Synthetic analysis of seismic back-projection using 3D dynamic rupture simulations of the 2018, Palu Sulawesi earthquake

2020 ◽  
Author(s):  
Thomas Ulrich ◽  
Bo Li ◽  
Alice-Agnes Gabriel
Keyword(s):  
Seismic Energy ◽  
Fault System ◽  
Beam Power ◽  
Small Scale ◽  
Back Projection ◽  
Secondary Phases ◽  
Dynamic Rupture ◽  
Rupture Dynamics ◽  
Projection Techniques ◽  
Spatio Temporal

<p>Back-projection uses the time-reversal property of the seismic wavefield recorded at large aperture dense seismic arrays. Seismic energy radiation is imaged by applying array beam-forming techniques. The spatio-temporal rupture complexity of large earthquakes can be imaged simply and rapidly with a limited number of assumptions, which makes back-projection techniques an important tool of modern seismology. However, back-projection analyses exhibit frequency and array dependency (e.g. Wu et al., AGU19). In addition, the method relies on station network geometry and data quality and can suffer from imaging artifacts (e.g., Fan and Shearer, 2017) and back-projection results may not be consistently interpreted.</p><p>The Mw7.5 Palu, Sulawesi earthquake that occurred on September 28, 2018, ruptured a 180 km long section of the Palu-Koro fault. The earthquake triggered a localized but powerful tsunami within Palu Bay, which swept away houses and buildings. The supershear earthquake and unexpected tsunami led to more than 4000 fatalities. Ulrich et al. (2019) propose a physics-based, coupled earthquake-tsunami scenario of the event, tightly constrained by observations. The model matches key observed earthquake characteristics, including moment magnitude, rupture duration, fault plane solution, teleseismic waveforms, and inferred horizontal ground displacements. It suggests that time-dependent earthquake-induced uplift and subsidence could have sourced the observed tsunami within Palu Bay.</p><p>Back-projection has been used to track the rupture propagation of the Palu earthquake. Bao et al. (2019) image unilateral rupture traveling at a supershear rupture speed. Their results show array dependent ruptures, from a rather relatively linear rupture using the Australian array, to a spatio-temporally more scattered image using the seismic array in Turkey. In addition, they do not resolve any portion of the rupture as traveling at sub-Rayleigh speeds, while Wei et al. (AGU19) suggest a gradually accelerating rupture.</p><p>In this study, we build upon the dynamic rupture model of Ulrich et al. (2019) to investigate the reliability of standard back-projection techniques using a realistic and perfectly known earthquake model. In particular, we investigate whether or not rupture transfers across the segmented fault system, and the effect of specific geometric features of the fault system, such as fault bends, on rupture dynamics, leave a clear signal on the inferred beam power. Also, we investigate the effect of secondary phases, such as reflections from the free-surface or from fault segment boundaries, naturally captured by dynamic rupture modeling. In addition, we study the effect of small-scale source heterogeneities on the back-projection results by including different levels of fault roughness in the dynamic rupture simulations. Finally, we investigate the array dependence of back-projection results.</p><p>Overall, this study should help to better understand which features of rupture dynamics back-projection can capture. Our results are a first step towards fundamental analysis to better understand which features can be captured by back-projection and to provide guidelines for back-projection interpretation.</p>

Exploring the dynamics of the Dead-Sea Transform fault using data-integrated numerical models

2021 ◽  
Author(s):  
Thomas Ulrich ◽  
Alice-Agnes Gabriel ◽  
Yann Klinger ◽  
Jean-Paul Ampuero ◽  
Percy Galvez ◽  
...  
Keyword(s):  
Dead Sea ◽  
Strike Slip ◽  
Fault System ◽  
Gulf Of Aqaba ◽  
Back Projection ◽  
Earthquake Cycle ◽  
Transform Fault ◽  
Dynamic Rupture ◽  
Dead Sea Transform ◽  
The Dead

