National Earthquake Response Support Service

Abstract The Changbaishan volcanic field, which includes the Tianchi and Wangtian'e volcanoes in China and the Namphothe volcano in North Korea, is one of the most complex and hazardous volcanic regions on Earth due to its tectonic setting and explosive Holocene activity. Although the magmatic structure beneath the Tianchi volcano has been widely studied, the subsurface systems beneath the Wangtian'e and Namphothe volcanoes remain unknown. Using receiver function data from a dense seismic array, we present the first high‐resolution images of crustal thickness and V p / V s structure across the Tianchi and Wangtian'e volcanic fields. stacking shows that the Tianchi volcano is underlain by a thick crust with high V p / V s , while the Wangtian'e volcano has normal crustal thickness but similarly elevated V p / V s . Common Conversion Point imaging reveals strong negative phases at depths of 5–10 km beneath both volcanoes, marking the likely tops of major crustal magma chambers. Ambient noise tomography, derived from the densest broadband data set collected in this region, also detects pronounced low‐velocity zones at similar depths. These results indicate that the two volcanoes form an interconnected magmatic system: mantle‐derived melts pool near the Moho beneath the Tianchi volcano and then migrate laterally to supply separate magma reservoirs beneath the Tianchi and Wangtian'e volcanoes. Notably, we identify partial melt beneath the Wangtian'e, a volcano long regarded as dormant or extinct, underscoring the need for continued monitoring and further geophysical studies of the entire Changbaishan volcanic complex.

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.

As the most immediately impacted population, survivors’ mobility and distribution characteristics are closely linked to earthquake emergency response. Accurate population distribution and mobility data are vital foundational resources for post-disaster decision-making. With the help of mobile phone location data within the earthquake-stricken area, we explore novel rapid assessment approaches to identifying perceived impact area.The widespread adoption of smart mobile devices in China has led to the installation of numerous third-party applications. These applications rely on push notification services enabled by Push Software Development Kits (SDKs) provided by mobile service providers. These SDKs, compliant with data security standards, collection scopes, and transmission protocols, utilize built-in functional modules to periodically collect user-authorized geolocation data. This data encompasses device identifiers, GPS coordinates, WiFi signatures, cellular base station logs, and network connectivity metadata. After encryption, these multi-source data streams inputs are aggregated into structured mobile location datasets. They can provide high-resolution insights into population mobility patterns during disasters.At 9:05 (Beijing time) on 7 January 2025, an earthquake with magnitude Ms 6.8 hit Dingri County, Tibet Autonomous Region, which killed 126 people. We collected a mobile phone location dataset covering a 150 km radius from the epicenter, spanning 38 hours from 9:00 on 6 January to 1:00 on 8 January. It comprises 146,123 records. Each record includes four mobile location-based indicators, namely (1) Active Base Stations (base stations scanned and periodically reported by mobile devices), (2) Active Wi-Fi Hotspots (Wi-Fi hotspots scanned and periodically reported by mobile devices), (3) Mobile Devices (mobile devices that obtain services through multiple positioning methods) and (4) Wi-Fi-Connected Devices (mobile devices connected to Wi-Fi hotspots). Utilizing natural neighbor interpolation and Thiessen polygon interpolation methods, we analyzes changes in four mobile location-based indicators and their spatiotemporal distribution characteristics before and after the earthquake, summarizing crowd movement patterns and communication behaviors after the Dingri earthquake. The results indicate an uneven distribution of population and differing dynamics in mobile phone signal activity. This reflects different behavioral patterns and the potential perceived extent of the earthquake. Within 50 km of the epicenter, all four indicators showed varying degrees of decline post-earthquake, while areas beyond 100 km exhibited short-term surges, reflecting differentiated behavioral responses based on seismic impact severity. In areas experiencing strong shaking, risk avoidance behavior predominated, while in areas where shaking was noticeable but less severe, communication behavior was more prominent. Mobile data decline zones showed high spatial correlation with intensity VIII+ regions, proving their effectiveness as rapid indicators for identifying strongly affected areas. Notably, mobile location data enabled accurate identification of strongly affected zones within 30 min post-earthquake.The research establishes a theoretical-technical framework supporting three critical post-disaster applications: (1) dynamic population distribution sensing, (2) behavioral pattern analysis of affected populations, and (3) rapid evaluation of seismic perception zones.

