一种应用于三峡滑坡探测的新方法—SNMR法

三峡库区蓄水后,由于水位上升滑坡地区的软弱层遇到水就会变软并引起滑塌。核磁共振是当今世界尖端技术之一,我们首先将该技术应用于滑坡探查。本文探讨了用SNMR探查滑坡的方法技术以及通过SNMR实测数据求取与滑坡稳定性有关的水文地质参数,通过一个三峡的实例说明了该方法是一种快速有效的探测滑坡的方法。     

面向用户的地震地质灾害信息系统

工程选址、防震减灾规划与经济发展规划等均需要地震地质灾害基础数据,但地震地质灾害种类多、信息量大,如何进行有效管理,并实现共享,是当前亟待解决的问题。本文基于GIS技术,以ArcGIS为系统平台,建立了面向用户的地震地质灾害信息系统。首先,基于地震地质灾害的特点及对应的分析评价方法,构建了地震地质灾害信息系统平台的总体架构;对构建地震地质灾害系统存在的数据标准化等关键性问题进行了讨论,并给出了解决方案;最后以唐山地震为例,建立了示范性地震地质灾害GIS系统。     

Radioxenon Time Series and Meteorological Pattern Analysis for CTBT Event Categorisation

Understanding radioxenon time series and being able to distinguish anthropogenic from nuclear explosion signals are fundamental issues for the technical verification of the Comprehensive Nuclear-Test-Ban Treaty. Every radioxenon event categorisation methodology must take into account the background at each monitoring site to uncover anomalies that may be related to nuclear explosions. Feedback induced by local meteorological patterns on the equipment and on the sampling procedures has been included in the analysis to improve a possible event categorisation scheme. The occurrence probability of radioxenon outliers has been estimated with a time series approach characterising and avoiding the influence of local meteorological patterns. A power spectrum estimator for radioxenon and meteorological time series was selected; the randomness of the radioxenon residual time series has been tested for white noise by Kolmogorov–Smirnov and Ljung–Box tests. This methodological approach was applied to radioxenon data collected at two monitoring sites located at St. John’s, Canada and Charlottesville, USA, equipped with two different noble gas systems. It shows different feedback with local meteorological patterns and randomness for the radioxenon data recorded at the selected sites of St. John’s and Charlottesville as well as a different occurrence probability of the outliers in the normalized radioxenon original and residual time series.     

A Quantitative Assessment of DInSAR Measurements of Interseismic Deformation: The Southern San Andreas Fault Case Study

We investigate the capabilities and limitations of the Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques, in particular of the Small BAseline Subset (SBAS) approach, to measure surface deformation in active seismogenetic areas. The DInSAR analysis of low-amplitude, long-wavelength deformation, such as that due to interseismic strain accumulation, is limited by intrinsic trade-offs between deformation signals and orbital uncertainties of SAR platforms in their contributions to the interferometric phases, the latter being typically well approximated by phase ramps. Such trade-offs can be substantially reduced by employing auxiliary measurements of the long-wavelength velocity field. We use continuous Global Positioning System (GPS) measurements from a properly distributed set of stations to perform a pre-filtering operation of the available DInSAR interferograms. In particular, the GPS measurements are used to estimate the secular velocity signal, approximated by a spatial ramp within the azimuth-range radar imaging plane; the phase ramps derived from the GPS data are then subtracted from the available set of DInSAR interferograms. This pre-filtering step allows us to compensate for the major component of the long-wavelength range change that, within the SBAS procedure, might be wrongly interpreted and filtered out as orbital phase ramps. With this correction, the final results are obtained by simply adding the pre-filtered long-wavelength deformation signal to the SBAS retrieved time series. The proposed approach has been applied to a set of ERS-1/2 SAR data acquired during the 1992–2006 time interval over a 200?×?200?km area around the Coachella Valley section of the San Andreas Fault in Southern California, USA. We present results of the comparison between the SBAS and the Line Of Sight (LOS)—projected GPS time series of the USGC/PBO network, as well as the mean LOS velocity fields derived using SBAS, GPS and stacking techniques. Our analysis demonstrates the effectiveness of the presented approach and provides a quantitative assessment of the accuracy of DInSAR measurements of interseismic deformation in a tectonically active area.     