<p>The Dead-Sea Transform fault system, a 1200 km-long strike-slip fault forming the tectonic boundary between the African Plate and the Arabian Plate, poses a major seismic hazard to the eastern Mediterranean region. The Gulf of Aqaba, which terminates the Dead Sea fault system to the South, results from a succession of pull-apart basins along the Dead-Sea Transform fault system. The complexity of the fault system in the Gulf has been recently evidenced by Ribot et al. (2020), who compiled a detailed map of its fault traces, based on a new multibeam bathymetric survey of the Gulf. Part of the Gulf of Aqaba was ruptured by an Mw 7.3 earthquake in 1995. Teleseismic data analysis suggests that it may have been a multi-segment rupture (Klinger et al., 1999). This event occurred offshore, in a poorly instrumented region, and therefore the exact sequence of faults that ruptured is not precisely known. The detailed fault mapping of Ribot et al. (2020) offers a fresh view of this earthquake. In particular, it identifies many oblique faults between the major strike-slip faults, which may have linked these segments.</p><p>Relying on this new dataset, on a new back-projection study, and on 3D dynamic rupture modeling with SeisSol (https://github.com/SeisSol/SeisSol), we revisit the 1995 Aqaba earthquake. Using back projection, we identify 2 strong radiators, which we associate with 2 step-overs. Using 3D dynamic rupture modeling, we propose scenarios of the 1995 earthquake, compatible with the various dataset available. Our modeling allows constraining the regional state of stress in the region, acknowledging transtension, offers constraints on the nucleation location and confirms the role of the oblique faults in propagating the rupture to the North. It offers new constraints on the regional seismic hazard, in particular on the expected maximum moment magnitude.</p><p>Finally, we explore the dynamics of the Gulf of Aqaba fault system using earthquake cycle modeling. For that purpose, we rely on QDYN (https://github.com/ydluo/qdyn), a boundary element software, which simulates earthquake cycles under the quasi-dynamic approximation on faults governed by rate-and-state friction and embedded in elastic media. We inform our parameterization of the earthquake cycle modeling using the previously described datasets and modeling results. Recently Galvez et al. (2020) demonstrated the capability of the method to model the dynamics of complex fault system in 3D. Here new code developments are required to adapt the method to the Gulf of Aqaba fault system, e.g. to allow accounting for normal stress changes and for variations in the fault rake.</p><p>Overall, we aim to better understand how large earthquakes may nucleate, propagate, and interact across a complex transform fault network. Our findings, e.g. on fault segmentation or the conditions that promote larger earthquakes, will have important implications for other large strike-slip fault systems worldwide.</p>

scholarly journals Effective friction law for small-scale fault heterogeneity in 3D dynamic rupture

2011 ◽  
Vol 116 (B10) ◽  
Author(s):  
S. Latour ◽  
M. Campillo ◽  
C. Voisin ◽  
I. R. Ionescu ◽  
J. Schmedes ◽  
...  
Keyword(s):  
Small Scale ◽  
Dynamic Rupture ◽  
Friction Law ◽  
Fault Heterogeneity ◽  
Effective Friction

scholarly journals Small-scale spatio-temporal characteristics of accumulation rates in western Dronning Maud Land, Antarctica

2008 ◽  
Vol 54 (185) ◽  
pp. 315-323 ◽  
Author(s):  
Helgard Anschütz ◽  
Daniel Steinhage ◽  
Olaf Eisen ◽  
Hans Oerter ◽  
Martin Horwath ◽  
...  
Keyword(s):  
Accumulation Rate ◽  
Temporal Variations ◽  
Spatial Variations ◽  
Small Scale ◽  
Accumulation Rates ◽  
Order Of Magnitude ◽  
Dronning Maud Land ◽  
Spatio Temporal ◽  
Ground Penetrating ◽  
Firn Core

AbstractSpatio-temporal variations of the recently determined accumulation rate are investigated using ground-penetrating radar (GPR) measurements and firn-core studies. The study area is located on Ritscherflya in western Dronning Maud Land, Antarctica, at an elevation range 1400–1560 m. Accumulation rates are derived from internal reflection horizons (IRHs), tracked with GPR, which are connected to a dated firn core. GPR-derived internal layer depths show small relief along a 22 km profile on an ice flowline. Average accumulation rates are about 190 kg m−2 a−1 (1980–2005) with spatial variability (1σ) of 5% along the GPR profile. The interannual variability obtained from four dated firn cores is one order of magnitude higher, showing 1σ standard deviations around 30%. Mean temporal variations of GPRderived accumulation rates are of the same magnitude or even higher than spatial variations. Temporal differences between 1980–90 and 1990–2005, obtained from two dated IRHs along the GPR profile, indicate temporally non-stationary processes, linked to spatial variations. Comparison with similarly obtained accumulation data from another coastal area in central Dronning Maud Land confirms this observation. Our results contribute to understanding spatio-temporal variations of the accumulation processes, necessary for the validation of satellite data (e.g. altimetry studies and gravity missions such as Gravity Recovery and Climate Experiment (GRACE)).

