Clustering and visualization of earthquake data in a grid environment

Yuen, D. A.; Kadlec, Benjamin J.; Bollig, E. F.; Dzwinel, Witold; Garbow, Zachary A.; Silva, Cesar R. S. da

doi:10.1007/s10069-005-0023-z

Cited by 21 publications

(9 citation statements)

References 21 publications

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“…The magnitude of completeness (MC) of the catalogue for earthquake dataset is determined first before implements clustering process. It uses the Gutenberg-Richter frequency size distribution [12]. Magnitude of completeness also called as threshold or cut-off magnitude.…”

Section: A Data Acquisitionmentioning

confidence: 99%

“…Good clustering results will produce an accurate representation of the behaviour of a system [8]. This concept is to extract the most similar (or dissimilar) separated sets of objects according to a given similarity (dissimilarity) measure [12]. Thus, this algorithm is the basis for extracting the dataset to obtain useful information for finding pattern recognition in the data [8].…”

Section: B Cluster Analysismentioning

confidence: 99%

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Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia

Shodiq

Kusuma

Rifqi

et al. 2018

JOIV : Int. J. Inform. Visualization

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Abstract-A model of artificial neural networks (ANNs) is presented in this paper to predict aftershock during the next five days after an earthquake occurrence in selected cluster of Indonesia with magnitude equal or larger than given threshold. The data were obtained from Indonesian Agency for Meteorological, Climatological and Geophysics (BMKG) and United States Geological Survey's (USGS). Six cluster was an optimal number of cluster base-on cluster analysis implementing Valley Tracing and Hill Climbing algorithm, while Hierarchical K-means was applied for datasets clustering. A quality evaluation was then conducted to measure the proposed model performance for two different thresholds. The experimental result shows that the model gave better performance for predicting an aftershock occurrence that equal or larger than 6 Richter's scale magnitudes.

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Section: A Data Acquisitionmentioning

confidence: 99%

Section: B Cluster Analysismentioning

confidence: 99%

Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia

Shodiq

Kusuma

Rifqi

et al. 2018

JOIV : Int. J. Inform. Visualization

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“…Yuen et al [27] presented a webbased system for visualizing seismic data in a grid environment. A conventional approach to analyze the field-collected data sets was to study earthquake induced effects as time histories by plotting and comparing discrete waveforms.…”

Section: Visualization Of Field-measured Seismic Datamentioning

confidence: 99%

“…A major energy release was defined as an event that emerged at the surface in an area of nearly 10 km in radius with accelerations of 350 gal and larger. They pointed out that major energy releases happened at 11,14,15,16,18,19,20,21,23,27,29,30,32,34,43, and 50 seconds from origin time. Some of the major energy releases were from adjacent active faults triggered by the bursts of extensive energy releases of the mainshock.…”

Section: Visualization Of Field-measured Seismic Datamentioning

confidence: 99%

Visualizing field-measured seismic data

Hsieh

Chen

2010

2010 IEEE Pacific Visualization Symposium (PacificVis)

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This paper presents visualization of field-measured, time-varying multidimensional earthquake accelerograph readings. Direct volume rendering is used to depict the space-time relationships of seismic readings collected from sensor stations in an intuitive way such that the progress of seismic wave propagation of an earthquake event can be directly observed. The resulting visualization reveals the sequence of seismic wave initiation, propagation, attenuation over time, and energy releasing events. We provide a case study on the magnitude scale M w 7.6 Chi-Chi earthquake in Taiwan, which is the most thoroughly recorded earthquake event ever in the history. More than 400 stations recorded this event, and the readings from this event increased global strong-motion records five folds. Each station measured east-west, north-south, and vertical component of acceleration for approximately 90 seconds. The sensor network released the initial raw data within minutes after the ChiChi mainshock. It is essential to have a visualization system for fast data exploring and analyzing, offering crucial visual analytical information for scientists to make quick judgments. Raw data requires preprocessing before it can be rendered. We generated a sequence of ground-motion wave-field maps of 350 × 200 regular grid covers the entire Taiwan island from the sensor network readings. The result is a total of 1000 ground-motion wave-field maps with 0.1 second interval, forming a 1000 × 350 × 200 volume data set. We show that visualizing the time-varying component of the data spatially uncovers the changing features hidden in the data.

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“…Dzwinel et al applied cluster analysis on the observed and synthetic seismic catalogs to discover the multi-resolution structure of earthquake patterns, with clustering results illustrated by 3D visualization [9]. Yuen et al [36] used a web client-server service to cluster and visualize seismic catalog data in a grid environment. Very recently, scientists have begun to study the relationship between seismic events and other observational data.…”

Section: Related Workmentioning

confidence: 99%

Scalable Multi-variate Analytics of Seismic and Satellite-based Observational Data

Yuan

Guo

et al. 2010

IEEE Trans. Visual. Comput. Graphics

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Fig. 1. Seismic and satellite-based observational data are visualized by our scalable multi-variate visualization system in a tiled display wall visualization environment with 32 LCD panels. Abstract-Over the past few years, large human populations around the world have been affected by an increase in significant seismic activities. Both for conducting basic scientific research and for setting critical government policies, it is crucial to be able to explore and understand seismic and geographical information obtained through all scientific instruments. In this work, we present a visual analytics system that enables explorative visualization of seismic data together with satellite-based observational data, and introduce a suite of visual analytical tools. Seismic and satellite data are integrated temporally and spatially. Users can select temporal and spatial ranges to zoom in on specific seismic events, as well as to inspect changes both during and after the events. Tools for designing high dimensional transfer functions have been developed to enable efficient and intuitive comprehension of the multi-modal data. Spreadsheet style comparisons are used for data drill-down as well as presentation. Comparisons between distinct seismic events are also provided for characterizing event-wise differences. Our system has been designed for scalability in terms of data size, complexity (i.e. number of modalities), and varying form factors of display environments.

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Clustering and visualization of earthquake data in a grid environment

Cited by 21 publications

References 21 publications

Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia

Neural Network for Earthquake Prediction Based on Automatic Clustering in Indonesia

Visualizing field-measured seismic data

Scalable Multi-variate Analytics of Seismic and Satellite-based Observational Data

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