西藏羌北高原多格错仁盐湖中更新世晚期以来的环境演化记录

通过对西藏藏北高原多格错仁盐湖湖岸3101cm高度剖面进行地形地貌、地层沉积特征、矿物学特征及粒度、频率磁化率等气候环境变化指标的分析研究发现,整个剖面反映出大致6个较大的气候变化过程：233. 3kaBP～223. 5kaBP气候波动较大,总体趋势气候趋于干冷,期间出现过两次较温暖气候,之后气候逐渐变冷;在223. 5kaBP～213. 6kaBP总体变化为气温大幅度上升,但在期间有一次较大的相对冷干过程;213. 6kaBP～170kaBP之间总体变化气候趋于变冷,中间有2次明显的气候变暖湿过程及两次冷干过程;170kaBP～117. 1kaBP气候转为明显湿热;117. 1kaBP～75. 6kaBP气候变化趋势明显降低;75. 6kaBP～56. 7kaBP气候又明显上升达到湿热状态。以上气候波动规律与极地冰芯记录及深海氧同位素记录的古气候波动规律有很好的一致性,同时本盐湖区与柴达木盆地察尔汗盐湖区的CH0310钻孔及青海湖南岸二郎剑阶地的 QH 86钻孔所揭示的中更新世晚期以来的气候变化的分析对比,发现西藏羌北的多格错仁盐湖区与青海的察尔汗盐湖区及青海湖湖区在更新世中晚期以来的气候环境变迁存在极好的可比性,说明青藏高原的气候演化在中晚更新世以来基本具有一致性,在时间上的微小超前与滞后具有区域上的细微变化,说明气候变迁在不同的区域又具有各自的独特性。     

基于信息量模型的云南东川泥石流易发性评价

以东川泥石流为研究对象,选取高程、坡度、坡向、起伏度、曲率、工程岩组、距断层距离、距水系距离、土地利用类型9个影响因子,以研究区144条泥石流为样本数据,建立了东川泥石流易发性评价体系。基于GIS平台,采用信息量模型计算各个评价指标状态分级的信息量值,以小流域为评价单元使用自然间断法将研究区泥石流易发程度分为极高、高、中和低4个易发区等级。结果表明：研究区极高易发区和高易发区发生泥石流灾害数量占比94.44%,AUC值为0.876,表明选取评价指标合理,信息量模型适用于东川泥石流易发性评价研究。     

江苏金坛和尚墩旧石器遗址地层的古地磁年代与磁化率研究

北方黄土研究中磁化率分析已越来越多运用到第四纪风尘堆积研究中。采用古地磁极性柱结合磁化率曲线比对定年的方法,初步确定江苏金坛和尚墩遗址750cm厚的地层年龄为330kaBP,对应磁化率曲线判断该区域分别经历了24kaBP左右、110kaBP左右、190—240kaBP左右、300kaBP左右的4个暖湿期,其中间隔有3个干冷时期,具有完整的3个气候旋回。对应地层发育的4个古土壤层、3个黄土层的风尘堆积—古土壤序列。研究表明,虽然古地磁极性定年的方法有一定的局限性,但在以磁化率信息为辅助的条件下,可在一定程度上消除这些局限,获得较为准确的地层年代,多种证据表明这种方法是可靠的。     

Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous areas.In this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility mapping.In the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)dataset.Additionally,a total of fifteen landslide influencing factors were selected.The relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)method.Finally,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,respectively.In terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,respectively.Moreover,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization capability.Meanwhile,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO model.Therefore,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area considered due to its excellent accuracy and efficiency.     

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.     

