Seismic Hazard Visualization from Big Simulation Data: Construction of a Parallel Distributed Processing System for Ground Motion Simulation Data

2016 ◽  
Vol 11 (2) ◽  
pp. 265-271 ◽  
Author(s):  
Takahiro Maeda ◽  
◽  
Hiroyuki Fujiwara
Keyword(s):  
Numerical Simulation ◽  
Data Mining ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Large Scale ◽  
Distributed Processing ◽  
Processing System ◽  
Great Earthquake ◽  
Simulation Data ◽  
Parallel Distributed Processing

We have developed a data mining system of parallel distributed processing system which is applicable to the large-scale and high-resolution numerical simulation of ground motion by transforming into ground motion indices and their statistical values, and then visualize their values for the seismic hazard information. In this system, seismic waveforms at many locations calculated for many possible earthquake scenarios can be used as input data. The system utilizes Hadoop and it calculates the ground motion indices, such as PGV, and statistical values, such as maximum, minimum, average, and standard deviation of PGV, by parallel distributed processing with MapReduce. The computation results are being an output as GIS (Geographic Information System) data file for visualization. And this GIS data is made available via the Web Map Service (WMS). In this study, we perform two benchmark tests by applying three-component synthetic waveforms at about 80,000 locations for 10 possible scenarios of a great earthquake in Nankai Trough to our system. One is the test for PGV calculation processing. Another one is the test for PGV data mining processing. A maximum of 10 parallel processing are tested for both cases. We find that our system can hold the performance even when the total tasks is larger than 10. This system can enable us to effectively study and widely distribute to the communities for disaster mitigation since it is built with data mining and visualization for hazard information by handling a large number of data from a large-scale numerical simulation.

A Novel Bio Inspired Algorithm Based on Echolocation Mechanism of Bats for Seismic States Prediction

2017 ◽  
Vol 8 (3) ◽  
pp. 1-18 ◽  
Author(s):  
Mohamed Elhadi Rahmani ◽  
Abdelmalek Amine ◽  
Reda Mohamed Hamou
Keyword(s):  
Data Mining ◽  
Seismic Hazard ◽  
Ground Motion ◽  
Geographic Area ◽  
Seismic Hazards ◽  
Data Discovery ◽  
Distance Calculation ◽  
Hard Problems ◽  
Recorded Data ◽  
Np Problems

Bio-inspired algorithms are sort of implementation of natural solutions to solve hard problems – so called NP problems. A seismic hazard is the probability that an earthquake will occur in a given geographic area, within a given window of time, and with ground motion intensity exceeding a given threshold. Seismic hazards prediction is one of the fields where data mining plays an important role. This paper presents a new bio-inspired algorithm motivated by the echolocation behavior of bats for seismic hazard states prediction in coal mines based on previously recorded data. It is a distance calculation based approach, Results were very satisfactory in a manner that encourage us to continue working on this approach. The implementation of the algorithm touches three fields of studies, data discovery or so called data mining, bio inspired techniques, and seismic hazards predictions.

Large-Scale Data Mining and Distributed Processing in Big Data Internet

2014 ◽  
Vol 989-994 ◽  
pp. 4594-4597
Author(s):  
Chun Zhi Xing
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Decision Tree ◽  
Large Scale ◽  
Distributed Processing ◽  
Processing Method ◽  
Decision Tree Algorithm ◽  
Data Query ◽  
Large Scale Data ◽  
Scale Data

With the development of Internet, various Internet-based large-scale data are facing increasing competition. With the hope of satisfying the need of data query, it is necessary to use data mining and distributed processing. As a consequence, this paper proposes a large-scale data mining and distributed processing method based on decision tree algorithm.

