Conference Agenda

Ground subsidence caused by the exploitation of mineral resources is not only an important factor to be considered in the development and utilization of land space, but also an obvious indication for the area of underground illegal mining. The mining distribution of mineral resources is wide and scattered, so it is very necessary to quickly and accurately identify and extract the spatial distribution of mining subsidence in large areas.In this paper, the multitemporal difference interferometric phase diagram of subsidence mining area is obtained by using synthetic aperture radar differential interferometry (DInSAR) technology, FCN-8s, PSPNet Deeplabv3 and U-Net models are used to train the network. The results show that the U-Net model has high detection accuracy and takes short time. In order to improve the semantic segmentation and extraction accuracy of mining subsidence, the efficient channel attention (ECA) module is introduced into the traditional U-Net model for training. The ECA-UNet results show that compared with the traditional model, the intersection union ratio (IOU) corresponding to mining subsidence is increased by 2.54%.

The deformation of reservoir bank slopes and water conservancy and hydropower engineering facilities is related to the operation safety of water conservancy and hydropower projects. Based on the multi-temporal Sentinel-1 images, the time series InSAR technique was used to carry out a demonstration application in the Xiaolangdi Multipurpose Dam Project. Four small areas with large deformation rate monitoring points were found on the slope of the reservoir bank in the key area of the north bank of Xiaolangdi. The deformation rate of the monitoring points mainly concentrated in -10mm~ -25mm/yr. In the central area of the crest of the Xiaolangdi Dam, a large deformation accumulation area with an annual deformation amount of -60mm/year was found in the satellite line of sight. Before January 2021, the deformation amount increased rapidly. From January to March 2021, the deformation is slowing down. The InSAR technique can quickly obtain the deformation information of water conservancy and hydropower engineering facilities and reservoir bank slopes, which can be used as a common monitoring method for monitoring the safe operation of water conservancy projects.

The collapse of buildings is the main cause for casualties after earthquakes. Real-time and accurate positioning of the building areas are crucial to make an effective implementation of emergency rescue after an earthquake. This work carries out an investigation into a building recognition method using a single post-earthquake SAR image with the complex background and the scatter distribution pattern. In the SAR image, the sparse and scattered building identification, the large parts of highlighted mountains and the large-scale structures significantly affect the identification of building areas. In order to solve these problems in assessing building damage, we propose to use sparse villages in low spatial resolution SAR images, scattered buildings in high spatial resolution SAR image, oriented buildings and a set of construction features, and develop an algorithm based on the recognition of linear ridgelines and the planar highlighted mountain elements. We apply the algorithm to identify point structures and planar structures, which are used to build the identification of buildings after an earthquake. In this report, we will present a novel recognition method based on spatial association modelling and self-group spatial magnetic expression for performing building damage survey and assessment, this method makes use of the background of building objects combined with semantic features to great extent. It comprises of the following components: target restoration recognition using geometric feature matching, the local space matching recognition algorithm of standardized shape primitives, the ridgeline detection algorithm combining terrain features and linear elements, the recognition method based on layered local texture feature sequence, the oriented building recognition based on linear feature tracking and the effective feature optimization. We also present preliminary assessment results on the building damage of the 2021 Haiti earthquake.

Pre-earthquake anomalous variations in Earth’s ionosphere and lithosphere were examined in ~800 km radius for two major earthquake episodes in China – M6.0 in Arzak, occurred on 19^th January 2020., and M6.3 occurred in Xizang on 22^nd July 2020. The study has built on a previously conducted Empirical Orthogonal Function and Principal Component Analysis (EOF and PCA), utilizing ESA’s satellite SWARM A, B, and C geomagnetic data. Eight observed significant PC time series were selected for modelling using a LSTM neural network architecture on a three-month and 1-year time scales, each of them is split into training and testing subsets. Strong departure from normal behaviour was noted on 9^th January 2020 in Arzak region, and on 14^th July 2020 in Xizang, corresponding to results previously obtained through EOF and PCA. Several additional anomalous events were observed in a period of two weeks and one month prior to the earthquake events, which further investigations are under way.

