45 research outputs found
Crazyseismic: A MATLAB GUIâBased Software Package for Passive Seismic Data Preprocessing
We introduce an openâsource MATLAB software package, named Crazyseismic, for passive seismic data preprocessing. Builtâin core functions such as seismic phase travelâtime calculation and multichannel cross correlation significantly improve the efficiency of data processing. Compared with conventional commandâlineâstyle toolboxes, all functions in Crazyseismic are embedded in one single graphic user interface (GUI). The humanâmachine interactive nature of GUI facilitates data quality control. The simplicity of the software allows users to process Seismic Analysis Code format seismic data with great ease and also provides a means by which users can tailor the software for their specific needs. We demonstrate the power of our software through two field examples: one for Pâwave arrivalâtime picking and the other for receiver function calculation. The software can essentially be used for analyzing all major bodyâwave phases in seismology
Crazyseismic: A MATLAB GUIâBased Software Package for Passive Seismic Data Preprocessing
We introduce an openâsource MATLAB software package, named Crazyseismic, for passive seismic data preprocessing. Builtâin core functions such as seismic phase travelâtime calculation and multichannel cross correlation significantly improve the efficiency of data processing. Compared with conventional commandâlineâstyle toolboxes, all functions in Crazyseismic are embedded in one single graphic user interface (GUI). The humanâmachine interactive nature of GUI facilitates data quality control. The simplicity of the software allows users to process Seismic Analysis Code format seismic data with great ease and also provides a means by which users can tailor the software for their specific needs. We demonstrate the power of our software through two field examples: one for Pâwave arrivalâtime picking and the other for receiver function calculation. The software can essentially be used for analyzing all major bodyâwave phases in seismology
Crazyseismic: A MATLAB GUIâBased Software Package for Passive Seismic Data Preprocessing
Crustal structure of the central Tibetan plateau and geological interpretation
Based on teleseismic data obtained from 225 stations from two networks in the central Tibetan plateau, we have generated detailed crustal structure images using P-wave receiver function techniques with more accurate piercing-depth-correction and time-depth-correction than what have previously been available. Our images indicate an undulatory Moho beneath the Tibetan plateau with a steep jump beneath the northern Himalaya, and obviously different structures in proximity to the Bangong-Nujiang suture. In several sections of the Tibetan plateau, the lower crust is characterized by pervasive high-velocity regions, which are consistent with the preservation of eclogite bodies beneath the plateau, whose presence affects the dynamics of the Tibetan plateau.China Earthquake Administration (Grant 201308013)National Natural Science Foundation (China) (Grants 40974034, 41174086, 41074052 and 41021003
Revisiting the Design Patterns of Composite Visualizations
Composite visualization is a popular design strategy that represents complex
datasets by integrating multiple visualizations in a meaningful and aesthetic
layout, such as juxtaposition, overlay, and nesting. With this strategy,
numerous novel designs have been proposed in visualization publications to
accomplish various visual analytic tasks. These well-crafted composite
visualizations have formed a valuable collection for designers and researchers
to address real-world problems and inspire new research topics and designs.
However, there is a lack of understanding of design patterns of composite
visualization, thus failing to provide holistic design space and concrete
examples for practical use. In this paper, we opted to revisit the composite
visualizations in VIS publications and answered what and how visualizations of
different types are composed together. To achieve this, we first constructed a
corpus of composite visualizations from IEEE VIS publications and decomposed
them into a series of basic visualization types (e.g., bar chart, map, and
matrix). With this corpus, we studied the spatial (e.g., separated or
overlaying) and semantic relationships (e.g., with same types or shared axis)
between visualizations and proposed a taxonomy consisting of eight different
design patterns (e.g., repeated, stacked, accompanied, and nested).
Furthermore, we analyzed and discussed common practices of composite
visualizations, such as the distribution of different patterns and correlations
between visualization types. From the analysis and examples, we obtained
insights into different design patterns on the utilities, advantages, and
disadvantages. Finally, we developed an interactive system to help
visualization developers and researchers conveniently explore collected
examples and design patterns
NeighViz: Towards Better Understanding of Neighborhood Effects on Social Groups with Spatial Data
Understanding how local environments influence individual behaviors, such as
voting patterns or suicidal tendencies, is crucial in social science to reveal
and reduce spatial disparities and promote social well-being. With the
increasing availability of large-scale individual-level census data, new
analytical opportunities arise for social scientists to explore human behaviors
(e.g., political engagement) among social groups at a fine-grained level.
However, traditional statistical methods mostly focus on global, aggregated
spatial correlations, which are limited to understanding and comparing the
impact of local environments (e.g., neighborhoods) on human behaviors among
social groups. In this study, we introduce a new analytical framework for
analyzing multi-variate neighborhood effects between social groups. We then
propose NeighVi, an interactive visual analytics system that helps social
scientists explore, understand, and verify the influence of neighborhood
effects on human behaviors. Finally, we use a case study to illustrate the
effectiveness and usability of our system.Comment: Symposium on Visualization in Data Science (VDS) at IEEE VIS 202