association with horizontal and vertical mixing processes by the CO₂-infused water injection. Additionally, the mixing ratios of each monitoring well were calculated, quantitatively confirming the influence of these mixing processes on the distribution of radon concentrations. Moreover, one monitoring well showed a high positive relationship between ²²²Rn and Total dissolved inorganic carbon (TIC) by the carrier gas effect of CO₂ through volatilization from the CO₂ plume. It indicated the applicability of ²²²Rn as a sensitive tracer to directly monitor CO₂ leakage. When with a little effect of carrier gas, natural ²²²Rn in groundwater can be used to compute mixing ratio of CO₂-infused water indicative of CO₂ migration pathways. CO₂ carrier gas effect can possibly increase ²²²Rn concentration in groundwater and, if fully verified with more field tests, will pose a great potential to be used as a natural tracer for CO₂.     

用短期降水预报做洪水预报与调度的应用试验

利用2003～2007年潘家口水库流域10场较大降水及其对应的河北省气象局MM5的面雨量预报数据,采用降水预报格点数据与水文洪水预报耦合的方法进行水库的洪水预报和调度,增长了水文洪水预报的预见期,在不增加水库工程投资,不增加上下游和工程风险的前提下,在汛期实施风险调度,以此发挥水库在洪水资源化调度中作用,解决防洪与蓄水的矛盾.     

甘肃北山及其邻区地壳稳定性模糊综合评价

地壳稳定性评价是高放核废物处置库选址的前期研究课题。本文以模糊数学为基础 ,建立地壳稳定性模糊综合评价模式 ;然后按地壳结构、地震、活动断裂和现代构造应力场等 4个方面的特征把研究区划分为 8个不同稳定性的区域。     

紫球藻藻红蛋白的分离纯化及光谱特性研究

紫球藻（ｐｏｒｐｈｙｒｉｄｉｕｍ ｃｒｕｅｎｔｕｍ）的水溶性色素粗提物经过固体硫酸铵沉淀和羟基磷灰石（ＨＡ）柱层析后,可以分离出较纯的Ｂ－藻红蛋白（Ｂ－ＰＥ）,纯度（ＯＤ５４５ｎｍ／ＯＤ２８０ｎｍ）为３．０。测定了Ｂ－ＰＥ的紫外可见光吸收光谱和荧光光谱以及紫球藻活体吸收光谱。Ｂ－ＰＥ在５４５ｎｍ和５６３ｎｍ各有一个峰,在４９８ｎｍ有一肩峰,与活体吸收光谱相比较只是最大吸收峰向短波方向移动了５ｎｍ。     

几种稀土元素对螺旋藻生长的影响

研究了镧、饵、钇这三种稀土元素对螺旋藻生长的影响。结果表明,单种稀土元素螺旋藻生长繁殖都有明显地促进作用,浓度范围普遍在５μｇ／Ｌ～３０μｇ／Ｌ之间,其中三氯化镧、硝酸镧、三氯化钇在１５μｇ／Ｌ、三氯化饵在２５μｇ／Ｌ的深度时对螺旋藻的促进作用最显著。