Search Results (59)

In the optical band, very few pulsars can be directly detected, but some of the pulsar binary companions can be observed. This study leverages high-precision astrometric data from Gaia Data Release 3 (DR3) to identify pulsar companions in binary systems. Cross-matching the Australia Telescope National Facility (ATNF) Pulsar Catalogue with Gaia DR3 yielded 58 astrometric pairs, including 9 newly confirmed companions—primarily in the southern hemisphere—expanding the known pulsar distribution there. Among newly confirmed companions, eight are redback pulsars, offering insights into millisecond pulsar evolution and companion composition. All 58 companions are classified as main-sequence stars, neutron stars, white dwarfs, or ultra-light companion stars, with ∼40% being spider pulsars. Gaia’s exceptional astrometric precision advances pulsar studies, enabling gravitational wave detection via Pulsar Timing Arrays (PTAs) and improved reference frame link. Future multi-wavelength research will benefit from Gaia DR4, International Pulsar Timing Array (IPTA) collaborations (including Five-hundred-meter Aperture Spherical radio Telescope (FAST)), and Very Long Baseline Interferometry (VLBI) networks like the Chinese VLBI Network (CVN). Full article

Coal mines play an important role in the global energy supply. Monitoring the displacement of open-pit mines is crucial to preventing geological disasters, such as landslides and surface displacement, caused by high-intensity mining activities. In recent years, multi-temporal Synthetic Aperture Radar Interferometry (InSAR) technology has advanced and become widely used for monitoring the displacement of open-pit mines. However, the scattering characteristics of surfaces in open-pit mining areas are unstable, resulting in few coherence points with uneven distribution. Small BAseline Subset InSAR (SABS-InSAR) technology struggles to extract high-density points and fails to capture the overall displacement trend of the monitoring area. To address these challenges, this study focused on the Shengli West No. 2 open-pit coal mine in eastern Inner Mongolia, China, using 201 Sentinel-1 images collected from 20 May 2017 to 13 April 2024. We applied both SBAS-InSAR and distributed scatterer InSAR (DS-InSAR) methods to investigate the surface displacement and long-term behavior of the open-pit coal mine over the past seven years. The relationship between this displacement and mining activities was analyzed. The results indicate significant land subsidence was observed in reclaimed areas, with rates exceeding 281.2 mm/y. The compaction process of waste materials was the main contributor to land subsidence. Land uplift or horizontal displacement was observed over the areas near the active working parts of the mines. Compared to SBAS-InSAR, DS-InSAR was shown to more effectively capture the spatiotemporal distribution of surface displacement in open-pit coal mines, offering more intuitive, comprehensive, and high-precision monitoring of open-pit coal mines. Full article

Land subsidence poses a significant risk in built-up environments, particularly in geologically complex and tectonically active regions. In this study, we integrated Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) techniques—Persistent Scatterer Interferometry (PS-InSAR) and Small Baseline Subset (SBAS)—with Global Navigation Satellite System (GNSS) observations to assess ground deformation in the Metamorphosis (MET0) area of Attica, Greece. A Kalman filtering approach was applied to fuse displacement measurements from GNSS, PS-InSAR, and SBAS, reducing noise and improving temporal consistency. Additionally, the PS and SBAS vertical displacement data were fused using Kalman filtering to enhance spatial coverage and refine displacement estimates. The results reveal significant subsidence trends ranging between −10 mm and −24 mm in localized zones, particularly near hydrographic networks and active fault systems. Fault proximity, fluvial processes, and unconsolidated sediments were identified as key drivers of displacement. Random Forest regression analysis, coupled with Partial Dependence analysis, demonstrated that distance to faults, proximity to streams, and the presence of stream drops and debris zones were the most influential factors affecting displacement patterns. This study highlights the effectiveness of integrating multi-sensor remote sensing techniques with data-driven machine learning analysis (Kalman filtering) to improve land subsidence assessment. The findings highlight the necessity of continuous geospatial monitoring for infrastructure resilience and geohazard risk mitigation in the Attica region. Full article

