Blind Source Separation for MT-InSAR Analysis With Structural Health Monitoring Applications

Martín, Gabriel; Hooper, Andrew; Wright, Tim; Selvakumaran, Sivasakthy

doi:10.1109/jstars.2022.3190027

Cited by 5 publications

(3 citation statements)

References 67 publications

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“…BSS is effective in recovering the original signals from the mixture, and it was first introduced to solve the Cocktail Party Problem [16]. Nowadays, BSS is widely used and performs well with the assumption of a linear mixing procedure [17][18][19][20][21], in which nonlinear components are neglected. However, in fact, the nonlinear effect is non-negligible in underwater acoustic channels, such as nonlinear distortion caused by hydrodynamics and the adiabatic relation between pressure and density [22], nonlinearity in devices like power amplifiers [23][24][25][26], the nonlinear interaction of collimated plane waves [27], the thermal current and nonlinear fluids such as relaxing fluids, bubbly liquids and fluids in saturated porous solids [28][29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Underwater Acoustic Nonlinear Blind Ship Noise Separation Using Recurrent Attention Neural Networks

Song,

Feng,

Wang

et al. 2024

Remote Sensing

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Ship-radiated noise is the main basis for ship detection in underwater acoustic environments. Due to the increasing human activity in the ocean, the captured ship noise is usually mixed with or covered by other signals or noise. On the other hand, due to the softening effect of bubbles in the water generated by ships, ship noise undergoes non-negligible nonlinear distortion. To mitigate the nonlinear distortion and separate the target ship noise, blind source separation (BSS) becomes a promising solution. However, underwater acoustic nonlinear models are seldom used in research for nonlinear BSS. This paper is based on the hypothesis that the recovery and separation accuracy can be improved by considering this nonlinear effect in the underwater environment. The purpose of this research is to explore and discover a method with the above advantages. In this paper, a model is used in underwater BSS to describe the nonlinear impact of the softening effect of bubbles on ship noise. To separate the target ship-radiated noise from the nonlinear mixtures, an end-to-end network combining an attention mechanism and bidirectional long short-term memory (Bi-LSTM) recurrent neural network is proposed. Ship noise from the database ShipsEar and line spectrum signals are used in the simulation. The simulation results show that, compared with several recent neural networks used for linear and nonlinear BSS, the proposed scheme has an advantage in terms of the mean square error, correlation coefficient and signal-to-distortion ratio.

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Section: Introductionmentioning

confidence: 99%

Underwater Acoustic Nonlinear Blind Ship Noise Separation Using Recurrent Attention Neural Networks

Song,

Feng,

Wang

et al. 2024

Remote Sensing

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“…Interferometric Synthetic Aperture Radar (InSAR) measures relative surface displacement within the sensor's LOS (line of sight), whose imagery is not affected by daylight or cloud cover [1,2]. Thus, InSAR has attracted attention from scholars in the field of Earth sciences for a long time [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Post-Tunneling Aging Buildings Using the InSAR Nonuniform Settlement Index

et al. 2023

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Tunneling work, including the construction of municipal tunnels and metro lines, may disturb the structural health of aging buildings in densely built urban areas. Deformation monitoring and risk assessments of aging buildings are crucial to mitigate incidents and prevent losses of people’s lives and properties. Time-series InSAR reveals spatio-temporal information about observed targets by extracting persistent scatterers of the structures, which can achieve the wide-range monitoring of buildings and infrastructure. However, solely relying on InSAR-derived general parameters (deformation rates and time series of specific points) cannot objectively assess the safety conditions of buildings. To address this issue, this study proposes an InSAR Nonuniform Settlement Index. First, the point targets of buildings are extracted through time-series InSAR processing. Then, using the points as inputs, the Nonuniform Settlement Index calculates the 3D settlement plane and the inclination angle of the plane corresponding to each building. In this way, the proposed Nonuniform Settlement Index acts as a subsequent analysis method of time-series InSAR to characterize the safety statuses of buildings. In our study, 147 scenes of COSMO-SkyMed images from 2013 to 2022 were used to inverse the nine-year deformation evolution of the tested area. After time-series InSAR processing and index analysis based on the above SAR datasets, cross-validation was implemented with static-level and manual investigation data. The approach was to use one aging, collapsed building affected by tunneling work, as well as the eight adjacent aging buildings. The results showed high consistency with the in situ data, which proves the efficiency of the proposed approach.

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“…The exploration of InSAR time series through artificial intelligence has also been used to mitigate uncertainty sources of the data, such as atmospheric effects [23][24][25] or unwrapping errors [26]. Two recent trends in this field are the separation of effects coming from different sources [27,28] and the prediction of future InSAR observations [29,30].…”

Section: Introductionmentioning

confidence: 99%

SARClust—A New Tool to Analyze InSAR Displacement Time Series for Structure Monitoring

Roque

Falcão

Perissin³

et al. 2023

Sustainability

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Interferometric Synthetic Aperture Radar (InSAR) has proved its efficiency for displacement monitoring in urban areas. However, the large volume of data generated by this technology turns the retrieval of information useful for structure monitoring into a big data problem. In this study, a new tool (SARClust) to analyze InSAR displacement time series is proposed. The tool performs the clustering of persistent scatterers (PSs) based on dissimilarities between their displacement time series evaluated through dynamic time warping. This strategy leads to the formation of clusters containing PSs with similar displacements, which can be analyzed together, reducing data dimensionality, and facilitating the identification of displacement patterns potentially related to structural damage. A proof of concept was performed for downtown Lisbon, Portugal, where ten distinct displacement patterns were identified. A relationship between clusters presenting centimeter-level displacements and buildings located on steep slopes was observed. The results were validated through visual inspections and comparison with another tool for time series analysis. Agreement was found in both cases. The innovation in this study is the attention brought to SARClust’s ability to (i) analyze vertical and horizontal displacements simultaneously, using an unsupervised procedure, and (ii) characterize PSs assisting the displacement interpretation. The main finding is the strategy to identify signs of structure damage, even on isolated buildings, in a large amount of InSAR data. In conclusion, SARClust is of the utmost importance to detect potential signs of structural damage in InSAR displacement time series, supporting structure safety experts in more efficient and sustainable monitoring tasks.

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Blind Source Separation for MT-InSAR Analysis With Structural Health Monitoring Applications

Cited by 5 publications

References 67 publications

Underwater Acoustic Nonlinear Blind Ship Noise Separation Using Recurrent Attention Neural Networks

Underwater Acoustic Nonlinear Blind Ship Noise Separation Using Recurrent Attention Neural Networks

Evaluation of Post-Tunneling Aging Buildings Using the InSAR Nonuniform Settlement Index

SARClust—A New Tool to Analyze InSAR Displacement Time Series for Structure Monitoring

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