At 20:08 on September 18, 2024, Beijing time, an earthquake of magnitude 4.7 occurred in Feidong, Anhui Province (31.98°N, 117.6°E). The China Earthquake Early Warning Network presented the first early warning results 8.9 s after the earthquake. The China Earthquake Networks Center (CENC) released the automatic rapid report results 163 s after the earthquake and the official rapid report results 8 min after the earthquake. At the same time, the CENC reported a series of emergency products, including source parameters, seismic tectonics, historical earthquakes, focal mechanism, instrument seismic intensity and predicted intensity. The results showed that the earthquake was located at the junction of the southern section of the Tanlu Fault and the Feizhong Fault, with aftershocks distributed in the NEE direction. The focal mechanism solution indicates that the earthquake is essentially a strike-slip event. The predicted intensity in the vicinity of the epicenter reaches up to VI, involving 23 towns that cover an area of about 1 359 km2. Only one station near the epicenter shows a peak acceleration value greater than the fortification standard of the area, which may cause slight damage to some adjacent houses, consistent with the actual damage to buildings.
Rainfall-induced landslides are often highly destructive. Reviewing and analyzing the causes, processes, impacts, and deficiencies in emergency response is critical for improving disaster prevention and management. From the night of July 21 to the morning of July 22, 2024, the Kencho Shacha Gozdi Village in Gezei Gofa, Southern Nations, Nationalities, and Peoples' Region, Ethiopia, suffered heavy rainfall that triggered two landslides. By July 25, this event had claimed at least 257 lives. This study presents a detailed characterization of the landslides using multi-source data. By analyzing the landslide disaster process, this study summarizes key lessons and provides suggestions for preventing rainfall-induced geological hazards. The results indicate that rainfall has the greatest impact on the occurrence of landslides, while lithology and human activities have promoted and strengthened the landslide disaster. Despite the active disaster response in the local area, many problems were still exposed in the emergency response work. This analysis offers valuable insights for mitigating rainfall-induced geological hazards and enhancing emergency response capabilities.
This paper presents a prospective forecast of the locations of the next MW ≥ 6.5 earthquakes in California and Nevada based on the locations and rates of occurrence of M ≥ 4.0 earthquakes during the past 30 years, called here preshocks. The time period of the forecast is arbitrarily set at 33 years. The forecast faults are the Anza section of the San Jacinto Fault, the Calaveras Fault, the creeping section of the San Andreas Fault, the Maacama Fault, the San Bernardino section of the San Jacinto Fault, and the southern San Andreas Fault, all strike-slip faults in California, and the normal-faulting Wassuk Range Fault in Nevada. The suspected preshocks have occurred randomly along the expected future fault ruptures at rates of at least 0.5 events per year. The temporal history of preshocks for past M ≥ 6.5 earthquakes in California do not indicate when the future mainshock will occur. Outside of California, preshock activity was observed before the 2016 MW 7.0 Kumamoto, Japan earthquake, the 2023 MW 7.8 Kahramanmaras, Turkey earthquake, and the 2017 MW 6.5 Jiuzhaigou, China earthquake, all strike-slip events, as well as the 2008 MW 7.9 Wenchuan, China thrust earthquake. The two mainshocks in China had preshock rates less than 0.5 events per year. By publishing this spatial earthquake forecast, seismologists in the future can evaluate whether or not this forecast was a total success, a total failure, or a partial success. The probability of just one of the forecast events actually taking place during the forecast time period is less than 2%.
Rapidly obtaining spatial distribution maps of secondary disasters triggered by strong earthquakes is crucial for understanding the disaster-causing processes in the earthquake hazard chain and formulating effective emergency response measures and post-disaster reconstruction plans. On April 3, 2024, a MW 7.4 earthquake struck offshore east of Hualien, Taiwan, China, which triggered numerous coseismic landslides in bedrock mountain regions and severe soil liquefaction in coastal areas, resulting in significant economic losses. This study utilized post-earthquake emergency data from China's high-resolution optical satellite imagery and applied visual interpretation method to establish a partial database of secondary disasters triggered by the 2024 Hualien earthquake. A total of 5 348 coseismic landslides were identified, which were primarily distributed along the eastern slopes of the Central Mountain Range watersheds. In high mountain valleys, these landslides mainly manifest as localized bedrock collapses or slope debris flows, causing extensive damage to highways and tourism facilities. Their distribution partially overlaps with the landslide concentration zones triggered by the 1999 Chi-Chi earthquake. Additionally, 6 040 soil liquefaction events were interpreted, predominantly in the Hualien Port area and the lowland valleys of the Hualien River and concentrated within the IX-intensity zone. Widespread surface subsidence and sand ejections characterized soil liquefaction. Verified against local field investigation data in Taiwan, rapid imaging through post-earthquake remote sensing data can effectively assess the distribution of coseismic landslides and soil liquefaction within high-intensity zones. This study provides efficient and reliable data for earthquake disaster response. Moreover, the results are critical for seismic disaster mitigation in high mountain valleys and coastal lowlands.
