Towards synergy of GNSS and InSAR mining deformation monitoring with Sentinel-1 data

2021 ◽  
Author(s):  
Damian Tondaś ◽  
Maya Ilieva ◽  
Witold Rohm ◽  
Jan Kapłon
Keyword(s):  
Time Series ◽  
Real Time ◽  
Laser Scanning ◽  
Large Scale ◽  
Ground Deformation ◽  
Dimensional Space ◽  
Satellite Navigation ◽  
Deformation Monitoring ◽  
North East ◽  
The Impact

<p>The determination of ground deformation may be carried out by applying various measurement methods such as levelling, laser scanning, satellite navigation systems, Synthetic Aperture Radar (SAR) and many others. In this work, we focus on the comparison of the deformation effects measured by Global Navigation Satellite Systems (GNSS) and satellite Interferometric SAR (InSAR) methods in the Upper-Silesian coal mining region (SW Poland).</p><p>An unquestionable advantage of GNSS technology is the possibility of continuous monitoring of deformations in three-dimensional space. Moreover, the evolution of real-time (RT) techniques such as: near real-time (NRT), ultra-fast NRT or RT allows to obtain a high precise position determination with a relatively slight latency (ranging from a few seconds to less than one hour). The limitation of the satellite navigation technology is the spatial range of the measurements. The deformation can only be observed at the point where the GNSS antenna is located. Furthermore, the acquisition, installation and maintenance of the equipment may also involve high costs.</p><p>In contrast to the GNSS technique, the InSAR methods enable measurement of the large-scale subsidence areas with possibility to use free products and software (e.g. Sentinel-1 and SNAP). The large-scale InSAR investigations provide a better overview of local terrain changes. Unfortunately, InSAR methods also have some limitations related to data acquisition technology:  </p><ul><li>a few days latency in acquiring a new image,</li> <li>insensitivity to changes in the northern component,</li> <li>acquiring deformation only in the LOS direction.</li> </ul><p>The main goal of this research is to analyse the deformation results obtained using GNSS and InSAR methods with respect to the capabilities and limitations of these two techniques. We investigated the level of agreement of eight GNSS and InSAR time series in areas with no subsidence, together with results acquired on seven regions of mining deformation where the maximum subsidence velocity exceeds 50 cm/year. The mean RMS time series fitting error obtained in subsidence basin is more than 5 cm and in non-deformed areas is equal to 2 cm. The study also investigated the effect of coherence threshold levels (0.3 ÷ 0.6) with introduction of the northern GNSS component on the InSAR decomposition process. Assuming the same GNSS deformation value in each directions (north, east, and up), the impact of the northern component was estimated as 10% of the total LOS subsidence.</p>

scholarly journals Subsidence Monitoring of Fill Area in Yan’an New District Based on Sentinel-1A Time Series Imagery

2021 ◽  
Vol 13 (15) ◽  
pp. 3044
Author(s):  
Mingjie Liao ◽  
Rui Zhang ◽  
Jichao Lv ◽  
Bin Yu ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Ground Deformation ◽  
Dynamic Change ◽  
Deformation Monitoring ◽  
Chinese Loess Plateau ◽  
Ground Subsidence ◽  
Shanxi Province ◽  
Environment Change ◽  
Loess Area

In recent years, many cities in the Chinese loess plateau (especially in Shanxi province) have encountered ground subsidence problems due to the construction of underground projects and the exploitation of underground resources. With the completion of the world’s largest geotechnical project, called “mountain excavation and city construction,” in a collapsible loess area, the Yan’an city also appeared to have uneven ground subsidence. To obtain the spatial distribution characteristics and the time-series evolution trend of the subsidence, we selected Yan’an New District (YAND) as the specific study area and presented an improved time-series InSAR (TS-InSAR) method for experimental research. Based on 89 Sentinel-1A images collected between December 2017 to December 2020, we conducted comprehensive research and analysis on the spatial and temporal evolution of surface subsidence in YAND. The monitoring results showed that the YAND is relatively stable in general, with deformation rates mainly in the range of −10 to 10 mm/yr. However, three significant subsidence funnels existed in the fill area, with a maximum subsidence rate of 100 mm/yr. From 2017 to 2020, the subsidence funnels enlarged, and their subsidence rates accelerated. Further analysis proved that the main factors induced the severe ground subsidence in the study area, including the compressibility and collapsibility of loess, rapid urban construction, geological environment change, traffic circulation load, and dynamic change of groundwater. The experimental results indicated that the improved TS-InSAR method is adaptive to monitoring uneven subsidence of deep loess area. Moreover, related data and information would provide reference to the large-scale ground deformation monitoring and in similar loess areas.

