Conference Agenda

Opencast mining, which involves huge quantities of overburden removal, dumping and backfilling in excavated areas, is a classical operation mode of large coal mines worldwide. With the continuous expansion of open pit mining areas, the mining angle has also increased sharply, resulting in frequent landslide disasters and significant safety threats to mining production operations. Therefore, it is of vital significance for the safety of personnel, mining operation equipment and infrastructures to perform continuous displacement monitoring of opencast mines and their surroundings. In recent decades, with the continuous enrichment of satellite Synthetic Aperture Radar (SAR) data resources, Multi-temporal SAR Interferometry (MT-InSAR) technique has become a fundamental tool to estimate surface displacements with high spatial resolution, short temporal revisit interval, wide coverage and millimeter accuracy.

In this paper, multi-temporal InSAR technology is adopted to monitor the line of sight (LOS) displacement of Fushun West Opencast Coal Mine (FWOCM) and its surrounding areas in Northeast China using Sentinel-1 SAR images acquired from 2018 to 2022. The spatial-temporal evolution of urban subsidence and the south-slope landslide are both analyzed in detail. Comparison with ground measurements and cross-correlation analysis via cross-wavelet transform with monthly precipitation data are also conducted to analyze the influence factors of displacements in FWOCM. The monitoring results show that a subsidence basin appeared in the urban area near the eastern part of the north slope in 2018, with the settlement center located at the intersection of E3000 and fault F1. The Qian Tai Shan (QTS) landslide on the south slope, which experienced rapid sliding from 2014 to 2016, presents seasonal deceleration and acceleration with precipitation, with the maximum displacement in the vicinity of the Liushan Paleochannel. The results of this paper have fully taken into account the complications of large topographic relief, geological conditions, spatial distribution, and temporal evolution characteristics of surface displacements in opencast mining areas. The wide range and long time series dynamic monitoring of opencast mines is of great significance to ensure mine safety, production, and geological disaster prevention in the investigated mining area.

In recent years，thermal anomalies prior to large and hazardous earthquakes have been extensively detected by microwave remote sensing techniques. In order to effectively detect microwave brightness temperature (MBT) anomalies caused by seismic factors, a wavelet-based two-step difference (WTSD) method is proposed in this paper. In the WTSD method, the radiation received by the microwave sensor comprises of two components if no earthquakes happen, which are stable radiation and random radiation respectively. Since the radiation caused by topography, surface coverage and seasonal change has strong regularity and varies little over the years, it is therefore considered as stable contribution to the microwave radiation. On the other hand, radiation caused by meteorological conditions (e.g., precipitation and temperature change, etc.) frequently changes within several days, which has no regularities, and it is therefore considered as random contribution to the microwave radiation. The stable components and random components are removed step by step in the WTSD method. The key steps prior to difference calculation rely on reliable retrieval of the stable component (which is the background MBT), and on successful elimination of the random component (which is the meteorological factor) as well, which is realized by adopting the hierarchical clustering and wavelet analysis. Then, the proposed WTSD method was used to detect seismic MBT anomalies prior to three strong earthquakes happened in the Central and Eastern Qinghai-Tibet Plateau, including the Ms 7.1 earthquake in Yushu in 2010, the Ms 5.5 earthquake in Dingqing in 2016 and the Ms 7.4 earthquake in Maduo in 2021. Surprisingly, the MBT anomalies prior to the three earthquakes are generally similar in terms of location, shape and evolution characteristics. Preliminary mechanistic analysis suggests that the pre-earthquake MBT anomalies are consistent with spatial distribution of the NE-oriented normal faults and geothermal activities in this region. The pre-earthquake thermal anomalies may becaused by intensified extrusion of the Indian plate to the Eurasian plate and the increased crustal stress in this area

Assessing the impact of the February 6^th earthquake, which occurred in South-eastern Turkey near the Turkey-Syria border, on cultural heritage sites is crucial to ascertain the cultural and historical cost of the disaster. These twin quakes, which had a magnitude of 7.8 and an after-shock magnitude of 6.7, resulted in widespread damages with the official death toll figure rising to 55,000+ and over 107,000 injured across the eleven cities most affected. The zone of occurrence of this earthquake is a hotbed for seismic activity because of the complicated network of plate boundaries underlying the area. This zone is under-laid by three major plate boundaries namely the Anatolian plate, the Arabian plate and the African plate. It is characterized by series of lateral strike-slip fault movement which ultimately results in series of frequent earthquakes of varying magnitude.

