Deformation Monitoring Applications Research Articles

Theoretical Prediction of Monolayer BeP2O4H4 with Excellent Nonlinear-Optical Properties in Deep-Ultraviolet Range.

Most 2D nonlinear optical (NLO) materials do not have an ultrawide bandgap, therefore, they are unsuitable for working in the deep-ultraviolet spectral range (< 200nm). Herein, the theoretical prediction of an excellent monolayer BeP2O4H4 (ML-BPOH) is reported. DFT analyses suggest a low cleavage energy (≈45meV per atom) from a naturally existed bulk-BPOH material, indicating feasible exfoliation. This novel 2D material exhibits excellent properties including an ultrawide bandgap (Eg) of 7.84eV, and a strong second-order nonlinear susceptibility ( = 0.43pmV-1), which is comparable to that of benchmark bulk-KBBF crystal (d16=0.45pmV-1). The wide bandgap and large SHG effect of ML-BPOH are mainly derived from the (PO2H2)- tetrahedron. Notably, ML-BPOH exhibits an outstanding 50% variation in dsheet under minor stress stimuli (±3%) due to rotation of structurally rigid (PO2H2)- tetrahedron. This indicates significant potential for application in material deformation monitoring.

On the RFI Detection in Differential Interferometric Synthetic Aperture Radar

Abstract. Synthetic Aperture Radar (SAR) can image the ground with a wide area and high resolution at all times and in all weather and has become an important means of remote sensing. Differential interferometry SAR technology can obtain high-precision surface deformation information by processing more than two SAR images before and after deformation. In recent years, it has attracted widespread attention and research. However, due to the increasing number of ground electronic devices, ground radio frequency interference (RFI) has become one of the main problems in differential interferometry SAR processing, seriously affecting the performance of differential interferometry SAR imaging and differential interferometry surface deformation monitoring applications. In this paper, a clutter cancellation interference enhancement detection algorithm is proposed. By clutter suppression in the primary and secondary images, the interference-to-signal ratio is increased, which effectively improves the interference detection capabilities. The effectiveness of the algorithm in this paper is verified by the on-orbit measured data of the Lutan-1 satellites.

Accurate monitoring of coal pillar deformation based on distributed optical fiber

In many structural deformation monitoring applications, determination of the response length of embedded optical fibers is difficult due to the use of strain integration method, which increases the measurement errors. For systematic investigation of the deformation law of coal pillars under mining action, distributed fiber optic technology was used herein to analyze its response length. This study discusses how non-uniform strain distribution can increase the accuracy error of fiber optic displacement measurement, and verifies the effectiveness of theoretical analysis by using coupled FEM–DEM numerical simulation method. Furthermore, the relationship between the Brillouin gain spectrum characteristics of different sampling points and the non-uniformity of strain distribution was explained. To reduce accuracy errors, a new method was proposed herein for determining the response length of optical fibers. The feasibility of this method was verified through indoor experiments. Next, this method was applied to the deformation monitoring of coal pillars in a section of Huojitu Well. By using the proposed method, the deformation of coal pillars can be accurately monitored.

An integer ambiguity resolution method based on baseline vector predictions in landslide monitoring

Accurate integer ambiguity resolution (IAR) is pivotal for high-precision and high-reliability Global Navigation Satellite Systems (GNSS) positioning. However, traditional IAR methodologies, encompassing integer estimation and validation, often face significant challenges in harsh landslide environments. This paper proposes a new IAR method based on baseline vector predictions. Initially, the real-time baseline vectors predictions are used to substitute the true values of baseline vectors in the mixed integer least-squares (MILS) estimation equation, and the MILS estimation criterion is constructed which contains the quadratic form of baseline vectors. Subsequently, a novel baseline vector validation procedure, built on real-time dynamic predictions, is established. Experiments were carried out at two GNSS landslide monitoring area using GPS + BDS dual-frequency measurement data. These stations underwent both stable deformation stages within complex measurement environments and accelerated deformation stages in open environments. Results indicate that the proposed method overcomes the limitations associated with conventional ambiguity validation methods based on hypothesis testing, addressing the errors of “abandoning trueness” and “accepting mistake” simultaneously. Notably, the ambiguity fixed rate improved by 80.7 % for single-frequency experiments and 39.8 % for dual-frequency experiments at one monitoring station located in a complex environment. This work provides valuable IAR approach for GNSS deformation monitoring applications.

