To address the limitations of the algebraic expansion of the meridian arc length, this study is based on the principles of equivalent transformation and order-increment invariance. Using the first eccentricity or the third flattening, this study reconstructs polynomial function models for the meridian arc length and the meridian radius of curvature, respectively. Additionally, a constrained least squares method is derived for the direct estimation of the model constant coefficients. The results indicate that, regardless of whether the first eccentricity or the third flattening is used, the polynomial function models facilitate the high-order expansion and correctness verification of the algebraic expansion of the meridian arc length. Furthermore, their estimation accuracy can meet the requirements of practical applications such as high-precision geodesy and map projection.

Aiming at the critical influence of universal time 1 (UT1) prediction accuracy on satellite precise orbit determination, this paper quantitatively analyzes the prediction performance of 116 batches of Bulletin A released by the international Earth rotation and reference systems service (IERS). Taking multi-orbit targets including the TianQin-3 satellite, space station and Beidou satellites as examples, the magnitude estimation formula for the influence of UT1 prediction error on orbit determination accuracy is verified. A rapid evaluation of the three-dimensional orbital deviation of satellites is realized only based on orbital radius and universal time error. The analysis results show that the UT1 prediction error of Bulletin A exhibits obvious uncertainty and increases significantly with the growth of time scale. Its 30-day mean prediction error (3.399 ms) causes orbital deviations ranging from 1.673 m to 24.79 m for satellites from low Earth orbit to deep space exploration represented by the TianQin-3 satellite. The quantitative results reveal that the accuracy of existing prediction products has a non-negligible impact on future high-precision aerospace missions, and demonstrate the necessity of developing China's independent and high-precision universal time measurement and prediction system.

Currently, there is still a lack of long-term systematic monitoring of broadcast ephemeris orbit frames for multi-GNSS systems. We systematically investigate the long-term performance of the spatial reference frame for broadcast ephemeris orbits of the four GNSS satellite navigation systems (BDS-3, GPS, GLONASS, and Galileo) based on Helmert seven-parameter transformations between broadcast ephemeris and precise orbits, and the derived root sum squares (RSS) comprehensive index, as well as BDS-3/GLONASS analysis results from the data exchanged with the TsNIIMash, Russia, covering the period from August 2020 to December 2024. The results show that Galileo orbit demonstrates the best overall stability in realizing the spatial frame, with a RSS 7-parameter value of approximately 4.7-5.2 cm, following by GPS and BDS-3 (7.7-9.0/7.5-13.8 cm), while GLONASS has an uncertainty of 19.9-23.3 cm. Among the four systems, the Z-component of Helmert translation parameters for GPS, Galileo, and BDS-3 all exhibit significant annual periodic variations, which are likely related to deficiencies in the solar radiation pressure models used during orbit determination. Since the optimization of the Earth orientation parameters (EOP) update strategy in 2023, the BDS-3 system has shown a significant improvement in orbit frame maintenance accuracy, with the fluctuations of rotation parameters R_X/R_Y/R_Z components reduced from ±6/±8/±8 mas to ±2/±4/±3 mas, and the overall frame accuracy clearly improved, as RMS of RSS₇ decreased from 12.2 cm to 7.5 cm. This finding is consistent with the analysis results from TsNIIMash.

We analyze the prediction effectiveness of seismic gaps before M_S≥5 earthquakes in Gansu region from 1984 to 2023. The results show that seismic gaps in Gansu and its adjacent regions have an accuracy rate of 0.75, a false rate of 0.25, and a missing rate of 0.38 for forecasting moderate-strong earthquakes in Gansu. The R-value is 0.5, which exceeds the R₀ value of 0.23 at the 97.5% confidence level. This demonstrates that seismic gaps have relatively good forecasting capability for moderate-strong earthquakes in Gansu. The mainshocks generally occurred inside the seismic gaps or near their edges, and the duration of these seismic gaps does not exceed 8 years. The number of broken and unbroken seismic gaps before mainshocks are comparable. The probability of the mainshock occurring within 1 year after the broken gap is about 89%, and about 67% within 4 months. There is no obvious correlation existed between the duration of seismic gap and the mainshock magnitude. However, there is a linear relationship between the minimum magnitude of seismic gap with initial magnitude of M_L3.0~4.0, and the long axis of seismic gap has a good correlation with the mainshock magnitude. The seismic gap can be used as one of the important indicators for predicting moderate-strong earthquakes in western Qilian mountains, the southeastern part of eastern Qilian mountains and Minxian-Diebu section. It is also a significant indicator for predicting M_S≥6 earthquakes in Gansu. The probability of two seismic gaps appearing before mainshock is very high, and the mainshock is most likely to occur within 4 months after the broken gap, located inside or near the overlapping region of the two seismic gaps.

Taking the 2021 Horqin M_S4.7 earthquake as a case study, this paper uses the Biot-Savart law and the Monte-Carlo method, and employs the abnormal amplitude of the anti-phase geomagnetic vertical component at observation stations to invert and locate the information of these earthquake-induced currents. It reveals the temporal and spatial evolution characteristics of the induced currents related to the pre-earthquake geomagnetic anti-phase anomalies, as well as their correlations and influences with seismogenic structures and nearby/far-field earthquakes in the surrounding areas. Spatial distribution of induced currents: 1)The distribution trend of the peak value of induced currents is similar to the nature of the seismogenic fault. 2)Conjugate-shaped induced currents appear before the earthquake. Temporal sequence analysis of induced current depth: 1)The depth of induced currents during the earthquake occurrence period is very close to the focal depth. 2)As the earthquake approaches in time, the first-order difference of the shallowest depth of induced currents shows a convergent trend. 3)Surrounding earthquake events can cause the phenomenon of "shrinking before the earthquake and increasing after the earthquake" in the maximum depth of induced currents.This study represents a new attempt in the application of the electromagnetic discipline to the location of earthquake precursor anomalies, and provides new ideas for the short-term and imminent earthquake prediction methods based on electromagnetic anomalies.

