This paper takes the Earth rotation parameters(ERP) obtained by each individual measurement means as research data, and outputs the processing results in the time domain and frequency domain respectively. Then the index parameters obtained from each analysis are compared with the data analysis results obtained by joint calculation in international Earth rotation service(IERS). The results show that the time-varying characteristics of ERP obtained by various measurement methods are different.

This article is based on the kinematic GNSS observation from the 13th Chinese Arctic Ocean scientific expedition, which marked China's first arrival at the North Pole. It compares and analyzes the observation quality of different signal from BDS and GPS in terms of carrier-to-noise ratio, multipath effects, pseudorange noise, ionospheric delay rate of change, and evaluates their kinematic positioning performance. The results show that the BDS's visible satellite count is still maintained at 8 to 14 close to the North Pole, and that the PDOP value is superior to that of GPS. In the Arctic region, the RMS of multipath combination observation for BDS is around 0.2 m. The RMS of multipath combination observation for B1C and B2a is smaller than the B1Ⅰ and B3Ⅰ signal, but their pseudorange noise is higher. The results of kinematic pseudorange positioning show that the positioning accuracy of BDS is comparable to the GPS, with the B1C/B2a ionosphere-free combination yields better positioning performance than the B1Ⅰ/B3Ⅰ combination.

The epicenter of 2022 Honghe M_S5.0 earthquake is located near the southern section of Red River fault zone, which consists of two nearly parallel faults: the Bolumu-Maocaoping fault and the Nanhun-Nabing fault. The Nanhun-Nabing fault is an active fault in the late Pleistocene, and significant water system dislocation deformation exists in the earthquake area. The field investigation after earthquake shows that a 1.2 km long surface rupture appeared on the ridge between Nanhun river and Nanchu river, and its distribution characteristics are consistent with the linear topography of Nanhun-Nabing fault. The deformation properties exhibited by the surface rupture in this area are consistent with the motion characteristics of earthquake rupture. Combined with the focal mechanism solution, it is believed that the Nanhun-Nabing fault is the seismogenic structure of Honghe M_S5.0 earthquake.

The strain monitoring data from the Guza station in 2022 to 2023 were analyzed using time series analysis. By comparing the difference in strain signals at the observation site during the seismic and non-seismic periods, it is concluded that during earthquakes above M_S4.5, there are obvious trend anomalies in both the detrended surface strain and the corrected principal strain direction. The likelihood of earthquakes increases when the detrended surface strain decreases at 1.1 times and the variance increases significantly. The trend of the corrected principal strain directions during seismicity is similar to the drug metabolism concentration curve model. The fluctuation period is small and the amplitude is large, which is significantly different from the nearly straight-line trend during the steady state.

Using continuous seismic waveforms from the Chongqing seismic network for one week following the Wulong M_S5.0 earthquake(November 23 to November 30, 2017), we employed the seismic detection and location program PALM to conduct microseismic detection and precise relocation of this earthquake sequence. The detection resulted in 1 558 seismic events, which is 4.2 times the number of events in the Chongqing seismic network catalog. After relocation, the minimum magnitude of completeness(M_C) of the microseismic catalog decreased to M_L0.1, significantly enhancing the completeness of the earthquake catalog. This indicates that PALM has high efficiency and accuracy in microseismic detection. The detected microseismic sequence clustered in an area approximately 5 km from the Wenfu fault, with depths concentrated between 5 km and 10 km, located at the bottom of the Wulong triangular tectonic zone. Based on the spatial distribution of the aftershocks, it is inferred that the causative structure of the earthquake might be a concealed fault at the base of the Wenfu fault.