The aim of this study is to detect damaged cultural heritage sites in the earthquake zone in Turkey, by using SAR (Synthetic-Aperture Radar) images. The affected cities are home to some of Turkey’s most iconic heritage sites. In this study, TerraSAR-X high-resolution X-band data and open access Sentinel-1 data was used. At some locations we also used Google Earth images as a reference images.

To detect damages on cultural heritage sites, two methods were adopted. First, since TerraSAR-X have high resolution spotlight mode, we tried to ‘visually recognize’ damages on historical buildings by comparing SAR images with terrestrial and UAV photos from the area taken by locals, archaeologists, and reporters. Second, we processed open access Sentinel-1 data of different dates, before and after the earthquake using ‘coherence change detection’ to detect the changes in specific structures in the city. The research will focus to a large extent the cultural and historical cost of the impact of earthquake and also highlight the further impacts of after-shock on damaged cultural heritage sites through time series analysis of images. We realized that some damaged buildings continued to collapse several days later as a result of subsequent aftershocks which shows the need to initiate mitigative measures as fast as possible to save what is left of the important monuments.

Looting is an ongoing global threat to cultural heritage. Detecting looting activities is therefore of the utmost importance. Remote sensing offers a possibility to detect looting in remote and inaccessible areas. The all-weather and continuous observation capabilities of SAR would be extremely beneficial for any practical implementation. However, SAR data is difficult to interpret and suffers from speckle noise, making the detection of small changes challenging.

The detectability of large-scale looting activity in high-resolution SAR images, for example in the context of the Syrian civil war, has been shown before. Many other looting activities are rather small scale and do not reach the almost industrialized looting activities witnessed in this conflict.

Therefore, the detectability of small-scale looting will be analyzed in this work. Based on an experimental setup with two different sized artificial looting holes, we analyze the detectability of these activities in SAR images of different resolution, polarization, looking angle, orbit, etc. Detectability in amplitude and coherence are being analyzed.

The results will provide deeper insight into the requirements in terms of resolution and other imaging parameters for looting detection.

Regular and continuous monitoring of surface deformation and structural instability is crucial for cultural heritage protection. Increasing urbanization and development are one of the causes of ground subsidence. In the last decade, the city of Wuhan (China) has experienced major threats in urban areas due to rapid expansion and ground deformation, as revealed by recent studies published prior to the Dragon-5 SARrchaeology project by the ASI, Wuhan University and CNR-ISAC team in the framework of the WUHAN-CSK project (Jiang et al., 2021, Tapete et al., 2021) and the follow-on research within SARrchaeology (Jiang et al., 2023). While the whole InSAR literature on Wuhan so far has focused on the relationships between urbanization and land subsidence, as well as on impacts on modern structures and infrastructures, no studies have been undertaken to assess the effect on the conservation of heritage buildings spread across the city.

To fill this gap, high-resolution COSMO-SkyMed and TerraSAR-X satellite imagery is used in this work for assessing potential deformation of cultural heritage in Wuhan using the PSInSAR technique, which allows object subsidence monitoring up to millimeter-level accuracy. However, for long-term observations of a highly dynamic urban environment, such as Wuhan, several assumptions of PSInSAR, like PS stability over the acquisition period or linear deformation, are unsuitable. Changes to the processing framework are therefore necessary and are tested in this work.

In the final paper, we will demonstrate the effectiveness of applying a modified PSInSAR technique for the analysis of high-resolution SAR images for long-term monitoring of subsidence. The effect and potential damage to different cultural heritage sites will be discussed.