China Seismo-Electromagnetic Satellite (CSES-1) is Chinese first satellite that is dedicated to monitoring ionospheric disturbance caused by earthquakes. It transits in a solar synchronous orbit with an altitude of 507 km and revisits the same place every 5 days. In order to more effectively realize the coverage monitoring of Chinese domestic seismic belts, the satellite orbits has been specially optimized for the Chinese seismic belts. The payload of the satellite consists of eight kinds of scientific detection instruments.

Using the revisited orbit design of CSES-1, we analyzed the electron density (Ne) data of 10 orbits from 30 days before the earthquake to 15 days after the earthquake. Before analysis, we take the data that exceeds the mean value by 6 times, greater than and less than or equal to 0 as erroneous data, and replaced them with null values. After replaced the erroneous data, the average value of Ne of the 6 orbits before the earthquake is treated as the background field, which is quite consistent with the 27-day solar cycle. After the background field is obtained, this paper compares the Ne on the day of the earthquake and within 15 days after the earthquake with the background field to try to extract the disturbance signal in the ionosphere which may cause by the earthquake.

The above method was successfully applied to the Yangbi Ms6.4 earthquake in Yunnan on May 21, 2021 and the Qinghai Maduo Ms7.4 earthquake on May 22, 2021. The results show that, about 20 days before the earthquakes, the disturbance signal began to appear in the earthquake epicenter and around the seismogenic area. While the earthquakes approaches, the anomalies appear more and more frequently, and then disappear quickly after the earthquakes.

We apply this method to M ≥ 6 earthquakes in the world and M ≥ 5 earthquakes in the mainland of China and adjacent areas. The statistical results of these earthquakes show that:

1. The number of anomalies increases with the approach of earthquakes, both the global and Chinese regions. And the number of anomalies decreases rapidly after the earthquake.
2. The number of anomalies increased significantly from 10 days before the earthquake to 5 days after the earthquake.
3. With the increase of earthquake magnitude, the number of anomalies increased, and the appearing time of anomalies is also advanced, and the duration of the anomalies after the earthquake is also extension.

The observed InSAR deformation in the Sichuan basin is probably caused by hydraulic fracturing for shale gas production. Some speculations are made based on such deformation patterns. Firstly, the surface deformation could be caused by long-term fluid injection or pumping which lasted several months in poroelasticity medium. Secondly, such deformation may be due to m­ultiple induced seismicities caused by pore pressure diffusion or fluid migration to vulnerable faults. Thirdly, long-term shale gas development could change the underground fluid mass. Loss or gain of fluids would change upper crustal gravity and produce the elastic response of the crust. We test these hypotheses based on numerical analysis of surface deformation patterns. Currently, the poroelasticity effects may exist in many geophysical exploitation activities, including underground water extraction, shale gas development, enhanced geothermal systems, etc. There are two main methods for poroelasticity forward modeling. One is the analytic solution or semi-analytical solution. The other one is the numerical simulation. The former cannot model spatially complicated medium, while the numerical method could approximate the poroelasticity problem of the real stratum as much as possible. Following Rongjiang Wang’s poroelasticity semi-analytical solution, we enhance the accuracy of the numerical method and verify the consistency of the parameters in both solutions. We then make numerical simulations to model the observed InSAR deformation in the Sichuan basin.

The Sentinel-1 mission provides dual-polarization (VV and VH) images for free, but only the VV channel is widely used for deformation measurement due to its larger amplitude value. Through optimizing a cost function related to one pixel selection criteria, polarimetric persistent scatterer interferometry (PolPSI) offers us a tool to combine both VV and VH channels as one optimum channel. Like other single polarization channels, the optimum channel can be used to implement practical applications including deformation monitoring. In order to analyze how the VH channel helps improve the measurement results, two experiments over Barcelona and Alcoy in Spain were carried out. In these experiments, we use amplitude dispersion (DA) as the pixel selection criteria and employ coherent pixels technique (CPT) as PSI processing method.