According to the China Earthquake Networks Center (CENC), an MS6.8 earthquake occurred in Dingri County, Tibet Autonomous Region, China, on January 7, 2025, with a focal depth of 10 km. The epicenter was located in the southern part of the Tibetan Plateau. Due to the northward push of the Indian Plate, a series of north-south trending rifts have formed within the block containing the epicenter. The seismogenic fault is identified as the Dengmocuo Fault in the southern segment of the Shenzha-Dinggye Rift. Within one week after the mainshock, 55 earthquakes of MS≥3.0 were recorded, including one aftershock of MS≥5.0—an MS 5.0 event on January 13th. Focal mechanism solutions from different institutions consistently indicate that the mainshock was an extensional rupture event with a nearly north-south striking plane, essentially consistent with the trend of the Shenzha-Dinggye Rift. Based on the CENC catalog, earthquakes of ML≥3.0 within the aftershock zone are overall distributed along a north-south orientation. The epicentral distribution map shows that, bounded by latitudes 28.8°N and 28.6°N, the aftershock zone can be divided into three main areas: northern, central, and southern. The mainshock is located in the southern area. ML≥3.0 aftershocks are primarily distributed in the northern and southern areas, with fewer and more scattered events in the central area.We collected waveform data for earthquakes of M≥3.0 within the aftershock zone from January 7 to 14. After quality screening, we determined the focal mechanism solutions for moderate and small earthquakes based on P-wave first motions, obtaining solutions for a total of 30 events. The results show that the focal mechanisms in the northern and central areas are predominantly strike-slip, although the number of solutions from the central area is limited. The focal mechanisms in the southern area are relatively complex, mainly characterized by extension with a subordinate strike-slip component. Subsequently, we inverted the regional stress field. Given that the aftershocks are basically aligned north-south with a narrow east-west distribution, we calculated the stress field from south to north at intervals of 0.2 degrees using a radius of 20 km. The calculation results show that the orientation of the maximum principal compressive stress (σ1) within the aftershock zone is essentially north-south, indicating that the overall rupture is dominated by east-west extension. Furthermore, the R-values from north to south are 0.8, 0.5, 0.1, and 0.2, respectively. This reveals a gradational stress pattern across the entire aftershock zone: "strong compression in the north → weak planar stress in the central area → weak compression in the south," with no abrupt changes. This suggests that the post-mainshock stress adjustment is continuous and controlled by the regional tectonic setting, with no significant stress discontinuity. A transition in the stress state from compression in the north to extension in the south is observed.

Crustal movement and deformation monitoring are important methods for reflecting changes in crustal stress. Crustal deformation data can be used to accurately describe the movement and deformation characteristics of active blocks and their boundary zones, providing effective data constraints for earthquake prediction and scientific research. Crustal deformation monitoring mainly includes crustal movement monitoring (such as Global Navigation Satellite System (GNSS) observations), surface strain monitoring (such as borehole strain observations), and surface tilt monitoring (such as vertical pendulum tilt observations). The high-precision and high temporal resolution data generated are widely used in the study of slow earthquakes, volcanic activity, and earthquake precursors. Given the close relationship between geophysical instruments and observation environments, crustal deformation monitoring instruments can not only record structural signals, but also interference signals caused by changes in surrounding loads. This article is based on the analysis displacement solution caused by the point load model, and derives formulas for calculating the surrounding tilt field and strain field, providing a theoretical basis for the quantitative calculation of the influence of surrounding loads in crustal deformation monitoring. In addition, this article also proposes a method for calculating the strain effects of two-dimensional and three-dimensional irregular shaped loads. Finally, based on the four component borehole strain observation data from Guza borehole strainmeter and the observation data of the surrounding river water level, the applicability of this analytical solution in quantitatively calculating the degree of influence of irregular load models in the surrounding area was verified under the conditions of setting the surrounding medium parameters (elastic modulus and Poisson's ratio). The results indicate that: (a) for the two-dimensional irregular shaped load model problem, vector superposition calculation can be performed after load scattering; (b) For the problem of three-dimensional irregular shaped load models, different weights can be assigned to scattering points after load scattering, and the two-dimensional irregular shaped load method can be used for calculation. The convergence process during vector superposition proves the correctness and feasibility of this method. This study provides a research foundation for the quantitative analysis of the influence of surrounding load interference in crustal deformation monitoring; (c) There is a high possibility that the data disturbance information of the four component drilling strain observation data at Guzan Station in summer is affected by the disturbance of the water level data of nearby rivers. This research work can quantitatively explain the degree of influence of load type interference factors on high-precision geophysical observation data, providing a quantitative interpretation scheme for the extraction of earthquake precursor anomalies.Fund support: Ningxia Natrual Science Foundation Project (2024AAC03436); National Natural Science Foundation of China (41704062); National Key Research and Development Program of China (2021YFC3000705-06).Figure 1 Example of the influence of irregularly shaped loads in the near field. In the figure: (a) represents the positional relationship between the Guzan borehole strainmeter and the Dadu River; (b) Representing the comparison of the changes in actual observed data with the results of irregular load model calculations.