An Improved Filtering Method and Application of Landslide Deformation Monitoring

Real-time early warning based on deformation monitoring is an important measure to reduce the threat related to landslides. However, due to the complexity of landslide deformation, equipment accuracy, and data transmission interference, the displacement obtained by existing monitoring equipment has random errors, resulting in inaccurate landslide warning. In this paper, a tangent angle model based on the improved filtering method is proposed for the early warning of landslides. An improved filtering method is proposed based on the least-square method to smooth the measurement errors of monitoring equipment, considering both “data smoothing effect” and “accelerated deformation real-time determination”. By comparing the least-squares method and the improved filtering method, the results demonstrate that the improved filtering method can more effectively smooth the fluctuation of the deformation rate. The amplitude of the error-induced tangent angle warning value fluctuation can be reduced, which provides correct early warning. The improved method provides a new approach for the real-time filtering of subsequent landslide deformation data.

Analysis of Multifrequency GNSS Signals and an Improved Single-Epoch RTK Method for Medium-Long Baseline

In recent years, the quantity of visible satellites has increased significantly due to multiple satellite systems that leaped forward. The BeiDou Navigation Satellite System (BDS) and Galileo satellite navigation system (Galileo) broadcast triple-frequency signals and above to users, thus enhancing the reliability, continuity, and availability of the single-epoch real-time kinematic (RTK) positioning. In this study, an improved single-epoch multifrequency multisystem RTK method is successfully developed for the medium-long baseline. First, the Galileo and BDS extra-wide-lane (EWL) ambiguities are fixed at a high success rate, and the Galileo and BDS wide-lane (WL) ambiguity is achieved via the transformation process. Second, the fixed WL ambiguities of Galileo and BDS are exploited to elevate the fixed rate of GPS WL ambiguity. Third, the parametric strategies for ionospheric delay are carried out to upregulate the narrow-lane (NL) ambiguity-fixed rate of GPS. Further, the real-time data are adopted for verifying the feasibility of the method developed in this study. The experimental results demonstrate the optimal carrier-to-noise density ratio (C/N0) of full operational capability (FOC) E5a/E5b at all frequencies, followed by IIR-M L1, and IIR-A/B L2 exhibits the worst performance. Generally, the multipath combination (MPC) of Galileo signals shows root mean square (RMS) values within 0.4 m, ordered as follows: E 1 &gt; E 5 b &gt; E 5 a . For the BDS-2, the B3 signal exhibits optimal performance, while the B1 signal is the worst. The RMS of MPC errors of L1 signals is smaller than the L2 signals for the GPS. Furthermore, under the 50 km baseline, the GPS NL ambiguity-fixed rate using the ionosphere-free (IF) combination reaches only 47.74% at the ratio threshold of 2. Finally, compared to the ionosphere-free combination method, the GPS NL ambiguity-fixed rate is increased by 45.52% with the presented method. The proposed approach broadens the future application of deformation monitoring in medium-long baseline scenarios.

BDS Dual-Frequency Carrier Phase Multipath Hemispherical Map Model and Its Application in Real-Time Deformation Monitoring.

The BDS multipath delay error is highly related to the surrounding monitoring environment, which cannot be eliminated or mitigated by applying the double difference observation model. In the actual monitoring environment, due to the complexity of the BDS constellation, it is difficult for existing algorithms to consider GEO, IGSO, MEO and other different orbital types of satellites for real-time and efficient multipath error reduction. Therefore, we propose a novel BDS dual-frequency multipath error reduction method for real deformation monitoring for BDS considering various satellite orbit types. This method extracts the single error residual of each satellite based on the assumption of "zero mean" and divides the appropriate grid density of GEO and IGSO/MEO, respectively, to construct a dual-frequency multipath hemispherical map model suitable for BDS satellites with different orbital types. This method can realize the multipath error elimination of the observed values of different orbits and different frequencies. The results of simulation experiments and real deformation monitoring data demonstrate that this method can effectively eliminate low-frequency multipath delay errors in the observation domain and coordinate domain. After multipath correction, the precision of the horizontal coordinates and height coordinates are 1.7 mm and 4.6 mm. The precision of the horizontal coordinate and height coordinate is increased by 50% and 60%, respectively. The fixed rate of ambiguity increased by 5-7%.

