Incorrect unwrapping of dense interferometric fringes caused by large gradient displacements make it difficult to measure mining subsidence using conventional Interferometric Synthetic Aperture Radar (InSAR). This paper presents a Range Split Spectrum Interferometry assisted Phase Unwrapping (R-SSIaPU) method for the first time. The R-SSIaPU method takes advantage of (i) the capability of Range Split Spectrum Interferometry of measuring surface displacements with large spatial gradients, and (ii) the capability of conventional InSAR of being sensitive to surface displacements with limited spatial gradients. Both simulated and real experiments show that the R-SSIaPU method can monitor large gradient mining-induced surface movements with high precision. In the case of the Tangjiahui mine, the R-SSIaPU method agreed with GPS with differences of approximately 4.2 cm, whilst conventional InSAR deviated from GPS with differences of nearly 1 m. The R-SSIaPU method makes phase unwrapping less challenge, especially in the cases with large surface displacements. In addition to mining subsidence, it is believed that the R-SSIaPU method can be used to monitor surface displacements caused by landslides, earthquakes, volcanic eruptions, and glacier movements.

The Nyingchi Mw 6.4 earthquake on 17 November 2017 is the first large event since 1950 at the southeast end of the Jiali fault. This event was captured by interferometric synthetic aperture radar (InSAR) measurements from the European Space Agency (ESA) Sentinel-1A radar satellite, which provide the potential to determine the fault plane, as well as the co-seismic slip distribution, and understand future seismic hazards. However, due to the limited magnitude of surface displacements and the strong topography variations, InSAR-derived co-seismic signals are contaminated by strong tropospheric effects which makes it difficult (if not impossible) to determine the source parameters and co-seismic slip distribution. In this paper, we employ the Generic Atmospheric Correction Online Service for InSAR (GACOS) to generate correction maps for the co-seismic interferograms, and successfully extract co-seismic surface displacements for this large event. The phase standard deviation after correction for a seriously-contaminated interferogram reaches 0.8 cm, significantly improved from the traditional phase correlation analysis (1.13 cm) or bilinear interpolation (1.28 cm) methods. Our best model suggests that the seismogenic fault is a NW–SE striking back-thrust fault with a right-lateral strike slip component. This reflects the strain partitioning of NE shortening and eastward movement of the Eastern Tibetan plateau due to the oblique convergence between the Indian and Eurasian plates.

The Daguangbao mega-landslide (China), induced by the 2008 Wenchuan earthquake (Mw=7.9), with an area of approximately 8km2, is one of the largest landslides in the world. Experts predicted that the potential risk and instability of the landslide might remain for many decades, or even longer. Monitoring the activity of such a large landslide is hence critical. Terrain Observation by Progressive Scans (TOPS) mode from the Sentinel-1 satellite provides us with up-to-date high-quality Synthetic Aperture Radar (SAR) images over a wide ground coverage (250×250km), enabling full exploitation of various InSAR applications. However, the TOPS mode introduces azimuth-dependent Doppler variations to radar signals, which requires an additional processing step especially for SAR interferometry. Sentinel-1 TOPS data have been widely applied to earthquakes, but the performance of TOPS data-based time series analysis requires further exploitation. In this study, Sentinel-1 TOPS data were employed to investigate landslide post-seismic activities for the first time. To deal with the azimuth-dependent Doppler variations, a processing chain of TOPS time series interferometry approach was developed. Since the Daguangbao landslide is as a result of the collapse of a whole mountain caused by the 2008 Mw 7.9 Wenchuan earthquake, the existing Digital Elevation Models (DEMs, e.g. SRTM and ASTER) exhibit height differences of up to approximately 500m. Tandem-X images acquired after the earthquake were used to generate a high resolution post-seismic DEM. The high gradient topographic errors of the SRTM DEM (i.e. the differences between the pre-seismic SRTM and the actual post-seismic elevation), together with low coherence in mountainous areas make it difficult to derive a precise DEM using the traditional InSAR processing procedure. A re-flattening iterative method was hence developed to generate a precise TanDEM-X DEM in this study. The volume of the coseismic Daguangbao landslide was estimated to be of 1.189±0.110×109m3 by comparing the postseismic Tandem-X DEM with the preseismic SRTM DEM, which is consistent with the engineering geological survey result. The time-series results from Sentinel-1 show that some sectors of the Daguangbao landslide are still active (and displaying four sliding zones) and exhibiting a maximum displacement rate of 8cm/year, even eight years after the Wenchuan earthquake. The good performance of TOPS in this time series analysis indicates that up-to-date high-quality TOPS data with spatiotemporal baselines offer significant potential in terms of future InSAR applications.