As one of the most seismically active regions, Sichuan basin is a key area of seismological studies in China. This study applies a neural network model with attention mechanisms, simultaneously picking the P-wave arrival times and determining the first-motion polarity. The polarity information is subsequently used to derive source focal mechanisms. The model is trained and tested using small to moderate earthquake data from June to December 2019 in Sichuan. We apply the trained model to predict first-motion polarity directions of earthquake recordings in Sichuan from January to May 2019, and then derive focal mechanism solutions using HASH algorithm with predicted results. Compared with the source mechanism solutions obtained by manual processing, the deep learning method picks more polarities from smaller events, resulting in more focal mechanism solutions. The catalog documents focal mechanism solutions of 22 events (ML 2.6-4.8) from analysts during this period, whereas we obtain focal mechanism solutions of 53 events (ML 1.9-4.8) through the deep learning method. The derived focal mechanism solutions for the same events are consistent with the manual solutions. This method provides an efficient way for the source mechanism inversion of small to moderate earthquakes in Sichuan region, with high stability and reliability.
The Bayan Har block, one of China's most seismically active regions, has experienced multiple major earthquakes (≥M 7.0) in recent years. It is a key area for investigating the interactions between the Qinghai-Xizang (Qingzang) Plateau and adjacent blocks, plateau uplift, and strong earthquake mechanisms. P-wave velocity and crustal composition provide key constraints on the properties of distinct tectonic units and their evolutionary modification processes. Based on the results of 8 Deep Seismic Sounding (DSS) profiles completed in the Bayan Har block and surrounding areas over the past 20 years, We constructed one-dimensional P-wave velocity models for the crust of Bayan Har block, Qilian fold belt, Qinling fold belt, Alxa block, Ordos block and Sichuan basin. Furthermore, crustal composition models for different tectonic units were established based on these results. The results reveal that the crustal thickness of the Bayan Har block gradually decreases towards the NNE, NE, and SE directions, while the average crustal velocity increases correspondingly. The felsic layer in the crust accounts for more than half of the total crustal thickness. The mafic content within the crust of different tectonic units exhibits notable variations, which may reflect that the Bayan Har block, Qilian fold belt, and Qinling fold belt have experienced more intensive lithospheric evolution processes compared to Ordos basin and Sichuan basin. The seismicity distribution in this region is significantly controlled by crustal velocity and composition heterogeneity across the Bayan Har block and adjacent areas, which demonstrates that earthquakes within and around the Bayan Har block exhibit both high frequency and larger magnitudes. These seismic characteristics primarily result from intense crustal stress accumulation and release during the outward expansion of the Qingzang Plateau.
The travel-time corrections for the primary seismic phases of 72 stations in the Guangdong seismic network, relative to the 1D South China travel-time model, were determined using joint hypocentral determination (JHD) and statistical analysis methods. The travel-time corrections for the Pg phase of 72 stations range between −0.25 s and 0.14 s, while the corrections for the Sg phase range between 0.27 s and 0.35 s, and those for the Pn phase are between −0.86 s and 0.07 s. The spatial distribution of travel-time corrections for Pg, Sg, and Pn phases of 72 stations correlates well with the geological structure in this region. This indicates that the travel-time corrections for Pg and Sg phases are mainly caused by the discrepancy between the actual crustal velocity structure beneath the stations and the 1D South China travel-time model. These corrections empirically compensate for systematic travel-time errors arising from such discrepancies. The primary factor contributing to the travel-time corrections for the Pn phase is the Moho undulations or tilt. These corrections are intended to compensate for systematic errors in travel time caused by variations in the actual Moho. By integrating the obtained travel-time corrections into the HYPO-SAT location algorithm, test results showed an obvious improvement in location accuracy and origin time precision for explosion events. The variation of horizontal distance between repeating earthquake pairs has also improved, with 86% of the repeating earthquake pair spacing being more accurately estimated after correction. This suggests the crucial significance of travel-time correction in earthquake location, and the consideration of travel-time correction exerts a notable impact on enhancing earthquake location accuracy.