EDM-GNSS distance comparison at the EURO5000 calibration baseline: preliminary results

Abstract At the Pieniny Klippen Belt in Poland, the novel primary reference baseline EURO5000 is required as part of the European Research project GeoMetre to both validate refractivity-compensated EDM prototypes and investigate the metrological traceability of GNSS-based distances. Since the aimed uncertainty is 1 mm at 5 km (k = 2), the design, construction, and validation must be carefully prepared to fulfil the high standards of the GeoMetre field campaigns which are planned to be carried out in May 2022. This contribution describes the main features of the EURO5000 and presents the results of the preliminary validation which includes a first comparison between the results obtained by using precise currently available EDMs as well as GNSS techniques following the standard GNSS geodetic processing algorithms, on the one hand, and the improved GNSS-Based Distance Meter (GBDM+) approach developed at UPV, on the other hand. The preliminary validation presented in this contribution also permits (1) to detect potential problems in the use of the baseline such as potential geodynamic problems, atmospheric refraction or multipath limitations, (2) to produce a set of reliable results, and (3) to pave the way for the final field comparisons between the novel EDMs and the GBDM+ approach. The result of this metrological experiment may significantly contribute to overcome the limitations of current high-precision deformation monitoring applications that require their scale to be consistent with the SI-metre within 0.1 ppm in several km.

Learning to sense three-dimensional shape deformation of a single multimode fiber

Optical fiber bending, deformation or shape sensing are important measurement technologies and have been widely deployed in various applications including healthcare, structural monitoring and robotics. However, existing optical fiber bending sensors require complex sensor structures and interrogation systems. Here, inspired by the recent renewed interest in information-rich multimode optical fibers, we show that the multimode fiber (MMF) output speckles contain the three-dimensional (3D) geometric shape information of the MMF itself. We demonstrate proof-of-concept 3D multi-point deformation sensing via a single multimode fiber by using k-nearest neighbor (KNN) machine learning algorithm, and achieve a classification accuracy close to 100%. Our results show that a single MMF based deformation sensor is excellent in terms of system simplicity, resolution and sensitivity, and can be a promising candidate in deformation monitoring or shape-sensing applications.

Terrestrial reference frame SC20 for monitoring crustal deformation in the adjacent areas of the South China block

SUMMARYFor the application of crustal deformation monitoring, users in East Asia need to have a terrestrial reference frame (TRF) that is consistent with the latest International Terrestrial Reference Frame 2014 (ITRF2014). We selected 12 core stations having 10 yr of continuous GPS observation (2010–2020) by the enhanced criteria and established a stable South China Reference Frame 2020 (SC20). The SC20 is defined as having no-net rotation within the stable South China block and an angular velocity from IGS14 to SC20 in Cartesian coordinates of (−0.1736 ± 0.0088, −0.4901 ± 0.0195, 1.0258 ± 0.0114) milliarcsecond yr−1 is obtained. The root-mean-square velocities of the core stations are 0.23 mm yr−1 in the north, 0.22 mm yr−1 in the east, and 0.30 mm yr−1 vertical and the SC20 frame can be confidently used within 8 yr beyond its useful life (2010–2020) without causing any deviation in position. Finally, the three-component velocities of 194 stations with respect to SC20 are obtained in East Asia. The deformation result can fully highlight the relative deformation of the boundary zone of the active block, which is beneficial to the crustal determination related to the location of strong earthquakes. The SC20 also degrades over time just as with ITRF, so we will be regularly updating it every few years by adding more reference stations and longer periods of observations and in time after major geophysical events.

Experimental Evaluation of Smartphone Accelerometer and Low-Cost Dual Frequency GNSS Sensors for Deformation Monitoring.