In recent decades, the interferometric synthetic aperture radar (InSAR) technique has emerged as a powerful tool for monitoring ground subsidence and geohazards. Various satellite SAR systems with different modes, such as Sentinel-1 and Lutan-1, have produced abundant SAR datasets with wide coverage and large historical archives, which have significantly influenced long-term deformation monitoring applications. However, large-scale InSAR data have posed significant challenges to conventional InSAR methods. These issues include the computational burden and storage of multi-temporal InSAR (MT-InSAR) methods, as well as temporal decorrelation for coherent scatterers with long temporal baselines. In this study, we propose a stepwise MT-InSAR with a temporal coherent scatterer method to address these problems. First, a batch sequential method is introduced in the algorithm by grouping the SAR dataset in the time domain based on the average coherence distribution and then applying permanent scatterer interferometry to each temporal subset. Second, a multi-layer network is employed to estimate deformation for partially coherent scatterers using small baseline subset interferograms, with permanent scatterer deformation parameters as the reference. Finally, the final deformation rate and displacement time series were obtained by incorporating all the temporal subsets. The proposed method efficiently generates high-density InSAR deformation measurements for long-time series analysis. The proposed method was validated using 9 years of Sentinel-1 data with 229 SAR images from Jakarta, Indonesia. The deformation results were compared with those of conventional methods and global navigation satellite system data to confirm the effectiveness of the proposed method. Full article

Deformation monitoring is a critical task for dam operators to guarantee safe operation. Given an increasing number of extreme weather events caused by climate change, the precise prediction of dam deformations has become increasingly important. Traditionally, multiple linear regression models have been employed, utilizing in situ data from pendulum systems or trigonometric measurements. These methods sometimes suffer from sparse data, which typically represent deformations only at specific points on the dam, if such data are available at all. Technical advances in multi-temporal synthetic aperture radar interferometry (MT-InSAR), particularly Persistent Scatterer Interferometry (PSI), address these limitations by enabling monitoring in high spatial and temporal resolution, capturing dam deformations with millimeter precision, and providing extensive spatial coverage. This study advances traditional methods of dam monitoring by employing data-driven techniques and integrating Sentinel-1 C-band Persistent Scatterer (PS) time series alongside in situ data. Through a comprehensive evaluation of advanced data-driven approaches, we demonstrated considerable improvements in predicting dam deformations and evaluating their drivers. The analysis provided evidence for the following insights: First, the accuracy of current modeling approaches can be greatly improved by utilizing advanced feature engineering and data-driven model selection. The prediction performance of the pendulum data was improved by utilizing data-driven algorithms, reducing the mean absolute error from 0.51 mm in the baseline model (R² = 0.92) to as low as 0.05 mm using the full model search space (R² = 0.99). Although the model accuracy for the PS datasets (MAE_max: 0.81 mm) was about one order of magnitude lower than that for pendulum data, the mean absolute errors could be reduced by up to 0.25 mm. Second, by incorporating freely available PS time series into deformation prediction, dams can be monitored in higher spatial resolution, making PSI a valuable tool for dam operators. This requires adequate dataset filtering to eliminate noisy PS points. Third, extended representations of water level and temperature, including interaction effects, can improve model accuracy and reduce prediction errors. With these insights, we recommend incorporating the proposed methodology into the monitoring program of gravity dams to enhance the accuracy in predicting their expected deformations. Full article

High-redshift active galactic nuclei (AGN) provide key insights into early supermassive black hole growth and cosmic evolution. This study investigates the parsec-scale properties of 86 radio-loud quasars at z ≥ 3 using very long baseline interferometry (VLBI) observations. Our results show predominantly compact core and core-jet morphologies, with 35% having unresolved cores, 59% with core–jet structures, and only 6% with core–double jet morphology. Brightness temperatures are generally lower than expected for highly radiative sources. The jets’ proper motions are surprisingly slow compared to those of lower-redshift samples. We observe a high fraction of young and/or confined peak-spectrum sources, providing insights into early AGN evolution in dense environments during early cosmic epochs. The observed trends may reflect genuine evolutionary changes in AGN structure over cosmic time, or selection effects favoring more compact sources at higher redshifts. These results stress the complexity of high-redshift radio-loud AGN populations and emphasize the need for multi-wavelength, high-resolution observations to fully characterize their properties and evolution through cosmic history. Full article