Researching and comprehending the characteristics of destructive seismic motions is essential for the seismic design of critical infrastructure. This study employs historical data from the M 7.5 earthquake that occurred in 1850 to simulate the impacts of a M 7.5 event on hydropower stations located in proximity to Xichang. Key factors taken into account in the simulation of seismic motion encompass uncertainties, mixed-source models, and the placement of asperities. Through these simulations, we acquired the peak ground acceleration (PGA), acceleration time histories, and acceleration response spectra for the hydropower facilities affected by the earthquake. To perform a comprehensive analysis, we utilized a multi-scenario stochastic finite fault simulation method to estimate parameters including the minimum, average, and maximum values of PGA and pseudo-spectral acceleration (PSA) response spectra. Additionally, we assessed the 50th, 84th, and 95th percentiles values of the peak ground acceleration and pseudo-spectral acceleration response spectra. The simulation results also include peak ground acceleration field maps and peak ground velocity (PGV) field maps and intensity distribution maps pertaining to the earthquake. The findings demonstrate that the intensity maps produced through the stochastic finite fault method closely correspond with the intensity contour maps published of historical seismic records. These findings offer significant insights for the seismic safety evaluation and design of the specified hydropower stations. Moreover, this multi-scenario methodology can be effectively utilized for other critical infrastructure projects to derive dependable seismic motion parameters.
Ambient noise tomography is an established technique in seismology, where calculating single- or nine-component noise cross-correlation functions (NCFs) is a fundamental first step. In this study, we introduced a novel CPU-GPU heterogeneous computing framework designed to significantly enhance the efficiency of computing 9-component NCFs from seismic ambient noise data. This framework not only accelerated the computational process by leveraging the Compute Unified Device Architecture (CUDA) but also improved the signal-to-noise ratio (SNR) through innovative stacking techniques, such as time-frequency domain phase-weighted stacking (tf-PWS). We validated the program using multiple datasets, confirming its superior computation speed, improved reliability, and higher signal-to-noise ratios for NCFs. Our comprehensive study provides detailed insights into optimizing the computational processes for noise cross-correlation functions, thereby enhancing the precision and efficiency of ambient noise imaging.
The coastal region of Fujian contains numerous existing stone masonry structures, many of which are constructed on soft soil sites. Previous studies have shown that the soil-structure interaction (SSI) effect on soft soil foundations can prolong the structure's natural vibration period and enhance its seismic response. We develops a soil-structure interaction system model and a comparative rigid foundation model using the finite element software LS-DYNA to investigate the impact of SSI on the dynamic characteristics and seismic response of stone structures. The results indicate that the SSI effect alters stone structures' dynamic properties and seismic response. This alteration is evident in the extended natural vibration period, which reduces overall stiffness, increases interstory displacement angles, and slightly decreases the acceleration response. Under both SSI and FIX systems, the structural failure mode is characterized by the external collapse of the second-story stone walls, which causes the roof stone slabs to lose support and fall, leading to overall collapse. The FIX system demonstrates better structural integrity and stability with slower crack development. In contrast, the SSI system exhibits cracks that appear earlier and develop more rapidly, causing more severe damage. The research findings provide a theoretical basis for the seismic reinforcement of existing stone structures on soft soil foundations.
As a primary slope stabilization technique, anchor support encompasses traditional engineering anchors, green anchors, and ecological restoration methods. This review synthesizes two decades of literature to evaluate these approaches. Current research disproportionately focuses on engineering anchors, while green anchor systems remain less studied despite their dual advantages: reduced labor/economic costs and environmental benefits. Notably, most green anchor studies originate from low-altitude plains, with minimal attention to high-altitude cold-arid regions such as plateaus. We therefore identify slope reinforcement using green anchors in plateau environments as a critical emerging research frontier.
Remote sensing technologies play a vital role in our understanding of earthquakes and their impact on the Earth's surface. These technologies, including satellite imagery, aerial surveys, and advanced sensors, contribute significantly to our understanding of the complex nature of earthquakes. This review highlights the advancements in the integration of remote sensing technologies into earthquake studies. The combined use of satellite imagery and aerial photography in conjunction with geographic information systems (GIS) has been instrumental in showcasing the significance of fusing various types of satеllitе data sourcеs for comprеhеnsivе еarthquakе damagе assеssmеnts. However, remote sensing encounters challenges due to limited pre-event imagery and restricted post-earthquake site access. Furthеrmorе, thе application of dееp-lеarning mеthods in assеssing еarthquakе-damagеd buildings dеmonstratеs potеntial for furthеr progrеss in this fiеld. Overall, the utilization of remote sensing technologies has greatly enhanced our comprehension of earthquakes and their effects on the Earth's surface. The fusion of remote sensing technology with advanced data analysis methods holds tremendous potential for driving progress in earthquake studies and damage assessment.