Analysis of micro-seismicity in sea ice with deep learning and Bayesian inference: application to high-resolution thickness monitoring

In the perspective of upcoming seasonally ice-free Arctic, understanding the dynamics of sea ice in the changing climate is a major challenge in oceanography and climatology. In particular, the new generation of sea ice models will require fine parameterization of sea ice thickness and rheology. With the rapidly evolving state of sea ice, achieving better accuracy, as well as finer temporal and spatial resolutions of its thickness will set new monitoring standards, with major scientific and geopolitical implications. Recent studies have shown the potential of passive seismology to monitor the thickness, density and elastic properties of sea ice with significantly reduced logistical constraints. For example, human intervention is no longer required, except to install and uninstall the geophones. Building up on this approach, we introduce a methodology for estimating sea ice thickness with high spatial and temporal resolutions from the analysis of icequakes waveforms. This methodology is based on a deep convolutional neural network for automatic clustering of the ambient seismicity recorded on sea ice, combined with a Bayesian inversion of the clustered waveforms. By applying this approach to seismic data recorded in March 2019 on fast ice in the Van Mijen fjord (Svalbard), we observe the spatial clustering of icequakes sources along the shore line of the fjord. The ice thickness is shown to follow an increasing trend that is consistent with the evolution of temperatures during the four weeks of data recording. Comparing the energy of the icequakes with that of calibrated seismic sources, we were able to derive a power law of icequake energy, and to relate this energy to the size of the cracks that generate the icequakes.</p

Seismic surface wave focal spot imaging : numerical resolution experiments

Numerical experiments of seismic wave propagation in a laterally homogeneous layered medium explore subsurface imaging at subwavelength distances for dense seismic arrays. We choose a time-reversal approach to simulate fundamental mode Rayleigh surface wavefields that are equivalent to the cross-correlation results of three-component ambient seismic field records. We demonstrate that the synthesized 2-D spatial autocorrelation fields in the time domain support local or so-called focal spot imaging. Systematic tests involving clean isotropic surface wavefields but also interfering body wave components and anisotropic incidence assess the accuracy of the phase velocity and dispersion estimates obtained from focal spot properties. The results suggest that data collected within half a wavelength around the origin is usually sufficient to constrain the used Bessel functions models. Generally, the cleaner the surface wavefield the smaller the fitting distances that can be used to accurately estimate the local Rayleigh wave speed. Using models based on isotropic surface wave propagation we find that phase velocity estimates from vertical-radial component data are less biased by P-wave energy compared to estimates obtained from vertical-vertical component data, that even strong anisotropic surface wave incidence yields phase velocity estimates with an accuracy of 1 per cent or better, and that dispersion can be studied in the presence of noise. Estimates using a model to resolve potential medium anisotropy are significantly biased by anisotropic surface wave incidence. The overall accurate results obtained from near-field measurements using isotropic medium assumptions imply that dense array seismic Rayleigh wave focal spot imaging can increase the depth sensitivity compared to ambient noise surface wave tomography. The analogy to elastography focal spot medical imaging implies that a high station density and clean surface wavefields support subwavelength resolution of lateral medium variations.Peer reviewe

Analyse et traitement de la matrice de covariance de données enregistrées sur des réseaux de stations sismiques

