In recent years, GNSS-derived total electron content (TEC) measurements have emerged as an effective method for detecting natural hazards through their ionospheric manifestations. Seismo-atmospheric disturbances generated by earthquakes, tsunamis, and volcanic eruptions propagate as traveling ionospheric disturbances (TIDs) that modify ionospheric electron density. Despite this potential, specialized open-source tools for such analyses remain limited. We present IonKit-NH, a MATLAB-based toolkit enabling systematic processing of multi-GNSS data (GPS, GLONASS, Galileo, BDS) through dual-frequency combination analysis for TEC derivation. The software implements automated generation of time-distance diagrams and 2D TEC perturbation maps, enabling quantitative characterization of TID propagation parameters associated with natural hazards. This toolkit enhances standardized analysis of ionospheric precursors and co-seismic signals across global navigation satellite systems.

Earthquakes can cause significant damage and loss of life, necessitating immediate assessment of the resulting fatalities. Rapid assessment and timely revision of fatality estimates are crucial for effective emergency decision-making. This study using the February 6, 2023, M_S 8.0 and M_S 7.9 Kahramanmaraş, Türkiye earthquakes as an example to estimate the ultimate number of fatalities. An early Quick Rough Estimate (QRE) based on the number of deaths reported by the Disaster and Emergency Management Presidency of Türkiye (AFAD) is conducted, and it dynamically adjusts these estimates as new data becomes available. The range of estimates of the final number of deaths can be calculated as 31 384-56 475 based on the "the QRE of the second day multiplied by 2-3″ rule, which incorporates the reported final deaths 50 500. The Quasi-Linear and Adaptive Estimation (QLAE) method adaptively adjusts the final fatality estimate within two days and predicts subsequent reported deaths. The correct order of magnitude of the final death toll can be estimated as early as 13 hr after the M_S 8.0 earthquake. In addition, additional earthquakes such as May 12, 2008, M_S 8.1 Wenchuan earthquake (China), September 8, 2023, M_S 7.2 Al Haouz earthquake (Morocco), November 3, 2023, M_S 5.8 Mid-Western Nepal earthquake, December 18, 2023, M_S 6.1 Jishishan earthquake (China), January 1, 2024, M_S 7.2 Noto Peninsula earthquake (Japan) and August 8, 2023, Maui, Hawaii, fires are added again to verified the correctness of the model. The fatalities from the Maui fires are found to be approximately equivalent to those resulting from an MS 7.4 earthquake. These methods complement existing frameworks such as Quake Loss Assessment for Response and Mitigation (QLARM) and Prompt Assessment of Global.

The National Strong-Motion Observation Network System of China has collected over 12 000 strong-motion recordings from 2007 to December 2020. This study assembled the source-related metadata of 1 920 earthquakes associated with assembled well-processed recordings of China. The earthquake basic information, focal mechanisms, and the fault geometry were collected from various institutes and literature. We recommended the M_W values for 900 earthquakes, the fault types for 1 064 earthquakes, and the fault geometries for 18 large earthquakes. We also performed the statistical analysis for establishing the empirical conversions of M_W-M_S, and M_L, and providing the empirical relationships between M_W and ruptured area, aspect ratio, respectively. Moreover, the ruptured fault geometries of large earthquakes were used to preliminarily divide all earthquakes considered into 1 141 mainshocks, and 779 aftershocks. The finite-fault distances (R_JB and R_rup) of strong-motion recordings from the 18 large earthquakes were calculated, and then used to yield the statistic relationships between the point-source distances (R_epi and R_hyp) and finite-fault distances. We finally provided the earthquake source database freely accessible at website. The source-related metadata can be directly applied to develop the ground motion prediction equations of China.

Accurate and rapid determination of source locations is of great significance for surface microseismic monitoring. Traditional methods, such as diffraction stacking, are time-consuming and challenging for real-time monitoring. In this study, we propose an approach to locate microseismic events using a deep learning algorithm with surface data. A fully convolutional network is designed to predict source locations. The input data is the waveform of a microseismic event, and the output consists of three 1D Gaussian distributions representing the probability distribution of the source location in the x,y, and z dimensions. The theoretical dataset is generated to train the model, and several data augmentation methods are applied to reduce discrepancies between the theoretical and field data. After applying the trained model to field data, the results demonstrate that our method is fast and achieves comparable location accuracy to the traditional diffraction stacking location method, making it promising for real-time microseismic monitoring.

