Institute of Geodesy and Geophysics

Abstract On 24 July 2024, an ML 4.6 earthquake occurred at 26.27° N, 104.74° E in Liupanshui City, Guizhou Province, China. The strongest ground shaking was felt in Fa’er Town, an area with several coal mines. To explore the seismogenic mechanism of this event, we analyzed broadband seismic waveforms from the China National Seismic Network to determine its source parameters, including its focal mechanism, centroid depth, and horizontal location. Because the earthquake was very close to the mining areas, we then constructed an elastic finite-element model to calculate the change in Coulomb failure stress (ΔCFS), aiming to evaluate the impact of mining activities on this event. The results show that the Liupanshui earthquake exhibited a thrust-slip focal mechanism with a centroid depth of ∼2 km. The relocated hypocenter was located directly beneath the coal mining area. Although the calculated ΔCFS in this region was relatively modest (∼4.5 kPa), the shallow depth and spatial correlation strongly suggest that the earthquake was likely induced by local mining activities.

The Atomic Clock Ensemble in Space (ACES) mission, currently operating aboard the International Space Station (ISS), is designed to provide high-precision time and frequency measurements and to test fundamental aspects of relativistic physics. Gravitational redshift (GRS), a fundamental prediction of General Relativity (GR), implies that clocks positioned at different gravitational potentials experience relative time dilation. Previous GRS experiments have focused primarily on microwave technologies, with negligible experimental coverage in the optical domain, particularly for ground-to-space links. Motivated by the European Laser Timing (ELT) experiment and the high-precision laser-cooled cesium clock aboard ACES, we introduce and evaluate an optical time-transfer method designed to achieve high-accuracy measurements of GRS. In the absence of actual ELT/ACES optical data, a high-fidelity numerical simulation framework was developed to assess the performance of this method. The framework incorporates representative ELT/ACES mission parameters, including the space-based cesium clock and the H-MASER clock located at the reference ground station, both providing frequency stability at the level of 10−15 for 1000 s averaging time. Applying a ±1σ filtering criterion, we obtain a simulated dataset comprising 33 ELT/ACES passes, representing a total observation time of 4.38 h over a single week. Analysis of this high-fidelity dataset reveals a GRS deviation from GR of (−7.19±0.63)×10−5, achieving a 3.4 orders of magnitude improvement over the best previous laser-ranging experiment conducted at the University of Maryland (UMD), USA, 51 years ago. These simulation results demonstrate that the optical time-transfer link constitutes a powerful tool for testing fundamental physics and, when combined with next-generation optical atomic clocks, enables unprecedented capabilities in space-based timekeeping and geoscience applications.

Abstract Obtaining ionospheric Total Electron Content (TEC) with high spatial and temporal resolution is crucial for satellite navigation and space weather monitoring. Global Ionospheric Maps (GIMs) are key sources of TEC data. However, the temporal resolution of GIMs from ionosphere associate analysis centers does not satisfy the requirements for space weather monitoring. To address this issue, the Massachusetts Institute of Technology (MIT) Haystack Observatory has provided high‐time‐resolution TEC maps since 1998, using data from ∼5,000 Global Navigation Satellite System (GNSS) tracking stations. However, our research reveals a significant systematic bias in MIT's TEC maps. Unlike the biases between GIMs, it exhibits an unexpected pattern of variation and is also present in comparisons with radio occultation TEC. Further analysis indicates that the systematic bias in MIT TEC decreases with increasing geomagnetic latitude and is correlated with solar activity. In low‐latitude regions, the bias can reach up to 30 TECU, while in high‐latitude regions, it can drop to nearly 0 TECU. During diurnal and seasonal variations, the systematic bias in MIT TEC follows TEC patterns but appears unaffected by small‐scale ionospheric structures. Finally, we analyze the factors potentially contributing to the systematic bias and find that Differential Code Biases (DCBs) provided by MIT exhibits errors of a magnitude comparable to the TEC bias, with a strong correlation between the two. Through further investigation, we confirm that the systematic bias in MIT TEC primarily stems from its DCB estimation strategy and choice of mapping function, and this bias persists both before and after MIT updated its DCB estimation approach.

Abstract High-precision geomagnetic data is crucial for resource exploration and navigation. This paper proposes a reconstruction method integrating spatially adaptive mean compensation with patch-based sparse coding (MC-SC), which features a resolution-self-adaptive joint dictionary training strategy and a spatially adaptive MC module to preserve low-frequency backgrounds and recover high-frequency details. Experiments on three aeromagnetic survey regions show that MC-SC outperforms existing methods iterative back projection convolutional neural network (IBP and CNN) in reconstruction accuracy and detail fidelity. At a 50 m grid scale, MC-SC achieves a peak signal-to-noise ratio of 55.57 dB in Herat, significantly higher than IBP (52.89 dB) and CNN (52.64 dB). Moreover, under noisy conditions (up to 10 nT Gaussian noise), MC-SC exhibits superior robustness with slower performance degradation and more concentrated error distributions. The method provides a reliable solution for high-resolution geomagnetic data reconstruction from sparse samples, with strong potential for navigation and mapping applications.