Motif Discovery on Seismic Amplitude Time Series: The Case Study of Mt Etna 2011 Eruptive Activity

Algorithms searching for similar patterns are widely used in seismology both when the waveforms of the events of interest are known and when there is no a priori-knowledge. Such methods usually make use of the cross-correlation coefficient as a measure of similarity; if there is no a-priori knowledge, they behave as brute-force searching algorithms. The disadvantage of these methods, preventing or limiting their application to very large datasets, is computational complexity. The Mueen–Keogh (MK) algorithm overcomes this limitation by means of two optimization techniques—the early abandoning concept and space indexing. Here, we apply the MK algorithm to amplitude time series retrieved from seismic signals recorded during episodic eruptive activity of Mt Etna in 2011. By adequately tuning the input to the MK algorithm we found eight motif groups characterized by distinct seismic amplitude trends, each related to a different phenomenon. In particular, we observed that earthquakes are accompanied by sharp increases and decreases in seismic amplitude whereas lava fountains are accompanied by slower changes. These results demonstrate that the MK algorithm, because of its particular features, may have wide applicability in seismology.     

GPS连续站水平位置坐标时间序列插值的一种新方法

GPS连续站多年的运行实践表明,其观测值在时间序列上并非时时连续,数据整体不完整率甚至已经达到了较高的比例。研究发现:在地壳形变的分析中,这样的结果直接影响着某种形变信息的正确提取。因此,插值已成为一项提高资料利用率、获取更多有效信息的必要途径。由于通常插值方法不注重空间上的联系,故对GPS的时间序列常难以给出恰如其分的插值结果。本文根据GPS连续观测资料各种信息的特征,提出了一种新的综合插值方法——"欧拉—周期—滤波—自噪"插值法。本文以天津GPS连续观测网为例,给出了实际操作方法,计算结果表明该插值法可以最大程度地保证各种信息不失真,较其它方法更为有效。     

煤矿采空塌陷特征与危险性预测研究——以北京西山地区为例

本文主要利用可拓理论及层次分析法进行了西山地区采空塌陷危险性预测研究,并在典型地段利用概率积分法、数值模拟方法进行了采空塌陷预测。主要思路是探讨解决采空塌陷危险性预测、区划中人为干扰的问题,为开展地质灾害预警预报工作提供基础资料。     

云南GNSS时间序列共模分量提取分析

针对共模分量的精确获取问题,以2011—2018年云南31个GNSS连续站垂向时间序列为基础,选用区域叠加法和主成分分析法,提取得到共模分量的主要成分,对比分析了这两种方法的效果。结果表明:(1)由于云南垂向非线性运动空间一致性较好(测站间相关系数平均值为0.88),两种方法得到的共模分量较为一致;(2)两种方法提取得到的共模分量与全球水文负荷和大气负荷模型给出的位移时间序列接近(相关系数均为0.9),说明共模分量的主要成分为地表负荷变化引发的地壳垂向非构造运动;(3)共模分量不能用周期模型完全表示,还包含了年际间的运动差异等信息;(4)两种方法的空间滤波效果非常接近(WRMS减速比平均值都为0.70),测站的空间滤波效果与测站间相关系数呈显著的正相关。由于区域叠加法对测站数据完整率要求相对较低,因此建议当测站较少或者数据缺失较多时,采用区域叠加法;在测站较多且数据完整率较高时,建议采用主成分分析法。     

汶川地震滑坡危险性评价——以武都区和文县为例

利用GIS技术详细研究汶川地震在甘肃省陇南市武都区和文县触发的滑坡地质灾害的分布规律及其与地震烈度、地形坡度、断层、高程、地层岩性的相关关系,采用基于GIS的加权信息量模型的崩塌滑坡危险性评价方法,对研究区的地震滑坡危险性进行学科分析。结果表明：极高危险区在高程上主要分布在集水高程区,高度危险区主要沿白水江、白龙江等主干河流两侧极高易发区的边界向两侧扩展,轻度和极轻度危险区面积占比较小,主要分布在低烈度、活动断裂不发育、人类活动微弱的高海拔地区,另外国道G215沿极高危险性区域分布明显;利用危险性等级分区结果统计人口公里格网数据,得到武都区和文县潜在影响人口,发现研究区约78万人将受到地震滑坡灾害的潜在影响。     

A Discrete Element Analysis of the Sliding Friction Heat in High-Speed and Long-Runout Landslides

Earthquake is one of the main causes of high-speed and long-runout landslides. Generally, the heat generated in the sliding zone is significant in such devastating landslides. In this study, we establish a two dimensional slope model which includes 0.2 million elements to simulate the development of high speed and long-runout landslides using the discrete element software MatDEM. The model not only suggests that heat is produced by friction and fracturing, but also simulates the process of tension generation in cracks and the generation of a high heat zone near the sliding region. Besides, the heat field graph indicates a banded high heat belt that is related to the location of the thickest sliding body. The logarithms of the total calorific value and the highest value in the heat zone during the sliding process are linearly related to the logarithm of the landslide height.     

