Earthquake early warning (EEW) systems aim to forecast the shaking intensity rapidly after an earthquake occurs and send warnings to affected areas before the onset of strong shaking. The system relies on rapid and accurate estimation of earthquake source parameters. However, it is known that source estimation for large ruptures in real‐time is challenging, and it often leads to magnitude underestimation. In a previous study, we showed that machine learning, HR‐GNSS, and realistic rupture synthetics can be used to reliably predict earthquake magnitude. This model, called Machine‐Learning Assessed Rapid Geodetic Earthquake model (M‐LARGE), can rapidly forecast large earthquake magnitudes with an accuracy of 99%. Here, we expand M‐LARGE to predict centroid location and fault size, enabling the construction of the fault rupture extent for forecasting shaking intensity using existing ground motion models. We test our model in the Chilean Subduction Zone with thousands of simulated and five real large earthquakes. The result achieves an average warning time of 40.5 s for shaking intensity MMI4+, surpassing the 34 s obtained by a similar GNSS EEW model. Our approach addresses a critical gap in existing EEW systems for large earthquakes by demonstrating real‐time fault tracking feasibility without saturation issues. This capability leads to timely and accurate ground motion forecasts and can support other methods, enhancing the overall effectiveness of EEW systems. Additionally, the ability to predict source parameters for real Chilean earthquakes implies that synthetic data, governed by our understanding of earthquake scaling, is consistent with the actual rupture processes.
Although infrequent, large (Mw7.5+) earthquakes can be extremely damaging and occur on subduction and intraplate faults worldwide. Earthquake early warning (EEW) systems aim to provide advanced warning before strong shaking and tsunami onsets. These systems estimate earthquake magnitude using the early metrics of waveforms, relying on empirical scaling relationships of abundant past events. However, both the rarity and complexity of great events make it challenging to characterize them, and EEW algorithms often underpredict magnitude and the resulting hazards. Here, we propose a model, M‐LARGE, that leverages deep learning to characterize crustal deformation patterns of large earthquakes for a specific region in real‐time. We demonstrate the algorithm in the Chilean Subduction Zone by training it with more than six million different simulated rupture scenarios recorded on the Chilean GNSS network. M‐LARGE performs reliable magnitude estimation on the testing data set with an accuracy of 99%. Furthermore, the model successfully predicts the magnitude of five real Chilean earthquakes that occurred in the last 11 years. These events were damaging, large enough to be recorded by the modern high rate global navigation satellite system instrument, and provide valuable ground truth. M‐LARGE tracks the evolution of the source process and can make faster and more accurate magnitude estimation, significantly outperforming other similar EEW algorithms. This is the first demonstration of our approach. Future work toward generalization is outstanding and will include the addition of more training and testing data, interfacing with existing EEW methods, and applying the method to different tectonic settings to explore performance in these regions.
more » « less- NSF-PAR ID:
- 10372164
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Solid Earth
- Volume:
- 126
- Issue:
- 10
- ISSN:
- 2169-9313
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
Abstract -
Our research aims to improve the accuracy of Earthquake Early Warning (EEW) systems by means of machine learning. EEW systems are designed to detect and characterize medium and large earthquakes before their damaging effects reach a certain location. Traditional EEW methods based on seismometers fail to accurately identify large earthquakes due to their sensitivity to the ground motion velocity. The recently introduced high-precision GPS stations, on the other hand, are ineffective to identify medium earthquakes due to its propensity to produce noisy data. In addition, GPS stations and seismometers may be deployed in large numbers across different locations and may produce a significant volume of data consequently, affecting the response time and the robustness of EEW systems.In practice, EEW can be seen as a typical classification problem in the machine learning field: multi-sensor data are given in input, and earthquake severity is the classification result. In this paper, we introduce the Distributed Multi-Sensor Earthquake Early Warning (DMSEEW) system, a novel machine learning-based approach that combines data from both types of sensors (GPS stations and seismometers) to detect medium and large earthquakes. DMSEEW is based on a new stacking ensemble method which has been evaluated on a real-world dataset validated with geoscientists. The system builds on a geographically distributed infrastructure, ensuring an efficient computation in terms of response time and robustness to partial infrastructure failures. Our experiments show that DMSEEW is more accurate than the traditional seismometer-only approach and the combined-sensors (GPS and seismometers) approach that adopts the rule of relative strength.more » « less
-
Abstract We present a real-data test for offshore earthquake early warning (EEW) with distributed acoustic sensing (DAS) by transforming submarine fiber-optic cable into a dense seismic array. First, we constrain earthquake locations using the arrival-time information recorded by the DAS array. Second, with site effects along the cable calibrated using an independent earthquake, we estimate earthquake magnitudes directly from strain rate amplitudes by applying a scaling relation transferred from onshore DAS arrays. Our results indicate that using this single 50 km offshore DAS array can offer ∼3 s improvement in the alert time of EEW compared to onshore seismic stations. Furthermore, we simulate and demonstrate that multiple DAS arrays extending toward the trench placed along the coast can uniformly improve alert times along a subduction zone by more than 5 s.
-
Abstract Rapid earthquake magnitude estimation from real-time space-based geodetic observation streams provides an opportunity to mitigate the impact of large and potentially damaging earthquakes by issuing low-latency warnings prior to any significant and destructive shaking. Geodetic contributions to earthquake characterization and rapid magnitude estimation have evolved in the last 20 yr, from post-processed seismic waveforms to, more recently, improved capacity of regional geodetic networks enabled real-time Global Navigation Satellite System seismology using precise point positioning (PPP) displacement estimates. In addition, empirical scaling laws relating earthquake magnitude to peak ground displacement (PGD) at a given hypocentral distance have proven effective in rapid earthquake magnitude estimation, with an emphasis on performance in earthquakes larger than ∼Mw 6.5 in which near-field seismometers generally saturate. Although the primary geodetic contributions to date in earthquake early warning have focused on the use of 3D position estimates and displacements, concurrent efforts in time-differenced carrier phase (TDCP)-derived velocity estimates also have demonstrated that this methodology has utility, including similarly derived empirical scaling relationships. This study builds upon previous efforts in quantifying the ambient noise of three-component ground-displacement and ground-velocity estimates. We relate these noise thresholds to expected signals based on published scaling laws. Finally, we compare the performance of PPP-derived PGD to TDCP-derived peak ground velocity (PGV), given several rich event datasets. Our results indicate that TDCP-PGV is more likely than PPP-PGD to detect intermediate magnitude (∼Mw 5.0–6.0) earthquakes, albeit with greater magnitude estimate uncertainty and across smaller epicentral distances. We conclude that the computationally lightweight TDCP-derived PGV magnitude estimation is complementary to PPP-derived PGD magnitude estimates, which could be produced at the network edge at high rates and with increased sensitivity to ground motion than current PPP estimates.more » « less
-
The LIGO detectors are susceptible to high magnitude teleseismic events such as earthquakes, which can disrupt proper functioning, operation and significantly reduce their duty cycle. With advanced warning of impeding tremors, the impact can be suppressed in the isolation system and the down time can be reduced at the expense of increased instrumental noise. An earthquake early- warning system has been developed relying on near real-time earthquake alerts provided by the U.S. Geological Survey (USGS) and the National Oceanic and Atmospheric Administration (NOAA). The alerts can be used to estimate arrival times and ground velocities at the gravitational-wave detectors. We use machine learning algorithms to develop a prediction model and control strategy has to reduce LIGO downtime.more » « less