Global oceanic microseism sources as seen by seismic arrays and predicted by wave action models.

International audienceWe analyze global microseism excitation patterns between July 2000 and June 2001. Seismological observations are compared with modeling results to isolate robust activity features of relevant source processes. First, we use observations of microseism source locations estimated by Landès et al. (2010) based on array processing of ambient noise correlations. Second, we construct synthetic activity patterns by coupling sea state estimates derived from wave action models to the excitation theory for microseisms. The overall spatiotemporal evolution of both estimates is characterized by a seasonal character that is associated with strong activity during winter months. The distribution of landmass causes seasonal changes on the Northern Hemisphere (NH) to exceed the variability on the Southern Hemisphere (SH). Our systematic comparison of the two estimates reveals significant microseism excitation along coastlines and in the open ocean. Since coastal reflections are not accounted for in the modeling approach, the consistent mismatch between near-coastal observations and predictions suggests that relevant microseism energy arriving at the networks is generated in these areas. Simultaneously, systematic coincidence away from coastlines verifies the open ocean generation hypothesis. These conclusions are universal and robust with respect to the seismic network locations on the NH. The spatially homogeneous resolution of our synthetics provides a valuable resource for the assessment of the global microseism weather. Similar to previously identified hot spot areas in the North Atlantic, the modeled distributions hypothesize regions of strong localized activity on the SH, which are only partially confirmed by the analyzed data sets

Intensity-based sentiment analysis. The 2020 Aegean earthquake.

After an earthquake, it is necessary to understand its impact to provide relief and plan recovery. Social media (SM) and crowdsourcing platforms have recently become valuable tools for quickly collecting large amounts of first-hand data after a disaster. Earthquake-related studies propose using data mining and natural language processing (NLP) for damage detection and emergency response assessment. Using tex-data provided by the Euro-Mediterranean Seismological Centre (EMSC) collected through the LastQuake app for the Aegean Earthquake, we undertake a sentiment and topic analysis according to the intensities reported by their users in the Modified Mercalli Intensity (MMI) scale. There were collected 2,518 comments, reporting intensities from I to X being the most frequent intensity reported III. We use supervised classification according to a rule-set defined by authors and a two-tailed Pearson correlation to find statistical relationships between intensities reported in the MMI by LastQuake app users, polarities, and topics addressed in their comments. The most frequent word among comments was: “Felt.” The sentiment analysis (SA) indicates that the positive polarity prevails in the comments associated with the lowest intensities reported: (I-II), while the negative polarity in the comments is associated with higher intensities (III–VIII and X). The correlation analysis identifies a negative correlation between the increase in the reported MMI intensity and the comments with positive polarity. The most addressed topic in the comments from LastQuake app users was intensity, followed by seismic information, solidarity messages, emergency response, unrelated topics, building damages, tsunami effects, preparedness, and geotechnical effects. Intensities reported in the MMI are significantly and negatively correlated with the number of topics addressed in comments. Positive polarity decreases with the soar in the reported intensity in MMI demonstrated the validity of our first hypothesis, despite not finding a correlation with negative polarity. Instead, we could not prove that building damage, geotechnical effects, lifelines affected, and tsunami effects were topis addressed only in comments reporting the highest intensities in the MMI

Intensity-based sentiment and topic analysis. The case of the 2020 Aegean Earthquake

After an earthquake, it is necessary to understand its impact to provide relief and plan recovery. Social media (SM) and crowdsourcing platforms have recently become valuable tools for quickly collecting large amounts of first-hand data after a disaster. Earthquake related studies propose using data mining and natural language processing (NLP) for damage detection and emergency response assessment. Using tex-data provided by the Euro-Mediterranean Seismological Centre (EMSC) collected through the LastQuake app for the Aegean Earthquake, we undertake a sentiment and topic analysis according to the intensities reported by their users in the Modified Mercalli Intensity (MMI) scale. There were collected 2,518 comments, reporting intensities from I to X being the most frequent intensity reported III. We use supervised classification according to a rule-set defined by authors and a two-tailed Pearson correlation to find statistical relationships between intensities reported in the MMI by LastQuake app users, polarities, and topics addressed in their comments. The most frequent word among comments was: “Felt.” The sentiment analysis (SA) indicates that the positive polarity prevails in the comments associated with the lowest intensities reported: (I-II), while the negative polarity in the comments is associated with higher intensities (III–VIII and X). The correlation analysis identifies a negative correlation between the increase in the reported MMI intensity and the comments with positive polarity. The most addressed topic in the comments from LastQuake app users was intensity, followed by seismic information, solidarity messages, emergency response, unrelated topics, building damages, tsunami effects, preparedness, and geotechnical effects. Intensities reported in the MMI are significantly and negatively correlated with the number of topics addressed in comments. Positive polarity decreases with the soar in the reported intensity in MMI demonstrated the validity of our first hypothesis, despite not finding a correlation with negative polarity. Instead, we could not prove that building damage, geotechnical effects, lifelines affected, and tsunami effects were topis addressed only in comments reporting the highest intensities in the MMI