The Haiyuan-Liupan Shan (HY-LPS) arcuate tectonic belt, located at the junction of the growth front of the Tibetan Plateau and the North China Craton, serves as a natural laboratory for investigating continental collision. Investigating the fine structure and dynamic processes in this area not only deepens our understanding of the debated growth and deformation patterns of the plateau, but also clarifies the interactions between the plateau and the adjacent craton. In this study, we establish a high-resolution three-dimensional crustal shear-wave velocity structure surrounding the HY-LPS arcuate tectonic belt using the surface wave imaging technique, utilizing ambient noise data from 219 broadband stations. The shear-wave velocity structure in this region exhibits a strong correlation with geological tectonics, consistent with the transformation of boundary faults from strike-slip to thrust. Low-velocity bodies are extensively distributed in the middle crust of the Longxi block, which is located at the northeastern margin of the Tibetan Plateau. The formation of these low-velocity anomalies may result from multifactorial interactions. Our results indicate that the upper and lower crusts in the Longxi block are decoupled, and the mid-crustal low-velocity bodies act as a detachment layer. This decoupling mechanism facilitates the growth of the plateau margin by enabling the upper crust to overthrust onto the craton, thereby contributing to the formation of the Liupan Shan. Furthermore, the lower crust of the Longxi block is found thickened due to the obstruction imposed by the North China Craton and intruded into the cratonic lower crust. The cratonic crust has been compromised due to the combined effects of tectonic compression and thermal erosion associated with the northeastward expansion of the Tibetan Plateau, which has facilitated the development of wedge tectonics.

With the widespread deployment of Industrial Cyber-Physical Systems (ICPS), their inherent vulnerabilities have increasingly exposed them to sophisticated cybersecurity threats. Although existing protective mechanisms can block attacks at runtime, the risk of defense failure remains. To proactively evaluate and harden ICPS security, we design a distributed crowdsourced testing platform tailored to the four-layer cloud ICPS architecture-spanning the workshop, factory, enterprise, and external network layers. Building on this architecture, we develop a Distributed Input-Output Testing and Verification Framework (DIOTVF) that models ICPS as systems with spatially separated injection and observation points, and supports controllable communication delays and multithreaded parallel execution. The framework incorporates a dynamic test-task management model, an asynchronous concurrent testing mechanism, and an optional LLM-assisted thread controller, enabling efficient scheduling of large testing workloads under asynchronous network conditions. We implement the proposed framework in a prototype platform and deploy it on a virtualized ICPS testbed with configurable delay characteristics. Through a series of experimental validations, we demonstrate that the proposed framework can improve testing and verification speed by approximately 2.6 times compared to Apache JMeter.

In recent years, with the expansion of network infrastructure, the scale of network traffic has grown. The Internet is integral to our lives, with wireless networks, the Internet of Things, and vehicular networks relying on it. Detecting malicious traffic accurately to maintain network security is crucial. Deep learning methods for intrusion detection automatically extract traffic features and classify traffic, showing promising results in experiments. However, most intrusion detection models use a combination of CNN and LSTM, which suffer from issues like sequential processing limitations and inability to capture long-term relationships in traffic data, affecting detection performance. To address these challenges, we propose Channel-TransCNN: a hybrid network combining CNN and Transformer for traffic detection. We employ channel attention on CNN to focus on crucial channel features and divide CNN-processed data flow into channels for input into Transformer, avoiding complexity issues. Extensive experiments on three public network traffic datasets show that Channel-TransCNN outperforms other baselines with higher accuracy and lower false positive rates.