A comparative study of sequential minimal optimization-based support vector machines,vote feature intervals,and logistic regression in landslide susceptibility assessment using GIS

Landslide susceptibility assessment using GIS has been done for part of Uttarakhand region of Himalaya (India) with the objective of comparing the predictive capability of three different machine learning methods, namely sequential minimal optimization-based support vector machines (SMOSVM), vote feature intervals (VFI), and logistic regression (LR) for spatial prediction of landslide occurrence. Out of these three methods, the SMOSVM and VFI are state-of-the-art methods for binary classification problems but have not been applied for landslide prediction, whereas the LR is known as a popular method for landslide susceptibility assessment. In the study, a total of 430 historical landslide polygons and 11 landslide affecting factors such as slope angle, slope aspect, elevation, curvature, lithology, soil, land cover, distance to roads, distance to rivers, distance to lineaments, and rainfall were selected for landslide analysis. For validation and comparison, statistical index-based methods and the receiver operating characteristic curve have been used. Analysis results show that all these models have good performance for landslide spatial prediction but the SMOSVM model has the highest predictive capability, followed by the VFI model, and the LR model, respectively. Thus, SMOSVM is a better model for landslide prediction and can be used for landslide susceptibility mapping of landslide-prone areas.     

A comparative study of landslide susceptibility mapping using weight of evidence,logistic regression and support vector machine and evaluated by SBAS-InSAR monitoring: Zhouqu to Wudu segment in Bailong River Basin,China

The determining of landslide-prone areas in mountainous terrain is essential for land planning and hazard mitigation. In this paper, a comparative study using three statistical models including weight of evidence model (WoE), logistic regression model (LR) and support vector machine method (SVM) was undertaken in the Zhouqu to Wudu segment in the Bailong River Basin, Southern Gansu, China. Six conditionally independent environmental factors, elevation, slope, aspect, distance from fault, lithology and settlement density, were selected as the explanatory variables that may contribute to landslide occurrence based on principal component analysis (PCA) and Chi-square test. The relation between landslide distributions and these variables was analyzed using the three models and the results then used to calculate the landslide susceptibility (LS). The performance of the models was then evaluated using both the highly accurate deformation signals produced by using the Small Baseline Subset Interferometric Synthetic Aperture Radar technique and Receiver Operating Characteristic (ROC) curve. Results show more deformation points in areas with high and very high LS levels, and also more stable points in areas with low and very low LS levels for the SVM model. In addition, the SVM has larger area under the ROC curve. It indicates that the SVM has better prediction accuracy and classified ability. For the interpretability, the WoE derives the class of factors that most contributed to landsliding in the study area, and the LR reveals that factors including elevation, settlement density and distance from fault played major roles in landslide occurrence and distribution, whereas the SVM cannot provide relative weights for the variables. The outperformed SVM could be employed to determine potential landslide zones in the study area. Outcome of this research would provide preliminary basis for general land planning such as choosing new urban areas and infrastructure construction in the future, as well as for landslide hazard mitigation in Bailong River Basin.     

<Superscript>230</Superscript>Th/<Superscript>238</Superscript>U dating of hydrothermal sulfides from Duanqiao hydrothermal field,Southwest Indian Ridge

Duanqiao hydrothermal field is located between the Indomed and Gallieni fracture zones at the central volcano, at 50°28′E in the ultraslow-spreading Southwest Indian Ridge (SWIR). Twenty-eight subsamples from a relict chimney and massive sulfides were dated using the ²³⁰Th/²³⁸U method. Four main episodes of hydrothermal activity were determined according to the restricted results: 68.9–84.3, 43.9–48.4, 25.3–34.8, and 0.7–17.3 kyrs. Hydrothermal activity of Duanqiao probably started about 84.3 (±0.5) kyrs ago and ceased about 0.737 (±0.023) kyrs ago. The periodic character of hydrothermal activity may be related to the heat source provided by the interaction of local magmatism and tectonism. The estimated mean growth rate of the sulfide chimney is <0.02 mm/yr. This study is the first to estimate the growth rate of chimneys in the SWIR. The maximum age of the relict chimney in Duanqiao hydrothermal filed is close to that of the chimneys from Mt. Jourdanne (70 kyrs). The hydrothermal activity in Dragon Flag field is much more recent than that of Duanqiao or Mt. Jourdanne fields. The massive sulfides are younger than the sulfides from other hydrothermal fields such as Rainbow, Sonne and Ashadze-2. The preliminarily estimated reserves of sulfide ores of Duanqiao are approximately 0.5–2.9 million tons.     

结合宏观和微观视角的东莞城市建成区扩张特征分析

分析城市扩张过程中土地的供给现状、集约应用和利用监测等,对城市精准化管理与未来发展规划具有重要意义.本文基于东莞市历年遥感影像数据,提取城市用地信息,分别从宏观和微观角度量化城市用地变化和城市扩张过程,解释城市建成区扩张特征及城市扩张时空演化规律.研究表明:宏观上东莞市建城区面积1998-2018年逐渐扩张,直至201...     