Smartphone accelerometers and low-cost Global Navigation Satellite System (GNSS) equipment have faced rapid and important advancement, opening a new door to deformation monitoring applications such as landslide, plate tectonics and structural health monitoring (SHM). The precision potential and operational feasibility of the equipment play an important role in the decision making of campaigning for affordable solutions. This paper focuses on the evaluation of the empirical precision, including (auto)time correlation, of a common smartphone accelerometer (Bosch BMI160) and a low-cost dual frequency GNSS reference-rover pair (u-blox ZED-F9P) set to operate at high rates (50 and 5 Hz, respectively). Additionally, a high-rate (5 Hz) GPS-only baseline-based multipath (MP) correction is proposed for effectively removing a large part of this error and allowing to correctly determine the instrumental noise of the GNSS sensor. Furthermore, the benefit of smartphone-based validation for the tracking of dynamic displacements is addressed. The estimated East-North-Up (ENU) precision values () of , and 9.6 are comparable with the declared precision potential () of the smartphone accelerometer of . Furthermore, the acceleration noise shows only mild traces of (auto)correlation. The MP-corrected 3D (ENU) empirical precision values of , and mm were found to be better by 30–40% than the straight-out-of box precision of the GNSS sensor, attesting the usefulness of the MP correction. The GNSS sensors output position information with time correlation of typically tens of seconds. The results indicate exceptional precision potential of these low-power-consuming, small-scale, affordable sensors set to operate at a high-rate over small regions. The smartphone-based dynamic displacement validation shows that GNSS data of a low-cost sensor at a 5 Hz sampling rate can be successfully used for tracking dynamic processes.

Experimental study of pipeline deformation monitoring using the inverse finite element method based on the iBeam3 element

This paper presents a novel pipeline deformation monitoring method using the inverse finite element method (iFEM) based on the iBeam3 element. This element uses only two end nodes to reconstruct pipeline deformation without any material and/or loading information because only the strain–displacement relationship is employed in the algorithm. A series of experimental studies is conducted to validate the effectiveness and accuracy of the presented method. The maximum error of the pipe node displacement in the numerical simulation is 4.1%, and the average error in the experimental test combined with the data from fiber Bragg grating (FBG) strain sensors is 7.96%. An application of pipe deformation monitoring during the freeze–thaw process of soil is conducted, and the test results demonstrate that the presented method can effectively monitor the deformation process for a buried pipe during the freeze–thaw process of soil with a measurement error of 15.43%, showing potential in practical engineering.

An Improved Multipath Mitigation Method and Its Application in Real-Time Bridge Deformation Monitoring

A multipath is a major error source in bridge deformation monitoring and the key to achieving millimeter-level monitoring. Although the traditional MHM (multipath hemispherical map) algorithm can be applied to multipath mitigation in real-time scenarios, accuracy needs to be further improved due to the influence of observation noise and the multipath differences between different satellites. Aiming at the insufficiency of MHM in dealing with the adverse impact of observation noise, we proposed the MHM_V model, based on Variational Mode Decomposition (VMD) and the MHM algorithm. Utilizing the VMD algorithm to extract the multipath from single-difference (SD) residuals, and according to the principle of the closest elevation and azimuth, the original observation of carrier phase in the few days following the implementation are corrected to mitigate the influence of the multipath. The MHM_V model proposed in this paper is verified and compared with the traditional MHM algorithm by using the observed data of the Forth Road Bridge with a seven day and 10 s sampling rate. The results show that the correlation coefficient of the multipath on two adjacent days was increased by about 10% after residual denoising with the VMD algorithm; the standard deviations of residual error in the L1/L2 frequencies were improved by 37.8% and 40.7%, respectively, which were better than the scores of 26.1% and 31.0% for the MHM algorithm. Taking a ratio equal to three as the threshold value, the fixed success rates of ambiguity were 88.0% without multipath mitigation and 99.4% after mitigating the multipath with MHM_V. The MHM_V algorithm can effectively improve the success rate, reliability, and convergence rate of ambiguity resolution in a bridge multipath environment and perform better than the MHM algorithm.

Rapid Estimation of Entire Brain Strain Using Deep Learning Models.

