Calculating vertical deformation using a single InSAR pair based on singular value decomposition in mining areas

In this work we investigate the deformation signals affecting the coastal region of the megacity of Shangai (China) where, to satisfy the growing land demand for industrial and urban development, man-made lands reclaimed from the sea have been retrieved and used to build airports, harbors, and industrial areas. The presented analysis is carried out through the application of spaceborne Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) techniques. A general picture of the ground deformation field is provided by applying the Small Baseline Subset (SBAS) as well as the Permanent Scattereres (PS) approaches to archives of SAR images collected by ENVISAT and COSMO-SkyMed sensors from 2007 to 2014 over the investigated area.

<p>The large availability of Synthetic Aperture Radar (SAR) data collected over the last decade by several satellite missions, such as COSMO-SkyMed, TerraSAR-X and Sentinel-1 constellations, has been pushing toward the present Earth Observation (EO) scenario into a “golden age”, which is rapidly moving towards a real Big Data scenario. The widespread use of satellite SAR data have fostered the development of several SAR applications, one of those referred to as Differential SAR Interferometry (DInSAR) technology, which has deeply demonstrated to profitably detect the surface deformations over a wide spatial extent in both natural and anthropic hazard scenarios, through the generation of spatially dense deformation maps with millimetric accuracies. In particular, the advanced DInSAR algorithm referred to as Parallel Small BAseline Subset (P-SBAS) approach allows retrieving the temporal and spatial characteristics of the detected displacements at two spatial resolution scales, referred to as regional (spatial resolution in the 30-90 m range) and local (spatial resolution in the 3-10 m range) scales, suitable for a large variety of application fields, from natural hazards (volcano eruptions, seismic events, landslides) to anthropic contexts (urban areas, archaeological sites, oil-gas extraction, structures and transport infrastructures).</p><p>However, the interferometric processing performed at local scale needs to deal with hundreds of SAR acquisitions at full spatial resolution, i.e. to manage several hundreds of million points; consequently, the processing of such a data amount is particularly heavy from a computational point of view and can not be carried out in reasonable time frames through the traditional (sequential) implementation of the full resolution DInSAR processing chains.</p><p>Accordingly, to profitably benefit from the current SAR scenario, it is crucial to develop innovative solutions to automatically and efficiently handle large DInSAR data stacks. These solutions are principally based on the exploitation of advanced distributed computing environments, to achieve high efficiency in terms of scalability performances, as well as on the development of much more advanced DInSAR methodologies (and codes) able to effectively maximize the information related to these huge amount of DInSAR data.</p><p>This work is aimed at describing an innovative DInSAR solution, based on the exploitation of distributed HPC and Cloud Computing environments, which benefits from parallel programming techniques (multi-node, multi-threads, GPUs) implemented within an automatic full resolution P-SBAS processing pipeline. Starting from large SAR datasets acquired by the COSMO-SkyMed constellation, the developed parallel full resolution P-SBAS processing chain allows retrieving in short time frame (less than 24 hours) displacement time series and deformation maps, at the single buildings/infrastructure level, relevant to extended urban areas. The presented experimental results and the related performance analyses are achieved by applying the developed parallel P-SBAS pipeline to a number of large full resolution COSMO-SkyMed datasets acquired over some important Italian cities (e.g. Rome and Naples urban areas).</p>

Microwave remote sensing is truly the most rapidly emerging field of satellite remote sensing today. In a very short span of time, it has found variety of applications in almost every field which had seen optical remote sensing dominance. This shows that its both state of the art and a versatile non-evasive remote sensing tool. It has added advantage of all-weather availability and penetration abilities which the optical remote sensing lacks. Hence can be easily used for areas and months with dense cloud cover. Of the many applications of SAR (Synthetic Aperture RADAR) or Microwave remote sensing is its ability to map and monitor land surface deformations shown by both natural and man-made features over a period, which helps as an early warning for many possible disastrous events like structural failures in dams. The technique involved is Differential SAR Interferometry or DInSAR. DInSAR involves a repeat pass configuration of SAR Interferometry with minimum baseline. The presence of any random motion on the surface or surface features on earth, tends to decorrelate the backscatter signals and add noise to phase difference. Small change due to surface movements, causes change in phase difference which is measured precisely and accurately by this technique. This study aims to measure the deflection that has come over a period of one year on the walls of Bhakhra Dam in Himachal Pradesh in India, due to the change in water levels and pressure exerted by it. Using DInSAR, the deflection was measured to be around 0.952cm from August 2017 to October 2017.