References

Jiang H., Balz T., Cigna F., Tapete D. (2021) Land Subsidence in Wuhan Revealed Using a Non-Linear PSInSAR Approach with Long Time Series of COSMO-SkyMed SAR Data. Remote Sens., 13, 1256. https://doi.org/10.3390/rs13071256

Jiang H., Balz T., Cigna F., Tapete D., Li J., Y. Han (2023). Multi-Sensor InSAR Time Series Fusion for Long-term Land Subsidence Monitoring. Geo-spatial Information Science. https://doi.org/10.1080/10095020.2023.2178337

Tapete D., Cigna F., Balz T., Tanveer H., Wang J., Jiang H. (2021) Multi-Temporal InSAR and Target Detection with COSMO-SkyMed SAR Big Data to Monitor Urban Dynamics in Wuhan (China). 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, Brussels, Belgium, 2021, pp. 3793-3796, doi: 10.1109/IGARSS47720.2021.9554360

Groundwater overexploitation and its resulting surface subsidence have been critical issues in the North China Plain (NCP) for the last half-century. This problem, however, is being alleviated by the implementation of the South-to-North Water Diversion (SNWD) Project since 2015. Here, we monitor surface deformation and investigate aquifer physical properties in NCP by combining Interferometric Synthetic Aperture Radar (InSAR), Global Positioning System (GPS), and hydraulic head data observed during 2015-2019.

We process data from the ascending track 142 of the Sentinel-1A/1B satellites, with a total of 92 acquisitions among 5 consecutive frames during 4 years. The InSAR time series are generated using the StaMPS software package, and all of the interferograms are formed with respect to one reference image. By dividing the study area into overlapping patches, we use parallel computing algorithms and cluster job management system to reduce the computational overburden. With this method, we effectively reduce computation time and successfully obtain the InSAR time series in NCP with full resolution for the first time. The atmospheric phase screen (APS) is estimated and reduced using a combined method, in which the first-order APS is estimated using the ERA5 global atmosphere model, and the residual APS is estimated using the Common Scene Stacking method.

Geodetic observations reveal widespread and remarkable subsidence in the NCP, with an average rate of ~30 mm/yr, and ~100 mm/yr for the maximum. We successfully extract seasonal and long-term deformation components caused by different hydrogeological processes. By joint analysis of the seasonal deformation and hydraulic head changes, we estimate the storativity of 0.07~12.04*10^-3 and the thickness of clay lenses of 0.08~2.00 m for the confined aquifer system, and attribute their spatial distribution patterns to the alluvial and lacustrine sediments of the subsystem layers. Our study also reveals fulfilment of the SNWD Project in alleviating the groundwater shortage. About 57% of the NCP is found to have experienced subsidence deacceleration, mostly along the SNWD aqueduct lines, by a total of 37.0 mm on average during 2015-2019. The subsidence was reduced by 4.1 mm on average for the entire NCP, suggesting that although subsidence was still ongoing, the trend was reversed, particularly for some major cities along the routes of the SNWD Project. A distinct difference in subsidence rates is found across the borderline between the Hebei and Shandong Provinces, resulting from differences in groundwater use management. Our study demonstrates that the integration of geodetic and hydrological data can be effectively used for the assessment of groundwater circulation and to assist groundwater management and policy formulation.

With fast shale gas exploitation in Sichuan basin in China in recent years, numerous micro-seismicities and even some medium-sized earthquakes occurred. Some studies show that shale gas exploitations can generate detectable surface deformation. We used ALOS-2 InSAR data to measure the surface deformation over the Changning shale gas block and find significant ground deformation that may be caused by massive shale gas production. Meanwhile, we also did time-series analysis of Sentinel-1 satellite radar data to measure the surface deformation of the Sichuan basin during the active periods of shale gas exploitation, which shows strong correlations between the surface deformation and three major shale gas blocks, namely the Changning, Weiyuan, and Fulin blocks. So the observed InSAR deformation in the tectonic-stable Sichuan basin is probably caused by hydraulic fracturing for shale gas production.

Some speculations on deformation sources could be 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 a poroelasticity medium. Secondly, such deformation may be due to multiple induced seismicities or fault creeping caused by pore pressure diffusion or fluid migration to vulnerable faults. Thirdly, the long-term shale gas development in the Sichuan basin could change the underground fluid mass. Injection or pumping of fluids into the crust would change upper crustal gravity and produce the elastic response of the crust, called the mass loading effect. We test these hypotheses based on numerical analysis of surface deformation patterns from InSAR data.