Origin of deep ocean microseisms by using teleseismic body waves.

International audienceRecent studies of oceanic microseisms have concentrate on fundamental-mode surface waves. Extraction of fundamental-mode Rayleigh and Love wave Green functions from station-station correlations of ambient seismic noise has recently been demonstrated to be a very powerful tool for imaging of the Earth's crust and uppermost mantle. In this study we concentrate on energetic arrivals in two frequency bands around the primary (14s) and the secondary (7s) microseismic peaks that appear at near-zero times in noise cross-correlations. Thanks to a polarisation analysis of data from the the ETSE network (Turkey), we identify this "near-zero time" signal as an upcoming P wave in the secondary microseismic frequency band (5-10s). In a second step, analysing noise cross-correlations from three different arrays ( in Yellowstone, in Turkey and in Kyrgyzstan), we determine the origin of these signals by means of beamforming analysis and its projection on the Earth. Our results show that, in the 0.1-0.3 Hz frequency band, the energetic "near-zero" time arrivals in seismic noise cross-correlations are mainly formed by teleseismic P, PP, and PKP waves. Generation of this ambient body waves in the secondary microseismic band presents a marked seasonal behaviour with sources located in southern and northern oceans during summer and winter, respectively. Moreover, body wave array analysis is accurate enough to confirm that significant amount of the microseism energy is generated far from the coast in deep oceans

Explaining global patterns of microbarom observations with wave action models

International audienceS U M M A R Y We present a methodology to model the spatio-temporal variations of microbarom detections at a global scale. Our model combines the source term resulting from the non-linear ocean-wave interaction and a simplified description of the long-range infrasound propagation through the stratospheric waveguide. We compare model predictions with observations at infrasound stations of the International Monitoring System between 2008 and 2009. Our results show a first-order consistency between the observed and modelled trends of microbarom backazimuth detections for most stations. Taking into account stratospheric wind effect on the infrasound propagation systematically improves the fit between the observations the model predictions. However, correctly predicting patterns of weekly variation of detections turns out to be more challenging and would require further improving the source and the propagation models. Short-term and regional quantitative comparisons could then be carried out based on the metrics developed in this study

Origin of deep ocean microseisms by using teleseismic body waves.

International audienceRecent studies of oceanic microseisms have concentrate on fundamental-mode surface waves. Extraction of fundamental-mode Rayleigh and Love wave Green functions from station-station correlations of ambient seismic noise has recently been demonstrated to be a very powerful tool for imaging of the Earth's crust and uppermost mantle. In this study we concentrate on energetic arrivals in two frequency bands around the primary (14s) and the secondary (7s) microseismic peaks that appear at near-zero times in noise cross-correlations. Thanks to a polarisation analysis of data from the the ETSE network (Turkey), we identify this "near-zero time" signal as an upcoming P wave in the secondary microseismic frequency band (5-10s). In a second step, analysing noise cross-correlations from three different arrays ( in Yellowstone, in Turkey and in Kyrgyzstan), we determine the origin of these signals by means of beamforming analysis and its projection on the Earth. Our results show that, in the 0.1-0.3 Hz frequency band, the energetic "near-zero" time arrivals in seismic noise cross-correlations are mainly formed by teleseismic P, PP, and PKP waves. Generation of this ambient body waves in the secondary microseismic band presents a marked seasonal behaviour with sources located in southern and northern oceans during summer and winter, respectively. Moreover, body wave array analysis is accurate enough to confirm that significant amount of the microseism energy is generated far from the coast in deep oceans