How an earthquake rupture propagates strongly influences potentially destructive ground shaking. Complex ruptures often involve slip along multiple faults, masking information on the frictional behaviour of fault zones. Geometrically smooth ocean transform fault plate boundaries offer a favourable environment to study fault dynamics, because strain is accommodated along a single, wide fault zone that offsets homogeneous geology. Here we present an analysis of the 2016 M7.1 earthquake on the Romanche fracture zone in the equatorial Atlantic, using data from both nearby seafloor seismometers and global seismic networks. We show that this rupture had two phases: (1) upward and eastward propagation towards a weaker region where the transform fault intersects the mid-ocean ridge, then (2) unusual back-propagation westwards at super-shear speed toward the centre of the fault. We suggest that deep rupture into weak fault segments facilitated greater seismic slip on shallow locked zones. This highlights that even earthquakes along a single distinct fault zone can be highly dynamic. Observations of back-propagating ruptures are sparse, and the possibility of reverse propagation is largely absent in rupture simulations and unaccounted for in hazard assessments.
<p>We report on a multi-technique analysis using publicly available data for investigating the huge, accidental explosion that struck the city of Beirut, Lebanon, on August 4, 2020. Its devastating shock wave led to thousands of injured with more than two hundred fatalities and caused immense damage to buildings and infrastructure. Our combined analysis of seismological, hydroacoustic, infrasonic and radar remote sensing data allows us to characterize the source as well as to estimate the explosive yield. The latter ranges between 0.8 and 1.1 kt TNT (kilotons of trinitrotoluene) equivalent and is plausible given the reported 2.75 kt of ammonium nitrate as explosive source. Data from the International Monitoring System of the CTBTO are used for infrasound array detections. Seismometer data from GEOFON and IRIS complement the source characterization based on seismic and acoustic signal recordings, which propagated in solid earth, water and air. Copernicus Sentinel data serve for radar remote sensing and damage estimation. As there are strict limitations for an on-site analysis of this catastrophic explosion, our presented approach based on openly accessible data from global station networks and satellite missions is of high scientific and social relevance that furthermore is transferable to other explosions.</p>
The archive consists of six surface displacement maps of the 1995 Gulf of Aqaba earthquake. Five of these maps have been derived from differential radar interferometry (using phase information) and, a one map (suffix *off) was derived from azimuth pixelm offset tracking (using amplitude information). The data is provided in native KITE format. KITE is a python package for postprocessing of Interferometric synthetic aperture radar (InSAR) derived deformation maps and can be found here: www.pyrocko.org/kite
The archive consists of six surface displacement maps of the 1995 Gulf of Aqaba earthquake. Five of these maps have been derived from differential radar interferometry (using phase information) and, a one map (suffix *off) was derived from azimuth pixelm offset tracking (using amplitude information). The data is provided in native KITE format. KITE is a python package for postprocessing of Interferometric synthetic aperture radar (InSAR) derived deformation maps and can be found here: www.pyrocko.org/kite
<p>We present a modular open-source software framework - Kite (http://pyrocko.org) - for rapid post-processing of spaceborne InSAR-derived surface displacement maps. The software enables swift parametrisation, post-processing and sub-sampling of the displacement measurements that are compatible with common InSAR processors (e.g. SNAP, GAMMA, ISCE, etc.) and online processing centers delivering unrwapped InSAR data products, such as NASA ARIA or LiCSAR. The post-processing capabilities include removal of first-order atmospheric phase delays through elevation correlation estimations and regional atmospheric phase screen (APS) estimations based on atmospheric models (GACOS), masking of displacement data, adaptive data sub-sampling using quadtree decomposition and data error covariance estimation.</p><p>Kite datasets integrate into forward modelling and optimisation frameworks Grond (Heiman et al., 2019) and BEAT (Vasyura-Bathke et al., 2019), both software packages aim to ease and streamline the joint optimisation of earthquake parameters from InSAR and GPS data together with seismological waveforms. These data combinations will improve the estimation of earthquake rupture parameters. Establishing this data processing software framework we want to bridge the gap between InSAR processing software and seismological modelling frameworks, to contribute to a timely and better understanding of earthquake kinematics. This approach paves the way to automated inversion of earthquake models incorporating space-borne InSAR data.</p><p>Under development is the processing of InSAR displacement time series data to link simultaneous modelling of co- and post-seismic transient deformation processes from InSAR observations to physical earthquake cycle models.</p><p>We demonstrate the framework&#8217;s capabilities with an analysis of the 2019 Ridgecrest earthquakes from InSAR surface displacements (provided by NASA ARIA) combined with GNSS displacements using the Bayesian bootstrapping strategy from the Grond inverse modelling tool.</p>
Abstract We report on a multi-technique analysis using publicly available data for investigating the huge, accidental explosion that struck the city of Beirut, Lebanon, on August 4, 2020. Its devastating shock wave led to thousands of injured with more than two hundred fatalities and caused immense damage to buildings and infrastructure. Our combined analysis of seismological, hydroacoustic, infrasonic and radar remote sensing data allows us to characterize the source as well as to estimate the explosive yield. The latter is determined within 0.13 to 2 kt TNT (kilotons of trinitrotoluene). This range is plausible given the reported 2.75 kt of ammonium nitrate as explosive source. As there are strict limitations for an on-site analysis of this catastrophic explosion, our presented approach based on data from open accessible global station networks and satellite missions is of high scientific and social relevance that furthermore is transferable to other explosions.
