Global seismic networks cannot reliably detect earthquakes below a certain magnitude threshold.
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Seismic monitoring networks universally rely on detection thresholds and magnitude of completeness metrics, meaning they cannot reliably record earthquakes falling below specific magnitude limits.
All nuclear explosions are banned by the Comprehensive Nuclear-Test-Ban Treaty. In the context of the treaty a verification regime was put into place to detect, locate, and characterize nuclear explosions at any time, by anyone and everywhere on the Earth. The International Monitoring System, which plays a key role in the verification regime, was set up by the Preparatory Commission of the Comprehensive Nuclear-Test-Ban Treaty Organization. Out of the several different monitoring techniques applied in the International Monitoring System the seismic waveform approach is the most effective and reliable technology for monitoring nuclear explosions underground. This study introduces a deterministic method of threshold monitoring that allows to asses a lower body wave magnitude limit of a potential seismic event in a certain geographical region, that can be detected by those seismic stations being part of the International Monitoring System network. The method is based on measurements of ambient seismic noise levels at the individual seismic stations along with global distance corrections terms for the body wave magnitude. The results suggest that an average global detection capability of approximately body wave magnitude 4.0 can be achieved using only stations from the primary seismic network of the International Monitoring System. The incorporation of seismic stations from the auxiliary seismic network leads to a slight improvement of the detection capability, while the use and analysis of wave arrivals from distances greater than 120∘\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$^\circ$$\end{document} results in a significant improvement of the detection capability. Temporal variations in terms of hourly and monthly changes of the global detection capability can not be observed. Overall, comparisons between detection capability and manually retrieved body wave magnitudes from the Reviewed Event Bulletin suggest, that our method yields a more conservative estimation of the detection capability and that in reality detection thresholds might be even lower than estimated.
The China Earthquake Administration has currently launched an ambitious nationwide seismicity monitoring network project that will increase the number of stations from ∼950 to 2000 for the broadband seismic stations used to compile the earthquake catalog. The new network is planned to go online by the end of 2023. For more than half of Chinese mainland, the interstation distance of the broadband seismic network will soon be smaller than 100 km, for 27% smaller than 50 km, and for 6% smaller than 25 km. Of all possible ways to characterize the higher-resolution monitoring of the frequent smaller earthquakes expected inside Chinese mainland, the completeness magnitude (Mc) remains one of the most commonly used. Using the prior model of the Bayesian magnitude of completeness method calibrated on the Chinese earthquake catalog from 1 January 2009 to 26 June 2022, we predict the spatial distribution of Mc for the new network based on the planned network configuration. If almost the entire Chinese mainland is at present covered down to Mc=3.3, this threshold will fall to Mc=2.9 in the near future. This means approximately two times more earthquakes will be recorded in the complete catalog available for statistical analysis per year (for a = 6.77 and b = 0.80 in the Gutenberg–Richter law log10N=a−b·M, in which N represents the number of events of magnitude larger than or equal to M and M≥Mc). Based on the observation that abnormal seismicity as precursors are most likely to be observed at least at three units below the mainshock magnitude, and assuming earthquakes to be potentially damaging at M ≥ 5, the new seismic network shall achieve the goal of 76% coverage for optimal seismic-based earthquake prediction research.
Thanks to the new permanent seismic stations installed in the Franz Joseph Land and Severnaya Zemlya arctic archipelagoes, it has become possible at present to record earthquakes occurring in the eastern Gakkel ridge with a much lower detection threshold than that provided by the global network. At present, the lowest recorded magnitude is ML 2.4 and the magnitude of completeness is 3.4. We examined the results of seismic monitoring conducted from December 2016 through January 2020 to show that the earthquake epicenters are not uniformly distributed both in space and over time within the eastern part of the ridge. There were periods of quiescence and seismic activity. Most of the epicenters are confined to the area between 86° and 95.0° E. Relative location techniques were used to locate the single major swarm of earthquakes recorded so far. Most earthquakes were recorded by two or three stations only, so that relative location techniques have been able to yield reliable data for an analysis of the swarm. We showed that there have been actually two swarms that contained different numbers of events. The earthquakes in the larger swarm were occurring nonuniformly over time and clustered at certain depths. The ML scale was calibrated for the Eurasian Arctic based on records of the seismic stations installed in the Svalbard Archipelago, Franz Joseph Land, and on Severnaya Zemlya: −logA0(R)=1.5×logR100+1.0×10−4(R−100)+3.0. The results will help expand our knowledge of the tectonic and magmatic processes occurring within the ultraslow Gakkel ridge, which are reflected in the local seismicity.
Instrumental earthquake monitoring in Greece started in 1899-1906 with the first five seismic stations of the National Observatory of Athens, Institute of Geodynamics (NOA). Subsequent upgrades and expansions led to today’s unified national seismic network, which includes almost all permanent seismic stations in Greece and provides waveform and parametric data. We examine the detection capabilities of the Greek seismic networks using phase, location, and magnitude data from the International Seismological Centre (ISC) and the various National Institutes. We apply two methods to measure the network performance. In one, we form a grid, and find the 50 per cent probability detection threshold for each station-grid cell pair for different times. In the other, we find the probability-based magnitude of completeness grid for every three months from 1970 through 2014. Both methods show that in 1990 the detection threshold improved significantly in the north part of Greece. A much greater improvement took place in 2010 throughout Greece, yielding a completeness magnitude of Mp ∼ 1.
Deep learning enhances earthquake monitoring capabilities by mining seismic waveforms directly. However, current neural networks, trained within specific areas, face challenges in generalizing to diverse regions. Here, we employ a data recombination method to create generalized earthquakes occurring at any location with arbitrary station distributions for neural network training. The trained models can then be applied universally with different monitoring setups for earthquake detection and parameter evaluation from continuous seismic waveform streams. This allows real-time Earthquake Early Warning (EEW) to be initiated at the very early stages of an occurring earthquake. When applied to substantial earthquake sequences across Japan and California (US), our models reliably report most earthquake locations and magnitudes within 4 seconds of the initial P-wave arrival, with mean errors of 2.6-7.3 km and 0.05-0.32, respectively. The generalized neural networks facilitate global applications of real-time EEW, eliminating complex empirical configurations typically required by traditional methods.
This review synthesizes the state of the art on the integration of digital technologies, particularly machine learning, the Internet of Things (IoT), and advanced image processing techniques, for enhanced hazard monitoring. Focusing on air pollution, earthquakes, floods, and fires, we analyze articles selected from Scopus published between 2015 and 2024. This study classifies the selected articles based on hazard type, digital technology application, geographical location, and research methodology. We assess the effectiveness of various approaches in improving the accuracy and efficiency of hazard detection, monitoring, and prediction. The review highlights the growing trend of leveraging multi-sensor data fusion, deep learning models, and IoT-enabled systems for real-time monitoring and early warning. Furthermore, we identify key challenges and future directions in the development of robust and scalable hazard monitoring systems, emphasizing the importance of data-driven solutions for sustainable environmental management and disaster resilience.
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