The dynamic loads of fluid impact and static loads, such as the gravity of a rock mass during the formation of debris flows, exhibit a coupled effect of mutual influence. Under this coupling effect, surface monitoring points in disaster areas experience displacement. However, existing methods do not consider the dynamic–static coupling effects of debris flows on the surface. Instead, they rely on GNSS or InSAR technology for dynamic or static single-scale monitoring, leading to high Mean Absolute Percentage Error (MAPE) values and low warning accuracy. To address these limitations and improve debris flow warning accuracy, a multi-scale warning method was proposed based on Global Navigation Satellite System (GNSS) and Synthetic Aperture Radar Interferometry (InSAR) technology. GNSS technology was utilized to correct coordinate errors at monitoring points, thereby enhancing the accuracy of monitoring data. Surface deformation images were generated using InSAR and Small Baseline Subset (SBAS) technology, with time series calculations applied to obtain multi-scale deformation data of the surface in debris flow disaster areas. A debris flow disaster morphology classification model was developed using a support vector mechanism. The actual types of debris flow disasters were employed as training labels. Digital Elevation Model (DEM) files were utilized to extract datasets, including plane curvature, profile curvature, slope, and elevation of the monitoring area, which were then input into the training model for classification training. The model outputted the classification results of the hidden danger areas of debris flow disasters. Finally, the dynamic and static coupling variables of surface deformation were decomposed into valley-type internal factors (rock mass static load) and slope-type triggering factors (fluid impact dynamic load) using the moving average method. Time series prediction models for the variable of the dynamic–static coupling effects on surface deformation were constructed using polynomial regression and particle swarm optimization (PSO)–support vector regression (SVR) algorithms, achieving multi-scale early warning of debris flows. The experimental results showed that the error between the predicted surface deformation results using this method and the actual values is less than 5 mm. The predicted MAPE value reached 6.622%, the RMSE value reached 8.462 mm, the overall warning accuracy reached 85.9%, and the warning time was under 30 ms, indicating that the proposed method delivered high warning accuracy and real-time warning. Full article

The positions of millisecond pulsars (MSPs) can be determined with sub-milliarcsecond (mas) accuracy using both Very Long Baseline Interferometry (VLBI) and timing, referenced to the International Celestial Reference Frame (ICRF) and planetary ephemerides frame, respectively, representing kinematic and dynamical reference frames. The two frames can be connected through observations of common celestial objects, MSPs observed with VLBI and timing. However, previous attempts to establish this connection were unreliable due to the limited number of MSPs observed by both techniques. Currently, 23 MSPs have been precisely measured using both multiple timing and VLBI networks. Among them, 17 MSPs are used to link the two reference frames, marking a significant three-fold increase in the number of common MSPs used for frame linking. Nevertheless, six MSPs located near the ecliptic plane are excluded from frame linkage due to positional differences exceeding 20 mas measured by VLBI and timing. This discrepancy is primarily attributed to errors introduced in fitting positions in timing methods. With astrometric parameters obtained via both VLBI and timing for these MSPs, the precision of linking DE436 and ICRF3 has surpassed 0.4 mas. Furthermore, thanks to the improved timing precision of MeerKAT, even with data from just 13 MSPs observed by both MeerKAT and VLBI, the precision of linking DE440 and ICRF3 can also exceed 0.4 mas. The reliability of this linkage depends on the precision of pulsar astrometric parameters, their spatial distribution, and discrepancies in pulsar positions obtained by the two techniques. Notably, proper motion differences identified by the two techniques are the most critical factors influencing the reference frame linking parameters. The core shift of the calibrators in VLBI pulsar observations is one of the factors causing proper motion discrepancies, and multi-wavelength observations are expected to solve it. With the improvement in timing accuracy and the application of new observation modes like multi-view and multi-band observations in VLBI, the linkage accuracy of the dynamical and kinematic reference frames is expected to reach 0.3 mas. Full article

Deep space exploration utilizing high-orbit vehicles is a vital approach for extending beyond near-Earth space, with orbit information serving as the foundation for all functional capabilities. The performance of orbit determination is primarily influenced by observation types, errors, geometrical structures, and physical perturbations. Currently, research on orbit determination for high-orbit spacecraft predominantly focuses on single observation methods, error characteristics, multi-source fusion techniques, and algorithms. However, these approaches often suffer from low observation accuracy and increased costs. This paper advocates for the comprehensive utilization of existing multi-source observation methods, such as GNSS (Global Navigation Satellite System), SLR (Satellite Laser Ranging), and VLBI (Very Long Baseline Interferometry), in research. The decoupled Kalman filter reveals a positive correlation between measurement positioning accuracy and orbit determination accuracy, and it derives a simple orbit performance evaluation model that considers the influence of observation value types and geometric configurations, without the need to introduce complex dynamic models. Simulations are then employed to verify and analyze antenna gain, observation values, and performance evaluation. The results indicate the following: (1) Under simulated conditions, the optimal strategy involves employing the SLR/VLBI dual system during periods when VLBI orbit determination is feasible, yielding an average Weighted Position Dilution of Precision (WPDOP) of 26.79. (2) For periods when VLBI orbit determination is not feasible, the optimal approach is to utilize the GNSS/SLR/VLBI triple system, resulting in an average WPDOP of 16.32. (3) The orbit determination performance of the triple system is not significantly impacted by the use of global SLR stations compared to using only Chinese SLR stations. However, the global network enables continuous, round-the-clock orbit determination capabilities. Full article

