scholarly journals Distributed visualization of gridded geophysical data: the Carbon Data Explorer, version 0.2.3

2016 ◽  
Vol 9 (1) ◽  
pp. 383-392 ◽  
Author(s):  
K. A. Endsley ◽  
M. G. Billmire
Keyword(s):  
Climate Models ◽  
Software Tool ◽  
Scientific Data ◽  
Geophysical Data ◽  
Data Publishing ◽  
Data Sets ◽  
Earth System ◽  
Data Set ◽  
Uncertainty Estimates ◽  
Share Data

Abstract. Due to the proliferation of geophysical models, particularly climate models, the increasing resolution of their spatiotemporal estimates of Earth system processes, and the desire to easily share results with collaborators, there is a genuine need for tools to manage, aggregate, visualize, and share data sets. We present a new, web-based software tool – the Carbon Data Explorer – that provides these capabilities for gridded geophysical data sets. While originally developed for visualizing carbon flux, this tool can accommodate any time-varying, spatially explicit scientific data set, particularly NASA Earth system science level III products. In addition, the tool's open-source licensing and web presence facilitate distributed scientific visualization, comparison with other data sets and uncertainty estimates, and data publishing and distribution.

scholarly journals Distributed visualization of gridded geophysical data: a web API for carbon flux

2015 ◽  
Vol 8 (7) ◽  
pp. 5741-5761
Author(s):  
K. A. Endsley ◽  
M. G. Billmire
Keyword(s):  
Carbon Flux ◽  
Climate Models ◽  
Software Tool ◽  
Data Publishing ◽  
Spatially Explicit ◽  
Time Varying ◽  
Earth System ◽  
Web Based ◽  
Web Presence ◽  
Uncertainty Estimates

Abstract. Due to the proliferation of geophysical models, particularly climate models, the increasing resolution of their spatiotemporal estimates of Earth system processes, and the desire to easily share results with collaborators, there is a genuine need for tools to manage, aggregate, visualize, and share datasets. We present a new, web-based software tool – the Carbon Data Explorer – that provides these capabilities for gridded geophysical datasets. While originally developed for visualizing carbon flux, this tool can accommodate any time-varying, spatially explicit scientific dataset, particularly NASA Earth system science Level III products. In addition, the tool's open-source licensing and web presence facilitate distributed scientific visualization, comparison with other datasets and uncertainty estimates, and data publishing and distribution.

scholarly journals Methods for estimating uncertainty in factor analytic solutions

2014 ◽  
Vol 7 (3) ◽  
pp. 781-797 ◽  
Author(s):  
P. Paatero ◽  
S. Eberly ◽  
S. G. Brown ◽  
G. A. Norris
Keyword(s):  
Environmental Protection Agency ◽  
Synthetic Data ◽  
Analytic Solutions ◽  
Data Sets ◽  
Random Errors ◽  
Data Set ◽  
Factor Analytic ◽  
Uncertainty Estimates ◽  
Multilinear Engine ◽  
Analytic Models

Abstract. The EPA PMF (Environmental Protection Agency positive matrix factorization) version 5.0 and the underlying multilinear engine-executable ME-2 contain three methods for estimating uncertainty in factor analytic models: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement of factor elements (BS-DISP). The goal of these methods is to capture the uncertainty of PMF analyses due to random errors and rotational ambiguity. It is shown that the three methods complement each other: depending on characteristics of the data set, one method may provide better results than the other two. Results are presented using synthetic data sets, including interpretation of diagnostics, and recommendations are given for parameters to report when documenting uncertainty estimates from EPA PMF or ME-2 applications.

Feature-Based Uncertainty Visualization

Big Data ◽  
2016 ◽  
pp. 261-287
Author(s):  
Keqin Wu ◽  
Song Zhang
Keyword(s):  
Scientific Data ◽  
Data Sets ◽  
Data Set ◽  
Uncertainty Visualization ◽  
Scalar Data ◽  
Critical Issues ◽  
Contour Level ◽  
Feature Based ◽  
2D Data ◽  
The Impact

While uncertainty in scientific data attracts an increasing research interest in the visualization community, two critical issues remain insufficiently studied: (1) visualizing the impact of the uncertainty of a data set on its features and (2) interactively exploring 3D or large 2D data sets with uncertainties. In this chapter, a suite of feature-based techniques is developed to address these issues. First, an interactive visualization tool for exploring scalar data with data-level, contour-level, and topology-level uncertainties is developed. Second, a framework of visualizing feature-level uncertainty is proposed to study the uncertain feature deviations in both scalar and vector data sets. With quantified representation and interactive capability, the proposed feature-based visualizations provide new insights into the uncertainties of both data and their features which otherwise would remain unknown with the visualization of only data uncertainties.