Many recent studies suggest that brain deformation resulting from head impacts are linked to the corresponding clinical outcome, such as mild traumatic brain injury (mTBI). Even if several finite element (FE) head models have been developed and validated to calculate brain deformation based on impact kinematics, the clinical application of these FE head models is limited due to the time-consuming nature of FE simulations. This work aims to accelerate the brain deformation calculation and thus improve the potential for clinical applications. We propose a deep learning head model with a five-layer deep neural network and feature engineering, and trained and tested the model on 2511 head impacts from a combination of head model simulations and on-field college football and mixed martial arts impacts. The proposed deep learning head model can calculate the maximum principal strain (Green Lagrange) for every element in the entire brain in less than 0.001 s with an average root mean squared error of 0.022 and a standard deviation of 0.001 over twenty repeats with random data partition and model initialization. Trained and tested using the dataset of 2511 head impacts, this model can be applied to various sports in the calculation of brain strain with accuracy, and its applicability can even further be extended by incorporating data from other types of head impacts. In addition to the potential clinical application in real-time brain deformation monitoring, this model will help researchers estimate the brain strain from a large number of head impacts more efficiently than using FE models.

Joint time–frequency mask and convolutional neural network for real-time separation of multipath in GNSS deformation monitoring

As one of the leading technologies of real-time dynamic deformation monitoring, global navigation satellite system (GNSS) has been widely used in deformation monitoring. Multipath error is the primary error source that limits the application of GNSS high-precision deformation monitoring, which cannot be eliminated by the double-differenced technique. It is a problem to separate and mitigate multipath in the GNSS deformation monitoring series in real-time. Therefore, we propose an approach to solve this problem, named time–frequency mask and convolutional neural network (TFM–CNN). The specific processes are as follows: (1) TFM–CNN network construction. We add a full-band deformation to the multipath and obtain the spectrogram of the signal by the short-time Fourier transform (STFT); meanwhile, the ideal ratio mask (IRM) is used to obtain the corresponding time–frequency mask matrix based on the spectrogram; furthermore, one-dimensional CNN mines the mapping relationship between the spectrogram and the time–frequency mask matrix. (2) Multipath separation. We obtain the spectrogram of the GNSS real-time deformation monitoring series by STFT. Then, we estimate its time–frequency mask matrix by the network. The matrix is multiplied by the spectrogram of the monitoring series to obtain the spectrogram of the multipath. Finally, we perform the inverse STFT to obtain the multipath in the GNSS monitoring series. The experimental results show that by training GNSS data in only a specific direction (such as the north direction), the mapping relationship between the spectrogram of multipath and the time–frequency mask matrix can be obtained, which can effectively separate multipath of the GNSS monitoring series in different observation environment in real-time. TFM-CNN significantly improves the monitoring accuracy and achieves millimeter level dynamic deformation monitoring.

Frequency Domain Panoramic Imaging Algorithm for Ground-Based ArcSAR.

The ground-based arc-scanning synthetic aperture radar (ArcSAR) is capable of 360° scanning of the surroundings with the antenna fixed on a rotating arm. ArcSAR has much wider field of view when compared with conventional ground-based synthetic aperture radar (GBSAR) scanning on a linear rail. It has already been used in deformation monitoring applications. This paper mainly focuses on the accurate and fast imaging algorithms for ArcSAR. The curvature track makes the image focusing challenging and, in the classical frequency domain, fast imaging algorithms that are designed for linear rail SAR cannot be readily applied. This paper proposed an efficient frequency domain imaging algorithm for ArcSAR. The proposed algorithm takes advantage of the angular shift-invariant property of the ArcSAR signal, and it deduces the accurate matched filter in the angular-frequency domain, so panoramic images in polar coordinates with wide swath can be obtained at one time without segmenting strategy. When compared with existing ArcSAR frequency domain algorithms, the proposed algorithm is more accurate and efficient, because it has neither far range nor narrow beam antenna restrictions. The proposed method is validated by both simulation and real data. The results show that our algorithm brings the quality of image close to the time domain back-projection (BP) algorithm at a processing efficiency about two orders of magnitude better, and it has better image quality than the existing frequency domain Lee’s algorithm at a comparable processing speed.

Asynchronous RTK Method for Detecting the Stability of the Reference Station in GNSS Deformation Monitoring.