Green's function estimation from ambient seismic noise relies on the strong hypothesis that noise seismic sources are evenly distributed in the medium. Yet, observations of seismic data show that the noise sources do not provide such good conditions in real cases. Strongly coherent seismic sources or directional noise seismic sources may exist, and are harmful to the application of this in ambient seismic imaging. Several signal processing techniques are nowadays routinely applied to individual seismograms in frequency and temporal domain to improve the quality of reconstruction of the Green's function. The present work takes place in this context. Our approach is inspired by array-processing techniques, and is particularly focused on the covariance matrix of data recorded on seismic arrays. We show that the eigenstructure of this matrix provides crucial information about the seismic wavefield degree of coherence, as a function of time and frequency. This information is important because it allows to identify the time and frequency zones where the Green's function quality is ensured. An original array-processing technique is finally proposed, which consider the equalization of the covariance matrix eigenvalues, to attenuate the wavefield anisotropy such as earthquake-related signals or directive noise sources. We interpret this last method as an extension of the spectral whitening technique widely used in seismology to the spatial dimension encoded by the covariance matrix. We also invoke the analogy with the time-reversal technique that have recently led to a class of passive inverse filtering techniques.L’estimation des fonctions de Green par corrélation de bruit sismique ambiant repose entre autres sur l’hypothèse forte que les sources de bruit sont réparties de façon homogène dans le milieu. Or, les données sismiques réelles montrent que ces sources de bruit ne respectent pas ces conditions dans la plupart des cas. En particulier, les signaux très cohérents générés par des séismes tectoniques ou des sources de bruit directionnelles existent et sont néfastes à l’utilisation du bruit sismique en imagerie passive. Des techniques de traitement du signal temporelles et fréquentielles sont alors habituellement appliquées aux sismogrammes individuels afin d'améliorer la qualité de reconstruction des fonctions de Green. C'est dans la continuité des études visant à traiter les sismogrammes que ce travail s'inscrit. En s'inspirant de méthodes de traitement d'antenne, nous analysons la matrice de covariance de données enregistrées sur des réseaux de stations sismiques. À travers l'analyse des valeurs propres de cette matrice, nous montrons qu'il est possible de dégager des informations cruciales sur le champ d'onde mesuré, comme le degré de cohérence spatiale dans les dimensions temporelles et fréquentielles. Cette information est importante, car elle permet d'identifier les domaines temps-fréquence du champ d'onde propice à l'estimation des fonctions de Green. Finalement, une méthode d'égalisation des valeurs propres de la matrice de covariance visant à atténuer l'anisotropie des sources est proposée. Cette dernière méthode est interprétée comme une extension de la normalisation spectrale à la dimension spatiale, et également dans l'analogie du retournement temporel, au filtre inverse passif qui a vu le jour récemment

Analyse et traitement de la matrice de covariance de données enregistrées sur des réseaux de stations sismiques

Green's function estimation from ambient seismic noise relies on the strong hypothesis that noise seismic sources are evenly distributed in the medium. Yet, observations of seismic data show that the noise sources do not provide such good conditions in real cases. Strongly coherent seismic sources or directional noise seismic sources may exist, and are harmful to the application of this in ambient seismic imaging. Several signal processing techniques are nowadays routinely applied to individual seismograms in frequency and temporal domain to improve the quality of reconstruction of the Green's function. The present work takes place in this context. Our approach is inspired by array-processing techniques, and is particularly focused on the covariance matrix of data recorded on seismic arrays. We show that the eigenstructure of this matrix provides crucial information about the seismic wavefield degree of coherence, as a function of time and frequency. This information is important because it allows to identify the time and frequency zones where the Green's function quality is ensured. An original array-processing technique is finally proposed, which consider the equalization of the covariance matrix eigenvalues, to attenuate the wavefield anisotropy such as earthquake-related signals or directive noise sources. We interpret this last method as an extension of the spectral whitening technique widely used in seismology to the spatial dimension encoded by the covariance matrix. We also invoke the analogy with the time-reversal technique that have recently led to a class of passive inverse filtering techniques.L’estimation des fonctions de Green par corrélation de bruit sismique ambiant repose entre autres sur l’hypothèse forte que les sources de bruit sont réparties de façon homogène dans le milieu. Or, les données sismiques réelles montrent que ces sources de bruit ne respectent pas ces conditions dans la plupart des cas. En particulier, les signaux très cohérents générés par des séismes tectoniques ou des sources de bruit directionnelles existent et sont néfastes à l’utilisation du bruit sismique en imagerie passive. Des techniques de traitement du signal temporelles et fréquentielles sont alors habituellement appliquées aux sismogrammes individuels afin d'améliorer la qualité de reconstruction des fonctions de Green. C'est dans la continuité des études visant à traiter les sismogrammes que ce travail s'inscrit. En s'inspirant de méthodes de traitement d'antenne, nous analysons la matrice de covariance de données enregistrées sur des réseaux de stations sismiques. À travers l'analyse des valeurs propres de cette matrice, nous montrons qu'il est possible de dégager des informations cruciales sur le champ d'onde mesuré, comme le degré de cohérence spatiale dans les dimensions temporelles et fréquentielles. Cette information est importante, car elle permet d'identifier les domaines temps-fréquence du champ d'onde propice à l'estimation des fonctions de Green. Finalement, une méthode d'égalisation des valeurs propres de la matrice de covariance visant à atténuer l'anisotropie des sources est proposée. Cette dernière méthode est interprétée comme une extension de la normalisation spectrale à la dimension spatiale, et également dans l'analogie du retournement temporel, au filtre inverse passif qui a vu le jour récemment