To quantitatively interpret the surface deformation with shale gas production, we model the deformation sources as multiple fluid injection and pumping processes in a poroelasticity layer by spatiotemporal Green’s function method, rather than the simple elastic volcanic-like sources, which may misinterpret the physical parameters of the shale gas production. Then we invert for the production parameters in a least-squares solution and compare our results with limited open production data as a verification. The details will be reported in the meeting.

In 2013, the European Space Agency (ESA) launched a constellation of three satellites: known collectively as the SWARM satellites. Their mission is to monitor variations in the Earth’s magnetic field. It has long been theorised that anomalous fluctuations in the Earth’s ionosphere could herald the beginnings of major earthquakes. However, the ability to accurately capture the frequency and extent of these anomalies has proven to be a persistent challenge to the scientific community. Anomalies are defined as data points which lie outside of the scope of normal data. High-intensity anomalies are comparatively easy to detect, but it is difficult to distinguish low-intensity anomalies from normal data, using purely mathematical or statistical means. The aim of this research is to apply Q-Learning and Deep Q-Networks to SWARM (and possibly CSES) satellite data and solve this problem. The proposed method uses kNN machine learning algorithms; a modified version of Matrix Profiles and planar wave functions to construct a Q-Learning Table for our agent. Double Deep Q Networks could also be trained using the kNN and modified Matrix Profiles. This method eliminates the need for Active Learning (or human feedback) when training such algorithms. Reinforcement Learning could be the key to unlocking the Earth’s magnetic field, predicting earthquakes and saving countless lives.

The Tianshan orogenic belt (TSOB) is one of the most active regions in Eurasia. The far-range effect of the collision between the Indian and the Eurasian plates in the late Cenozoic led to the reactivation of the TSOB and the occurrence of intracontinental orogeny. At the same time, the TSOB expanded to the foreland basins on its both sides, forming multiple rows of décollement- and fault-related fold belts in the basin-mountain boundary zone. Global Positioning System (GPS) observations show that the shortening rate in the north-south direction across the TSOB gradually decreases from ~ 20 mm/yr in the west to ~ 8 mm/yr in the east. However, how the deformation is distributed inside the TSOB is controversial. Here, we determine the present-day kinematics of the major structural belts based on the Interferometric Synthetic Aperture Radar (InSAR) data of the Sentinel-1 satellites.

We process Synthetic Aperture Radar (SAR) data from 5 ascending tracks (T27;T129;T56;T158;T85) and 4 descending tracks (T107;T34;T136;T63) of the Sentinel-1A/1B satellites recorded between November 2014 and December 2020. We constructed a total of 1074 single-reference single-look interferometric pairs based on Gamma software covering a 790-km-length and 520-km-width area of the TSOB. Finally, the InSAR time series are processed using the StaMPS software package. The long-wavelength and elevation-dependent atmospheric errors from each date are mitigated using the TRAIN package and ECWMF ERA5 models.

Combining InSAR and GPS measurements, we show that the tectonic deformation is not evenly distributed in the TSOB. The convergence across the Tianshan ranges is approximately 15–24 mm/yr; the deformation gradient in the junction area between South Tianshan and Pamir is the largest and adjusts ∼68% of the total convergence deformation. South Tianshan is relatively stable without sharp gradients, and the remaining deformation is distributed in the intermontane faults and basin systems in the north of South Tianshan. We also find that the Kashi fold-thrust belt is the most active unit in this area, and the deformation is mainly concentrated on a series of folds: the Mushi, Kashi, and Atushi folds, and the faults between the folds, such as the Kashi, Atushi, and Toth Goubaz faults. As the boundary fault between the South Tianshan and the Tarim basin, the Maidan fault shows a clear deformation gradient. In the Keping nappe, the deformation is mainly concentrated on the Keping hill and Kepingtag fault in the front of the nappe. There are several remarkable deformation zones in the Kuche foreland. The deformation in the north of South Tianshan is dispersed in a series of intermountain active structures and the depression basins, unlike in the south side, where the deformation is mainly concentrated on the thrust folds. Furthermore, our study can provide constraints for deformation and slip partitioning patterns associated with the ongoing India-Eurasia collision in the TOSB.