Earthquakes are caused by the rapid slip along seismogenic faults. Whether large or small, there is inevitably a certain nucleation process involved before the dynamic rupture. At the same time, significant foreshock activity has been observed before some but not all large earthquakes. Understanding the nucleation process and foreshocks of earthquakes, especially large damaging ones, is crucial for accurate earthquake prediction and seismic hazard mitigation. The physical mechanism of earthquake nucleation and foreshock generation is still in debate. While the earthquake nucleation process is present in laboratory experiments and numerical simulations, it is difficult to observe such a process directly in the field. In addition, it is currently impossible to effectively distinguish foreshocks from ordinary earthquake sequences. In this article, we first summarize foreshock observations in the last decades and attempt to classify them into different types based on their temporal behaviors. Next, we present different mechanisms for earthquake nucleation and foreshocks that have been proposed so far. These physical models can be largely grouped into the following three categories: elastic stress triggering, aseismic slip, and fluid flows. We also review several recent studies of foreshock sequences before moderate to large earthquakes around the world, focusing on how different results/conclusions can be made by different datasets/methods. Finally, we offer some suggestions on how to move forward on the research topic of earthquake nucleation and foreshock mechanisms and their governing factors.

This paper focuses on the Qinghai-Xizang Plateau. It systematically reviews its seismic activity characteristics and extensive environmental effects under extreme climatic conditions in dry and cold seasons. Firstly, through detailed data analysis and literature review, it is revealed how the seasonal significant rainfall and temperature changes in the plateau establish potential links with key parameters such as the frequency and intensity of seismic activity. This process deeply analyzes how natural conditions such as extreme rainfall and temperature changes directly or indirectly affect the mechanism of earthquake preparation and triggering, which may promote or inhibit the occurrence of seismic activity. The close relationship between cold and dry seasons and seismic activity is emphasized, and the unique influence of these special climatic conditions on seismic activity patterns is discussed. In addition, the regional distribution characteristics of seismic activity in the plateau area are also analyzed, including key data such as annual occurrence number and magnitude distribution, which provides strong data support for formulating regional earthquake disaster response strategies. In addition, the characteristics of various secondary disasters that may be caused by earthquakes, such as landslides, debris flows, barrier lakes, etc., are analyzed, which deepens the understanding of the complexity of the earthquake disaster chain. The aim is to provide a scientific basis for future earthquake disaster prevention and control work and to promote the improvement of earthquake science research and disaster management levels in the Qingzang Plateau and even the world.

The 18^th Academic Conference of the Seismological Society of China was held in Guiyang, China, on August 7, 2023, fostering academic exchanges on the latest advancements in earthquake science. The conference featured 170 abstracts and nearly 300 academic presentations. In this paper, we classify and summarize the scholars' presentations, analyzing the current state and progress of earthquake science in China from four key perspectives: crustal structure dynamics, earthquake mechanisms, seismic resilience of urban and rural infrastructure, and innovative earthquake services. The presentations reveal that research primarily focuses on detecting crustal structures in southwest China, with seismic imaging technology and magnetotelluric detection being the most commonly used methods. Studies on earthquake mechanisms are centered on recent destructive events, such as the 2023 M_W 7.8 and M_W 7.6 Türkiye earthquakes, the 2022 M_W 6.7 Luding earthquake, and the 2021 M_W 7.4 Madoi earthquake. Regarding seismic resilience, the focus is on shock resistance and seismic isolation experiments involving large-scale hybrid structures, as well as the formation mechanisms and risk assessments of earthquake-triggered disaster chains. Additionally, significant progress has been made in smart earthquake services, particularly in rapid disaster assessment, earthquake disaster information extraction technology, the China Seismic Experimental Site, and the strong-motion Flatfile database for mainland China. Overall, this conference highlighted that earthquake science in China has reached a new level of development. However, numerous scientific challenges and critical technologies remain to be addressed, such as acquiring higher-resolution crustal structures and applying big data and artificial intelligence to diverse seismic models and earthquake services, which requires the continued collaboration of researchers in the field.