Monitoring Sea Ice Thickness and Mechanical Properties with Seismic Noise and Deep Learning

In the context of global warming, monitoring the thickness and mechanical properties of sea ice is a major challenge in modern climatology. In particular, the heavy logistical constraints of polar environments, and the lack of accuracy of satellite remote monitoring methods, are obstacles to improving climate models. As a result, the decline of sea ice, which has been accelerating over the last four decades, is difficult to predict on short or longer time scales. For example, while only 10 years ago, the Arctic was expected to be ice-free in summer from the 2050s, the latest forecasts indicate that this could happen as early as the 2030s. Accurate and regular measurements of pack ice properties are crucial to better anticipate future changes. In this presentation, we introduce methods to demonstrate that it is possible to monitor sea ice passively, based on the ambient seismic field recorded continuously in situ. In particular, we introduce analysis methods based on: - seismic noise interferometry to extract the Green's function of guided waves in ice - deep learning algorithms to classify the recorded signals - guided wave dispersion for recovering the thickness, Young's modulus, Poisson's ratio, and density of the ice pack, via Bayesian inference Based on these analyses, we demonstrate that it is possible to monitor the temporal and spatial evolution of these parameters at the scale of a few kilometers, with a temporal resolution of a few hours

Suivi de l'épaisseur de glace avec la sismique passive et l'apprentissage automatique

International audienceComprendre la dynamique de la glace de mer dans le changement climatique est un défi majeur en océanographie et en climatologie, d'autant plus dans la perspective à venir d'un océan Arctique dénué de glace de façon saisonnière. Le projet Icewaveguide a établi la preuve de concept de méthodes de surveillance sismique passive pour estimer simultanément l'épaisseur, la rigidité et la densité de la banquise, soit par l'interférométrie du bruit sismique enregistré dans la glace soit en exploitant les signaux des tremblements de glace

AI‐Based Unmixing of Medium and Source Signatures From Seismograms: Ground Freezing Patterns

International audienceSeismograms always result from mixing many sources and medium changes that are complexto disentangle, witnessing many physical phenomena within the Earth. With artificial intelligence (AI), we isolate the signature of surface freezing and thawing in continuous seismograms recorded in a noisy urban environment. We perform a hierarchical clustering of the seismograms and identify a pattern that correlates with ground frost periods. We further investigate the fingerprint of this pattern and use it to track the continuous medium change with high accuracy and resolution in time. Our method isolates the effect of the ground frost and describes how it affects the horizontal wavefield. Our findings show how AI-based strategies can help to identify and understand hidden patterns within seismic data caused either by medium or source changes

Suivi de l'épaisseur de glace avec la sismique passive et l'apprentissage automatique

International audienceComprendre la dynamique de la glace de mer dans le changement climatique est un défi majeur en océanographie et en climatologie, d'autant plus dans la perspective à venir d'un océan Arctique dénué de glace de façon saisonnière. Le projet Icewaveguide a établi la preuve de concept de méthodes de surveillance sismique passive pour estimer simultanément l'épaisseur, la rigidité et la densité de la banquise, soit par l'interférométrie du bruit sismique enregistré dans la glace soit en exploitant les signaux des tremblements de glace

AI‐Based Unmixing of Medium and Source Signatures From Seismograms: Ground Freezing Patterns

International audienceSeismograms always result from mixing many sources and medium changes that are complexto disentangle, witnessing many physical phenomena within the Earth. With artificial intelligence (AI), we isolate the signature of surface freezing and thawing in continuous seismograms recorded in a noisy urban environment. We perform a hierarchical clustering of the seismograms and identify a pattern that correlates with ground frost periods. We further investigate the fingerprint of this pattern and use it to track the continuous medium change with high accuracy and resolution in time. Our method isolates the effect of the ground frost and describes how it affects the horizontal wavefield. Our findings show how AI-based strategies can help to identify and understand hidden patterns within seismic data caused either by medium or source changes

Suivi de l'épaisseur de glace avec la sismique passive et l'apprentissage automatique

International audienceComprendre la dynamique de la glace de mer dans le changement climatique est un défi majeur en océanographie et en climatologie, d'autant plus dans la perspective à venir d'un océan Arctique dénué de glace de façon saisonnière. Le projet Icewaveguide a établi la preuve de concept de méthodes de surveillance sismique passive pour estimer simultanément l'épaisseur, la rigidité et la densité de la banquise, soit par l'interférométrie du bruit sismique enregistré dans la glace soit en exploitant les signaux des tremblements de glace