scholarly journals GROUND DEFORMATION MONITORING IN QINGDAO COASTAL AREAS BY TIME-SERIES TERRASAR-X IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 29-33
Author(s):  
A. Y. Hou ◽  
X. Qiao ◽  
D. Li
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Ground Deformation ◽  
Small Deformation ◽  
Coastal Areas ◽  
Deformation Monitoring ◽  
Rate Of Change ◽  
Short Period ◽  
Radar Satellite ◽  
Ground Deformation Monitoring

As a new generation of high resolution and short revisit period of radar satellite, TerraSAR-X is not only able to meet the requirements of monitoring large scale surface subsidence, but also make it possible to monitor the small deformation of the short period. This articles takes the coastal areas of the west coast of Qingdao as the research object. With Small baselines subsets interferometry synthetic aperture radar (SBASI), this paper obtained the period the average annual rate of change from the time series analysis of TerraSAR-X data from April 2015 to October 2014.In order to enrich the historical deformation data of the study area, it analyse the time series of ALOS images from December 2010 to October 2008 with the same method. Finally,it analyse and demonstrate the experimental results.

scholarly journals Offline-Online Change Detection for Sentinel-1 InSAR Time Series

2020 ◽  
Author(s):  
Ekbal Hussain ◽  
Alessandro Novellino ◽  
Colm Jordan ◽  
Luke Bateson
Keyword(s):  
Time Series ◽  
Real Time ◽  
Ground Deformation ◽  
Volcanic Eruptions ◽  
Deformation Monitoring ◽  
Real Time Processing ◽  
Spatial Continuity ◽  
Time Deformation ◽  
Population Standard Deviation ◽  
Gradient Change

Traditional applications of Interferometric Synthetic Aperture Radar (InSAR) data involved inverting an interferogram stack to determine the average displacement velocity. While this approach has useful applications in continuously deforming regions, much information is lost by simply fitting a line through the time series. Thanks to regular acquisitions across most of the the world by the ESA Sentinel-1 satellite constellation, we are now in a position to explore opportunities for near-real time deformation monitoring. In this paper we present a simple statistical approach for detecting offsets and gradient changes in InSAR time series. Our key assumption is that 5 years of Sentinel-1 data is sufficient to calculate the population standard deviation of the detection variables. Our offset detector identifies statistically significant peaks in the first, second and third difference series. The gradient change detector identifies statistically significant movements in the second derivative series. We exploit the high spatial resolution of Sentinel-1 data and the spatial continuity of geophysical deformation signals to filter out false positive detections that arise due to signal noise. When combined with near-real time processing of InSAR data these detectors, particularly the gradient change, could be used to detect incipient ground deformation associated with geophysical phenomena, for example from landslides or volcanic eruptions.

scholarly journals Offline-Online Change Detection for Sentinel-1 InSAR Time Series

2021 ◽  
Vol 13 (9) ◽  
pp. 1656
Author(s):  
Ekbal Hussain ◽  
Alessandro Novellino ◽  
Colm Jordan ◽  
Luke Bateson
Keyword(s):  
Time Series ◽  
Real Time ◽  
Ground Deformation ◽  
Volcanic Eruptions ◽  
Deformation Monitoring ◽  
Real Time Processing ◽  
Spatial Continuity ◽  
Time Deformation ◽  
Population Standard Deviation ◽  
Gradient Change

Traditional applications of Interferometric Synthetic Aperture Radar (InSAR) data involved inverting an interferogram stack to determine the average displacement velocity. While this approach has useful applications in continuously deforming regions, much information is lost by simply fitting a line through the time series. Thanks to regular acquisitions across most of the the world by the ESA Sentinel-1 satellite constellation, we are now in a position to explore opportunities for near-real time deformation monitoring. In this paper we present a statistical approach for detecting offsets and gradient changes in InSAR time series. Our key assumption is that 5 years of Sentinel-1 data is sufficient to calculate the population standard deviation of the detection variables. Our offset detector identifies statistically significant peaks in the first, second and third difference series. The gradient change detector identifies statistically significant movements in the second derivative series. We exploit the high spatial resolution of Sentinel-1 data and the spatial continuity of geophysical deformation signals to filter out false positive detections that arise due to signal noise. When combined with near-real time processing of InSAR data these detectors, particularly the gradient change, could be used to detect incipient ground deformation associated with geophysical phenomena, for example from landslides or volcanic eruptions.