Large-scale structures in stratified turbulent Couette flow and optimal disturbances

2020 ◽  
Vol 35 (1) ◽  
pp. 37-53
Author(s):  
Grigory V. Zasko ◽  
Andrey V. Glazunov ◽  
Evgeny V. Mortikov ◽  
Yuri M. Nechepurenko
Keyword(s):  
Numerical Simulation ◽  
Couette Flow ◽  
Large Scale ◽  
Spatial Scales ◽  
Static Stability ◽  
Simulation Data ◽  
Direct Numerical Simulation Data ◽  
Large Scale Structures ◽  
Scale Structures ◽  
Optimal Disturbances

AbstractDirect numerical simulation data of a stratified turbulent Couette flow contains two types of organized structures: rolls arising at neutral and close to neutral stratifications, and layered structures which manifest themselves as static stability increases. It is shown that both types of structures have spatial scales and forms that coincide with the scales and forms of the optimal disturbances of the simplified linear model of the Couette flow with the same Richardson numbers.

Mapping tectonic strain in the central Alpine-Himalayan Belt with Sentinel-1 InSAR and GNSS observations

2021 ◽  
Author(s):  
Chris Rollins ◽  
Tim Wright ◽  
Jonathan Weiss ◽  
Andrew Hooper ◽  
Richard Walters ◽  
...  
Keyword(s):  
High Resolution ◽  
Seismic Hazard ◽  
Strain Rate ◽  
Crustal Deformation ◽  
Large Scale ◽  
Surface Deformation ◽  
Processing System ◽  
Temporal Sampling ◽  
Himalayan Belt ◽  
Automated Processing

<p>Geodetic measurements of crustal deformation provide crucial constraints on a region’s tectonics, geodynamics and seismic hazard. However, such geodetic constraints have traditionally been hampered by poor spatial and/or temporal sampling, which can result in ambiguities about how the lithosphere accommodates strain in space and time, and therefore where and how often earthquakes might occur. High-resolution surface deformation maps address this limitation by imaging (rather than presuming or modelling) where and how deformation takes place. These maps are now within reach for the Alpine-Himalayan Belt thanks to the COMET-LiCSAR InSAR processing system, which performs large-scale automated processing and time-series analysis of Sentinel-1 InSAR data. Expanding from our work focused on Anatolia, we are combining LiCSAR products with GNSS data to generate high-resolution maps of tectonic strain rates across the central Alpine-Himalayan Belt. Then, assuming that the buildup rate of seismic moment (deficit) from this geodetically-derived strain is balanced over the long term by the rate of moment release in earthquakes, we pair these strain rate maps with seismic catalogs to estimate the recurrence intervals of large, moderate and small earthquakes throughout the region. We also use arguments from dislocation modeling to identify two key signatures of a locked fault in a strain rate field, allowing us to convert the strain maps to “effective fault maps” and assess the contribution of individual fault systems to crustal deformation and seismic hazard. Finally, we address how to expand these approaches to the Alpine-Himalaya Belt as a whole.</p>

scholarly journals Ground motion simulations for finite-fault earthquake scenarios on the Húsavík-Flatey Fault, North Iceland

2021 ◽  
Author(s):  
Claudia Abril ◽  
Martin Mai ◽  
Benedikt Halldórsson ◽  
Bo Li ◽  
Alice Gabriel ◽  
...  
Keyword(s):  
Seismic Hazard ◽  
Ground Motion ◽  
Large Scale ◽  
Velocity Model ◽  
Fault System ◽  
Fault Rupture ◽  
Ground Shaking ◽  
Earthquake Scenarios ◽  
3D Velocity Model ◽  
Finite Fault

