Slow slip detection with deep learning in multi-station raw geodetic time series validated against tremors in Cascadia

Slow slip events (SSEs) originate from a slow slippage on faults that lasts from a few days to years. A systematic and complete mapping of SSEs is key to characterizing the slip spectrum and understanding its link with coeval seismological signals. Yet, SSE catalogues are sparse and usually remain limited to the largest events, because the deformation transients are often concealed in the noise of the geodetic data. Here we present the first multi-station deep learning SSE detector applied blindly to multiple raw geodetic time series. Its power lies in an ultra-realistic synthetic training set, and in the combination of convolutional and attention-based neural networks. Applied to real data in Cascadia over the period 2007-2022, it detects 78 SSEs, that compare well to existing independent benchmarks: 87.5% of previously catalogued SSEs are retrieved, each detection falling within a peak of tremor activity. Our method also provides useful proxies on the SSE duration and may help illuminate relationships between tremor chatter and the nucleation of the slow rupture. We find an average day-long time lag between the slow deformation and the tremor chatter both at a global- and local-temporal scale, suggesting that slow slip may drive the rupture of nearby small asperities

Can Avalanche Deposits be Effectively Detected by Deep Learning on Sentinel-1 Satellite SAR Images?

International audienceAchieving reliable observations of avalanche debris is crucial for many applications including avalanche forecasting. The ability to continuously monitor the avalanche activity, in space and time, would provide indicators on the potential instability of the snowpack and would allow a better characterization of avalanche risk periods and zones. In this work, we use Sentinel-1 SAR (synthetic aperture radar) data and an independent in-situ avalanche inventory (ground truth) to automatically detect avalanche debris in the French Alps during the remarkable winter season 2017-18. Convolutional neural networks are applied on SAR image patches to locate avalanche debris signatures. We are able to successfully locate new avalanche deposits with as much as 77% confidence on the most susceptible mountain zone (compared to 53% with a baseline method). One of the challenges of this study is to make an efficient use of remote sensing measurements on a complex terrain. It explores the following questions: to what extent can deep learning methods improve the detection of avalanche deposits and help us to derive relevant avalanche activity statistics at different scales (in time and space) that could be useful for a large number of users (researchers, forecasters, government operators)

Evaluation of Personalised Canine Electromechanical Models

International audienceCardiac modelling aims at understanding cardiac diseases and predicting cardiac responses to therapies. By generating the elec-trical propagation, the contraction and the mechanical response, we are able to simulate cardiac motion from non-invasive imaging techniques. Four healthy canine clinical data (left ventricles) were provided by the STACOM'2014 challenge. Our study is based on Bestel-Clement-Sorine mechanical modelling, while the electrophysiological phenomena is driven by an Eikonal model. Our model has been calibrated by a quantitative sensitivity study as well as a personalized automatic calibration. Results and comparison with clinical measures are shown in terms of left ventricular volume, flow, pressure and ejection fraction

Sparse Bayesian Non-linear Regression for Multiple Onsets Estimation in Non-invasive Cardiac Electrophysiology

Best paper award FIMH 2017, category: ElectrophysiologyInternational audienceIn the scope of modelling cardiac electrophysiology (EP) for understanding pathologies and predicting the response to therapies, patient-specific model parameters need to be estimated. Although per-sonalisation from non-invasive data (body surface potential mapping, BSPM) has been investigated on simple cases mostly with a single pacing site, there is a need for a method able to handle more complex situations such as sinus rhythm with several onsets. In the scope of estimating cardiac activation maps, we propose a sparse Bayesian kernel-based regression (relevance vector machine, RVM) from a large patient-specific simulated database. RVM additionally provides a confidence on the result and an automatic selection of relevant features. With the use of specific BSPM descriptors and a reduced space for the myocardial geometry, we detail this framework on a real case of simultaneous biventricular pacing where both onsets were precisely localised. The obtained results (mean distance to the two ground truth pacing leads is 18.4mm) demonstrate the usefulness of this non-linear approach

Non-invasive personalisation of cardiac electrophysiological models from surface electrograms