ABSTRACTThe present work deals with the optimal placement of piezoelectric actuators on a thin plate using modified control matrix and singular value decomposition (MCSVD) approach. The MCSVD is considered as the fitness function and optimal positions of the actuators are obtained by maximizing it with MHGA (modified heuristic genetic algorithm). Vibration suppression has been studied for simply supported plate with piezoelectric patches in optimal positions to suppress first specified modes using LQR (linear quadratic regulator) controller. It is observed that the positions of patches obtained with this approach give greater vibration suppression, reduced computational requirements, and provide global optimum solution only.

Satellite interferometry for high-precision detection of ground deformation at a carbon dioxide storage site

The accurate prediction of vertical deformation (VD) in tidal flat terrains is crucial for their rational development and disaster warning. However, the high-precision and strong robustness prediction method of VD data in tidal flat terrain is still a great challenge due to the complex environmental conditions. In this study, VD data was collected over seven consecutive days using microelectromechanical system (MEMS) sensor arrays deployed in a tidal flat. Particle swarm optimization (PSO)-optimized variational mode decomposition (VMD) is applied to decompose the acquired VD data for noise removal. The fast Fourier transform (FFT) and Spearman correlation coefficient analysis were employed to identify the inducing factors by comparing them with the decomposed VD. Four machine learning algorithms were evaluated to predict the trend and periodic components of VD, and the results showed that long short-term memory (LSTM) neural network outperformed the other three, achieving a root mean square error (RMSE) less than 0.15 and an R-squared (<inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$R^{2}$ </tex-math></inline-formula>) greater than 0.90. The proposed combined PSO-VMD-LSTM model effectively addresses the challenges of feature recognition, noise removal, and deformation prediction of VD data in tidal flat terrains.

This research work will examine singular value decomposition (SVD), Fast Fourier Transform (FFT) and least square technique for the estimation of harmonics in power system. Harmonics in power system disturbs the quality of energy supplied to load, increased energy losses and reduce the reliability of the power system. In some cases, the large power converter systems produce not only typical harmonics but also a significant amount of inter harmonics which can significantly deteriorate the quality of power supply voltage. Inter harmonics are those components of current or voltage waveform which are not integer multiple of fundamental frequency component. Since poor power quality cause losses in power system that's why researchers are working to find techniques to estimate and control the harmonics in power system. Singular value decomposition approach is an ideal technique to estimate harmonics in power system located far away. Since in most of the cases, power system produce over determine equations which can easily be solved using SVD for optimal solution. Singular vector decomposition, SVD is really a mathematical method to lessen a dataset containing an extensive number of qualities to a dataset containing fundamentally less values, yet which still contains a huge portion of the variability introduce in the first information. SVD investigation brings about a more reduced representation of correlations, particularly with multivariate datasets and can give knowledge into spatial and temporal variations exhibited in the fields of data being analysed under different circumstance. Similarly traditional least square method is also one of the most prominent method to estimate the line of best fit. This research work will show the analysis of said techniques to find the optimal approximation of noisy and distorted wave form. To investigate effectiveness of said techniques; simulations will be carried out in MATLAB with same parameters.

The aim of a correspondence analysis is the graphical representation of the categories of variables in one frame of reference. This visualization is possible due to the decomposition of the basic matrix with the use of Singular Value Decomposition (SVD). There are three matrices used in the process of decomposition: right singular vectors, left singular vectors, and a singular value diagonal matrix. The aim of this paper is to compare four different approaches and algorithms of SVD methods used in a correspondence analysis. In the literature, four approaches are known to singular value decomposition, defined by: R.A. Fisher (1940), M.J. Greenacre (1984), E.B. Anderson (1991), and J.D. Jobson (1992). Those computational procedures will be presented and compared in this paper. Also, methods of determining the coordinates of the category column and line matrix, as well as the values of inertia will be defined for these approaches. A key problem is to compare the well-known approaches, since in the literature only one approach ‒ proposed by Greenacre ‒ is used for singular value decomposition. The reason of the superiority of this algorithm over the others may be the simplicity and ease of the mathematical calculations. Greenacre’s algorithm is also used in R statistical software, making its availability and popularity growing, however, other algorithms are worth presenting and focusing on.

