Periodic Reporting for period 1 - QUAKEHUNTER (Real-time monitoring of earthquake nucleation for faults near urban areas)
Période du rapport: 2023-03-01 au 2025-08-31
One of the main goals in geoscience is to protect cities from the dangers of earthquakes. Today’s early-warning systems only kick in once an earthquake has already begun—giving people just seconds to react, especially when the quake starts close to urban areas. This leaves little time for effective safety measures. For many years, scientists have been asking important questions: Do large, damaging earthquakes show any warning signs before they happen—something we could detect early enough to make a difference? Under what conditions do earthquakes display detectable nucleation processes?
Our project, QUAKEHUNTER, funded by the European Research Council, is exploring this question. We're using new methods, including artificial intelligence, to monitor subtle signs (called "nucleation processes") that might occur before earthquakes of moderate to large size. These signs could appear at different temporal and spatial scales and vary among geological settings.
Our project, QUAKEHUNTER, funded by the European Research Council, is exploring this question. We're using new methods, including artificial intelligence, to monitor subtle signs (called "nucleation processes") that might occur before earthquakes of moderate to large size. These signs could appear at different temporal and spatial scales and vary among geological settings.
A major breakthrough from the first two years of our work came with the devastating 2023 magnitude 7.8 earthquake in Kahramanmaraş, southern Türkiye, which tragically caused over 60,000 deaths in Türkiye and Syria. In our study (Kwiatek, Martínez-Garzón et al., Nature Communications, 2023), we identified a seismicity transient during the 8-months preceding the mainshock, representing a potential preparatory phase (Kwiatek, Martínez-Garzón et al., Nat. Comm., 2023). The seismicity during the previous months was composed of isolated spatio-temporal clusters within 65 km of future epicentre, displaying non-Poissonian inter-event time statistics, magnitude correlations and low Gutenberg-Richter b-values. This kind of seismic behavior hadn't been seen in the area for nearly a decade. When building a higher resolution catalog employing machine-learning techniques, the characteristics of the seismicity during these 8 months preceding the mainshock become even more unique (Núnez-Jara et al., in review).
While we are far from being able to predict the occurrence of an earthquake from these transients, these results yield important information to understand the conditions that promote the detectability of earthquake preparatory processes. To deepen our understanding, we jointly reviewed the observations of precursory signals available for 33 earthquake sequences on various tectonic settings and with different monitoring conditions. We compiled and analyzed at both seismic (earthquake-related) and aseismic (slow movement without shaking) signals. Our analysis found some common patterns, showing how geological structure, tectonic forces, and other factors all play a role in earthquake development.
While we are far from being able to predict the occurrence of an earthquake from these transients, these results yield important information to understand the conditions that promote the detectability of earthquake preparatory processes. To deepen our understanding, we jointly reviewed the observations of precursory signals available for 33 earthquake sequences on various tectonic settings and with different monitoring conditions. We compiled and analyzed at both seismic (earthquake-related) and aseismic (slow movement without shaking) signals. Our analysis found some common patterns, showing how geological structure, tectonic forces, and other factors all play a role in earthquake development.
We place particular emphasis on connecting observed phenomena to the underlying physical processes driving the sequences. From our findings, we propose a new way of thinking about how earthquakes happen: not as the result of one single process, but as the outcome of several overlapping physical processes, unfolding across different times and places, that together increase stress in the Earth's crust until a major quake is triggered (Martínez-Garzón and Poli, 2024).
Artificial intelligence (AI) is rapidly transforming how we study earthquakes. One major breakthrough in recent years has been the use of AI techniques to process massive amounts of seismological data. This has opened up exciting new possibilities—especially in understanding and potentially forecasting large earthquakes, including those created in controlled laboratory experiments that simulate fault behavior. As part of our ongoing research in the team, another key development has been an unsupervised AI-based method that analyzes patterns in earthquake catalog data. This approach does not rely on pre-labeled datasets, but instead detects subtle changes in seismic activity on its own (i.e. as an unsupervised machine learning method). It identifies distinct seismicity patterns, which we interpret as signals related to the changing stress along a fault—essentially, how close a fault is to slipping.