This paper proposes a novel in-situ stress inversion method. Combined with Michael's linear stress inversion and Vavryčuk's fault plane identification technology, a complete inversion workflow for in-situ stress parameters is established by adopting multi-constrained moment tensor inversion to determine focal mechanisms. Taking the carbonate hot dry rock (HDR) reservoir in the Subei basin as a case study, 90 high-quality microseismic events are selected for in-situ stress inversion. The relative magnitude of the three principal stresses follows the order of S_V>S_Hmax>S_Hmin. The orientation of the maximum horizontal principal stress S_Hmax is approximately NW44°, and the stress shape factor R is 0.74. The inverted results are in good agreement with the in-situ stress data measured by the anelastic strain recovery (ASR) method from core samples of the same well, in terms of stress regime and principal stress orientation. This confirms the reliability and applicability of the microseismic-based stress inversion method in the study area. The proposed method can efficiently calculate in-situ stress parameters during fracturing and quantitatively characterize dynamic stress field evolution, which provides solid technical support for deep reservoir stimulation.

We propose an outlier detection method for dam deformation based on ensemble empirical mode decomposition (EEMD) high-frequency differential conversion and gated recurrent unit (GRU) analysis. First, the EEMD technology is employed to decompose the original deformation monitoring data, yielding deformation components at different frequencies. Then, high-frequency components containing abnormal deformation information can be extracted for differential sequence conversion, and identification criteria for abnormal single-jump points are proposed. Finally, a high-frequency component time series model is established by GRU neural networks, which is then combined with the abnormal single-jump point identification pattern to recognize outliers. The results show that EEMD technology and high-frequency sequence differential conversion can effectively isolate deformation anomaly information. The proposed single-jump point detection model aligns with the characteristics of dam deformation anomalies. Combining GRU and the 3σ criterion for time series modeling analysis of differential sequences, all deformation anomalies can be accurately identified, and achieve higher accuracy than traditional methods.

Based on 228 Sentinel-1 images acquired from January 2018 to August 2022, we use the small baseline subset interferometric synthetic aperture radar (SBAS-InSAR) technique to derive surface deformation time series in Baotou. Multi-source datasets, including precipitation records, optical imagery, and mineral resource distribution data, were integrated to analyze deformation characteristics and their driving factors in different functional zones. The results indicate that the study area is generally stable, with approximately 98.85% of permanent scatterers (PS) exhibiting annual average deformation rates between -10 and 10 mm/a, while significant local variations are observed. In local built-up areas of the urban plain, evident subsidence occurs due to building loads and ash compaction, with a maximum rate of -73 mm/a. Open-pit mining activities in Donghe district induce pronounced deformation, with a maximum subsidence rate of -77 mm/a, and a stage-wise adjustment of deformation is observed after mining cessation. Time-series analysis results show that deformation in urban built-up areas exhibits weak sensitivity to precipitation, whereas deformation in mining areas presents a lagged response to precipitation at the monthly scale. Comparison with GNSS observations indicate that there is a good consistency between InSAR and the measured deformation trends. The results suggest that mining activities and urban construction are the primary controlling factors of regional surface deformation, while natural conditions play a modulating role in deformation evolution across different functional zones. The study highlights the application potential of SBAS-InSAR for surface deformation monitoring in resource-based cities.

SBAS-InSAR technology was employed to obtain the land surface deformation distribution in Xining city from October 2020 to November 2024. By integrating static features such as the spatial location of deformation points with dynamic climatic characteristics, a multi-feature-based BiLSTM prediction model was constructed to forecast deformation in areas with typical deformation characteristics. The main conclusions are as follows: 1) The average annual deformation rate in the study area ranges from -48 mm/a to 30.2 mm/a, with a maximum cumulative deformation value reaching -187.2 mm. 2) Compared with the LSTM model, the prediction accuracy of the multi-feature BiLSTM model in three typical deformation areas is improved by up to approximately 32.3%, and it can more accurately identify local details and abrupt change points. At last, the optimal model was used to predict the deformation trends in each typical deformation area for the next six months, providing a reasonable reference for surface deformation monitoring.

This study takes Jinyang county as the research area and integrates 332 Sentinel-1 SAR images from the past six years with multi-temporal optical imagery. Based on the SBAS-InSAR technique, regional landslide hazards were systematically identified and monitored. The results indicate that: 1) Combining InSAR-derived deformation with optical interpretation, and verified by field surveys, 14 landslide hazards were identified. They are mainly distributed along the right bank of the Jinsha river and both banks of the Jinyang river, with slope aspects predominantly facing due east and southeast. 2) In-depth analysis of a typical landslide shows a high consistency between the deformation area detected by InSAR (2018-01-02 to 2023-10-27) and the results from optical interpretation. Deformation is primarily concentrated in the middle part of the slope. Based on deformation rates, the area can be classified into zones of extremely strong, strong, weak, and stable deformation. The cumulative deformation in the extremely strong deformation zone exceeds -490 mm and shows ongoing activity. 3) Landslide deformation exhibits a lagged response to rainfall, and terrain slope is a key factor controlling its deformation pattern. This study can provide direct support for regional landslide risk management and scientific monitoring.