L'objectif de cette thèse est d'utiliser des données non-invasives (électrocardiogrammes, ECG) pour personnaliser les principaux paramètres d'un modèle électrophysiologique (EP) cardiaque pour prédire la réponse à la thérapie de resynchronisation cardiaque. La TRC est un traitement utilisé en routine clinique pour certaines insuffisances cardiaques mais reste inefficace chez 30% des patients traités impliquant une morbidité et un coût importants. Une compréhension précise de la fonction cardiaque propre au patient peut aider à prédire la réponse à la thérapie. Les méthodes actuelles se basent sur un examen invasif au moyen d’un cathéter qui peut être dangereux pour le patient. Nous avons développé une personnalisation non-invasive du modèle EP fondée sur une base de données simulée et un apprentissage automatique. Nous avons estimé l'emplacement de l'activation initiale et un paramètre de conduction global. Nous avons étendu cette approche à plusieurs activations initiales et aux ischémies au moyen d'une régression bayésienne parcimonieuse. De plus, nous avons développé une anatomie de référence afin d'effectuer une régression hors ligne unique et nous avons prédit la réponse à différentes stimulations à partir du modèle personnalisé. Dans une seconde partie, nous avons étudié l'adaptation aux données ECG à 12 dérivations et l'intégration dans un modèle électromécanique à usage clinique. L'évaluation de notre travail a été réalisée sur un ensemble de données important (25 patients, 150 cycles cardiaques). En plus d'avoir des résultats comparables avec les dernières méthodes d'imagerie ECG, les signaux ECG prédits présentent une bonne corrélation avec les signaux réels.The objective of this thesis is to use non-invasive data (body surface potential mapping, BSPM) to personalise the main parameters of a cardiac electrophysiological (EP) model for predicting the response to cardiac resynchronization therapy (CRT). CRT is a clinically proven treatment option for some heart failures. However, these therapies are ineffective in 30% of the treated patients and involve significant morbidity and substantial cost. The precise understanding of the patient-specific cardiac function can help to predict the response to therapy. Until now, such methods required to measure intra-cardiac electrical potentials through an invasive endovascular procedure which can be at risk for the patient. We developed a non-invasive EP model personalisation based on a patient-specific simulated database and machine learning regressions. First, we estimated the onset activation location and a global conduction parameter. We extended this approach to multiple onsets and to ischemic patients by means of a sparse Bayesian regression. Moreover, we developed a reference ventricle-torso anatomy in order to perform an common offline regression and we predicted the response to different pacing conditions from the personalised model. In a second part, we studied the adaptation of the proposed method to the input of 12-lead electrocardiograms (ECG) and the integration in an electro-mechanical model for a clinical use. The evaluation of our work was performed on an important dataset (more than 25 patients and 150 cardiac cycles). Besides having comparable results with state-of-the-art ECG imaging methods, the predicted BSPMs show good correlation coefficients with the real BSPMs

Personnalisation Non-invasive de Modèles Electrophysiologiques Cardiaques à Partir d’Electrogrammes Surfaciques

The objective of this thesis is to use non-invasive data (body surface potentialmapping, BSPM) to personalise the main parameters of a cardiac electrophysiological(EP) model for predicting the response to cardiac resynchronization therapy(CRT). CRT is a clinically proven treatment option for some heart failures.However, these therapies are ineffective in 30% of the treated patients and involvesignificant morbidity and substantial cost. The precise understanding of the patientspecificcardiac function can help to predict the response to therapy. Until now, suchmethods required to measure intra-cardiac electrical potentials through an invasiveendovascular procedure which can be at risk for the patient.We developed a non-invasive EP model personalisation based on a patientspecificsimulated database and machine learning regressions. First, we estimatedthe onset activation location and a global conduction parameter. We extended thisapproach to multiple onsets and to ischemic patients by means of a sparse Bayesianregression. Moreover, we developed a reference ventricle-torso anatomy in order toperform an common offline regression and we predicted the response to differentpacing conditions from the personalised model. In a second part, we studied theadaptation of the proposed method to the input of 12-lead electrocardiograms (ECG)and the integration in an electro-mechanical model for a clinical use. The evaluationof our work was performed on an important dataset (more than 25 patients and150 cardiac cycles). Besides having comparable results with state-of-the-art ECGimaging methods, the predicted BSPMs show good correlation coefficients with thereal BSPMsL’objectif de cette thèse est d’utiliser des données non-invasives (electrocardiogrammes,ECG) pour personnaliser les principaux paramètres d’un modèle électrophysiologique(EP) cardiaque pour prédire la réponse à la thérapie de resynchronisationcardiaque. La TRC est un traitement utilisé en routine clinique pourcertaines insuffisances cardiaques mais reste inefficace chez 30% des patients traitésimpliquant une morbidité et un coût importants. Une compréhension précise de lafonction cardiaque propre au patient peut aider à prédire la réponse à la thérapie.Les méthodes actuelles se basent sur un examen invasif au moyen d’un cathéter quipeut être dangereux pour le patient.Nous avons développé une personnalisation non-invasive du modèle EP fondéesur une base de données simulée et un apprentissage automatique. Nous avons estimél’emplacement de l’activation initiale et un paramètre de conduction global.Nous avons étendu cette approche à plusieurs activations initiales et aux ischémiesau moyen d’une régression bayésienne parcimonieuse. De plus, nous avons développéune anatomie de référence afin d’effectuer une régression hors ligne unique et nousavons prédit la réponse à différentes stimulations à partir du modèle personnalisé.Dans une seconde partie, nous avons étudié l’adaptation aux données ECG à12 dérivations et l’intégration dans un modèle électromécanique à usage clinique.L’évaluation de notre travail a été réalisée sur un ensemble de données important(25 patients, 150 cycles cardiaques). En plus d’avoir des résultats comparables avecles dernières méthodes d’imagerie ECG, les signaux ECG prédits présentent unebonne corrélation avec les signaux réels