In 1993 synthetic aperture radar (SAR) interferometry (InSAR) was introduced to the wider remote sensing community with the publication of the interferogram depicting the ground deformation caused by the Landers earthquake. Although the power of interferometry was demonstrated, the conventional technique has not always been applicable in all operational scenarios. Over the last few years, however, a number of technical developments have emerged that provide a higher precision of motion rates, the extraction of specific motion histories, and precise targeting. This paper examines uses of differential SAR interferometry (DifSAR) for monitoring geohazards. Limitations of DifSAR will be discussed: lack of coherence, atmospheric refraction and targeting. It will be shown how some of these limitations can be overcome with persistent scatterer interferometry (PSI), which detects slow ground motion with annual rates of as little as a few millimetres, reconstructing a motion history based on the European Space Agency's SAR image archive. The technique permits the estimation and removal of the atmospheric phase, achieving higher accuracies than DifSAR. PSI relies on the availability of pre-existing ground features that strongly and persistently reflect back the signal from the satellite. However, in highly vegetated regions, PSI may not be applicable because of the lack of natural scatterers. To ensure motion measurement of the ground or structures at targeted locations, the NPA Group is developing InSAR using artificial radar reflectors, such as Corner Reflectors (CRs) or Compact Active Transponders (CATs). Both reflector types are still undergoing validation tests, but results show a high phase stability in both cases.

In this work we present a processing chain we implemented to calculate, in a completely automatic way, the seismic source with distributed slip starting from the Differential Synthetic Aperture Radar Interferometry (DInSAR) coseismic displacement maps generated through the EPOSAR service.EPOSAR is a scientific service of EPOS (European Plate Observing System) Research Infrastructure, developed by CNR-IREA, that provides coseismic displacement maps at global scale. In particular, following the occurrence of an earthquake of a) magnitude greater and b) depth smaller than selected thresholds, EPOSAR automatically retrieves and process all the Copernicus Sentinel-1 data necessary to generate all the possible DInSAR coseismic maps within a monthly time window, so that the earthquake can be analyzed from different satellite paths. We further remark that the EPOSAR service is currently operative and the generated DInSAR products are freely available to the scientific community through the EPOS infrastructure.In this work we present the implementation of an automatic new modeling chain, by acting in cascade to the EPOSAR service, with a twofold aim: revealing the seismic source at the occurrence of every new event detectable through DInSAR and providing a complete database of sources that includes all the earthquakes occurred since the launch of Sentinel-1 satellites.The procedure starts from DInSAR data, produced by the EPOSAR service, and a focal mechanism automatically retrieved from several catalogs (USGS, Global CMT, INGV-TDMT). The non-linear inversion is implemented with two stages, coarse and refined, to get a robust and well centered, uniform slip solution; this source is then extended and subdivided into small elements to get the slip distribution via linear inversion. For every single step, a number of algorithms, based on two decades of experience in modeling at INGV, were implemented to face the large number of options and conditions usually handled by an expert user: image selection, setup and iterative update of the input parameters, definition of the regularization strength,&amp;#160; detection of specific conditions (point-source, poorly constraining data, etc.). The model is also automatically updated with the availability of new DInSAR data, always balancing the contribution from ascending and descending acquisitions.The developed tool is designed to deploy a service aimed at providing a quick and reliable automatic fault model solution and it has been tested and validated on hundred up to date events, characterized by different magnitudes, rupture mechanisms and locations. In this work, we present the main algorithm aspects and performances, addressing also the potentialities arising with the availability of a complete and homogeneous database of DInSAR-based source models: definition of updated scaling factors, systematic bias, etc.We finally remark that our tool will be soon operative and integrated within the EPOS infrastructure, thus allowing the user community to access the generated results and benefit from quick and reliable products on the source mechanisms of the more significant seismic events.

During the past 10 years, Timor-Leste has concentrated all its efforts on infrastructure development. However, it has not achieved enough due to unexpected ground deformation in mountainous areas that is seriously affecting road constructions, etc. In order to design roads and other infrastructure under such difficult conditions, it is important to know the present and future ground conditions. Continuous monitoring is a significant methods of detecting ground deformation and providing essential information to realize an effective design. The problem arises of “How can ground deformation be monitored in extensive areas, which are generally located in mountain areas that are difficult to access?” Differential Interferometry Synthetic Aperture Radar (DInSAR) has recently been applied to monitor displacement in extensive areas. In addition, Unmanned Aerial Vehicle (UAV) photogrammetry is useful for detecting the deformation in detail. Both methods are advantageous in that they do not require any sensors. Therefore, the combination of DInSAR and UAV photogrammetry is one of the solutions for monitoring the ground deformation in Timor-Leste. In this paper, DInSAR and UAV photogrammetry are applied to unstable ground in the Bobonaro region of Timor-Leste to find the recent ground deformation, since 2007, due to earthquakes and hard rainfall events. It is found that DInSAR is useful for screening usual and unusual ground behavior and that UAV photogrammetry is flexible to use and can detect displacements with cm accuracy after the DInSAR screening.

