Earthquake Science最新文献

I provide some science and reflections from my experiences working in geophysics, along with connections to computational and data sciences, including recent developments in machine learning. I highlight several individuals and groups who have influenced me, both through direct collaborations as well as from ideas and insights that I have learned from. While my reflections are rooted in geophysics, they should also be relevant to other computational scientific and engineering fields. I also provide some thoughts for young, applied scientists and engineers.

Among electromagnetic methods of short-term earthquake prediction, an approach is being actively developed based on the phenomenon of magnetic ultra-low-frequency (ULF) power depression occurring a few days before an earthquake. In particular, a nighttime geomagnetic power depression in the band 0.03–0.05 Hz was observed approximately 5 days before the catastrophic Tohoku 2011 earthquake. To verify the reliability of this method, we performed an extended analysis using data from magnetometer arrays JMA, MAGDAS, PWING, and INTERMAGNET. The selected stations included sites close to the epicenter (<300 km) and remote points (∼10000 km). The band-integrated spectral power of nighttime magnetic noise decreased significantly from March 6–9, several days before the earthquake. However, such variations occur simultaneously not only at nearby stations but also at distant stations. During this event, the ULF power depression was caused by low global geomagnetic activity, as evidenced by the planetary index SME. Thus, the depression of geomagnetic ULF noise cannot be considered a reliable short-term precursor.

Radon observation is an important measurement item of seismic precursor network observation. The radon detector calibration is a key technical link for ensuring radon observation accuracy. At present, the radon detector calibration in seismic systems in China is faced with a series of bottleneck problems, such as aging and scrap, acquisition difficulties, high supervision costs, and transportation limitations of radon sources. As a result, a large number of radon detectors cannot be accurately calibrated regularly, seriously affecting the accuracy and reliability of radon observation data in China. To solve this problem, a new calibration method for radon detectors was established. The advantage of this method is that the dangerous radioactive substance, i.e., the radon source, can be avoided, but only “standard instruments” and water samples with certain dissolved radon concentrations can be used to realize radon detector calibration. This method avoids the risk of radioactive leakage and solves the current widespread difficulties and bottleneck of radon detector calibration in seismic systems in China. The comparison experiment with the traditional calibration method shows that the error of the calibration coefficient obtained by the new method is less than 5% compared with that by the traditional method, which meets the requirements of seismic observation systems, confirming the reliability of the new method. This new method can completely replace the traditional calibration method of using a radon source in seismic systems.

The Arkhangelsk Seismic Network (ASN) of the N. Laverov Federal Center for Integrated Arctic Research of the Ural Branch of the Russian Academy of Sciences, founded in 2003, includes 10 permanent seismic stations located on the coasts of the White, Barents, and Kara Seas and on the Arctic archipelagos of Novaya Zemlya, Franz Josef Land, and Severnaya Zemlya. The network is registered with the International Federation of Digital Seismograph Networks and the International Seismological Center. We used not only ASN data to process earthquakes but also the waveforms of various international seismic stations. The 13,000 seismic events were registered using ASN data for 2012–2022, and for 5,500 of them, we determined the parameters of the earthquake epicenters from the European Arctic. The spatial distribution of epicenters shows that the ASN monitors not only the main seismically active zones but also weak seismicity on the shelf of the Barents and Kara Seas. The representative magnitude of ASN was M_{L, rep}=3.5. The level of microseismic noise has seasonal variations that affect the registration capabilities of each station included in the ASN and the overall sensitivity of the network as a whole. In summer, the sensitivity of the ASN decreased owing to the increasing microseismic and ambient noises, whereas in winter, the sensitivity of the ASN increased significantly because of the decrease.

As critical conduits for the dissemination of online public opinion, social media platforms offer a timely and effective means for managing emergencies during major disasters, such as earthquakes. This study focuses on the analysis of online public opinions following the Maduo M7.4 earthquake in Qinghai Province and the Yangbi M6.4 earthquake in Yunnan Province. By collecting, cleaning, and organizing post-earthquake Sina Weibo (short for Weibo) data, we employed the Latent Dirichlet Allocation (LDA) model to extract information pertinent to public opinion on these earthquakes. This analysis included a comparison of the nature and temporal evolution of online public opinions related to both events. An emotion analysis, utilizing an emotion dictionary, categorized the emotional content of post-earthquake Weibo posts, facilitating a comparative study of the characteristics and temporal trends of online public emotions following the earthquakes. The findings were visualized using Geographic Information System (GIS) techniques. The analysis revealed certain commonalities in online public opinion following both earthquakes. Notably, the peak of online engagement occurred within the first 24 hours post-earthquake, with a rapid decline observed between 24 to 48 hours thereafter. The variation in popularity of online public opinion was linked to aftershock occurrences. Adjusted for population factors, online engagement in areas surrounding the earthquake sites and in Sichuan Province was significantly high. Initially dominated by feelings of “fear” and “surprise”, the public sentiment shifted towards a more positive outlook with the onset of rescue operations. However, distinctions in the online public response to each earthquake were also noted. Following the Yangbi earthquake, Yunnan Province reported the highest number of Weibo posts nationwide; in contrast, Qinghai Province ranked third post-Maduo earthquake, attributable to its smaller population size and extensive damage to communication infrastructure. This research offers a methodological approach for the analysis of online public opinion related to earthquakes, providing insights for the enhancement of post-disaster emergency management and public mental health support.