<strong class="journal-contentHeaderColor">Abstract.</strong> Particle bulk optical property parameters play an essential role in evaluating air quality, however, both parameters can be substantially enhanced under humid atmospheric conditions via hygroscopic growth. Here we use 532-nm polarization lidar observations and ERA5 humidity data during 2010&ndash;2024 to retrieve vertical profiles of ambient and dry aerosol backscatter and extinction coefficients of anthropogenic pollution over central China. Particle hygroscopic growth led to enhanced particle backscatter coefficient by 11&ndash;46% below 2 km on an annual basis for the considered time frame. Anthropogenic Aerosol Optical Depth (AOD) was 30.7% higher under ambient atmospheric conditions. We found values of AOD_amb=0.404 and AOD_dry=0.309. During China&rsquo;s rapid air-cleaning period of 2010&ndash;2017,&nbsp; AOD_amb declined significantly by &minus;0.068 yr^-1; in contrast, the rate of decrease of AOD_dry was &minus;0.049 yr^-1 which is 28% slower, but the decrease of the dry aerosols more accurately captures aerosol emission reductions. Hygroscopic-growth-induced net AOD ( &Delta;AOD_RH) dropped sharply in 2011&ndash;2014, most likely as a result of emission mitigation and drier atmospheric conditions, then rebounded in 2014&ndash;2019 as rising humidity conditions and the presence of hydrophilic aerosols. Since 2020, &Delta;AOD_RH has remained high attributing to rising humidity conditions but weakening hygroscopicity. While AOD_amb suggests peak pollution in summer, AOD_dry identifies winter as the true air-pollution maximum. These results highlight the significant impact of aerosol water uptake on its optical properties; therefore, it must be accounted for to ensure accurate air quality assessments.

SUMMARY Traditional seafloor mapping relies on shipborne soundings which have limited spatial coverage. The Surface Water and Ocean Topography (SWOT) wide-swath altimetry satellite holds the potential for predicting more detailed seafloor topography. In this study, we integrate SWOT gravity data with single-beam shipborne depths to construct seafloor topography models in the Northwestern Pacific using the deep neural network (DNN) method. Compared to shipborne depth checkpoints, the root mean square (RMS) error of the differences between topography model predicted by DNN method and shipborne depths is approximately 97.5 m, improving by 19.5 per cent and 9.9 per cent compared to the gravity-geologic (GGM) method and the Smith and Sandwell (SAS) method respectively. Compared to traditional data, the integration of SWOT gravity data universally enhances prediction accuracy. Furthermore, the DNN method effectively demonstrates superior capability in balancing the characterization of overall structures with the retention of authentic topography features, which we demonstrated in the Mariana region of the NW Pacific Ocean. However, limited by spatial heterogeneity and physical mechanisms, accurate prediction of such complex, fine-scale topography using gravity data remains a significant challenge.

<strong class="journal-contentHeaderColor">Abstract.</strong> Lidar-derived particle backscatter coefficient is commonly used to assess air pollution levels; however, hygroscopic growth can amplify particle backscatter and hinder accurate assessment of particle concentration. This study investigated the hygroscopic growth characteristics of urban anthropogenic aerosols in Wuhan (30.5&deg; N, 114.4&deg; E), central China, using ground-based 532-nm polarization lidar observations during 2010&ndash;2024. A total of 192 cases were identified based on the following criteria: (1) the presence of a layer thicker than 300 m; (2) a lidar-derived backscatter coefficient that increases monotonically with simultaneously-measured relative humidity (RH) from radiosonde, and (3) limited variations in key meteorological parameters, including water vapor mixing ratio, potential temperature, and wind speed and direction. Using the H&auml;nel parameterization method, the hygroscopic growth parameter &gamma; was estimated as 0.62 (&plusmn;0.24), corresponding to a backscatter coefficient enhancement factor of 2.36 at 85 % RH. No evident differences in &gamma; were observed between the boundary layer (0.63&plusmn;0.25) and free troposphere (0.60&plusmn;0.24). The annual mean &gamma; increased from 0.49 in 2014 to 0.63 in 2017 and stabilized within 0.6&ndash;0.7 after 2018, closely following the evolution of the annual mean NO₂-to-SO₂ concentration ratio. The minimum seasonal average &gamma; occurred in winter (0.56), while the maximum was observed in autumn (0.64). These results provide a comprehensive characterization of the long-term and seasonal hygroscopicity of pollutants over central China, enhancing our understanding of the influence of hygroscopic growth on lidar-observed particle backscatter coefficients and offering valuable insights for urban air pollution control strategies.

All-source sensor fusion is critical to enhancing the robustness of positioning, navigation, and timing (PNT) systems. A typical fusion paradigm is that all sensors are pre-configured and hard-wired with respect to a specific environment or task, which means that such a fusion is exclusively customized to a pre-defined PNT task. Even the plug-and-play PNT systems still rely on specific types and numbers of sensors and positioning algorithms. As a consequence, it would be difficult for this established fusion to be freely and promptly adapted to fit another PNT environment or task, and PNT users would have to re-configure the hardware and re-compile the software PNT systems. In this study, we propose a novel multi-sensor fusion framework, i.e., “componentized PNT”, where all candidate sensors, regardless of their types and quantities, are taken as plug-and-play components to be freely connected to a central PNT platform with regard to switching environments and tasks. Our componentized PNT is featured by an edge-computing architecture and a standard hardware/software interface definition, and as a result, the sensor-specific processing algorithm is deployed in the edge sensors while the sensor-shared fusion algorithm deployed in the central PNT platform. This standardized interface allows a PNT fusion solution for a new task or environment to be rapidly established without modifying the platform software or reconfiguring hardware interfaces, thereby achieving a higher level of scalability compared with typical plug-and-play systems. A hardware prototype was developed and validated through real-time experiments involving sensor addition, disconnection, and replacement in complex environments. As sensor components are integrated, the system consistently achieves 34-78% improvement in 3D positioning accuracy, demonstrating the system’s true plug-and-play capability and autonomous reconfiguration across multiple domains and dynamic tasks.