High-frequency ground-motion variability for rough-fault ruptures  

2021 ◽  
Author(s):  
Jagdish Chandra Vyas ◽  
Martin Galis ◽  
Paul Martin Mai
Keyword(s):  
Ground Motion ◽  
Large Scale ◽  
Earthquake Ground Motion ◽  
Small Scale ◽  
Dynamic Rupture ◽  
Roughness Effects ◽  
Ground Shaking ◽  
Fault Surface ◽  
Fault Roughness ◽  
Ground Motion Variability

<p>Geological observations show variations in fault-surface topography not only at large scale (segmentation) but also at small scale (roughness). These geometrical complexities strongly affect the stress distribution and frictional strength of the fault, and therefore control the earthquake rupture process and resulting ground-shaking. Previous studies examined fault-segmentation effects on ground-shaking, but our understanding of fault-roughness effects on seismic wavefield radiation and earthquake ground-motion is still limited.  </p><p>In this study we examine the effects of fault roughness on ground-shaking variability as a function of distance based on 3D dynamic rupture simulations. We consider linear slip-weakening friction, variations of fault-roughness parametrizations, and alternative nucleation positions (unilateral and bilateral ruptures). We use generalized finite difference method to compute synthetic waveforms (max. resolved frequency 5.75 Hz) at numerous surface sites  to carry out statistical analysis.  </p><p>Our simulations reveal that ground-motion variability from unilateral ruptures is almost independent of  distance from the fault, with comparable or higher values than estimates from ground-motion prediction equations (e.g., Boore and Atkinson, 2008; Campbell and Bozornia, 2008). However, ground-motion variability from bilateral ruptures decreases with increasing distance, in contrast to previous studies (e.g., Imtiaz et. al., 2015) who observe an increasing trend with distance. Ground-shaking variability from unilateral ruptures is higher than for bilateral ruptures, a feature due to intricate seismic radiation patterns related to fault roughness and hypocenter location. Moreover, ground-shaking variability for rougher faults is lower than for smoother faults. As fault roughness increases the difference in ground-shaking variabilities between unilateral and bilateral ruptures increases. In summary, our simulations help develop a fundamental understanding of ground-motion variability at high frequencies (~ 6 Hz) due small-scale geometrical fault-surface variations.</p>

Kalmag: a high spatio temporal model of the Geomagnetic field

2021 ◽  
Author(s):  
Julien Baerenzung ◽  
Matthias Holschneider
Keyword(s):  
Geomagnetic Field ◽  
Learning Algorithm ◽  
Dynamical Evolution ◽  
Small Scale ◽  
Large Set ◽  
The Core ◽  
Resolution Model ◽  
Auto Regressive ◽  
Spatio Temporal ◽  
Marine Vessels

<p>We present a new high resolution model of the Geomagnetic field spanning the last 121 years. The model derives from a large set of data taken by low orbiting satellites, ground based observatories, marine vessels, airplane and during land surveys. It is obtained by combining a Kalman filter to a smoothing algorithm. Seven different magnetic sources are taken into account. Three of them are of internal origin. These are the core, the lithospheric  and the induced / residual ionospheric fields. The other four sources are of external origin. They are composed by a close, a remote and a fluctuating magnetospheric fields as well as a source associated with field aligned currents. The dynamical evolution of each source is prescribed by an auto regressive process of either first or second order, except for the lithospheric field which is assumed to be static. The parameters of the processes were estimated through a machine learning algorithm with a sample of data taken by the low orbiting satellites of the CHAMP and Swarm missions. In this presentation we will mostly focus on the rapid variations of the core field, and the small scale lithospheric field.  We will also discuss the nature of model uncertainties and the limitiations they imply.</p>

scholarly journals Spatio-temporal patterns and movement analysis of pigs from smallholder farms and implications for African swine fever spread, Limpopo province, South Africa