After two phases of on-site construction and testing (2010–2013 and 2013–2017), the Shanghai Tianma Radio Telescope (TMRT) can work well, with efficiencies better than 50% from 1.3 to 50.0 GHz, mainly benefiting from its low-noise cryogenic receivers and active surface system. Pulsars were chosen as important targets of research at the TMRT because of their important scientific and applied values. To meet the demands of pulsar-related observations, TMRT is equipped with some necessary backends, including a digital backend system (DIBAS) supporting normal pulsar observation modes, a real-time fast-radio-burst-monitoring backend, and baseband backends for very-long-baseline interferometry (VLBI) observations. Utilizing its high sensitivity and simultaneous dual-frequency observation capacity, a sequence of pulsar research endeavors has been undertaken, such as long-term pulsar timing, magnetar monitoring, multi-frequency (or high-frequency) observations, interstellar scintillation, pulsar VLBI, etc. In this paper, we give a short introduction about pulsar observation systems at the TMRT and briefly review the results obtained by these pulsar research projects. Full article

In mountainous regions, high-locality landslides have the characteristics of a latent disaster process with a wide disaster range, which can easily cause large casualties. Therefore, early landslide identification and dynamic stability evaluation are significant. We first used multi-temporal synthetic aperture radar data to detect potential landslides at Yezhi Site Area during the 2015–2020 period, identifying and mapping a total of 18 active landslides. The study area was found to have an average deformation rate between −15 and 10 mm/y during the period. Then, time series and spatiotemporal deformation characteristics of landslides were examined using interferogram stacking and small baseline interferometry techniques. The results show that the majority of the landslide deformations detected exhibit a periodic variation trend, and the study area was in a slow deformation state before 2017. Finally, combined with detection results, Google Earth optical images, and field investigations, it is concluded that the main factors affecting the time series deformation and spatial distribution of landslides in the study area are rainfall, geological factors, and engineering activities. The results of this study provide valuable technical references and support for early identification and dynamic stability evaluation of regional active landslides in complex terrain, especially for high-locality landslides. Full article

International terrestrial reference frame (ITRF) input data, generated by Global Navigation Satellite Systems (GNSS), Satellite Laser Ranging (SLR), Very Long Baseline Interferometry (VLBI), and Doppler Orbitography and Radiopositioning integrated by satellite (DORIS) combination centers (CCs), are considered to be relatively high-quality and accurate solutions. Every few years, these input data are submitted to the three ITRS combination centers, namely Institut Géographique National (IGN), Deutsches Geodätisches Forschungsinstitut at the Technische Universität München (DGFI-TUM), and Jet Propulsion Laboratory (JPL), to establish a multi-technique combined terrestrial reference frame (TRF). Generally, these solutions have undergone three rounds of outlier removal: the first at the technique analysis centers during solution generations and the second during the technique-specific combination by the CCs; ITRS CCs then perform a third round of outlier removal and preprocessing during the multi-technique combination of TRFs. However, since the primary objective of CCs is to release the final TRF product, they do not emphasize the publication of analytical preprocessing results, such as the outlier rejection rate. In this paper, our specific focus is on assessing the precision improvement of ITRF input data from 2014 to 2020, which includes evaluating the accuracy of coordinates, the datum accuracy, and the precision of the polar motions, for all four techniques. To achieve the above-mentioned objectives, we independently propose a TRF stacking approach to establish single technical reference frameworks, using software developed by us that is different from the ITRF generation. As a result, roughly 0.5% or less of the SLR observations are identified as outliers, while the ratio of DORIS, GNSS, and VLBI observations are below 1%, around 2%, and ranging from 1% to 1.2%, respectively. It is shown that the consistency between the SLR scale and ITRF has improved, increasing from around −5 mm in ITRF2014 datasets to approximately −1 mm in ITRF2020 datasets. The scale velocity derived from fitting the VLBI scale parameter series with all epochs in ITRF2020 datasets differs by approximately 0.21 mm/year from the velocity obtained by fitting the data up to 2013.75 because of the scale drift of VLBI around 2013. The decreasing standard deviations of the polar motion parameter (XPO, YPO) offsets between Stacking TRFs and 14C04 (20C04) indicate an improvement in the precision of polar motion observations for all four techniques. From the perspective of the weighted root mean square (WRMS) in station coordinates, since the inception of the technique, the station coordinate WRMS of DORIS decreased from 30 mm to 5 mm for X and Y components, and 25 mm to 5 mm for the Z component; SLR WRMS decreased from 20 mm to better than 10 mm (X, Y and Z); GNSS WRMS decreased from 4 mm to 1.5 mm (X and Y) and 5 mm to 2 mm (Z); while VLBI showed no significant change. Full article