scholarly journals GROUND DEFORMATION MONITORING IN QINGDAO COASTAL AREAS BY TIME-SERIES TERRASAR-X IMAGES

2016 ◽  
Vol XLI-B7 ◽  
pp. 29-33
Author(s):  
A. Y. Hou ◽  
X. Qiao ◽  
D. Li
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Ground Deformation ◽  
Small Deformation ◽  
Coastal Areas ◽  
Deformation Monitoring ◽  
Rate Of Change ◽  
Short Period ◽  
Radar Satellite ◽  
Ground Deformation Monitoring

As a new generation of high resolution and short revisit period of radar satellite, TerraSAR-X is not only able to meet the requirements of monitoring large scale surface subsidence, but also make it possible to monitor the small deformation of the short period. This articles takes the coastal areas of the west coast of Qingdao as the research object. With Small baselines subsets interferometry synthetic aperture radar (SBASI), this paper obtained the period the average annual rate of change from the time series analysis of TerraSAR-X data from April 2015 to October 2014.In order to enrich the historical deformation data of the study area, it analyse the time series of ALOS images from December 2010 to October 2008 with the same method. Finally,it analyse and demonstrate the experimental results.

scholarly journals Offline-Online Change Detection for Sentinel-1 InSAR Time Series

2021 ◽  
Author(s):  
Alessandro Novellino ◽  
Ekbal Hussain ◽  
Colm Jordan ◽  
Luke Bateson
Keyword(s):  
Time Series ◽  
Real Time ◽  
Ground Deformation ◽  
Volcanic Eruptions ◽  
Deformation Monitoring ◽  
Real Time Processing ◽  
Spatial Continuity ◽  
Time Deformation ◽  
Population Standard Deviation ◽  
Gradient Change

<p>Traditional applications of Interferometric Synthetic Aperture Radar (InSAR) data involved inverting an interferogram stack to determine the average displacement velocity. While this approach has useful applications in continuously deforming regions, new tools are needed for automatically and regularly identifying changes in the time series. Thanks to regular acquisitions across most of the world by the ESA Sentinel-1 satellites constellation, we are now in a position to explore opportunities for near-real time deformation monitoring. In this paper we present a statistical approach for detecting offsets and gradient changes in InSAR time series. Our key assumption is that 5 years of Sentinel-1 data is sufficient to calculate the population standard deviation of the detection variables. Our offset detector identifies statistically significant peaks in the first, second and third difference series. The gradient change detector identifies statistically significant movements in the second derivative series. We exploit the high spatial resolution of Sentinel-1 data and the spatial continuity of geophysical deformation signals to filter out false positive detections that arise due to signal noise. When combined with near-real time processing of InSAR data these detectors, particularly the gradient change, could be used to detect incipient ground deformation associated with geohazards such as landslides or volcanic eruptions.</p>

Machine-Learning for the Prediction of Lost Circulation Events - Time Series Analysis and Model Evaluation

2021 ◽  
Author(s):  
Arturo Magana-Mora ◽  
Mohammad AlJubran ◽  
Jothibasu Ramasamy ◽  
Mohammed AlBassam ◽  
Chinthaka Gooneratne ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Real Time ◽  
Large Scale ◽  
Model Comparison ◽  
Drilling Fluid ◽  
Sensor Data ◽  
False Alarms ◽  
Suitable Model ◽  
Lost Circulation