<p>The Tjörnes Fracture Zone (TFZ) in North Iceland is the largest and most complex zone of transform faulting in Iceland, formed due to a ridge-jump between two spreading centers of the Mid-Atlantic Ridge, the Northern Volcanic Zone and Kolbeinsey Ridge in North Iceland. Strong earthquakes (Ms>6) have repeatedly occurred in the TFZ and affected the North Icelandic population. In particular the large historical earthquakes of 1755 (Ms 7.0) and 1872 (doublet, Ms 6.5), have been associated with the Húsavı́k-Flatey Fault (HFF), which is the largest linear strike-slip transform fault in the TFZ, and in Iceland. We simulate fault rupture on the HFF and the corresponding near-fault ground motion for several potential earthquake scenarios, including scenario events that replicate the large 1755 and 1872 events. Such simulations are relevant for the town of Húsavı́k in particular, as it is located on top of the HFF and is therefore subject to the highest seismic hazard in the country. Due to the mostly offshore location of the HFF, its precise geometry has only recently been studied in more detail. We compile updated seismological and geophysical information in the area, such as a recently derived three-dimensional velocity model for P and S waves. Seismicity relocations using this velocity model, together with bathymetric and geodetic data, provide detailed information to constrain the fault geometry. In addition, we use this 3D velocity model to simulate seismic wave propagation. For this purpose, we generate a variety of kinematic earthquake-rupture scenarios, and apply a 3D finite-difference method (SORD) to propagate the radiated seismic waves through Earth structure. Slip distributions for the different scenarios are computed using a von Karman autocorrelation function whose parameters are calibrated with slip distributions available for a few recent Icelandic earthquakes. Simulated scenarios provide synthetic ground motion and time histories and estimates of peak ground motion parameters (PGA and PGV) at low frequencies (<2 Hz) for Húsavík and other main towns in North Iceland along with maps of ground shaking for the entire region [130 km x 110 km]. Ground motion estimates are compared with those provided by empirical ground motion models calibrated to Icelandic earthquakes and dynamic fault-rupture simulations for the HFF. Directivity effects towards or away from the coastal areas are analyzed to estimate the expected range of shaking. Thick sedimentary deposits (up to ∼4 km thick) located offshore on top of the HFF (reported by seismic, gravity anomaly and tomographic studies) may affect the effective depth of the fault's top boundary and the surface rupture potential. The results of this study showcase the extent of expected ground motions from significant and likely earthquake scenarios on the HFF. Finite fault earthquake simulations complement the currently available information on seismic hazard for North Iceland, and are a first step towards a systematic and large-scale earthquake scenario database on the HFF, and for the entire fault system of the TFZ, that will enable comprehensive and physics-based hazard assessment in the region.</p>

scholarly journals Toward a comprehensive understanding of EEG and its analyses

2020 ◽  
Author(s):  
A. F. Rocha
Keyword(s):  
Large Scale ◽  
Temporal Discrimination ◽  
Distributed Processing ◽  
Processing System ◽  
Point Of View ◽  
Human Cognition ◽  
Eeg Analysis ◽  
Cortical Column ◽  
Scale Free ◽  
Different Types

SummaryBackgroundEEG is the oldest tool for studying human cognitive function, but it is blamed to be useless because its poor spatial resolution despite it excellent temporal discrimination. Such comments arise from a reductionist point of view about the cerebral function. However, if the brain is assumed to be a distributed processing system recruiting different types of cells widely distribute over the cortex, then EEG temporal resolution and the many different tools available for its analysis, turn it the tool of choice to investigate human cognition.ProposalTo better understand the different types of information encoded in the recorded cortical electrical activity, a clear model of the cortical structure and function of the different cortical column layers is presented and discussed. Taking this model into consideration, different available techniques for EEG analysis are discussed, under the assumption that tool combination is a necessity to provide a full comprehension of dynamics of the cortical activity supporting human cognition.MethodologyThe present approach combines many of the existing methods of analysis to extract better and richer information from the EEG, and proposes additional analysis to better characterize many of the EEG components identified by these different methods.AnalysisData on language understanding published elsewhere are used to illustrate how to use this combined and complex EEG analysis to disclose important details of cognitive cerebral dynamics, which reveal that cognitive neural circuits are scale free networks supporting entrainment of a large number of cortical column assemblies distributed all over the cortex.ConclusionsReasoning is then assumed to result from a well orchestrated large scale entrainment

scholarly journals Performance analysis of large-scale parallel-distributed processing with backup tasks for cloud computing

2014 ◽  
Vol 10 (1) ◽  
pp. 113-129 ◽  
Author(s):  
Tsuguhito Hirai ◽  
◽  
Hiroyuki Masuyama ◽  
Shoji Kasahara ◽  
Yutaka Takahashi ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Performance Analysis ◽  
Large Scale ◽  
Distributed Processing ◽  
Parallel Distributed Processing
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