The extraction of real geological environment information is a key factor in accurately evaluating the vulnerability to geological hazards. Yanghe Township is located in the mountainous area of western Sichuan and lacks geological survey data. Therefore, it is important predict the spatial and temporal development law of landslide debris flow in this area and improve the effectiveness and accuracy of monitoring changes in landslide debris flow, this article proposes a method for extracting information on the changes in landslide debris flows combined with NDVI variation, which is based on short baseline interferometry (SBAS-InSAR) and optical remote sensing interpretation. In this article, we present relevant maps based on six main factors: vegetation index, slope, slope orientation, elevation, topographic relief, and formation lithology. At the same time, different remote sensing images were compared to improve the accuracy of landslide debris flow sensitivity assessments. The research showed that the highest altitude of the region extracted by multi-source remote sensing technology is 2877 m, and the lowest is 630 m, which can truly reflect the topographic relief characteristics of the region. The pixel binary model’s lack of regional restrictions enables a more accurate estimation of the Normalized Difference Vegetation Index (NDVI), bringing it closer to the actual vegetation situation. The study uncovered a bidirectional relationship between vegetation coverage changes and landslide deformation in the study area, revealing spatial–temporal evolution patterns. By employing multi-source remote sensing technology, the research effectively utilized changes in multi-period imagery and feature extraction methods to accurately depict the development process and distribution characteristics of landslide debris flow. This approach not only offers technical support but also provides guidance for evaluating the vulnerability of landslide debris flow in the region. Full article

InSAR capabilities allow us to understand ground deformations in large metropolitan areas, this is key to assessing site conditions in areas in an inherently expanding context. The multi-temporal interferometry of SAR data records ground surface displacement velocities over large metropolitan areas, identifying anomalous and potential geological hazards. The metropolitan city of Concepción, Chile, is an alluvial basin in one of the world’s most seismically active subduction zones, where many subduction earthquakes have occurred throughout history. In this study, we monitored the deformations of the ground surface in the metropolitan area of Concepción using two interferometric techniques, the first being Persistent Scatterer Interferometry (PSI) and the second, the Small Baseline Subset (SBAS) technique. To do this, we have used the same Sentinel-1 dataset, obtaining ground movement rates between 2019 and 2021. The velocities were aligned with the GNSS station available in the area. Ground deformation patterns show local deformations depending on factors such as soil type and heterogeneity, and regional deformations due to geographical location in the subduction area. Our results highlight the similarity of the deformation rates obtained with different processing techniques and have also allowed us to identify areas of deformation and compare them to site conditions. These results are essential to evaluate ground conditions and contribute to urban planning and risk management in highly seismic areas. Full article

The detection performance of power transmission towers in mountainous areas using SAR amplitude images is obviously influenced by the strong layover background (mainly including vegetation and soil) clutter interference around the towers. In this paper, power transmission tower detection in a mountainous layover area, using single-baseline SAR interferometry coherence images, which show better feature enhancement effectiveness compared to SAR amplitude images, is presented. Moreover, a novel feature enhancement method, that of generating multi-baseline SAR interferometry-correlated synthesis images for power transmission tower detection in a mountain layover area, is proposed. It demonstrates better feature enhancement (layover background cluster suppression) than that using single-baseline SAR interferometry coherence images. Theoretical analysis illustrates that the mountainous layover background clutter interference can be suppressed in the proposed single-baseline/multi-baseline SAR interferometry-correlated synthesis image. Experiments including over 12 repeat-pass TerraSAR-X staring spotlight mode acquisitions were conducted, and the results demonstrate that the detection performance with the use of multi-baseline SAR interferometry-correlated synthesis images showed an improvement of more than 43.6%, compared with the traditional method of using SAR amplitude images when benchmark deep learning-based detectors are used, i.e., Faster RCNN and YOLOv7. Full article