Abstract Objective/Scope. Lost circulation events (LCEs) are among the top causes for drilling nonproductive time (NPT). The presence of natural fractures and vugular formations causes loss of drilling fluid circulation. Drilling depleted zones with incorrect mud weights can also lead to drilling induced losses. LCEs can also develop into additional drilling hazards, such as stuck pipe incidents, kicks, and blowouts. An LCE is traditionally diagnosed only when there is a reduction in mud volume in mud pits in the case of moderate losses or reduction of mud column in the annulus in total losses. Using machine learning (ML) for predicting the presence of a loss zone and the estimation of fracture parameters ahead is very beneficial as it can immediately alert the drilling crew in order for them to take the required actions to mitigate or cure LCEs. Methods, Procedures, Process. Although different computational methods have been proposed for the prediction of LCEs, there is a need to further improve the models and reduce the number of false alarms. Robust and generalizable ML models require a sufficiently large amount of data that captures the different parameters and scenarios representing an LCE. For this, we derived a framework that automatically searches through historical data, locates LCEs, and extracts the surface drilling and rheology parameters surrounding such events. Results, Observations, and Conclusions. We derived different ML models utilizing various algorithms and evaluated them using the data-split technique at the level of wells to find the most suitable model for the prediction of an LCE. From the model comparison, random forest classifier achieved the best results and successfully predicted LCEs before they occurred. The developed LCE model is designed to be implemented in the real-time drilling portal as an aid to the drilling engineers and the rig crew to minimize or avoid NPT. Novel/Additive Information. The main contribution of this study is the analysis of real-time surface drilling parameters and sensor data to predict an LCE from a statistically representative number of wells. The large-scale analysis of several wells that appropriately describe the different conditions before an LCE is critical for avoiding model undertraining or lack of model generalization. Finally, we formulated the prediction of LCEs as a time-series problem and considered parameter trends to accurately determine the early signs of LCEs.

scholarly journals Automated terrestrial laser scanning with near-real-time change detection – monitoring of the Séchilienne landslide

2017 ◽  
Vol 5 (2) ◽  
pp. 293-310 ◽  
Author(s):  
Ryan A. Kromer ◽  
Antonio Abellán ◽  
D. Jean Hutchinson ◽  
Matt Lato ◽  
Marie-Aurelie Chanut ◽  
...  
Keyword(s):  
Change Detection ◽  
Real Time ◽  
Laser Scanning ◽  
Terrestrial Laser Scanning ◽  
Cost Effective ◽  
Deformation Monitoring ◽  
Time Change ◽  
High Temporal Resolution ◽  
Atmospheric Conditions ◽  
Detection Processing

Abstract. We present an automated terrestrial laser scanning (ATLS) system with automatic near-real-time change detection processing. The ATLS system was tested on the Séchilienne landslide in France for a 6-week period with data collected at 30 min intervals. The purpose of developing the system was to fill the gap of high-temporal-resolution TLS monitoring studies of earth surface processes and to offer a cost-effective, light, portable alternative to ground-based interferometric synthetic aperture radar (GB-InSAR) deformation monitoring. During the study, we detected the flux of talus, displacement of the landslide and pre-failure deformation of discrete rockfall events. Additionally, we found the ATLS system to be an effective tool in monitoring landslide and rockfall processes despite missing points due to poor atmospheric conditions or rainfall. Furthermore, such a system has the potential to help us better understand a wide variety of slope processes at high levels of temporal detail.

scholarly journals Regression-Based Identification of Behavior-Encoding Neurons During Large-Scale Optical Imaging of Neural Activity at Cellular Resolution

2011 ◽  
Vol 105 (2) ◽  
pp. 964-980 ◽  
Author(s):  
Andrew Miri ◽  
Kayvon Daie ◽  
Rebecca D. Burdine ◽  
Emre Aksay ◽  
David W. Tank
Keyword(s):  
Time Series ◽  
Optical Imaging ◽  
Neural Activity ◽  
Laser Scanning ◽  
Large Scale ◽  
Neural Encoding ◽  
Quantitative Measurements ◽  
Cellular Resolution ◽  
Activity Measurements ◽  
General Regression

The advent of methods for optical imaging of large-scale neural activity at cellular resolution in behaving animals presents the problem of identifying behavior-encoding cells within the resulting image time series. Rapid and precise identification of cells with particular neural encoding would facilitate targeted activity measurements and perturbations useful in characterizing the operating principles of neural circuits. Here we report a regression-based approach to semiautomatically identify neurons that is based on the correlation of fluorescence time series with quantitative measurements of behavior. The approach is illustrated with a novel preparation allowing synchronous eye tracking and two-photon laser scanning fluorescence imaging of calcium changes in populations of hindbrain neurons during spontaneous eye movement in the larval zebrafish. Putative velocity-to-position oculomotor integrator neurons were identified that showed a broad spatial distribution and diversity of encoding. Optical identification of integrator neurons was confirmed with targeted loose-patch electrical recording and laser ablation. The general regression-based approach we demonstrate should be widely applicable to calcium imaging time series in behaving animals.

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