We first developed and tested this method using laboratory earthquake data, where we can precisely measure stress changes using a machine that applies controlled pressure to rock samples (Karimpouli et al., 2024). The AI method closely followed changes in fault stress throughout these experiments, suggesting it could be a powerful new tool for identifying when a fault is approaching failure. Encouragingly, we’re now beginning to apply this technique to real-world data.
Currently, the first applications of this method to two earthquakes (2023 MW 7.8 Kahramanmaras, Türkiye and 2009 MW 6.1 L’Aquila) are promising (Karimpouli et al., submitted). Preliminary results from two major earthquakes—the 2023 magnitude 7.8 Kahramanmaraş earthquake in Türkiye and the 2009 magnitude 6.1 L’Aquila earthquake in Italy—are promising (Karimpouli et al., submitted). These applications show potential for using AI to track fault stress in the field, which is an essential step toward real-world earthquake forecasting.
The 2023 Kahramanmaraş earthquake caused catastrophic loss of life and destruction. While attention understandably focused on this tragic event, our research continues to highlight another highly vulnerable region: the eastern Sea of Marmara, near the densely populated city of Istanbul. This area is considered one of the highest-risk seismic zones in Europe (Crowley et al., 2020). In the next phase of our project, we will focus on better understanding the conditions that might allow us to detect seismic or aseismic precursors—early warning signs—of major earthquakes in this region. We also aim to refine our AI methods to identify when a fault is transitioning from a stable to an unstable state, a critical clue that could indicate an impending rupture.Our ultimate goal is to move toward near-real-time testing, applying these techniques to ongoing seismic activity as it happens. While we're still far from being able to predict exactly when and where an earthquake will occur, our work is paving the way toward earlier detection and, hopefully, better preparation and resilience for communities at risk.
Artificial intelligence (AI) is rapidly transforming how we study earthquakes. One major breakthrough in recent years has been the use of AI techniques to process massive amounts of seismological data. This has opened up exciting new possibilities—especially in understanding and potentially forecasting large earthquakes, including those created in controlled laboratory experiments that simulate fault behavior. As part of our ongoing research in the team, another key development has been an unsupervised AI-based method that analyzes patterns in earthquake catalog data. This approach does not rely on pre-labeled datasets, but instead detects subtle changes in seismic activity on its own (i.e. as an unsupervised machine learning method). It identifies distinct seismicity patterns, which we interpret as signals related to the changing stress along a fault—essentially, how close a fault is to slipping.
We first developed and tested this method using laboratory earthquake data, where we can precisely measure stress changes using a machine that applies controlled pressure to rock samples (Karimpouli et al., 2024). The AI method closely followed changes in fault stress throughout these experiments, suggesting it could be a powerful new tool for identifying when a fault is approaching failure. Encouragingly, we’re now beginning to apply this technique to real-world data.
Currently, the first applications of this method to two earthquakes (2023 MW 7.8 Kahramanmaras, Türkiye and 2009 MW 6.1 L’Aquila) are promising (Karimpouli et al., submitted). Preliminary results from two major earthquakes—the 2023 magnitude 7.8 Kahramanmaraş earthquake in Türkiye and the 2009 magnitude 6.1 L’Aquila earthquake in Italy—are promising (Karimpouli et al., submitted). These applications show potential for using AI to track fault stress in the field, which is an essential step toward real-world earthquake forecasting.
The 2023 Kahramanmaraş earthquake caused catastrophic loss of life and destruction. While attention understandably focused on this tragic event, our research continues to highlight another highly vulnerable region: the eastern Sea of Marmara, near the densely populated city of Istanbul. This area is considered one of the highest-risk seismic zones in Europe (Crowley et al., 2020). In the next phase of our project, we will focus on better understanding the conditions that might allow us to detect seismic or aseismic precursors—early warning signs—of major earthquakes in this region. We also aim to refine our AI methods to identify when a fault is transitioning from a stable to an unstable state, a critical clue that could indicate an impending rupture.Our ultimate goal is to move toward near-real-time testing, applying these techniques to ongoing seismic activity as it happens. While we're still far from being able to predict exactly when and where an earthquake will occur, our work is paving the way toward earlier detection and, hopefully, better preparation and resilience for communities at risk.