Seismic engineering, a critical field with significant societal implications, often presents communication challenges due to the complexity of its concepts. This paper explores the role of Artificial Intelligence (AI), specifically OpenAI’s ChatGPT, in bridging these communication gaps. The study delves into how AI can simplify intricate seismic engineering terminologies and concepts, fostering enhanced understanding among students, professionals, and policymakers. It also presents several intuitive case studies to demonstrate the practical application of ChatGPT in seismic engineering. Further, the study contemplates the potential implications of AI, highlighting its potential to transform decision-making processes, augment education, and increase public engagement. While acknowledging the promising future of AI in seismic engineering, the study also considers the inherent challenges and limitations, including data privacy and potential oversimplification of content. It advocates for the collaborative efforts of AI researchers and seismic experts in overcoming these obstacles and enhancing the utility of AI in the field. This exploration provides an insightful perspective on the future of seismic engineering, which could be closely intertwined with the evolution of AI.

In 2022, four earthquakes with M_S≥6.0 including the Menyuan M_S6.9 and Luding M_S6.8 earthquakes occurred in the North-South Seismic Zone (NSSZ), which demonstrated high and strong seismicity. Pattern Informatics (PI) method, as an effective long and medium term earthquake forecasting method, has been applied to the strong earthquake forecasting in Chinese mainland and results have shown the positive performance. The earthquake catalog with magnitude above M_S3.0 since 1970 provided by China Earthquake Networks Center was employed in this study and the Receiver Operating Characteristic (ROC) method was applied to test the forecasting efficiency of the PI method in each selected region related to the North-South Seismic Zone systematically. Based on this, we selected the area with the best ROC testing result and analyzed the evolution process of the PI hotspot map reflecting the small seismic activity pattern prior to the Menyuan M_S6.9 and Luding M_S6.8 earthquakes. A “forward” forecast for the area was carried out to assess seismic risk. The study shows the following. 1) PI forecasting has higher forecasting efficiency in the selected study region where the difference of seismicity in any place of the region is smaller. 2) In areas with smaller differences of seismicity, the activity pattern of small earthquakes prior to the Menyuan M_S6.9 and Luding M_S6.8 earthquakes can be obtained by analyzing the spatio-temporal evolution process of the PI hotspot map. 3) The hotspot evolution in and around the southern Tazang fault in the study area is similar to that prior to the strong earthquakes, which suggests the possible seismic hazard in the future. This study could provide some ideas to the seismic hazard assessment in other regions with high seismicity, such as Japan, California, Turkey, and Indonesia.

The 2022 M_W6.7 Menyuan earthquake ruptured the western end of the Tianzhu seismic gap, providing an opportunity to study the regional seismogenic characteristics and seismic hazards. Here we use interferometric synthetic aperture radar (InSAR) and seismic data to study the mainshock rupture, early afterslip and the second largest aftershock of the 2022 Menyuan earthquake sequences. Our modeling results show that the mainshock ruptured the Lenglongling fault and the Tuolaishan fault with a maximum slip of ∼3 m. Rapid postseismic transient deformation occurred at the center of the Lenglongling fault. Our afterslip modeling reveals that the majority of afterslip occurred in the deeper part of the Lenglongling fault. A high-angle conjugated faulting event is found at the middle section of the Lenglongling fault. We use the stress inversion to investigate the possible triggering mechanism of the conjugated rupture event. The results indicate the maximum principal stress direction is in ∼222°, forming a ∼22° angle between the conjugated fault of second largest aftershock and the mainshock. The calculated normal stress changes indicate the region is within a pull-apart stress field, which favors such a conjugated rupturing event. Our study will help understand the rupture behavior of such kind of conjugated fault in other regions.