Earth and Space Science Open Archive This work has been accepted for publication in Journal of Geophysical Research - Solid Earth. Version of RecordESSOAr is a venue for early communication or feedback before peer review. Data may be preliminary. Learn more about preprints. preprintOpen AccessYou are viewing the latest version by default [v1]Estimation of seismic moment tensors using variational inference machine learningAuthorsAndreasSteinbergiDHannesVasyura-BathkeiDPeterGaebleriDMatthiasOhrnbergeriDLarsCerannaiDSee all authors Andreas SteinbergiDCorresponding Author• Submitting AuthorFederal Institute for Geosciences and Natural ResourcesiDhttps://orcid.org/0000-0001-7328-636Xview email addressThe email was not providedcopy email addressHannes Vasyura-BathkeiDUniversity of PotsdamiDhttps://orcid.org/0000-0002-3826-0663view email addressThe email was not providedcopy email addressPeter GaebleriDBGR HannoveriDhttps://orcid.org/0000-0001-7331-3399view email addressThe email was not providedcopy email addressMatthias OhrnbergeriDUniversity of PotsdamiDhttps://orcid.org/0000-0003-1068-0401view email addressThe email was not providedcopy email addressLars CerannaiDBGRiDhttps://orcid.org/0000-0002-1159-935Xview email addressThe email was not providedcopy email address
This are the Grond reports of the seismic moment tensor inversion done for the manuscript submitted to GJI titled: "The January 2022 Hunga Volcano explosive eruption from the multi-technological perspective of CTBT monitoring" You can view the summary figures of the inversions in the subfolders for each event manually if you wish. However to view the reports interactively you need to have the pyrocko and grond softwares installed. See here for installation instruction for pyrocko: https://pyrocko.org/ and here for grond https://pyrocko.org/grond/docs/current/ After correct installation you can view the reports in any browser by executing the command "grond report --so" in the folder which contains the unpacked "report" folder.
With the upcoming availability of the next generation of high quality orbiting hyperspectral sensors, a major step toward improved regional soil mapping and monitoring and delivery of quantitative soil maps is expected. This study focuses on the determination of the prediction accuracy of spectral models for the mapping of common soil properties based on upcoming EnMAP (Environmental Mapping and Analysis Program) satellite data using semi-operational soil models. Iron oxide (Fed), clay, and soil organic carbon (SOC) content are predicted in test areas in Spain and Luxembourg based on a semi-automatic Partial-Least-Square (PLS) regression approach using airborne hyperspectral, simulated EnMAP, and soil chemical datasets. A variance contribution analysis, accounting for errors in the dependent variables, is used alongside classical error measurements. Results show that EnMAP allows predicting iron oxide, clay, and SOC with an R2 between 0.53 and 0.67 compared to Hyperspectral Mapper (HyMap)/Airborne Hyperspectral System (AHS) imagery with an R2 between 0.64 and 0.74. Although a slight decrease in soil prediction accuracy is observed at the spaceborne scale compared to the airborne scale, the decrease in accuracy is still reasonable. Furthermore, spatial distribution is coherent between the HyMap/AHS mapping and simulated EnMAP mapping as shown with a spatial structure analysis with a systematically lower semivariance at the EnMAP scale.
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