Measurement Of Surface Deformation Research Articles

Light-tracing based surface deformation measurement strategy for large radio telescopes

Light-tracing based surface deformation measurement strategy for large radio telescopes

Untrained single scanning phase retrieval neural network for surface deformation measurement of large-aperture antennas

Research on data-driven Fourier phase retrieval neural networks is well-established. However, in many practical applications, such as phase retrieval-based antenna surface deformation measurements in astronomy, obtaining a sufficient number of ground truth images for training is unlikely. In this paper, we propose a purely physics-driven neural network model for retrieving the aperture-field phase of large antennas. This is accomplished through the integration of the physical model describing the interaction between the aperture-field and the far-field with the neural network. Our simulations and experiments demonstrate accurate reconstruction of the aperture-field phase using only a single far-field intensity image. Furthermore, we present a solution tailored for scenarios with low signal-to-noise ratio.

A novel deformation measurement method for rotating blade based on PSO-ILS image correlation matching and mismatch correction

Abstract Aiming at the problem of small measurement range and difficult measurement of traditional contact sensor method in the rotating state of aero-engine blades, this paper proposes a novel deformation measurement method for rotating blade based on image correlation matching and mismatch correction. Firstly, a Particle Swarm Optimization-Iterative Local Search image intelligent matching algorithm is proposed, which effectively balances global search and local optimization, and the image matching displacement accuracy reaches 10−3 pixel. Secondly, a mismatch point detection method based on multi-scale local Root Mean Square is proposed, and the high-precision detection of mismatch points is realized by considering the influence of local structure. Finally, through the hierarchical refinement of the sub-pixel level mismatch point correction method, the mismatch point is corrected in the two iterative stages of global search and local optimization. The corrected mismatch point response value K is reduced by 99% compared with that before correction, which further improves the accuracy of deformation field calculation. In the experiment, the deformation of the rotating blade of 4500 RPM is measured, which proves that the image intelligent matching algorithm and the deformation field calculation method proposed in this paper can provide new methods and technical support for the accurate measurement of the blade surface deformation.

Interferometric Synthetic Aperture Radar (InSAR)-Based Absence Sampling for Machine-Learning-Based Landslide Susceptibility Mapping: The Three Gorges Reservoir Area, China

The accurate prediction of landslide susceptibility relies on effectively handling landslide absence samples in machine learning (ML) models. However, existing research tends to generate these samples in feature space, posing challenges in field validation, or using physics-informed models, thereby limiting their applicability. The rapid progress of interferometric synthetic aperture radar (InSAR) technology may bridge this gap by offering satellite images with extensive area coverage and precise surface deformation measurements at millimeter scales. Here, we propose an InSAR-based sampling strategy to generate absence samples for landslide susceptibility mapping in the Badong–Zigui area near the Three Gorges Reservoir, China. We achieve this by employing a Small Baseline Subset (SBAS) InSAR to generate the annual average ground deformation. Subsequently, we select absence samples from slopes with very slow deformation. Logistic regression, support vector machine, and random forest models demonstrate improvement when using InSAR-based absence samples, indicating enhanced accuracy in reflecting non-landslide conditions. Furthermore, we compare different integration methods to integrate InSAR into ML models, including absence sampling, joint training, overlay weights, and their combination, finding that utilizing all three methods simultaneously optimally improves landslide susceptibility models.

Coaxial panoramic 3D-DIC system for full inner surface deformation measurement

A coaxial panoramic three-dimensional digital image correlation (3D-DIC) system was developed for full 360o inner surface deformation measurement. The panoramic observation was realized by using a convex mirror. Two cameras were arranged coaxially to ensure the largest overlapping observation area. After the coordinate transformation and correlation calculation, the 3D displacement and strain distributions were reconstructed. The principles of the system were presented in detail, and the internal morphology and deformation of pipes subjected to compression loading were described to verify the effectiveness, practicality, and accuracy of the proposed system.