Based on the waveform data and seismic phase reports from the Xinjiang regional digital seismic network, the focal mechanism solution of Aheqi M_S5.2 earthquake and some aftershocks are inverted using the CAP method. Meanwhile, the double-difference localization method is used to relocate the earthquake and its sequence. The results show that the strike, slip and rake are 259°, 44°, and 90° for nodal plane I, and those are 79°, 46°, 90° for nodal plane II, respectively, with a moment magnitude of M_W4.76, and a depth of moment center of 5 km, which suggests that the earthquake is a reverse-type event. The relocation results show that Aheqi M_S5.2 earthquake sequence spreads linearly in the NW direction and is perpendicular to the fault strike, and the depth is concentrated in 3-6 km. Based on this, we comprehensively analyze the historical seismic mechanism solution, geological and tectonic background, and conclude that the Suogedangtawu fault, which is located at the root of Keping thrust tectonic, is the seismogenic structure of this earthquake. This study can provide a certain understanding of the seismic mechanism of strong earthquakes in the contact zone between south Tianshan orogenic belt and Keping thrust tectonic, and provide reference for regional earthquake trend analysis.

To mitigate the unmodeled multipath errors in traditional dynamic precise point positioning(PPP) and enhance the accuracy of high-rate GNSS real-time coseismic displacement measurements, this paper constructed an undifferenced and uncombined PPP multipath error correction model based on the multipath hemispherical map(MHM). This method employs undifferenced and uncombined PPP to resolve the residuals of pseudorange and phase. It then constructs a refined MHM model using multi-day residual sequences, which is subsequently applied to dynamic PPP real-time positioning on the target day. Using the example of the M_W8.2 earthquake that occurred south of Alaska in 2021, we analyzed the real-time coseismic displacement results before and after model correction. The average RMS reduction rates in the E, N, U directions were 52.9%, 55.0%, and 50.7%, respectively, leading to an overall improvement of 51.7%. Comparing the real-time coseismic displacement extracted by the method in this paper with that extracted by the strong seismometer after baseline correction, the former more accurately reflects the displacement changes during the earthquake, indicating the stability and reliability of the proposed model.

Based on the focal mechanism solutions of small earthquakes in Shanxi province since 2001, the time curve of 1D stress field is inverted. The results show that the azimuth angle change of the three main axes of the stress field in Shanxi province is generally stable, the plunge angles of σ₁ and σ₂ show synchronous reverse change, and the plunge angles of σ₃ is nearly horizontal, revealing that the study area is a long-term stable horizontal tension structural stress field environment. From the temporal change of plunge angles, the stress field is divided into two periods including 2001-02-06 to 2008-12-19 and 2008-12-20 to 2023-07-31. The first period is the stress field of strike-slipping property, and the second period is the stress field of normal-fault property. During these two periods, most of the moderate earthquakes in Shanxi province match the corresponding stress field characteristics. The M9.0 earthquake in Japan, 2001 did not significantly change the stress field in Shanxi province.

In order to understand the current surface deformation characteristics and stress state of Shanxi reservoir area, we obtain the cross-fault deformation rate field in the study area based on 57 Sentinel-1 radar images from 2022 to 2023 using SBAS-InSAR technology, and analyze the influencing factors of surface deformation. The results show that the crust in the study area is overall in a state of strain accumulation, and the short-term surface deformation near reservoir area is mainly controlled by water storage. The seismogenic fault is still accumulating strain in the inter-seismic phase, and continuous monitoring of surface deformation in reservoir area should be carried out to provide a basis for seismic hazard assessment.

To study the surface deformation caused by the 2020 Changbai mountain Tianchi volcanic swarm event, GNSS data from the Tianchi volcanic area were processed, treating GNSS observation points as SBAS-InSAR orbit refinement points. The results from GNSS and SAR data processing were compared and analyzed to obtain the deformation velocity field of Changbai mountain Tianchi volcano. The results show that the deformation near the Tianchi volcanic crater is significant, characterized by expansion and uplift near the crater and slow subsidence in the surrounding areas. Before 2022, the Tianchi volcanic area experienced large-scale uplift, presumably due to the sudden large-scale movement of underground fluids. After 2022, the deformation and deformation rate at the volcanic crater decreased. Specifically, the surface uplift in the north and southeast directions of the Tianchi volcanic crater is more pronounced, while there is a small area of subsidence in the northeast direction. The maximum deformation rate in the northwest direction is -12 mm/a, and in the southeast direction, it is 6.9 mm/a. The results of trend analysis indicate that the eastern and southwestern parts of Tianchi volcano may continue to contract, and the expansion trends of the northern and southern slopes have weakened, with the overall deformation rate being 10 to 14 mm/a lower than before 2023. This suggests that after the swarm event, the overall volcanic activity is weakening.