The real-time kinematic (RTK) positioning technique of global navigation satellite systems (GNSS) has been widely used for deformation monitoring in the past several decades. The RTK technique can provide relative displacements in a local reference frame defined by a highly stable reference station. However, the traditional RTK solution does not account for reference stations that experience displacement. This presents a challenge for establishing a near real-time GNSS monitoring system, as since the displacement of a reference station can be easily misinterpreted as a sign of rapid movement at the monitoring station. In this study, based on the reference observations in different time domains, asynchronous and synchronous RTK are proposed and applied together to address this issue, providing more reliable displacement information. Using the asynchronously generated time difference of a reference frame, the proposed approach can detect whether a measured displacement has occurred in the reference or the monitoring station in the current epoch. This allows for the separation of reference station movements from monitoring station movements. The results based on both simulated and landslide monitoring data demonstrate that the proposed method can provide reliable displacement determinations, which are critical in deformation monitoring applications, such as the early warning of landslides.

Constant Resistance and Yielding Support Technology for Large Deformations of Surrounding Rocks in the Minxian Tunnel

During the excavation of the Minxian tunnel, problems of large deformations of surrounding rocks and failure of support structures appeared frequently, which caused serious influences on construction safety and costs of the tunnel. Based on laboratory analysis of mineral composition and field investigations on deformation characteristics of the surrounding rocks, the large deformation mechanism of surrounding rocks of the tunnel was considered as water‐absorbing swelling molecules of carbonaceous slate and stress‐induced asymmetric structural deformations of the surrounding rocks. The structural deformations of surrounding rocks mainly include bending deformation, interlayer sliding, and crushing failure of local rock blocks. Then, a new constant resistance and yielding support technology based on the constant resistance and large deformation (CRLD) anchor cable was proposed to control large deformations of surrounding rocks. The field tests and deformation monitoring were carried out. The monitoring results showed that compared with original support measure, the surrounding rock deformations, stresses of primary supports, and permanent lining using new support technology decreased greatly. Among them, the maximum deformation of surrounding rock was only 73 mm. The effects of field application and results of deformation monitoring showed that the new support technology can effectively control large deformations of the surrounding rocks in the Minxian tunnel.

A new 3D laser-scanning and GPS combined measurement system

Terrestrial laser scanning (TLS) is widely used because of its ability to quickly acquire high-density and high-precision 3D image and topographic data. However, it can only acquire independent coordinate system points, which restricts its application in large-scale deformation monitoring. In this study, we constructed a measurement system to acquire global coordinate point cloud data by combining TLS and GPS (Global Positioning System). The coordinate values of retro-reflective targets could be acquired in different coordinate systems, the GPS coordinate and the TLS station coordinate, synchronously. Our experiments showed that, after registration with the homonymy points acquired by 30-min short-baseline differential GPS using the ICP algorithm, the positional accuracy of the TLS retro-reflective target center in the global coordinate was better than 10 mm. This high precision meets, for instance, the requirements of coal mining subsidence monitoring. We used our new combined measurement system to acquire and process the point cloud data of a frame structure. The measurements demonstrated the practicability and robustness of the new measurement system.

Research on quality improvement method of deformation monitoring data based on InSAR

In recent years, geological disasters caused by surface deformation frequently occur, which seriously threatens the safety of people's lives and property. Therefore, it is of great significance to strengthen the monitoring of surface deformation. With the continuous advancement of science and technology, traditional monitoring technology is difficult to meet the development requirements of modern society. As a new type of space-to-earth observation technology, INSAR technology has the advantages of high precision and real-time dynamic monitoring, and has been obtained in surface deformation monitoring widely used. This paper briefly analyzes the basic working principle of INSAR technology and its specific application in surface deformation monitoring. The algorithm parallelism of the ground-based SAR deformation monitoring process is analyzed, and the CPU + GPU heterogeneous platform is used to accelerate the implementation to improve the timeliness of deformation monitoring. BP imaging algorithm, interferogram generation, interferogram filtering and phase unwrapping algorithm are designed in parallel, and appropriate parallel granularity planning for multiple loops, adaptive division of optimal thread block size and use of shared memory to reduce duplicate data are adopted. Optimization strategies such as read time enable GPU acceleration processing. Compared with the implementation of CPU platform and CPU + GPU heterogeneous platform, the acceleration effect from tens to hundreds of times is accelerated, and the feasibility of GPU to improve the timeliness of deformation monitoring is verified.