Solving Groundwater Flow Problems with Time Series Analysis: You May Not Even Need Another Model

Time series analysis is a data-driven approach to analyze time series of heads measured in an observation well. Time series models are commonly much simpler and give much better fits than regular groundwater models. Time series analysis with response functions gives insight into why heads vary, while such insight is difficult to gain with black box models out of the artificial intelligence world. An important application is to quantify the contributions to the head variation of different stresses on the aquifer, such as rainfall and evaporation, pumping, and surface water levels. Time series analysis may be applied to answer many groundwater questions without the need for a regular groundwater model, such as what is the drawdown caused by a pumping station? Or, how long will it take before groundwater levels recover after a period of drought? Even when a regular groundwater model is needed to solve a groundwater problem, time series analysis can be of great value. It can be used to clean up the data, identify the major stresses on the aquifer, determine the most important processes that affect flow in the aquifer, and give an indication of the fit that can be expected. In addition, it can be used to determine calibration targets for steady-state models, and it can provide several alternative calibration methods for transient models. In summary, the overarching message of this paper is that it would be wise to do time series analysis for any application that uses measured groundwater heads.     

Identification and Explanation of a Change in the Groundwater Regime using Time Series Analysis

Time series analysis is applied to identify and analyze a transition in the groundwater regime in the aquifer below the sand ridge of Salland in the Netherlands, where groundwater regime refers to the range of head variations throughout the seasons. Standard time series analysis revealed a discrepancy between modeled and observed heads in several piezometers indicating a possible change in the groundwater regime. A new time series modeling approach is developed to simulate the transition from the initial regime to the altered regime. The transition is modeled as a weighted sum of two responses, one representing the initial state of the system, the other representing the altered state. The inferred timing and magnitude of the change provided strong evidence that the transition was the result of significant dredging works that increased the river bed conductance of the main river draining the aquifer. The plausibility of this explanation is corroborated by an analytical model. This case study and the developed approach to identify a change in the groundwater regime are meant to stimulate a more systematic application of time series analysis to detect and understand changes in groundwater systems which may easily go unnoticed in groundwater flow modeling.     

Hazard Assessment of Co-seismic Landslides Based on Information Value Method: A Case in 2018 MW6.6 Hokkaido Earthquake, Japan

An M_W6.6 earthquake occurred in eastern Hokkaido, Japan on September 6^th, 2018. Based on the pre-earthquake image from Google Earth and the post-earthquake image from high resolution (3 m) planet satellite, we manually interpret 9 293 coseismic landslides and select 7 influencing factors of seismic landslide, such as elevation, slope, slope direction, road distance, flow distance, peak ground acceleration (PGA) and lithology. Then, 9 293 landslide points are randomly divided into training samples and validation samples with a proportion of 7:3. In detail, the training sample has 6 505 landslide points and the validation sample has 2 788 landslide points. The hazard risk assessment of seismic landslide is conducted by using the information value method and the study area is further divided into five risk grades, including very low risk area, low risk area, moderate risk area high risk area and very high risk area. The results show that there are 7 576 landslides in high risk area and very high risk area, accounting for 81.52% of the total landslide number, and the landslide area is 22.93 km², accounting for 74.35% of the total area. The hazard zoning is in high accordance with the actual situation. The evaluation results are tested by using the curve of cumulative percentage of hazardous area and cumulative percentage of landslides number. The results show that the success rate of the information value method is 78.50% and the prediction rate is 78.43%. The evaluation results are satisfactory, indicating that the hazard risk assessment results based on information value method may provide scientific reference for landslide hazard risk assessment as well as the disaster prevention and mitigation in the study area.     

Using a Mesoscale Meteorological Model to Reduce the Effect of Tropospheric Water Vapour from DInSAR Data: A Case Study for the Island of Tenerife, Canary Islands

Measurements of ground displacement through classical Differential Interferometric SAR (DInSAR) and advanced DInSAR techniques have been carried out over the entire actively volcanic island of Tenerife, Canary Islands. However, a detailed analysis of the effect of tropospheric water vapour on DInSAR at Tenerife should be carried out to evaluate its influence, including correction models that might improve the accuracy of DInSAR derived deformation signals. Unlike water vapour correction models that are based on space platforms (e.g. MODIS and MERIS), we present an alternative approach that is based on precise water vapour estimations derived from mesoscale numerical meteorological models, in particular the Weather Research and Forecasting (WRF) model. The application of this approach to a set of DInSAR observations of the island of Tenerife shows encouraging results.     

Special Contribution: Pictorial Series of the Manifestations of the Dynamics of the Earth: 3. Historical Images of Landslides and Rock Falls

Studia Geophysica et Geodaetica -