For the physical interpretation of the optimization process by using the VH channel, a test site comprising structures with diverse geometrical features and orientations was selected. In many cases, the amplitude of VH channel is smaller than that of VV channel, but the DA value is improved thanks to the more stable amplitude provided by the VH channel, which allows PolPSI to select many additional pixels with good phase quality. For two Sentinel-1 datasets acquired from 2017 to 2021, the PS density in the optimum channel increased by around 130% compared with VV channel (under the condition: DA<0.25). Finally, the additional PSs with stable phase increase the coverage of the measurement area and the pixel linking network in CPT. Taking the experiment in Alcoy as an example (a city with landslides and consolidation settlements over small areas), results given by the optimum channel are more accurate than the ones provided by VV when compared with the available in-situ displacement data. All these results support using PolPSI to combine VV and VH channel for a better displacement measurement.

Active landslides pose a significant hazard in mining areas given their considerable potential to induce slope failures, which typically affect open pits and waste and tailing disposal facilities. In order to minimise the impact caused by slope failures in mining areas, much effort has been devoted in recent decades to the development of new approaches to obtain and update active landslides inventory maps with a particular focus on those approaches based on remote sensing. This work illustrates the potential of exploiting satellite InSAR and airborne LiDAR data, combined with data inferred through safety factor maps, to obtain and update inventory maps of active landslides in mining areas. The proposed approach is illustrated by analysing the region of Sierra de Cartagena-La Union (Murcia), a mountainous mining area in the southeast Spain. Firstly, we processed Sentinel-1 InSAR imagery acquired in both ascending and descending geometry during the period between October 2016 and November 2021. The obtained ascending and descending InSAR datasets were then post-processed to semi-automatically generate two active deformation areas (ADAs) maps. Subsequently, both two InSAR datasets were used to decompose the 2D LOS displacement into vertical and east-west components. Complementarily, open-access and non-customized LiDAR data were used to analyse surface changes. Furthermore, safety factor (SF) was calculated over the study area adopting an infinite slope stability model. Finally, the obtained InSAR-derived maps, the LiDAR-derived maps, the original inventory map and the SF map were jointly analysed to create a new active landslide inventory map. In a further step, the influence of rainfall on the activity of the mapped landslides was studied by analysing of the InSAR time series. The results highlight the effectiveness of different remote sensing techniques (i.e., InSAR and LiDAR) jointly with classical methods for slope stability evaluation to update inventory maps of active landslides in mining areas.

Multi-dimensional, long-term time series displacement monitoring is crucial for generating early warnings for active landslides and for mitigating geohazards. The synthetic aperture radar (SAR) interferometry method has been widely applied to achieve small-gradient landslide displacement monitoring; however, measuring the landslide displacement with a steep gradient has posed certain challenges. In comparison, the SAR offset tracking method is a powerful tool for mapping large-gradient landslide displacement in both the slant-range and azimuth directions. Nevertheless, there are some limitations in the existing SAR offset tracking approaches: (i) the measurement accuracy is greatly reduced by the extreme topography relief in high mountain areas, (ii) a fixed matching window from expert experience is usually adopted in the calculation of cross-correlation, (iii) estimating the long-term displacements between the SAR images from cross-platforms and with longer spatiotemporal baselines is a challenging task, and (iv) it is difficult to calculate the three-dimensional (3D) landslide displacements using a single SAR dataset. Additionally, only a few studies have focused on how to realize early warning of landslide deformation using SAR measurements. To address these issues, this paper presents an improved cross-platform SAR offset tracking method, which can not only estimate high-precision landslide displacements in two and three dimensions but also calculate long-term time series displacements over a decade using cross-platform SAR offset tracking measurements. Initially, we optimize the traditional SAR offset tracking workflow to estimate high-precision ground displacements, in which three improvements are highlighted: (i) an “ortho-rectification” operation is applied to restrain the effect of topography relief, (ii) an “adaptive matching window” is adopted in the cross-correlation computation, and (iii) a new strategy is proposed to combine all the possible offset pairs and optimally design the displacement inversion network based on the “optimization theory” of geodetic inversion. Next, robust mathematical equations are built to estimate the two-dimensional (2D) and 3D long-term time series landslide displacements using cross-platform SAR observations. The M-estimator is introduced into the 2D displacement inversion equation to restrain the outliers, and the total least squares criterion is adopted to estimate the 3D displacements considering the random errors in both the design matrix and observations. We take the Laojingbian landslide, Wudongde Reservoir Area (China), as an example to demonstrate the proposed method using ALOS/PALSAR-1 and ALOS/PALSAR-2 images. The results reveal that the proposed method significantly outperforms traditional methods. We also retrieve the movement direction of each pixel of the Laojingbian landslide using the proposed method, thus allowing us to understand the fine-scale landslide kinematics. Finally, we capture and analyze the acceleration characteristics of the landslide, perform an early warning of hazard, and forecast the future displacement evolution based on the 3D displacement time series coupled with the physical models of the rocks.

