We introduce the Ensemble Earthquake Early Warning System (E3WS), a set of Machine Learning algorithms designed to detect, locate and estimate the magnitude of an earthquake using 3 seconds of P waves recorded by a single station. The system is made of 6 Ensemble Machine Learning algorithms trained on attributes computed from ground acceleration time series in the temporal, spectral and cepstral domains. The training set comprises datasets from Peru, Chile, Japan, and the STEAD global dataset. E3WS consists of three sequential stages: detection, P-phase picking and source characterization. The latter involves magnitude, epicentral distance, depth and back-azimuth estimation. E3WS achieves an overall success rate in the discrimination between earthquakes and noise of 99.9%, with no false positive (noise mis-classified as earthquakes) and very few false negatives (earthquakes mis-classified as noise). All false negatives correspond to M ≤ 4.3 earthquakes, which are unlikely to cause any damage. For P-phase picking, the Mean Absolute Error is 0.14 s, small enough for earthquake early warning purposes. For source characterization, the E3WS estimates are virtually unbiased, have better accuracy for magnitude estimation than existing single-station algorithms, and slightly better accuracy for earthquake location. By updating estimates every second, the approach gives time-dependent magnitude estimates that follow the earthquake source time function. E3WS gives faster estimates than present alert systems relying on multiple stations, providing additional valuable seconds for potential protective actions.

State-of-the-art earthquake early warning systems use the early records of seismic waves to estimate the magnitude and location of the seismic source before the shaking and the tsunami strike. Because of the inherent properties of early seismic records, those systems systematically underestimate the magnitude of large events, which results in catastrophic underestimation of the subsequent tsunamis. Prompt elastogravity signals (PEGS) are low-amplitude, light-speed signals emitted by earthquakes, which are highly sensitive to both their magnitude and focal mechanism. Detected before traditional seismic waves, PEGS have the potential to produce unsaturated magnitude estimates faster than state-of-the-art systems. Accurate instantaneous tracking of large earthquake magnitude using PEGS has been proven possible through the use of a Convolutional Neural Network (CNN). However, the CNN architecture is sub-optimal as it does not allow to capture the geometry of the problem. To address this limitation, we design PEGSGraph, a novel deep learning model relying on a Graph Neural Network (GNN) architecture. PEGSGraph accurately estimates the magnitude of synthetic earthquakes down to Mw 7.6-7.7 and determines their focal mechanisms (thrust, strike-slip or normal faulting) within 70 seconds of the event’s onset, offering crucial information for predicting potential tsunami wave amplitudes. Our comparative analysis on Alaska and Western Canada data shows that the GNN outperforms the CNN, especially on test samples with low signal-to-noise ratios, providing more reliable rapid magnitude estimates and enhancing tsunami warning reliability.

A document by Gabriela Arias Mendez. Click on the document to view its contents.

The North Anatolian Fault (NAF) has produced numerous major earthquakes. After decades of quiescence, the Mw 6.8 Elazig earthquake (January 24, 2020) has recently reminded us that the East Anatolian Fault (EAF) is also capable of producing significant earthquakes. To better estimate the seismic hazard associated with these two faults, we jointly invert Interferometric Synthetic Aperture Radar (InSAR) and GPS data to image the spatial distribution of interseismic coupling along the eastern part of both the North and East Anatolian Faults. We perform the inversion in a Bayesian framework, enabling to estimate uncertainties on both long-term relative plate motion and coupling. We find that coupling is high and deep (0-20 km) on the NAF and heterogeneous and superficial (0-5 km) on the EAF. Our model predicts that the Elazig earthquake released between 200 and 250 years of accumulated moment, suggesting a bi-centennial recurrence time.

Until the Mw 6.8 Elazığ earthquake ruptured the central portion of the East Anatolian Fault (EAF) on January 24, 2020, the region had only experienced moderate magnitude (Mw 6.2) earthquakes over the last century. Here, we use geodetic data to constrain a model of subsurface fault slip. We adopt an unregularized Bayesian sampling approach relying solely on physically justifiable prior information and account for uncertainties in both the assumed elastic structure and fault geometry. The rupture of the Elazığ earthquake was bilateral, with two primary disconnected regions of slip. This rupture pattern may be controlled by structural complexity. Both the Elazığ and 2010 Mw 6.1 Kovancılar events ruptured portions of the central EAF that are believed to be coupled during interseismic periods, and the Palu segment is the last portion of the EAF showing a large deficit of fault slip which has not yet ruptured in the last 145 years.