In this report we will present the latest development of anomaly detection algorithms underpinned with Deep Neural Networks (DNN), which focuses on predicting and generating electromagnetic data from the Swarm historic data. We report our investigation into the two architectures of Recurrent Neural Networks (RNN) and generative adversarial network (GAN), particularly illustrating the development of Long-Short Term Memory (LSTM) based architectures and a flow-based generative model. The first RNN architecture is modelling with a stacked LSTM layers. There are several variations of this architecture, however our empirical analysis that the best result achieved is three LSTM layers structure. The second architecture is an architecture on three RNN models, called Encoder-Predictor-Decoder that is inspired by the work of Multi-head CNN–RNN for multi-time series anomaly detection. We will present the design of the architectures and their implementation, and compare the predicted and generated results of applying these approaches to the Swarm historic data. Based on the predicted and generated results, we will describe error metrics that can be used to measure the accuracy of reconstructed Swarm data reconstruction. Finally we will present our methods of detecting anomalies in the synthesized and true Swarm data along with possible applications in detecting seismic precursors from the synthesized and true Swarm data.

Building damage assessment is one of the most important parts of the earthquake damage assessment, the rapid and accurate damage assessment can help to reduce the disaster loss. A method built on using SAR images for building damage assessment is independent of weather conditions, and also using one post-earthquake SAR image to assess building damage is much quicker and more convenient than using the multi-source or multi-temporal data. PolSAR (fully-polarimetric SAR) data contain much more information than single- or dual-polarization SAR data, and the texture features extracted are very useful for recognizing ground objects in SAR image. However with PolSAR images, building damage recognition results directly generated by a polarimetric decomposition method always give rise to excessive assessment of damaged buildings. To overcome this deficit and improve the identification accuracy of building earthquake damage, we developed the two new texture feature parameters CV_AFI in the frequency domain and MSD in the spatial domain.
In SAR imagery, the scattering intensity of collapsed buildings is weaker than that of standing buildings, as the dihedral structures in collapsed buildings are destroyed. The standing oriented buildings (whose orientation is not parallel to the flight direction) have strong depolarization effect. Therefore, both collapsed buildings and oriented buildings are dominated by volume scattering, and they are easily misclassified. The oriented buildings always show banded textures with consistent arrangement, but collapsed buildings often show more random textures with a disordered distribution. The spatial frequency of SAR images can be clearly rendered in the frequency domain. For comparing the classification performance of texture features in the frequency domain and the spatial domain, we proposed the variable coefficient of angle domains based on the Fourier amplitude spectrum parameter (CV_AFI) and the mean standard deviation (MSD) parameter based on the statistical characteristics to discriminate oriented buildings and collapsed buildings.
We used CV_AFI and MSD, and combined the Yamaguchi four-component polarimetric decomposition method to extract building earthquake damage information, respectively. The double-bounce scattering components generated from Yamaguchi four-component decomposition are directly regarded as the intact buildings which is called DB intact buildings for short. The total power image as the intensity image of PolSAR data is used to compute CV_AFI and MSD. According to the threshold values of CV_AFI and MSD, the volume scattering components generated from the Yamaguchi four-component decomposition are classified into two categories of the collapsed buildings and the oriented buildings. The volume-dominated buildings corresponding to the CV_AFI or MSD values which are bigger than the thresholds are classified as the collapsed buildings, and the remaining volume-dominated buildings are classified as the oriented buildings. Finally, the oriented buildings are incorporated into the intact buildings.
The experimental results show that the overall correct building damage recognition accuracies of CV_AFI and MSD are 84.45% and 80.65%, respectively. The correct identification rates of CV_AFI and MSD for the collapsed buildings are 82.95% and 82.43%, respectively. The undamaged building correct identification accuracies of CV_AFI and MSD are 85.20% and 80.30%, respectively. The correct recognition accuracies of collapsed buildings and undamaged buildings and the building damage recognition overall accuracy with CV_AFI are higher than those of MSD. Especially, more oriented buildings are correctly detected using CV_AFI, that is, less oriented buildings are misclassified as collapsed buildings. Thus the identification accuracy of intact buildings is higher than that of collapsed buildings. This result well confirmed that the texture features in the frequency domain can better reflect the difference in the spatial distribution between oriented buildings and collapsed buildings. Therefore, the texture features in frequency domain are more effective for building damage recognition, and they should be given more consideration when developing earthquake damage assessment methods, in addition to applying the texture features in spatial domain.