Shack–Hartmann wavefront sensing: A new approach to time-resolved measurement of the stress intensity factor during dynamic fracture

The stress intensity factor describes the stress state around a crack tip in a solid material and is important for understanding crack initiation and propagation. Because stresses cannot be measured directly, the characterization of the stress intensity factor relies on the measurement of deformation around a crack tip. Such measurements are challenging for dynamic fracture of brittle materials where the deformation is small and the crack tip velocity can be high. Digital gradient sensing (DGS) is capable of full-field measurement of surface deformation with a sub-micrometer sensitivity and a sub-microsecond temporal resolution, but it has only been demonstrated on centimeter-scale specimens with a spatial resolution of ∼ 1 mm. This makes it challenging to measure deformations close to the crack tip. Here, we demonstrate the potential of Shack–Hartmann wavefront sensing (SHWFS), as an alternative to DGS, for measuring surface deformation during dynamic brittle fracture of millimeter-scale specimens. Using an opaque commercial glass ceramic as an example material, we demonstrate the capability of SHWFS to measure the surface slope evolution induced by a propagating crack with a micrometer spatial resolution and a sub-microsecond temporal resolution. The SHWFS apparatus has the additional advantage of being physically more compact than a typical DGS apparatus. We interpret our SHWFS measurements of the surface slope by comparing them with 2D analytical predictions and phase-field simulations as well as 3D linear-elastic FEM simulations, based on which we discuss the relevance of 3D effects around the crack tip. Then, we introduce our procedure for extracting the apparent stress intensity factor associated with the propagating crack tip using SHWFS measurements. We conclude by discussing potential future enhancements of this technique and how its compactness could enable the integration of SHWFS with other characterization techniques including x-ray phase-contrast imaging (XPCI) for multi-modal characterization of dynamic fracture.

Dynamic pressure surface deformation measurement based on a chromatic confocal sensor: erratum.

This erratum corrects errors in Fig.4 of the original paper, Appl. Opt.62, 1467 (2023)APOPAI0003-693510.1364/AO.482808.

Investigation of 2020–2022 extreme floods and droughts in Sichuan Province of China based on joint inversion of GNSS and GRACE/GFO data

The Global Navigation Satellite System (GNSS) surface deformation measurement and Gravity Recovery and Climate Experiment (GRACE)/GRACE Follow-on (GFO) satellite gravimetry provide complementary strengths in estimating terrestrial water storage (TWS) changes, which helps quantify the evolutions of extreme floods and droughts. We implemented a joint inversion framework that only incorporates the contributions of GNSS and GRACE/GFO to recover the TWS changes for monitoring extreme floods and droughts in the Sichuan Province of China (SCP). The performance of TWS changes from the joint inversion is evaluated through closed-loop simulation and independent hydrometeorological data (i.e., precipitation (P), evapotranspiration (ET), and runoff (R)) based on the water balance equation (named P-ET-R) in the spatio-temporal domains. The simulation analysis results show that joint inversion presents better performance in recovering TWS changes than GNSS-only and GRACE/GFO-only solutions in the spatio-temporal scales. The measured joint inversion results reveal more spatial details of TWS changes than GNSS-only and GRACE/GFO-only solutions and present better correlations with GLDAS- and ERA5-derived P-ET-R (0.77 vs. 0.79) than GNSS-only (0.49 vs. 0.54) and GRACE/GFO-only (0.61 vs. 0.68) in the spatial domain. Meanwhile, the joint inversion results effectively characterize the spatio-temporal dynamics of extreme floods and droughts in SCP during 2020–2022, the average TWS surpluses are 33.61 and 32.86 mm in 2020 and 2022 flood events and the average TWS deficits are −36.78 and −63.87 mm in 2021 and 2022 drought events. In addition, the drought propagation time generally presents an increasing trend from southwestern to northeastern SCP. Furthermore, the average and minimum recovery time of drought and flood in the Sichuan basin is longer than those in the western mountains during 2020–2022. The proposed approach provides an effective estimation strategy for recovering reliable TWS changes for the investigation of extreme floods and droughts in regions where GNSS stations are distributed.

Slip Model of the 2022 Mw 6.6 Luding Earthquake from Inversion of GNSS and InSAR with Sentinel-1

Abstract We use surface deformation measurements, including Interferometric Synthetic Aperture Radar data acquired by the Sentinel-1A satellites and Global Navigation Satellite System observations, to invert the fault geometry and coseismic slip distribution of the 2022 Mw 6.6 earthquake in Sichuan, China. The dip of the best-fitting model is 68°. The rupture of the 2022 Luding earthquake is dominated by northwest strike-slip movement, mainly concentrated over a length of about 20 km above a depth of 15 km. The maximum slip is at approximately 4 km depth with the maximum displacement of about 2.1 m. The results indicate that the 2022 Luding earthquake ruptured the shallow layer of the seismic zone. The slip distribution indicates that the Moxi–Shimian fault segment is fully locked from the surface down to 15 km, which is consistent with the estimated locking depth. Based on the Coulomb stress analysis and considering the strong locking state of the Anninghe fault, more attention should be paid to the possibility of earthquakes in the Anninghe fault.

