arXiv subjects

Pan Xiong

Publications and source records attributed to Pan Xiong.

4 recordsLinked to original sources

arXiv2026

Multi-instrument constraints on a hemispherically asymmetric positive ionospheric storm in the 60-180 deg E sector during the 12-13 November 2025 geomagnetic storm

Geomagnetic storms drive complex ionospheric responses through coupled electrodynamic and thermospheric processes, yet attributing storm-time TEC perturbations to specific mechanisms remains challenging. We investigate the ionospheric response to the 12-13 November 2025 intense geomagnetic storm (Dst minimum = -214 nT) in the 60-180 deg E sector using a coordinated multi-instrument dataset comprising JPL GIM TEC, dense regional GNSS networks, continuous BeiDou GEO links, COSMIC-2 radio occultation, ground ionosondes, Swarm in-situ electron density, HF Doppler soundings, and TIMED/GUVI thermospheric composition observations. The observations reveal a dayside-dominant positive TEC storm with pronounced hemispheric asymmetry, where Northern Hemisphere mid-to-low latitudes exhibit stronger and longer-lasting enhancement than the Southern Hemisphere. Joint analysis of radio occultation, ionosonde, and Swarm data indicates that the enhancement is density-dominated with NmF2 and foF2 increases but with no coherent, sector-scale peak-height uplift in hmF2 or h'F2, posing challenges for uplift-only electrodynamic interpretations. Coherent large-scale traveling ionospheric disturbances propagate across the equator during UT 1-6, while HF Doppler oscillations maximize later during UT 6-24, revealing a timing offset between integrated TEC responses and reflection-height dynamics. Southern Hemisphere O/N2 ratio depletion observed by TIMED/GUVI provides compositional context consistent with the faster positive-phase decay there, although concurrent Northern Hemisphere GUVI coverage is limited during this interval. These findings highlight the value of multi-observable diagnostics for developing testable constraints on storm-time mechanisms and improving sector-specific space weather nowcasting capabilities.

physics.geo-ph↗

arXiv2026

Cross-sphere Coupling and Source Inversion of Ionospheric Disturbances Associated with the 2025 Myanmar Strike-slip Earthquake from BeiDou GEO and Multi-GNSS Observations

Focusing on the M7.9 earthquake in Myanmar in 2025, this study comprehensively utilizes data from BeiDou geostationary satellites of the Chinese Continental Crustal Movement Observation Network and multi-system Global Navigation Satellite Systems (GNSS). The spatiotemporal evolution characteristics and physical mechanisms of pre-seismic ionospheric anomalies and co-seismic ionospheric disturbances were systematically analyzed. By employing the moving interquartile range method combined with solar-terrestrial environmental parameters, a negative Total Electron Content (TEC) anomaly associated with the seismogenic region was identified three days before the earthquake. The equatorial conjugate structure of this TEC anomaly revealed a multi-path coupling effect between the lithosphere, atmosphere, and ionosphere. The extraction of Coherent Ionospheric Disturbance (CID) signals based on wavelet transform and band-pass filtering indicated that the co-seismic ionospheric disturbances were dominated by acoustic-gravity waves in the 2-8 mHz frequency band, propagating at a speed of approximately 1.2 km/s, and exhibiting an asymmetric pattern in the southeast direction. A spatial density-weighted method for locating the source of ionospheric disturbances was proposed, elucidating the joint control mechanism of fault strike-slip motion, geomagnetic field modulation, and equatorial electrojet on the disturbance energy. The results confirm that the high spatiotemporal resolution of BeiDou GEO satellites and multi-system GNSS significantly enhances the capability to capture weak ionospheric anomaly signals associated with earthquakes. These results provide additional observational constraints on space-based Lithosphere-Atmosphere-Ionosphere Coupling (LAIC) processes and may contribute to the development of ionosphere-based earthquake monitoring techniques.

physics.geo-ph↗

arXiv2021

Self-repairing high entropy oxides

All biological organisms, from plants to living creatures, can heal minor wounds and damage. The realization of a similar self-healing capacity in inorganic materials has been a design target for many decades. This would represent a breakthrough in materials engineering, enabling many novel technological applications, since such materials would be able to resist damage caused by electromagnetic irradiation and/or mechanical impact. Here we demonstrate that a high-entropy oxide is intrinsically capable of undergoing an autonomous self-repairing process. Transmission electron microscopy revealed that the spinel structure of (AlCoCrCu0.5FeNi)3O4 can regrow and repair itself at the atomic level when damaged. Density functional theory calculations reveal that the extra enthalpy stored in the high entropy material during fabrication can be released to effectively heal macroscopic defects by regrowing into a partially ordered state. This extraordinary self-repairing phenomenon makes this new material highly desirable as a coating, enabling structures used in harsh environments to better withstand damage, such as cosmic irradiation in space, nuclear irradiation in nuclear power facilities, or tribological damage. Most importantly, our results set the general design principles for the synthesis of self-repairing materials.

cond-mat.mtrl-sci↗

arXiv2021

Towards advancing the earthquake forecasting by machine learning of satellite data

Amongst the available technologies for earthquake research, remote sensing has been commonly used due to its unique features such as fast imaging and wide image-acquisition range. Nevertheless, early studies on pre-earthquake and remote-sensing anomalies are mostly oriented towards anomaly identification and analysis of a single physical parameter. Many analyses are based on singular events, which provide a lack of understanding of this complex natural phenomenon because usually, the earthquake signals are hidden in the environmental noise. The universality of such analysis still is not being demonstrated on a worldwide scale. In this paper, we investigate physical and dynamic changes of seismic data and thereby develop a novel machine learning method, namely Inverse Boosting Pruning Trees (IBPT), to issue short-term forecast based on the satellite data of 1,371 earthquakes of magnitude six or above due to their impact on the environment. We have analyzed and compared our proposed framework against several states of the art machine learning methods using ten different infrared and hyperspectral measurements collected between 2006 and 2013. Our proposed method outperforms all the six selected baselines and shows a strong capability in improving the likelihood of earthquake forecasting across different earthquake databases.

physics.geo-ph↗