2015 ◽  
Vol 82 (1) ◽  
Author(s):  
Folorunso O. Fasina ◽  
Japhta M. Mokoele ◽  
B. Tom Spencer ◽  
Leo A.M.L. Van Leengoed ◽  
Yvette Bevis ◽  
...  
Keyword(s):  
South Africa ◽  
Smallholder Farmers ◽  
Animal Health ◽  
African Swine Fever ◽  
Limpopo Province ◽  
Small Scale ◽  
Network Analyses ◽  
Critical Control Points ◽  
Spatio Temporal ◽  
Disease Dispersal

Infectious and zoonotic disease outbreaks have been linked to increasing volumes of legal and illegal trade. Spatio-temporal and trade network analyses have been used to evaluate the risks associated with these challenges elsewhere, but few details are available for the pig sector in South Africa. Regarding pig diseases, Limpopo province is important as the greater part of the province falls within the African swine fever control area. Emerging small-scale pig farmers in Limpopo perceived pig production as an important means of improving their livelihood and an alternative investment. They engage in trading and marketing their products with a potential risk to animal health, because the preferred markets often facilitate potential longdistance spread and disease dispersal over broad geographic areas. In this study, we explored the interconnectedness of smallholder pig farmers in Limpopo, determined the weaknesses and critical control points, and projected interventions that policy makers can implement to reduce the risks to pig health. The geo-coordinates of surveyed farms were used to draw maps, links and networks. Predictive risks to pigs were determined through the analyses of trade networks, and the relationship to previous outbreaks of African swine fever was postulated. Auction points were identified as high-risk areas for the spread of animal diseases. Veterinary authorities should prioritise focused surveillance and diagnostic efforts in Limpopo. Early disease detection and prompt eradication should be targeted and messages promoting enhanced biosecurity to smallholder farmers are advocated. The system may also benefit from the restructuring of marketing and auction networks. Since geographic factors and networks can rapidly facilitate pig disease dispersal over large areas, a multi-disciplinary approach to understanding the complexities that exist around the animal disease epidemiology becomes mandatory.

scholarly journals Build, measure, understand: Pupils contributing to meteorological measurement campaigns.

2021 ◽  
Author(s):  
Martin Göber ◽  
Henning Rust ◽  
Thomas Kox ◽  
Bianca Wentzel ◽  
Christopher Böttcher ◽  
...  
Keyword(s):  
Weather Forecasting ◽  
Cold Pool ◽  
Small Scale ◽  
Atmospheric Measurements ◽  
High Resolution Data ◽  
Representative Study ◽  
Spatio Temporal ◽  
Observed Damage ◽  
Weather Risks ◽  
Resolution Data

<p>Voluntary weather measurements have a long tradition and the opportunities have recently expanded with that the advent of the Internet of Things. Atmospheric measurements are prototypical examples for the maker community and popular means to strengthen interest in STEM subjects. In two projects in Germany  (in Brandenburg, within the FESSTVaL (Field Experiment on submesoscale spatio-temporal variability in Lindenberg) measurement campaign initiated by the Hans-Ertel-Center for Weather Research, and in Bavaria, in the KARE-Citizen Science  project), we use a weather station to be assembled by pupils as a participatory vehicle to increase interest in and understanding of weather and climate, as well as of weather forecasting, and to generate high resolution data for research.</p><p>The devices measure e.g. temperature, humidity, radiation, pressure and precipitation in the students' immediate environment. They can be placed in almost any location, since they operate independent of W-LAN and external power supply. The data is visualized directly via a web app. Students report weather impacts, such as observed damage or their own exposure to weather. Due to the pandemic, only a few dozens pupils were able to participate and building their devices had to be done with digital guidance and video support. Further online materials on understanding weather forecasting and its uncertainty were provided.</p><p>Understanding of weather risks was surveyed before and after participation to detect any changes. Students were asked questions about thunderstorm, rain and heat events and climatic changes since 1880. The results show a good understanding of weather risks compared to a population of all ages representative study. In online workshops pupils together with the scientists scetched and discussed the influence of the placement of their stations on their measurements. Interesting meteorological phenomena were discovered in the dataset, e.g. a cold pool that can form during a thunderstorm and trigger new ones. Thus, our network of higher spatial and temporal resolution data collected by the pupils has the potential to study these small-scale phenomena in more detail than with professional networks of about 25 km spacing.</p>

scholarly journals Rupture-dependent breakdown energy in fault models with thermo-hydro-mechanical processes