Characterization of thermal expansion in additively manufactured continuous carbon fibre reinforced polymer composites using fibre Bragg grating sensors

This study investigates thermal strains in fibre reinforced polymeric samples manufactured using a modified Fused Deposition Modelling (FDM) method. The investigated material was a composition of polylactic acid (PLA) resin and continuous carbon fibres. Each test sample was equipped with two Bragg grating (FBG) sensors, one embedded inside and the other bonded to the surface. Both sensors monitored temperature-induced deformations during the conditioning of the specimens in a thermal chamber. Multiscale, analytical and finite element method based models were implemented to quantify the temperature deformations.Research has revealed that in investigated samples, bending occurs due to thermal loading. This can result in an inaccurate estimation of the coefficient of thermal expansion when relying on surface deformation measurements. A proposed solution involves the use of one FBG sensor embedded inside the specimen or two FBG sensors placed symmetrically, capable of measuring axial thermal deformation and averaging the effects associated with bending.

The role of pre-eruptive gas segregation on co-eruptive deformation and SO2 emissions

The presence of exsolved gas bubbles influences measurements of both volcanic surface deformation and SO2 emissions. In a closed-system, exsolved volatiles remain within the melt but in an open-system, the decoupled gas phase can either outgas or accumulate, leading to large variations magmatic gas fraction. Here we investigate the role of gas volume fraction and gas segregation processes on magma properties and co-eruptive monitoring data. First we use thermodynamic models of gas exsolution to model gas volume fraction and magma compressibility, and use these to calculate SO2 emissions and co-eruptive volume change. We find that volume change is equally sensitive to magma compressibility and chamber compressibility over realistic parameters ranges, and both must be considered when interpreting surface deformation data. Reservoir depth and magma composition are the dominant controls on gas volume fraction, but the initial content of H2O and S have strong influences on volume change and SO2 emissions, respectively. Pre-eruptive gas accumulation produces increased SO2 emissions and muted co-eruptive deformation, while degassing has the opposite effect. We then compare our models to a compilation of data from 20 recent eruptions where measurements of volume change, SO2 emissions and erupted volume are available. To the first order, shallow reservoirs produce smaller volume changes per volume erupted and silica-poor magmas yield greater co-eruptive volume changes than silica-rich systems, consistent with closed system degassing. Co-eruptive degassing causes high SO2 emissions during effusive eruptions. Comparison between model predictions and observations suggests that all magmatic systems experience a certain degree of outgassing prior to an eruption. Our findings are consistent with current conceptual models of transcrustal magmatic systems consisting of heterogeneous mixtures of gas and melt and have important implications for the interpretation of surface deformation and SO2 emission signals at all stages of the eruption cycle.

MCNN-DIC: a mechanical constraints-based digital image correlation by a neural network approach

Digital image correlation (DIC) is a widely used photomechanical method for measuring surface deformation of materials. Practical engineering applications of DIC often encounter challenges such as discontinuous deformation fields, noise interference, and difficulties in measuring boundary deformations. To address these challenges, a new, to the best of our knowledge, DIC method called MCNN-DIC is proposed in this study by incorporating mechanical constraints using neural network technology. The proposed method applied compatibility equation constraints to the measured deformation field through a semi-supervised learning approach, thus making it more physical. The effectiveness of the proposed MCNN-DIC method was demonstrated through simulated experiments and real deformation fields of nuclear graphite material. The results show that the MCNN-DIC method achieves higher accuracy in measuring non-uniform deformation fields than a traditional mechanical constraints-based DIC and can rapidly measure deformation fields without requiring extensive pre-training of the neural network.

Review of research progress and development trend of digital image correlation

PurposeThe purpose of this paper is to provide a comprehensive review of a non-contact full-field optical measurement technique known as digital image correlation (DIC).Design/methodology/approachThe approach of this review paper is to introduce the research pertaining to DIC. It comprehensively covers crucial facets including its principles, historical development, core challenges, current research status and practical applications. Additionally, it delves into unresolved issues and outlines future research objectives.FindingsThe findings of this review encompass essential aspects of DIC, including core issues like the subpixel registration algorithm, camera calibration, measurement of surface deformation in 3D complex structures and applications in ultra-high-temperature settings. Additionally, the review presents the prevailing strategies for addressing these challenges, the most recent advancements in DIC applications across quasi-static, dynamic, ultra-high-temperature, large-scale and micro-scale engineering domains, along with key directions for future research endeavors.Originality/valueThis review holds a substantial value as it furnishes a comprehensive and in-depth introduction to DIC, while also spotlighting its prospective applications.