一种小基线地表形变监测精度评价方法

针对地表形变监测中水准测量存在的缺陷,本文在介绍小基线（SBAS）技术的原理和数据处理流程的基础上,以宁波市32景Cosmo-SkyMed影像为数据源,获取地表形变平均速率与形变特征,提出了内符合和外符合精度评价方法,并阐述了水准比对流程,为SBAS技术的应用提供参考。结果显示,SBAS技术在城市地表形变监测中可以取得毫米级精度,成果可靠,具有广阔的应用前景。     

冰川和常年积雪常态化地理国情监测开展模式初探

应“十三五”开展常态化地理国情监测之需求,以冰川和常年积雪为监测对象,对冰雪常态化地理国情监测的开展模式进行了初步探索。主要内容有：冰雪监测内容、监测周期和监测方法,冰雪统计分析与评价。对于冰雪统计分析与评价,主要结合相关行业需求,重点在于指标构建。     

Shifting Correlation Between Earthquakes and Electromagnetic Signals: A Case Study of the 2013 Minxian–Zhangxian ML 6.5 (MW 6.1) Earthquake in Gansu,China

The shifting correlation method (SCM) is proposed for statistical analysis of the correlation between earthquake sequences and electromagnetic signal sequences. In this method, the two different sequences were treated in units of 1 day. With the earthquake sequences fixed, the electromagnetic sequences were continuously shifted on the time axis, and the linear correlation coefficients between the two were calculated. In this way, the frequency and temporal distribution characteristics of potential seismic electromagnetic signals in the pre, co, and post-seismic stages were analyzed. In the work discussed in this paper, we first verified the effectiveness of the SCM and found it could accurately identify indistinct related signals by use of sufficient samples of synthetic data. Then, as a case study, the method was used for analysis of electromagnetic monitoring data from the Minxian–Zhangxian M_L 6.5 (M_W 6.1) earthquake. The results showed: (1) there seems to be a strong correlation between earthquakes and electromagnetic signals at different frequency in the pre, co, and post-seismic stages, with correlation coefficients in the range 0.4–0.7. The correlation was positive and negative before and after the earthquakes, respectively. (2) The electromagnetic signals related to the earthquakes might appear 23 days before and last for 10 days after the shocks. (3) To some extent, the occurrence time and frequency band of seismic electromagnetic signals are different at different stations. We inferred that the differences were related to resistivity, active tectonics, and seismogenic structure.     

2013年四川芦山7.0级地震烈度遥感评估

2013年4月20日四川芦山MS7.0级地震发生后,在灾区应急获取了多种高分辨率航空和无人机遥感影像,并快速解译提取了灾区建筑物震害信息.采用地震烈度遥感定量评估方法,利用2008年汶川8.0级地震等震后震害遥感解译和现场调查研究确定的经验震害遥感定量评估模型,获得了芦山地震灾区126个主要居民点的地震烈度遥感评估结果,并据此圈画了地震烈度分布遥感评估图.结果显示,本次地震Ⅸ度区面积约150km2,Ⅷ度区面积约900km2.该结果在第一时间(4月21日晚)提供给了中国地震局地震现场应急指挥部.对比分析显示,地震烈度遥感快速评估结果与中国地震局4月25日公布的地震烈度图,以及与笔者在现场实地进行的建筑物震害详细调查结果基础上评定的地震烈度具有较高的一致性.表明强烈地震发生后,借助于快速获取的灾区高分辨率遥感影像,可以快速估计地震烈度分布,对地震灾区灾情估计和抗震救灾工作具有十分重要的参考意义.     

顾及多方向观测值权比反演地球重力场的动力积分法

考虑到不同坐标系下各个方向观测值对反演地球重力场的频谱贡献不同,建立了顾及多方向观测值权比的动力积分法,并利用该方法反演了高精度的GOCE HL-SST卫星重力场模型.首先,分析了不同坐标系下各个方向观测值与地球重力场信息的响应关系,其中惯性系(IRF)下X、Z方向的观测值分别对扇谐系数、带谐系数最为敏感,Z方向的解算精度在全频段均略高于X、Y方向;地固系(EFRF)下各个方向的独立解算精度均与能量守恒法的解算精度相当;局部指北坐标系(LNOF)下X、Z和Y三个方向的解算精度依次递减,且Y方向在47阶附近有明显"驼峰"现象.其次,比较了不同坐标系下顾及三个方向观测值权比的加权解算模型,其中加权联合解算模型精度在20至70阶次均明显优于等权解算模型,在带谐项和共振阶次精度提升明显,且LNOF下的加权联合解算精度要优于IRF和EFRF.最后,比较了GOCE和CHAMP卫星的模型解算精度,采用本文计算方法,仅利用2个月GOCE轨道观测值解算的模型精度优于包含更长观测时段信息的AIUB-CHAMP01S和EIGEN-CHAMP03S模型,且略优于ASU-GOCE-2months模型.     

不同仪器烈度算法在四川地区 历次地震中的比较应用

强震观测资料包括强震动时程记录资料和宏观震害调查资料两大类, 前者是定量的微观数据, 后者则是定性的宏观指标. 本文明晰给出了微观仪器烈度与宏观仪器烈度、 微观考察烈度与宏观考察烈度的概念, 并在此基础上提出了只有在宏观仪器烈度与宏观考察烈度之间进行比较才具有实质性的意义;同时对国内外常见的仪器烈度算法进行了比较性研究, 得出了袁一凡仪器烈度算法可靠性更高的结论;最后以四川地区历年来重要震例的强震动记录为依据, 对修正的袁一凡仪器烈度算法的可靠性进行了比较应用. 结果表明, 对袁一凡仪器烈度算法修正与扩展的应用是可行的, 同时也验证了将微观仪器烈度与宏观考察烈度直接进行比较会存在较大的差距.