Taking Muli county, Liangshan prefecture, Sichuan province as an example, based on the Sentinel-1A ascending and descending orbit SAR data provided by the European Space Agency, the visibility of SAR satellite surface monitoring and the sensitivity of slope deformation monitoring in different slope directions were quantitatively analyzed, and typical landslides in the region were selected to carry out fine monitoring and volume inversion research. Combined with the slope constraints, the three-dimensional deformation field of the lattice landslide in the area was inverted by using the LOS deformation results of the lifting rail. The results show that the landslide mainly moves horizontally and slides down the slope, with the maximum horizontal displacement rate exceeding 100 mm/a. The maximum annual deformation rate in the vertical and northward direction is between 40 and 60 mm/a, and the north-south and vertical deformation characteristics of the landslide are related to the local topography of the landslide surface.Based on the vector tilt method and the mass conservation method, the depth and volume of the landslide are inverted.The results show that the area with the greatest depth of the landslide is located in the middle of the slope, with a maximum depth of 52 meters, the volume of the landslide is between 2.2×10⁷ m³ and 2.8×10⁷ m³. And the results of the two methods have high consistency. This study provides a technical reference for the monitoring, disaster prevention and mitigation of typical landslides in southwest China, and the estimation of the impact range of landslides.

Based on the Sentinel-1 ascending and descending orbit images from 2014 to 2023, we use the time network orbit error correction method considering long-term structural rates to remove orbit errors, and the external GACOS atmospheric data and public main image superposition method are combined to remove atmospheric delay errors, obtaining high-precision and high-density InSAR interseismic velocity fields along Xianshuihe fault, revealing the fault movement characteristics dominated by left-lateral strike-slip. We calculate the fault-parallel velocity from InSAR data, which shows that there is a significant difference in the fault-parallel rate on both sides of far-field fault, about 8-11 mm/a. We also obtain the high-precision three-dimensional velocity field using GNSS data, and calculate the maximum shear rate strain field. The results show that the shallow creep movement is significant between Zhuwo to the middle of Kangding segment. Among them, the shear strain in the Songlinkou to the middle of Kangding segment is the strongest, with a maximum magnitude of 0.6 ustrain/a and a maximum creep rate of 7.0 mm/a. This study is helpful to understand the interseismic deformation and creep characteristics and provide high-quality data for interseismic coupling model inversion and earthquake risk assessment.

We use BDS data from continuous GNSS stations of China seismic experiment site(CSES) and crustal movement observation network of China(CMONOC) to study the static and dynamics displacement fields of 2022 Luding M_W6.6 earthquake. First, we employ the relative positioning method to process 30 s sampling data based on GAMIT/GLOBK software, and obtain daily solutions over the period of 8 days before and 6 days after the Luding earthquake. We extract co-seismic static displacements using the differences between average coordinates before and after the earthquake. The maximum displacement is determined at SYD5 site, about 39 km away from the epicenter, with 19.57±2.08 mm and 10.88±1.79 mm for EW and NS components, respectively. The displacements decrease with the increase of epicenter distance. The co-seismic displacements are less than 4 mm for stations more than 100 km far away from the epicenter. The static displacement field in spatial distribution shows a left-lateral slip characteristic. Then, we use PRIDE PPP-AR software and precise point positioning method to process 1 Hz data and obtain dynamic displacement waveforms for each station. We calculate static displacements using dynamics waveforms of 2 minutes before and after the earthquake. The results indicate that SYD5(Anshunchang), SYB1(Kangding), SYE0(Liziping) and SYD9(Mianning), relatively close to the epicenter, recorded prominent dynamic displacement waveforms, with dynamic displacement of EW component larger than that of NS component. The SYD5 and SYB1 sites which are closer to the epicenter show prominent static displacements, while the static displacements of other stations are not significant. Finally, a comparative analysis is conducted between the static displacements obtained from BDS and GPS data, which shows a good consistency between them. This study indicates that the Beidou system can clearly record the coseismic static displacement of moderate intensity earthquake occur in the CSES.

