Based on the seismic ambient noise cross-correlation method, we calculate the relative velocity variation within the underground medium. We utilize continuous waveform data from the CCH and BDS stations at Dongtan mine, spanning from 2 a.m. on February 8, 2022, to 7 a.m. on March 8, 2023. The analysis focuses on the correlation between these velocity changes and both the mining operation and seismic activity at the 63^up06 working face. The findings reveal a notable impact of the mining operation at the 63^up06 working face on the wave velocity within the underground medium. Additionally, a clear correlation exists between the changes in wave velocity and the incidence of mine earthquakes, highlighting the interplay between variations in underground stress levels and seismic events. Furthermore, the release of underground stress during mine earthquakes can lead to slight fluctuations in wave velocity.

Based on the measured ground motion data of the M_S4.5 earthquake in Luding, Sichuan in 2023, we conduct an asymmetric slope time-frequency domain data analysis. The results show that: 1) The PGA amplification factor at monitoring point 1^# on the right bank is highest, with horizontal and vertical values of 3.16 to 15.08 and 4.37, respectively. 2) The maximum Arias intensity on the right bank is 2.24 cm/s, and the maximum Arias intensity on the left bank is 0.79 cm/s, indicating that the seismic energy on the right bank is stronger. 3) The dominant frequency distribution of the monitoring points on the right bank of the slope is 2 to 9 Hz, while the dominant frequency on the left bank is 2 to 17 Hz. The energy presented by the seismic amplitude is more concentrated in the horizontal than in the vertical direction. On the right bank of the asymmetric high steep valley slope, the protruding peninsula shaped slope terrain has a stronger response than the straight shaped slope.

Utilizing the envelope cross-correlation method, we localize the very low frequency earthquakes(VLFEs) before the Jishishan M_S6.2 earthquake. We conduct preliminary localization over a larger area, followed by precise localization within a smaller area. Analysis by increasing or decreasing the number of stations in different directions from the epicenter reveals that an even distribution of stations in all directions is crucial, effectively reducing localization errors. The localization results indicate that the VLFEs before the Jishishan earthquake were primarily distributed near the fault zone, with their distribution orientation is consistent with the fault direction.

Aiming at the problem of missing data and gross errors in GRACE Level1B observations, we propose an optimization method to complete the missing data of SCA1B. At the same time, we use an optimization strategy to eliminate gross errors for KBR1B and kinematic orbit data, then use these data sets to invert the time-varying gravity field model. In addition, based on synthesized data and real data respectively, we analyze the impact of the error from ACC1B in the Y direction on the inversion results and propose an optimization correction strategy. The optimized completion method can recover the missing data of SCA1B effectively, and fully considers the variational characteristics of the observations throughout the arc. The accuracy of the result derived from ACC1B data after correction using the optimization strategy is 3.7 mm, which is higher than the result derived from the uncorrected data. It can be seen from the results that the overall accuracy of the gravity field model solved by the data processed using the optimization method is like that of the three official centers. However, the detailed signals performance of the results from different institutions in regionals are different. It shows that the optimized data preprocessing strategy is effective and feasible.

We calculate local terrain and isostatic correction using spectral combination and nested different topographic resolutions and determine the free air gravity anomaly grid by the gravity reduction method. The results show that the accuracy of terrain correction calculated by the spectral combination method is the best in the test area, and the accuracy of isostatic correction calculated by nested different resolutions in the central and edge regions is best. When the integral radius is 1° and 2°, there is little difference between the local terrain correction, and the isostatic correction results differ significantly. The accuracy of free air gravity anomaly grid results obtained from the complete Bouguer and isostatic gravity anomalies by gravity reduction is the same, and the accuracy of the results is much higher than that obtained from the direct grid of free air gravity anomalies at ground points.

Large earthquakes can simultaneously excite numerous normal modes of Earth's free oscillations. Due to the Earth's ellipticity, rotation, and internal anisotropy, these normal modes undergo splitting, with frequencies of splitting modes becoming very close(only a few μHz apart). This imposes higher demands on the detection of Earth's free oscillation modes. This article derives and verifies a new method for detecting free oscillation modes based on normal time-frequency transform(NTFT). Taking the detection of ₃S₁ splitting modes as an example, compared with the classical FT spectrum method and the latest OSE method, our method has higher frequency resolution for detecting free oscillation modes.

Based on the observation data from regional fixed geomagnetic stations, this study quantitatively investigates the large-scale geomagnetic daily variation anomalies that occurred in the Sichuan-Yunnan region before the Yangbi M_S6.4 earthquake on May 21, 2021. This article is based on Biot-Savart's law and the Monte-Carlo model to invert the distribution state and depth of underground current. The results show that: 1) The spatial distribution position and depth of underground current are consistent with the distribution of underground electrical high conductors, which is consistent with the crustal current position given by previous studies. 2) There is non-uniformity in both the horizontal and vertical interfaces below the research area, and the distribution of surface currents may be related to tectonic activity. 3) The upwelling of deep thermal fluids may be one of the reasons for the concentrated distribution of underground currents.

Tonga volcano violently erupted at 12:26:30 on January 15, 2022. The atmospheric pressure observation station of Yunnan Geophysical Network clearly recorded the intense pressure change process caused by the eruption. The atmospheric pressure change forms and characteristics of all observation stations were highly consistent, with the average peak amplitude of 2.16 hPa and the average propagation velocity of 314.30 m/s. The wave train has the properties of Lamb wave propagation, and the atmospheric pressure observation data are basically reliable. The water level observation in Yunnan deep well clearly recorded the water level change caused by the short-time pressure change. The static pressure efficiency of the observation well was estimated by the ratio of peak water level change to the amplitude of pressure change. We analyze by regression the short-time pressure change and well water level data, and take the determination coefficient, regression line slope and static pressure efficiency as indicators. Then we analyze the bearing property and stress-strain sensitivity of the borehole water-bearing system. The results show that the water level and pressure observation data of 25 wells, such as Kunming station, have high goodness of fit, small lag time, and good permeability of aquifer. The atmospheric pressure efficiency of 7 wells in Kunming station, Daguanxijiagou, Tengchong station, Ludianciyuan, Nanhua, Shiping and Zhaotong SK3 is slightly higher, and the sensitivity of well water level to stress and strain may be low. The larger the coefficient of determination for an observation well, the higher the probability that its monitoring and forecasting performance will be assessed as qualified.

In this paper, after preprocessing and various corrections of the Zigui station gPhone gravimeter data for the whole year of 2017, we use four methods to carry out a comparative study of drift correction including polynomial correction order selection, drift removal effect evaluation of each method, whether the BEADS algorithm is effective and other issues. The research results show that when the polynomial fitting method is used to remove the drift problem of gPhone gravimeter data, it will become stable when the order reaches about fourth and fifth orders. For general research, it is recommended to use fifth-order polynomials to remove drift, which can meet data processing requirements. When filtering to remove drift, we need to pay attention to the marginal effect. The EEMD method is highly operable and has more advantages than wavelet filtering. The BEADS algorithm can realize the function of fitting trend terms to remove drift, but can it be used in various studies? This requires further research. The research results of this article can provide an effective reference for drift correction of related spring-type gravimeters.