The 28 August 2009 Mw 6.3 Dachaidan (DCD) earthquake occurred at the Qaidam Basin’s northern side. To explain its postseismic deformation time series, the method of modeling them with a combination model of afterslip and viscoelastic relaxation is improved to simultaneously assess the time-dependent afterslip and the viscosity. The coseismic slip model in the layered model is first inverted, showing a slip pattern close to that in the elastic half-space. The postseismic deformation time series can be explained by the combination model, with a total root mean square (RMS) misfit of 0.37 cm. The preferred time-dependent afterslip mainly occurs at a depth from the surface to about 9.1 km underground and increases with time, indicating that afterslip will continue after 28 July 2010. By 334 days after the main shock, the moment released by the afterslip is 0.91 × 1018 N∙m (Mw 5.94), approximately 24.3% of that released by the coseismic slip. The preferred lower bound of the viscosity beneath the Qaidam Basin’s northern side is 1 × 1019 Pa·s, close to that beneath its southern side. This result also indicates that the viscosity structure beneath the Tibet Plateau may vary laterally.

After publication of the research paper [1] an error was recognized.[...]

On April 25, 2015, a destructive MW 7.8 earthquake struck the capital of Nepal, Kathmandu, killing more than 8800 people and destroying numerous historical structures. We analyze six coseismic interferograms from several satellites (ALOS-2, Sentinel-1A, and RADARSAT-2), as well as three-dimensional displacements at six GPS stations to investigate fault structure and slip distribution of the Gorkha earthquake. Using a layered crustal structure, the best-fit slip model shows that the preferred dip angle of the mainshock fault is 6±3.5° and the major slip is concentrated within depths of 8–15km. The maximum slip of ~6.0m occurs at a depth of 11km, 70km south east of the epicenter. The coseismic rupture extends ~150km eastward of the epicentre with a cumulative geodetic moment of 7.8×1020Nm, equivalent to an earthquake of MW 7.84. We also investigate the MW 7.2 aftershock on 12 May 2015 using another three postseismic interferograms from ALOS2, RADARSAT-2, and Sentinel-1A. The InSAR-based best-fit slip model of the largest aftershock implies that its major slip is next to the eastern lower end of the mainshock rupture with a similar maximum slip of ~6m at a depth of ~13km. This study generates various coseismic geodetic measurements to determine the source parameters of the MW 7.8 Gorkha earthquake and 12 May MW 7.2 afershock, providing an additional chance to understand the local fault structure and slip extent.

2012-04-11 08:38:37UT和10:43:09UT苏门答腊地区先后发生了M8.6级和M8.2级强烈地震.本文采用震中附近地区9个IGS连续跟踪站的GPS观测数据,对两次地震的同震电离层异常现象进行了探测与分析.结果表明两次地震之后均出现了明显的同震电离层异常现象,异常出现的时间和异常的幅度与距震中的距离有关,异常的最大值约为2 TECU,且震中偏北方向的异常最为显著.根据震中到穿刺点的距离,计算得出两次地震产生的大气声波的传播速度分别为0.73±0.06 km/s和0.69±0.04 km/s,与声波在0～450 km内的平均传播速度一致.

The computerized ionospheric tomography usually involves solving an ill-posed inversion problem. The sparsity of Global Positioning System (GPS) stations and the limitation of projection angles lead to insufficient data acquisition, thereby preventing the accurate reconstruction of ionospheric-electron-density distributions. In this paper, we investigate and propose a 3-D iterative reconstruction algorithm based on the minimization of total variation under quiescent and disturbed ionospheric conditions. Numerical experiments on GPS simulation data and real data are discussed. In contrast to the improved algebraic reconstruction technique, the proposed algorithm exhibits significantly reconstruction accuracy.

以2012年1月数据为例,分析了球谐函数模型的阶数和观测数据采样间隔对全球电离层模型建模时间和模型精度的影响,以及地磁场活动强度与模型精度的相关性.结果表明,模型阶数和采样间隔对建模时间和模型精度有很大影响;太阳和地磁场活动强度与模型精度的相关性为——模型精度随太阳和地磁活动强度的增强而降低.

针对全球加权平均温度模型(GWMT)在海洋地区存在显著异常的缺陷,利用2007～2011年NCEP再分析资料建立全球大气加权平均温度模型GWMT_N.利用2011年全球650个无线电探空站Tm数据和2011年COSMIC掩星Tm数据对GWMT_N模型的精度进行验证.结果表明,GWMT_N模型避免了GWMT模型的缺陷,且在陆地地区的精度也得到了提高;GWMT_N模型的整体精度与采用地面实测温度的Bevis公式相当,且无需已知地面实测温度.

以2010年001 ～031 d结果为例,分析加入GLONASS数据对全球电离层格网模型结果的影响.结果表明,利用GPS/GLONASS系统组合建立全球电离层格网模型的结果与单独采用GPS相差不大,加入GLONASS数据建立全球电离层模型并不能克服模型在海洋地区精度较低的缺陷.

In this study, the correlation between T m , a key variable in GNSS water vapor inversion, and surface temperature ( T s ) was calculated on a global scale based on the global geodetic observing system (GGOS) atmosphere T m data and European centre for medium-range weather forecasts (ECMWF) surface temperature data. The results show that their correlation is mainly affected by latitudes, and the correlation is stronger at high latitudes and weaker at low latitudes. Although the correlation is relatively weak in the tropic areas, the temperature changes so little in a year in these areas that we can still achieve good T m results by linear regression model. Based on these facts, “GGOS atmosphere” T m data and ECMWF T s data from 2005 to 2011 were used to establish the global latitude-related linear regression model. The new model has root mean square error (RMSE) of 3.2, 3.3, and 4.4 K, respectively, compared with respect to the “GGOS atmosphere” data, COSMIC data, and radiosonde data and is more accurate than the Bevis T m – T s relationship.