Micro bi-prism bi-telecentric single lens Stereo-DIC system using one-step calibration method

In recent years, the bi-prism bi-telecentric single lens stereo digital image correlation (BBSL Stereo-DIC) system has emerged as a viable approach for measuring stereo surface deformation. However, the reliance on a bi-prism that needs to be precisely aligned perpendicular to the optical axis of the bi-telecentric lens using a laser beam, as well as the need to remove the bi-prism during the calibration phase, impose limitations on the practical application of the existing method. To overcome these challenges, we propose a convenient one-step calibration method that eliminates the positional dependence on the bi-prism. This method incorporates the bi-prism’s attitude parameter and principal point coordinates as additional intrinsic parameters. Similar to traditional binocular calibration, the calibration process involves capturing a series of images of a calibration target at various positions and orientations, making it convenient and straightforward for DIC users. A numerical simulation calibration experiment is performed to illustrate the difference between the proposed method and the existing method. The result shows that the bi-prism’s attitude parameter has a significant impact on 3D reconstruction. A rigid-body translation experiment and a tensile experiment of an 800H alloy micro-specimen with a notch are conducted on a micro BBSL Stereo-DIC system. The results and the comparison with finite element simulation prove the accuracy of the proposed method, which demonstrates that the one-step calibration method can be easily employed for micro stereo surface deformation measurement.

A novel method for inverting coseismic 3D surface deformation using InSAR considering the weight influence of the spatial distribution of GNSS points

The combination of InSAR and GNSS data can provide more accurate measurements of coseismic 3D surface deformations, which are crucial in determining earthquake source parameters. Currently, the methods for inverting coseismic 3D surface deformations by integrating GNSS and InSAR data using the strain model (SM) (e.g., Simultaneous and integrated strain tensor estimation from geodetic measurements to obtain 3D displacement maps (SISTEM), SM-VCE) simply select GNSS points based on a certain number or distance, without considering the weight influence of the spatial distribution of G NSS points on the InSAR and GNSS data fusion results. To address this issue, this paper proposes a new method that uses Voronoi diagrams analyze the contribution of GNSS points to the deformation at target points. In this paper, GNSS points with high contribution can be used to construct observation value equations and optimize the internal weight of GNSS data. The proposed approach characterizes the contribution of GNSS data to target points by analyzing the changes in the Delaunay triangulation area formed before and after the target points are added to the GNSS data. Simulation experiments reveal that the proposed method outperforms conventional methods in accuracy and completeness. The proposed method is then applied to map the coseismic 3D surface deformations of the 2020 Mw6.5 Monte Cristo Range earthquake and find that the maximum deformation in the east–west, north–south, and vertical directions is about 13 cm, 11 cm, and 24 cm, respectively.

Phase-based motion estimation and SVR smooth for target-free 3D deformation measurement using stereophotogrammetry

Non-contact measurement methods based on computer vision have shown great potential for measuring structural surface displacement and strain due to their low cost and high efficiency. However, the traditional computer vision technology based on digital image correlation (DIC) is limited by its dependence on natural texture features of the surface and its susceptibility to environmental conditions, making it challenging to obtain effective and robust measurements of surface deformation. Additionally, these methods typically only focus on the structural response in the image plane, which makes it difficult to perform a comprehensive health assessment of complex structural components with asymmetry. Therefore, a time-domain visual measurement technique based on image phase information is proposed for measuring three-dimensional (3D) deformation of structures using stereophotogrammetry. The method involves extracting image phase information using 2D Gabor filters and applying it to dense optical flow estimation based on polynomial approximation and semi-global block matching (SGBM) for obtaining robust 3D displacement measurements. The support vector regression (SVR) is then used to perform hyperplane fitting of the original displacements for improving the smoothness of the 3D displacements. Finally, an intuitive strain conversion method is introduced to convert the 3D displacements into 3D strains. The performance of the proposed technique has been validated through numerical simulation experiments based on a physics-based graphics model (PBGM) in a synthetic environment. The robustness of the proposed method has been further proved through free attenuation vibration experiments on a steel ruler. The initial evaluation of the expected performance of the measurement plan has been enabled by performing a 1:1 reduction of the field measurement process in a synthetic environment. The results indicate that the proposed method is a promising method for accurately and reliably measuring structural deformation in both synthetic and real-world environments, especially for complex structural components with asymmetry.