We collect 243 descending-track Sentinel-1 SAR data covering the Longmenshan fault zone from October 2014 to April 2019 to obtain the post-seismic deformation of the Wenchuan M8.0 earthquake. Based on the co-seismic rupture model and regional layered media model, we conduct simulations of post-seismic viscoelastic relaxation and post-seismic afterslip inversion to estimate the relative contributions of the two mechanisms to post-seismic deformation. The results show that: 1)The post-seismic deformation on both sides of the Longmenshan fault zone exhibit distinct differences.The deformation in northwest side of the fault shows upward in LOS direction, with a maximum cumulative deformation of approximately 41.1 mm, located near Yingxiu. 2)The simulated values of viscoelastic relaxation cannot explain the actual post-seismic deformation observed by near-field InSAR. However, the deformation caused by viscoelastic relaxation in the middle-far-field region is consistent with the deformation observed by InSAR.3)Post-seismic afterslip mainly occurs in the vicinity of epicenter, primarily in areas where little or no slip occurred during the co-seismic. In conclusion, the post-seismic deformation mechanism of Wenchuan earthquake mainly involves afterslip and viscoelastic relaxation, the post-seismic afterslip dominated to the near-field deformation and viscoelastic relaxation contributed to the middle-far-field deformation.

This paper collects 227 marine gravity datasets obtained by Japan through ship borne surveys in its surrounding waters and the northwest Pacific. Statistical analysis, visualization mapping, and correlation analysis methods are used to analyze the gravity survey situation and data quality. This paper systematically analyzes the history, spatiotemporal distribution characteristics, and quality of gravity data surveys in Japan. The results show that ship measured gravity data has good consistency with GEBCO terrain data and DTU gravity model data, while the gravity data has a correlation of 0.98 and a root mean square error of 7.7 mGal with the DTU21. This paper establishes a trajectory intersection point difference model and analyzes the data measurement accuracy using intersection point difference error. The total crossover points are 73 979 with an crossover error of 11.85 mGal, the internal crossover point are 24 822 with an crossover error of 9.56 mGal, and the external crossover point are 49 157 with an crossover error of 12.85 mGal. The overall accuracy of the gravity data measurement accuracy is high, and the consistency is good.

The microtremor noise data of 24 stations in the western Anhui region are used to calculate site response based on HVSR method. The results showed that 4 out of 24 stations had abnormal HVSR curves due to observation system failures. All 20 stations have varying degrees of amplification(attenuation) effects, with 4 stations showing flat HVSR curves in the 0.5 Hz to 30 Hz frequency band and site response values of 1 to 2 times. The HVSR curves of the remaining 16 stations exhibit both unimodal and complex bimodal and multimodal shapes, with peak frequencies ranging from 8.5 Hz to 30 Hz, an average of 18.8 Hz, a median of 17.9 Hz, and a standard deviation of 5.66 Hz. The overall trend is relatively large, possibly due to the installation of seismometers on pendulum piers, which reduces the influence of sedimentary layers on measurement results; The peak amplitude ranges from 2.6 to 10.5 times, with an average of 6.0 times, a median of 5.3 times, and a standard deviation of 2.3 times. This may be due to the fact that surface seismometers are more susceptible to surface conditions.

In this paper, based on microseismic monitoring data and 49 microseismic feature parameters of a marble mine in Perugia, central Italy, we compare and analyze the accuracy of three machine learning models, namely, neural networks, decision trees and random forests, in the automated classification of microseismic signals, and optimize the model parameters to improve the performance of the model through the grid searcher algorithm, with an emphasis on discussing the effects of the feature parameter selection and the size of the classification set on the classification results. The results show that the random forest classifier obtains the highest accuracy in microseismic signal classification, with 98.11% classification accuracy after optimization, meanwhile, by reducing some of the feature parameters with small weights, the efficiency of the classification computation is greatly improved and the accuracy decreases less, and the optimal allocation ratio of the training set to the test set is 8 ∶2.