Xianhe Yang

Xianhe Yang


Research Focus & Metrics

Academic Career Length

4Years (2023-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

View All Publications
16 August 2023 in Remote Sensing
Verified
Journal Article
Open access
PDF file
JLJiang Liu
Jiang Liu
Xuemin ZhangXuemin Zhang
Xuemin Zhang
XYXianhe Yang
Xianhe Yang
MYMuping Yang
Muping Yang
TZTiebao Zhang
Tiebao Zhang
QLQIAN LU
QIAN LU
Taking the Luding Ms6.8 earthquake (EQ) on 5 September 2022 as a case study, we investigated the potential seismic anomalies of the ionosphere, infrared radiation, atmospheric electrostatic field (AEF), and hot spring ions in the seismogenic region. Firstly, we analyzed the multi-parameter anomalies in the ionosphere around the epicenter and found synchronous anomalous disturbances in the ground parameters, namely the global ionospheric map (GIM), GPS, TEC, and satellite parameters, such as the He+ and O+ densities on 26 August under relatively quiet solar–geomagnetic conditions (F10.7 < 120 SFU; Kp < 3; Dst > −30 nT; |AE| < 500 nT). Next, both the anomaly analysis of the infrared radiation and AEF, and the survey results of the Luding EQ scientific expedition on the hot spring ions showed pre-seismic anomalous variations at different time periods in the seismogenic region. The characteristics of Earth’s multi-sphere coupling anomalies in temporal evolution and spatial distribution were obvious, which validated the Lithosphere–Atmosphere–Ionosphere Coupling (LAIC) mechanism. Finally, combining the analysis results and the LAIC mechanism, we suggested that the multi-sphere coupling anomalies were more likely associated with the Luding Ms6.8 EQ, and that the differential motion and the regional crustal stress accumulation between the Chuandian block and the Bayan Har block might have led to this EQ. Furthermore, remote sensing and ground-based monitoring technologies can play an important role in corroborating and compensating each other, while further study of the multi-sphere coupling mechanism will provide a clearer understanding of the seismogenic process for major EQs.

3 July 2024 in Frontiers in Earth Science
Verified
Journal Article
Open access
PDF file
XLXuemei Liu
Xuemei Liu
XYXianhe Yang
Xianhe Yang
RENMAO YUANRENMAO YUAN
RENMAO YUAN
RXRui Xu
Rui Xu
CLChaohai Liu
Chaohai Liu
Seismic landslide susceptibility evaluation models are usually built on the basis of historical sample data; however, the evaluation results are often unsatisfactory when the environmental settings differ between the historical sample data region and application region. Therefore, similarity between the environmental settings is important for the application of such models. In this paper, a seismic landslide susceptibility evaluation model was first built using data from the 2008 Ms 8.0 Wenchuan earthquake-induced landslide, and the model was then used to evaluate the 2022 Ms 6.8 Luding earthquake area. In addition, the grade of susceptibility is typically represented by the landslide density, which is insufficient for capturing the details of landslides, such as their sizes, frequencies, and spatial distribution patterns. The authors therefore use a large and concentrated landslide as the susceptibility grade for the Luding earthquake area. The test results demonstrate that these two areas have similar background environments. The area under the curve (AUC) value of the receiver operating characteristics (ROC) curve of the evaluation accuracy for the model applied to the Luding earthquake area is 0.889, which indicates relatively high accuracy. Besides, the results also demonstrate that the evaluations are consistent with the disaster situation of the Moxi Platform, Wandong Village, as well as the Dagangshan Hydropower Station area. Therefore, it is reliable to apply the susceptibility evaluation model based on the Wenchuan earthquake data to the Luding earthquake area. These results show that better evaluations can be obtained based on environmental similarity tests between the areas used for historical data modeling and areas to which the models are applied.