scholarly journals Development of a tool-kit for the detection of healthy and injured cardiac tissue based on MR imaging

2017 ◽  
Vol 3 (2) ◽  
pp. 195-198
Author(s):  
Philip Westphal ◽  
Sebastian Hilbert ◽  
Michael Unger ◽  
Claire Chalopin
Keyword(s):  
Contrast Agent ◽  
Cardiac Tissue ◽  
Software Tool ◽  
Ease Of Use ◽  
Free Software ◽  
Patient Specific ◽  
Initial Contour ◽  
Data Sets ◽  
Heart Model ◽  
Data Set

AbstractPlanning of interventions to treat cardiac arrhythmia requires a 3D patient specific model of the heart. Currently available commercial or free software dedicated to this task have important limitations for routinely use. Automatic algorithms are not robust enough while manual methods are time-consuming. Therefore, the project attempts to develop an optimal software tool. The heart model is generated from preoperative MR data-sets acquired with contrast agent and allows visualisation of damaged cardiac tissue. A requirement in the development of the software tool was the use of semi-automatic functions to be more robust. Once the patient image dataset has been loaded, the user selects a region of interest. Thresholding functions allow selecting the areas of high intensities which correspond to anatomical structures filled with contrast agent, namely cardiac cavities and blood vessels. Thereafter, the target-structure, for example the left ventricle, is coarsely selected by interactively outlining the gross shape. An active contour function adjusts automatically the initial contour to the image content. The result can still be manually improved using fast interaction tools. Finally, possible scar tissue located in the cavity muscle is automatically detected and visualized on the 3D heart model. The model is exported in format which is compatible with interventional devices at hospital. The evaluation of the software tool included two steps. Firstly, a comparison with two free software tools was performed on two image data sets of variable quality. Secondly, six scientists and physicians tested our tool and filled out a questionnaire. The performance of our software tool was visually judged more satisfactory than the free software, especially on the data set of lower quality. Professionals evaluated positively our functionalities regarding time taken, ease of use and quality of results. Improvements would consist in performing the planning based on different MR modalities.

Rapid multi-scale analysis of near-surface geophysical anomaly maps: Application to an archaeo-geophysical data set

Geophysics ◽  
2020 ◽  
pp. 1-41 ◽  
Author(s):  
Jens Tronicke ◽  
Niklas Allroggen ◽  
Felix Biermann ◽  
Florian Fanselow ◽  
Julien Guillemoteau ◽  
...  
Keyword(s):  
Random Noise ◽  
Geophysical Data ◽  
Magnetic Data ◽  
Engineering Practice ◽  
Data Sets ◽  
Stripe Pattern ◽  
Data Set ◽  
Near Surface ◽  
Multi Scale ◽  
Topographic Data

In near-surface geophysics, ground-based mapping surveys are routinely employed in a variety of applications including those from archaeology, civil engineering, hydrology, and soil science. The resulting geophysical anomaly maps of, for example, magnetic or electrical parameters are usually interpreted to laterally delineate subsurface structures such as those related to the remains of past human activities, subsurface utilities and other installations, hydrological properties, or different soil types. To ease the interpretation of such data sets, we propose a multi-scale processing, analysis, and visualization strategy. Our approach relies on a discrete redundant wavelet transform (RWT) implemented using cubic-spline filters and the à trous algorithm, which allows to efficiently compute a multi-scale decomposition of 2D data using a series of 1D convolutions. The basic idea of the approach is presented using a synthetic test image, while our archaeo-geophysical case study from North-East Germany demonstrates its potential to analyze and process rather typical geophysical anomaly maps including magnetic and topographic data. Our vertical-gradient magnetic data show amplitude variations over several orders of magnitude, complex anomaly patterns at various spatial scales, and typical noise patterns, while our topographic data show a distinct hill structure superimposed by a microtopographic stripe pattern and random noise. Our results demonstrate that the RWT approach is capable to successfully separate these components and that selected wavelet planes can be scaled and combined so that the reconstructed images allow for a detailed, multi-scale structural interpretation also using integrated visualizations of magnetic and topographic data. Because our analysis approach is straightforward to implement without laborious parameter testing and tuning, computationally efficient, and easily adaptable to other geophysical data sets, we believe that it can help to rapidly analyze and interpret different geophysical mapping data collected to address a variety of near-surface applications from engineering practice and research.

scholarly journals Methods for estimating uncertainty in factor analytic solutions