Radio Telescope Surface Measurement via Deep Learning

This paper proposes a new method for accurately measuring the surface deformation of radio telescope antennas based on deep learning. A deep convolutional neural network is used to predict surface deformations by mapping the near-field intensity of the antenna, instead of relying entirely on a physical model. The proposed method could offer precise measurement of surface deformations in real time with only a single image of near-field intensity pattern. To optimize the deep learning model, a preliminary U-net based deep convolutional neural network (DCNN) model was developed based on a large data set generated by an approximate physical model, a partial differential equation (PDE). The network parameters were then fine-tuned using transfer learning with a small data set obtained by high precision numerical simulation. During this process, the fine-tuning layers that achieved optimal performance for the U-net network was studied. The final results show that the proposed method significantly improves the accuracy of antenna surface deformation recovery. Additionally, singular value decomposition (SVD) technology is employed to denoise the intensity image, which facilitates the application of the proposed method to actual deformation measurement.

Optimizing feature based image alignment for surface deformation measurements

A novel method for efficient and reliable detection of surface features on weakly structured surfaces is presented. This method enables the analysis of surface deformations without the need for extensive sample preparation. Our simplified workflow, which eliminates the need for preparation, focuses on feature filtering to produce reliable transformations between different surface configurations. The versatility and robustness of this approach is demonstrated by a series of surface tests under different loads that provide consistent results. This method shows promise for a wide range of applications in surface feature detection and comprehensive deformation analysis, and provides a practical tool for the precise investigation of weakly textured surfaces.

Making use of Digital Image Correlation to identify the true character of the applied load

Digital Image Correlation became one of the dominant optical full-field techniques used in experimental mechanics. It does not require sophisticated equipment to evaluate the full displacement vector field of the specimen under test with great accuracy. In this paper, we would like to put the reader's attention to the DIC technique as a solution that may identify the genuine character of the load applied to the object under test. Our experience with testing wind turbine blades using DIC proves that possibility. Full Text: PDF References E.M.C Jones, M.A. Iadicola, "A good practises guide for digital image correlation" International Digital Image Correlation Society, (2018), CrossRef B. Pan, "Digital image correlation for surface deformation measurement: historical developments, recent advances and future goals", Meas. Sci. Technol. 29, 082001 (2018), CrossRef B. Pan, L.P. Yu, Q.B. Zhang, "Review of single-camera stereo-digital image correlation techniques for full-field 3D shape and deformation measurement", Sci. China Technol. Sci. 61, 2 (2018), CrossRef S. Sharma, N. Iniyan Thiruselvam, S.J. Subramanian, G.S. Kumar, "Computation of strains from stereo digital image correlation using principal component analysis", Meas. Sci. Technol. 32, 105201 (2021), CrossRef IEC 61400-5:2020 Wind energy generation systems - Part 5: Wind turbine, DirectLink IEC 61400-23:2014 Wind turbines - Part 23: Full-scale structural testing of rotor blades, DirectLink

Integrating shallow head measurements and InSAR data to quantify groundwater-storage change in San Joaquin Valley, California (USA)

Monitoring groundwater storage is essential for sustainable groundwater management. Storage can be quantified by considering the two main components through which storage change is expressed: saturation changes and deformation of aquifer materials. Here, these components were quantified using a selected area in California’s San Joaquin Valley (USA). First, this involved following existing observational approaches: quantifying the component expressed through saturation changes by identifying head measurements from shallow wells and scaling by specific yield. In the San Joaquin Valley, existing approaches to estimate the deformation component are to ignore it or approximate it with a simple linear relation to measured head. However, head and deformation measurements made at extensometers revealed that assuming a linear relationship between deformation and head might provide a poor estimate, particularly during periods in which measured head is rising. Instead, InSAR-derived surface deformation measurements were used to quantify the deformation component of storage changes. This showed that the two components—saturation and deformation—accounted for storage declines of equal magnitude over 2015–2021, suggesting that the deformation component should not be neglected when estimating storage changes in regions with subsidence. Summing the two calculated components gave a new estimate of the total storage change that captured the major trends seen in independent estimates, while better accounting for the deformation component. An additional benefit is that this method accounts for the deformation component in the unconfined aquifer. This method to quantify total storage change can be a practical and effective tool to support groundwater management.