2020 ◽  
Author(s):  
Valère Lambert ◽  
Nadia Lapusta
Keyword(s):  
Material Property ◽  
Numerical Models ◽  
Slip Rate ◽  
Shear Cracks ◽  
Dynamic Rupture ◽  
Rupture Dynamics ◽  
Stress Changes ◽  
History Of ◽  
Thermal Pressurization ◽  
Pass Through

Abstract. Substantial insight into earthquake source processes has resulted from considering frictional ruptures analogous to cohesive-zone shear cracks from fracture mechanics. This analogy holds for slip-weakening representations of fault friction that encapsulate the resistance to rupture propagation in the form of breakdown energy, analogous to fracture energy, prescribed in advance as if it were a material property of the fault interface. Here, we use numerical models of earthquake sequences with enhanced weakening due to thermal pressurization of pore fluids to show how accounting for thermo-hydro-mechanical processes during dynamic shear ruptures makes breakdown energy rupture-dependent. We find that local breakdown energy is neither a constant material property nor uniquely defined by the amount of slip attained during rupture, but depends on how that slip is achieved through the history of slip rate and dynamic stress changes during the rupture process. As a consequence, the frictional breakdown energy of the same location along the fault can vary significantly in different earthquake ruptures that pass through. These results suggest the need for re-examining the assumption of pre-determined frictional breakdown energy common in dynamic rupture modeling and for better understanding of the factors that control rupture dynamics in the presence of thermo-hydro-mechanical processes.

scholarly journals Coastal Change Patterns from Time Series Clustering of Permanent Laser Scan Data

2020 ◽  
Author(s):  
Mieke Kuschnerus ◽  
Roderik Lindenbergh ◽  
Sander Vos
Keyword(s):  
Time Series ◽  
Laser Scanning ◽  
Clustering Algorithm ◽  
Coastal Areas ◽  
Small Scale ◽  
Temporal Data ◽  
Agglomerative Clustering ◽  
Data Set ◽  
Deformation Processes ◽  
Spatio Temporal

Abstract. Sandy coasts are constantly changing environments governed by complex interacting processes. Permanent laser scanning is a promising technique to monitor such coastal areas and support analysis of geomorphological deformation processes. This novel technique delivers 3D representations of a part of the coast at hourly temporal and centimetre spatial resolution and allows to observe small scale changes in elevation over extended periods of time. These observations have the potential to improve understanding and modelling of coastal deformation processes. However, to be of use to coastal researchers and coastal management, an efficient way to find and extract deformation processes from the large spatio-temporal data set is needed. In order to allow data mining in an automated way, we extract time series in elevation or range and use unsupervised learning algorithms to derive a partitioning of the observed area according to change patterns. We compare three well known clustering algorithms, k-means, agglomerative clustering and DBSCAN, and identify areas that undergo similar evolution during one month. We test if they fulfil our criteria for a suitable clustering algorithm on our exemplary data set. The three clustering methods are applied to time series of 30 epochs (during one month) extracted from a data set of daily scans covering a part of the coast at Kijkduin, the Netherlands. A small section of the beach, where a pile of sand was accumulated by a bulldozer is used to evaluate the performance of the algorithms against a ground truth. The k-means algorithm and agglomerative clustering deliver similar clusters, and both allow to identify a fixed number of dominant deformation processes in sandy coastal areas, such as sand accumulation by a bulldozer or erosion in the intertidal area. The DBSCAN algorithm finds clusters for only about 44 % of the area and turns out to be more suitable for the detection of outliers, caused for example by temporary objects on the beach. Our study provides a methodology to efficiently mine a spatio-temporal data set for predominant deformation patterns with the associated regions, where they occur.

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