Since the 21st century, the urbanization of human living environment has been accelerated, and a large number of various bridge facilities have emerged. With the increase of operation time and daily load, some bridges have started to experience different degrees of settlement, deformation, cracks and uplift, which seriously affect the safety of daily use of bridges. Therefore, the use of a reliable technology for periodic bridge deformation monitoring is of great importance to prevent public casualties and property damage caused by bridge collapse.

Compared with traditional contact monitoring means (GPS, level, etc.), which have the shortcomings of long monitoring period and are easily affected by the environment, InSAR technology is a non-contact monitoring means, and the monitoring of bridges, tall buildings and other infrastructures by InSAR technology has the characteristics of all-weather detection, high accuracy, low cost, and does not affect bridge operation. High-resolution SAR data can be applied to bridge fine deformation monitoring work with the advantages of higher monitoring point density and sensitivity.

This research plan is based on a five-span large-span continuous box girder bridge with variable cross-section in Shenyang, Liaoning Province - Xinlipu Bridge over Hun River. The data sources used are 30 images from March 2015 to April 2017 provided by TerraSAR-X satellite and 29 images from August 2015 to June 2017 provided by COSMO-SkyMed satellite, and the data set is processed by SBAS-InSAR technique to obtain the deformation information in the LOS direction of the bridge. The least-squares linear fitting method is applied to extract the temperature influence factor by combining the structural characteristics and material properties of the bridge, and to construct a bridge thermal dilation model to separate the thermal dilation and trend deformation of the bridge. The bridge deformation is the result of the combined effect of periodic thermal dilation and linear trend-type deformation, so separating the thermal dilation from the trend deformation can help us better study the deformation characteristic mechanism of the bridge. Then the multi-source LOS directional thermal dilation is combined with the bridge structure and sensor geometry parameters, based on the natural neighborhood interpolation method, to obtain the bridge along the bridge directional thermal dilation field. Based on the time and space interpolation methods and the principle of singular value decomposition, the LOS trend deformation obtained from the multi-source SAR data is geometrically aligned, interpolated and fused to solve the bridge deformation along the bridge and vertical deformation. Finally, for the visualization of the deformation of the complex structure of the bridge, the deformation points of different structural parts of the bridge are separated and extracted, so that the deformation patterns of different structural parts of the bridge can be better analyzed.

The results show that the thermal dilation of continuous box girder bridges is very obvious, and the method of constructing a bridge thermal dilation model based on the least squares method to extract the temperature influence factors of the bridge monitoring points can separate the periodic thermal dilation from the long-term trend deformation of the bridge. Based on various temporal and spatial interpolation methods, the multi-source SAR data fusion method applying the principle of singular value decomposition can obtain accurate bridge vertical and longitudinal deformation information, and the results show that the main span of Xinlibao Bridge has obvious vertical deformation in the middle of the main span, and the secondary span also has vertical and longitudinal displacement. InSAR technology can be used as a conventional deformation monitoring tool to extract and analyze the time series deformation of various bridges, which provides a reliable technology and data support for bridge health inspection.