藏北雁石坪地区中侏罗统玛托组高频层序及海平面变化分析

西藏北部雁石坪地区晚巴柔—早巴通期玛托组是一个以砂岩、泥岩为主夹少量灰岩组成的混积型陆棚环境的沉积。含有介壳的凝缩段、下超面及沟蚀面,它们是划分体系域的关键界面。体系域具有二元结构特征,即海侵—高水位体系域,且TST沉积旋回厚度>HST,准层序类型有3种,分别是以砂岩为主的准层序、以泥岩为主的准层序和以潮坪体系向上变浅的准层序,准层序叠置构成进积型和退积型准层序组。采用沉积体系分析方法,初步建立研究区玛托组相对海平面变化曲线,并与藏南及全球海平面曲线进行对比分析,结合碳、氧同位素和磁化率资料,探讨研究区晚巴柔—早巴通期玛托组海平面变化控制因素。研究认为全球海平面变化控制了雁石坪地区晚巴柔早期海平面变化,而班公湖—怒江逢合带向北俯冲构造活动引起的区域洋盆容积变化是晚巴柔晚期—早巴通期海平面变化的主要因素。     

近12000年以来北京地区地球磁场变化机理探讨

距今12000年以来北京地区地球磁场长期变化由长周期(大于1000年)和短周期(约500年)两部分组成，长周期和短周期分量分别受控于漂移场和稳定场的变化.与日本地区相比，北京地区地球磁场最显著的特征是在距今(5110-4670)±110年之间曾发生短极性漂移事件，这样的短极性地磁事件可能与地球外核流体运动的异常变化有关.     

Varved sediments of Lake Yoa (Ounianga Kebir,Chad) reveal progressive drying of the Sahara during the last 6100 years

The sedimentological and geochemical properties of a 7·47 m long laminated sequence from hypersaline Lake Yoa in northern Chad have been investigated, representing a unique, continuous 6100 year long continental record of climate and environmental change in the eastern Central Sahara. These data were used to reconstruct the Mid to Late Holocene history of this currently hyper‐arid region, in order to address the question of whether the Mid Holocene environmental transition from a humid to a dry Sahara was progressive or abrupt. This study involved a suite of analyses, including petrographic and scanning electron microscope examination of thin sections, X‐ray diffraction, X‐radiography, granulometry, loss on ignition and magnetic susceptibility. The potential of micro‐X‐ray fluorescence core scanning was tested at very high resolution. Detailed microscopic investigation revealed the sedimentary processes responsible for the formation of the fine laminations, identified the season during which they were formed, and confirmed their annually rhythmic nature. High‐resolution X‐ray fluorescence core scanning allowed the distinction of each individual lamination over the entire record, opening new perspectives for the study of finely laminated sediment sequences. Geochemical and mineralogical data reveal that, due to decreasing monsoon rainfall combined with continuous and strong evaporation, the hydrologically open and fresh Mid Holocene Lake Yoa slowly evolved into the present‐day hypersaline brine depleted in calcium, which has existed for about the past 1050 years. During the oldest part of the investigated period, Lake Yoa probably contained a permanently stratified lower water column that was nevertheless disrupted relatively frequently by mixing events. Deep‐water anoxia became more stable because of increased salinity‐driven density stratification. In parallel, the sediment grain‐size proxies record a progressive increase of aeolian input in the course of the last 6100 years. Altogether, all geochemical and sedimentological indicators point to a progressive drying of the eastern Central Sahara, strengthening previous conclusions based on palaeoecological indicators.     

Artificial neural network and liquefaction susceptibility assessment: a case study using the 2001 Bhuj earthquake data,Gujarat, India

This study pertains to prediction of liquefaction susceptibility of unconsolidated sediments using artificial neural network (ANN) as a prediction model. The backpropagation neural network was trained, tested, and validated with 23 datasets comprising parameters such as cyclic resistance ratio (CRR), cyclic stress ratio (CSR), liquefaction severity index (LSI), and liquefaction sensitivity index (LSeI). The network was also trained to predict the CRR values from LSI, LSeI, and CSR values. The predicted results were comparable with the field data on CRR and liquefaction severity. Thus, this study indicates the potentiality of the ANN technique in mapping the liquefaction susceptibility of the area.