Interpreting convolutional neural network decision for earthquake detection with feature map visualization, backward optimization and layer-wise relevance propagation methods

International audienceSUMMARY In the recent years, the seismological community has adopted deep learning (DL) models for many diverse tasks such as discrimination and classification of seismic events, identification of P- and S-phase wave arrivals or earthquake early warning systems. Numerous models recently developed are showing high accuracy values, and it has been attested for several tasks that DL models perform better than the classical seismological state-of-art models. However, their performances strongly depend on the DL architecture, the training hyperparameters, and the training data sets. Moreover, due to their complex nature, we are unable to understand how the model is learning and therefore how it is making a prediction. Thus, DL models are usually referred to as a ‘black-box’. In this study, we propose to apply three complementary techniques to address the interpretability of a convolutional neural network (CNN) model for the earthquake detection. The implemented techniques are: feature map visualization, backward optimization and layer-wise relevance propagation. Since our model reaches a good accuracy performance (97%), we can suppose that the CNN detector model extracts relevant characteristics from the data, however a question remains: can we identify these characteristics? The proposed techniques help to answer the following questions: How is an earthquake processed by a CNN model? What is the optimal earthquake signal according to a CNN? Which parts of the earthquake signal are more relevant for the model to correctly classify an earthquake sample? The answer to these questions help understand why the model works and where it might fail, and whether the model is designed well for the predefined task. The CNN used in this study had been trained for single-station detection, where an input sample is a 25 s three-component waveform. The model outputs a binary target: earthquake (positive) or noise (negative) class. The training database contains a balanced number of samples from both classes. Our results shows that the CNN model correctly learned to recognize where is the earthquake within the sample window, even though the position of the earthquake in the window is not explicitly given during the training. Moreover, we give insights on how a neural network builds its decision process: while some aspects can be linked to clear physical characteristics, such as the frequency content and the P and S waves, we also see how different a DL detection is compared to a visual expertise or an STA/LTA detection. On top of improving our model designs, we also think that understanding how such models work, how they perceive an earthquake, can be useful for the comprehension of events that are not fully understood yet such as tremors or low frequency earthquakes

Designing Convolutional Neural Network Pipeline for Near‐Fault Earthquake Catalog Extension Using Single‐Station Waveforms

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Testing machine learning models for seismic damage prediction at a regional scale using building-damage dataset compiled after the 2015 Gorkha Nepal earthquake

Assessing post-seismic damage on an urban/regional scale remains relatively difficult owing to the significant amount of time and resources required to acquire information and conduct a building-by-building seismic damage assessment. However, the application of new methods based on artificial intelligence, combined with the increasingly systematic availability of field surveys of post-seismic damage, has provided new perspectives for urban/regional seismic damage assessment. This study analyzes the effectiveness and relevance of a number of machine learning techniques for analyzing spatially distributed seismic damage after an earthquake at the regional scale. The basic structural parameters of a portfolio of buildings and the post-earthquake damage surveyed after the Nepal 2015 earthquake are analyzed and combined with macro-seismic intensity values provided by the United States Geological Survey ShakeMap tool. Among the methods considered, the random forest regression model provides the best damage predictions for specified ground motion intensity values and structural parameters. For traffic-light-based damage classification (three classes: green-, amber-, and red-tagged buildings based on post-earthquake damage grade), a mean accuracy of 0.68 is obtained. This study shows that restricting learning to basic features of buildings (i.e. number of stories, height, plinth area, and age), which could be readily available from authoritative databases (e.g. national census) or field-surveyed databases, yields a reliable prediction of building damage (4 features/3 damage grade accuracy: 0.64)