Underground mining can induce large vertical displacements that often lead to the loss of coherence in repeat-pass interferometric synthetic aperture radar (SAR) images. Using SAR intensity images, this paper employs the image offset tracking method to map SAR slant range changes due to ground deformation over areas of mining. The rationale of slant range offset measurement with respect to the vertical deformation is analysed and the prerequisite of applying the slant range offset method to monitor vertical deformation is discussed. Results from the slant range offset method are used to produce time-series of cumulative ground displacements via least square estimate. We use six Advanced Land Observation Satellite (ALOS)-Phased Array L-band SAR (PALSAR) images over two coalfields in Inner Mogolia, China, to illustrate the proposed method and its effectiveness. We achieve deformation measurements with a precision of ∼0.2 m, with the maximum vertical displacement over the mining sites reaching ∼4.5 m. Finally, we use time-series results to outline common features identified in mining-induced deformation. Our results are supported by in situ investigations.

Image enhancement and reconstruction is an important field of research in digital image analysis. To increase the quality of low-contrast images, a variety of image-enhancing technologies are available. Among these image enhancement techniques, singular value decomposition and discrete wavelet transform are the popular approaches for enhancing low-quality images. In this study, we have developed a modified singular value decomposition approach to enhance low-contrast and low-resolution close-circuit television images. Low-quality and low-resolution images with singular values near zero are used as input in the proposed approach. On the selected data, a threshold value was determined in the singular values of the diagonal matrix. Finally, the proposed modified singular value decomposition technique was applied to enhance the value of images with low quality and resolution. The datasets include 90 facial images of different individuals with low quality and resolution from a university database. The performance of the proposed approach is assessed using image entropy, peak signal to noise ratio, mean square error, contrast measurement and time computation. During the experiments performed on the obtained dataset, it was found that the proposed modified singular value decomposition approach outperformed the existing singular value decomposition, discrete wavelet transform-singular value decomposition and stationary wavelet transform methods.

It is well known that the singular value decomposition (SVD) offers an optimal approximation in F-norm for a given image. In this paper, we show that using a 2D-to-4D mapping in conjunction with optimal 4-D low rank approximation, improved compression rate can be achieved compared to that obtained from the conventional (2-D) SVD approach.

&amp;lt;p&amp;gt;Piton de la Fournaise volcano forms the southeastern part of La R&amp;amp;#233;union, an oceanic basaltic island in the southernmost part of Mascarene Basin (Indian Ocean). Five eruptions occurred at Piton in 2019, accompanied by seismic activity, lava flow, and lava fountaining. Here below, we focus on the fourth eruption occurred between August 11 and 15 on the southern-southeastern flank of the volcano, inside the Enclos Fouqu&amp;amp;#233; caldera. This eruption was characterized by the opening of two eruptive fissures. We retrieve the surface deformations induced by the eruptive activity through space-borne Differential Synthetic Aperture Radar Interferometry (DInSAR) measurements.&amp;amp;#160;First, we generated the coseismic deformation maps by applying the DInSAR technique to SAR data collected along ascending and descending orbits by the Sentinel-1 constellation of the European Copernicus Programme. The DInSAR technique allows us to analyze the deformation patterns caused by the 11 August 2019 eruption. We also retrieved the pre-eruptive deformation through the Small BAseline Subset (SBAS) DInSAR approach. Then, we modelled the DInSAR displacements to constrain the geometry and characteristics of the eruptive source. The modelling results suggest that the observed deformation can be attributed to the interaction between a shallow magma reservoir located at ~1.5-2 km depth below the summit, and the intrusion of a dike feeding the eruptive fissure inside the Enclos Fouqu&amp;amp;#233; caldera.&amp;lt;/p&amp;gt;&amp;lt;p&amp;gt;&amp;lt;em&amp;gt;This work is supported by: the 2019-2021 IREA-CNR and Italian Civil Protection Department agreement; the EPOS-SP project (GA 871121); and the I-AMICA (PONa3_00363) project.&amp;lt;/em&amp;gt;&amp;lt;/p&amp;gt;