When using total least squares to solve the regression model parameter,both the traditional SVD and iteration methods do not consider that corrections of the same elements in different positions of the augmented matrix formed by the coefficient matrix and the observation vector are different.This paper proposes a new iteration method which can effectively solve the shortage problem in traditional methods,puts the same element in different positions of an augmented matrix into the same correction.This method is more compatible to the actual situation.Adjustment precision can also be improved.Finally,a concrete example was conducted to verify the feasibility and effectiveness of this method.

We introduce a new global ionospheric modeling software—IonoGim, using ground-based GNSS data, the altimetry satellite and LEO (Low Earth Orbit) occultation data to establish the global ionospheric model. The software is programmed by C++ with fast computing speed and highly automatic degree, it is especially suitable for automatic ionosphere modeling. The global ionospheric model and DCBs obtained from IonoGim were compared with the CODE (Center for Orbit Determination in Europe) to verify its accuracy and reliability. The results show that IonoGim and CODE have good agreement with small difference, indicating that IonoGim owns high accuracy and reliability, and can be fully applicable for high-precision ionospheric research. In addition, through comparison between only using ground-based GNSS observations and multi-source data model, it can be demonstrated that the space-based ionospheric data effectively improve the model precision in marine areas where the ground-based GNSS tracking station lacks.

On April 20, 2013, an earthquake of M7.0 occurred in Lushan, Sichuan province, China. This paper investigates the coseismic ionospheric anomalies using GPS (Global Positioning System) data from 23 reference stations in Sichuan province that are a part of the Crustal Movement Observation Network of China (CMONOC). The recorded results show that a clear ionospheric anomaly occurred within 15min after the earthquake near the epicenter, and the occurrence time of the anomalies recorded by various stations is related to the distance from the epicenter. The maximum anomaly is 0.25 TECu, with a 2min duration and the distance of the recording station to the epicenter is 83km. Acoustic waves generated by the crustal vertical movement during the earthquake propagate up to the height of the ionosphere lead to the ionospheric anomaly, and the propagation speed of the acoustic wave is calculated as 0.72±0.04km/s based on the propagation time and propagation distance, consistent with the average speed of sound waves within a 0–450km atmospheric height.

On 20 April 2013, an earthquake of M =7.0 occurred in Lushan, Sichuan province, China. This paper investigates the coseismic ionospheric anomalies using GPS (Global Positioning System) data from 23 reference stations in Sichuan province that are a part of the Crustal Movement Observation Network of China (CMONOC). The recorded results show that a clear ionospheric anomaly occurred within 15 min after the earthquake near the epicenter, and the occurrence time of the anomalies recorded by various stations is related to the distance from the epicenter. The maximum anomaly is 0.25 TECu, with a 2 min duration and the distance of the recording station to the epicenter is 83 km. Acoustic waves generated by the crustal vertical movement during the earthquake propagate up to the height of the ionosphere lead to the ionospheric anomaly, and the propagation speed of the acoustic wave is calculated as 0.72 ± 0.04 km s-1 based on the propagation time and propagation distance, consistent with the average speed of sound waves within a 0–450 km atmospheric height.

Considering the limitations of current single pixel-based and function-based computerized ionospheric tomography (CIT) models, this paper proposes a new tomography model – COMBI, which combines these two models. COMBI model is able to reconstruct the three dimensional distribution of electron density with fewer parameters, and easy to compute, as well as very convenient to use. Through experiments with simulated data and measured data, it is verified that the new COMBI model not only can better describe refine structure of ionospheric electron density, but also is superior to these two pixel-based and function-based CIT models in application.

The pre-earthquake ionospheric anomalies that occurred before the global M = 7.0+ earthquakes in 2010 are investigated using the total electron content (TEC) from the global ionosphere map (GIM). We analyze the possible causes of the ionospheric anomalies based on the space environment and magnetic field status. Results show that some anomalies are related to the earthquakes. By analyzing the time of occurrence, duration, and spatial distribution of these ionospheric anomalies, a number of new conclusions are drawn, as follows: earthquake-related ionospheric anomalies are not bound to appear; both positive and negative anomalies are likely to occur; and the earthquake-related ionospheric anomalies discussed in the current study occurred 0–2 days before the associated earthquakes and in the afternoon to sunset (i.e. between 12:00 and 20:00 local time). Pre-earthquake ionospheric anomalies occur mainly in areas near the epicenter. However, the maximum affected area in the ionosphere does not coincide with the vertical projection of the epicenter of the subsequent earthquake. The directions deviating from the epicenters do not follow a fixed rule. The corresponding ionospheric effects can also be observed in the magnetically conjugated region. However, the probability of the anomalies appearance and extent of the anomalies in the magnetically conjugated region are smaller than the anomalies near the epicenter. Deep-focus earthquakes may also exhibit very significant pre-earthquake ionospheric anomalies.