In this paper, facing the demand of volcanic activity analysis in Tianchi, Changbaishan, the existing time-series InSAR deformation monitoring method and volcanic point source model are improved, and a set of volcano monitoring scheme suitable for Changbaishan is proposed. Firstly, to address the problem of high vegetation coverage and deformation monitoring being greatly affected by vegetation decoherence, a time-series InSAR deformation monitoring method based on normalized difference vegetation index (NDVI) constraint is proposed. Based on 33 Envisat ASAR images between 2004 and 2010 and 19 ALOS PALSAR images between 2018 and 2020, the accurate surface deformation parameters of the Changbaishan Tianchi crater and the surrounding area were extracted using the small baseline subset technique (SBAS-InSAR). Due to the lack of level data between 2018 and 2020 for comparison, the surface deformation parameters between 2018 and 2020 were also extracted using the persistent scatterer technique (PS-InSAR). The two sets of results were cross-validated and analyzed together with the seismic activity data of the same period. Secondly, we systematically analyzed the three-dimensional geometric relationship between the volcanic surface deformation field and the radar line of sight direction, established a generalized projection conversion equation from the horizontal and vertical deformation of the volcano to the LOS direction, improved the original point source model based on the horizontal and vertical deformation respectively to a point source model based on the LOS direction deformation, and inverted the magma chamber parameters for each time period of Changbaishan Tianchi volcano. Finally, based on the inversion results of the improved point source model, the surface deformation field of Tianchi volcano was orthorectified. The orthorectified results were compared and analyzed with seismic monitoring and fluid geochemical monitoring data to accurately assess the changes of magma chamber of Tianchi Volcano, and to explore the process of volcanic activity in Tianchi, which changed from strong to weak around the end of the disturbance period and gradually became active in the last two years. The results of this paper show that the Tianchi volcanic magma chamber first experienced a brief expansion between 2004 and 2010, with the 3.7 earthquake on September 8, 2004 as the turning point, and then began to enter a fluctuating gradual contraction after the earthquake until it stabilized in 2008. The volcanic magma chamber of Tianchi showed a fluctuating gradual expansion state between 2018 and 2020, and the whole change process was cyclical, with extreme values of deformation once the summer season. Similarly, the temporal deformation of PS-InSAR also has a cyclical trend, which is consistent with the results of SBAS-InSAR.

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In recent years, as the mining scale of open-pit mines continues to expand, a large number of high and steep slopes are formed, leading to increasingly serious slope instability disasters. Landslide disasters are complex, concealed and hazardous, and it is difficult to grasp the accurate deformation development law. Therefore, it is very important to carry out large-scale, long time series and high-precision dynamic monitoring of the slopes of open pit mines to ensure the safe production of mines.

Traditional deformation monitoring technology has the disadvantages of low efficiency, small monitoring range, high labor cost, and inability to obtain a wide range of monitoring data. Therefore, based on the demand for efficient, accurate and near real-time landslide disasters monitoring technology, the interferometic synthetic aperture radar (InSAR) is widely used in the field of landslide monitoring for its advantages of short revisit period, high measurement accuracy, low weather influence and large monitoring range. For large complex landslides with fast sliding, the interferometry technique based on phase information is plagued by the phase unwarpping and is only applicable to slowly deforming landslides with small deformation gradients. However, the Pixel Offset-Tracking (POT) technique based on SAR amplitude information is not affected by the phase unwarpping and space-time decoherence problems, and can overcome the limitation that InSAR can only acquire one-dimensional deformation and measure two-dimensional deformation in azimuth and line of sight (LOS) simultaneously. Under the high-resolution data condition, the deformation solution accuracy of POT technology can reach the decimeter level.

In this paper, a total of 34 scenes of Cosmo-SkyMed SAR data from June 4, 2014 to December 18, 2016 were acquired to monitor the landslide of Fushun West Open Pit Mine using the time-series SAR offset tracking technique, analyze the development pattern of the open pit slope deformation and study its deformation evolution mechanism. In addition, this paper analyzes the degree of influence of three influencing factors, namely, search window, oversampling factor and step size, on reliable pixel point extraction by setting up several groups of comparison experiments, and then selects the most suitable parameters for the analysis of temporal offset slippage deformation by considering the running time and experimental effect. Finally, the obtained monitoring results were verified with GPS observation data, and the comparison results existed high consistency, which further verified the high feasibility and applicability of the pixel offset tracking method in the application of large complex multi-gradient landslide monitoring, and the research results have important reference significance for the slope stability monitoring of Fushun West Open Pit Mine.