2013 ◽  
Vol 6 (4) ◽  
pp. 7593-7631 ◽  
Author(s):  
P. Paatero ◽  
S. Eberly ◽  
S. G. Brown ◽  
G. A. Norris
Keyword(s):  
Synthetic Data ◽  
Analytic Solutions ◽  
The Other ◽  
Data Sets ◽  
Random Errors ◽  
Data Set ◽  
Factor Analytic ◽  
Uncertainty Estimates ◽  
Multilinear Engine ◽  
Analytic Models

Abstract. EPA PMF version 5.0 and the underlying multilinear engine executable ME-2 contain three methods for estimating uncertainty in factor analytic models: classical bootstrap (BS), displacement of factor elements (DISP), and bootstrap enhanced by displacement of factor elements (BS-DISP). The goal of these methods is to capture the uncertainty of PMF analyses due to random errors and rotational ambiguity. It is shown that the three methods complement each other: depending on characteristics of the data set, one method may provide better results than the other two. Results are presented using synthetic data sets, including interpretation of diagnostics, and recommendations are given for parameters to report when documenting uncertainty estimates from EPA PMF or ME-2 applications.

scholarly journals Ein Instrument zur Schätzung von Holzernteproduktivitäten mittels der kNN-Methode

2012 ◽  
Vol 163 (4) ◽  
pp. 119-129
Author(s):  
Fabian Kostadinov ◽  
Renato Lemm ◽  
Oliver Thees
Keyword(s):  
Software Tool ◽  
Estimation Accuracy ◽  
Data Sets ◽  
Data Set ◽  
Wood Harvesting ◽  
Federal Institute ◽  
Nearest Neighbours ◽  
Order Of Magnitude ◽  
Landscape Research ◽  
Cable Crane

A software tool for the estimation of wood harvesting productivity using the kNN method For operational planning and management of wood harvests it is important to have access to reliable information on time consumption and costs. To estimate these efficiently and reliably, appropriate methods and calculation tools are needed. The present article investigates whether use of the method of the k nearest neighbours (kNN) is appropriate in this case. The kNN algorithm is first explained, then is applied to two sets of data “combined cable crane and processor” and “skidder”, both containing wood harvesting figures, and thus the estimation accuracy of the method is determined. It is shown that the kNN method's estimation accuracy lies within the same order of magnitude as that of a multiple linear regression. Advantages of the kNN method are that it is easy to understand and to visualize, together with the fact that estimation models do not become out of date, since new data sets can be constantly taken into account. The kNN Workbook has been developed by the Swiss Federal Institute for Forest, Snow and Landscape Research (WSL). It is a software tool with which any data set can be analysed in practice using the kNN method. This tool is also presented in the article.

scholarly journals A review and framework for the evaluation of pixel-level uncertainty estimates in satellite aerosol remote sensing

2020 ◽  
Vol 13 (2) ◽  
pp. 373-404 ◽  
Author(s):  
Andrew M. Sayer ◽  
Yves Govaerts ◽  
Pekka Kolmonen ◽  
Antti Lipponen ◽  
Marta Luffarelli ◽  
...  
Keyword(s):  
Uncertainty Propagation ◽  
Optimal Estimation ◽  
Sensitivity Analyses ◽  
Data Sets ◽  
Validation Data ◽  
Data Set ◽  
Formal Error ◽  
Uncertainty Estimates ◽  
Diagnostic Approaches

Abstract. Recent years have seen the increasing inclusion of per-retrieval prognostic (predictive) uncertainty estimates within satellite aerosol optical depth (AOD) data sets, providing users with quantitative tools to assist in the optimal use of these data. Prognostic estimates contrast with diagnostic (i.e. relative to some external truth) ones, which are typically obtained using sensitivity and/or validation analyses. Up to now, however, the quality of these uncertainty estimates has not been routinely assessed. This study presents a review of existing prognostic and diagnostic approaches for quantifying uncertainty in satellite AOD retrievals, and it presents a general framework to evaluate them based on the expected statistical properties of ensembles of estimated uncertainties and actual retrieval errors. It is hoped that this framework will be adopted as a complement to existing AOD validation exercises; it is not restricted to AOD and can in principle be applied to other quantities for which a reference validation data set is available. This framework is then applied to assess the uncertainties provided by several satellite data sets (seven over land, five over water), which draw on methods from the empirical to sensitivity analyses to formal error propagation, at 12 Aerosol Robotic Network (AERONET) sites. The AERONET sites are divided into those for which it is expected that the techniques will perform well and those for which some complexity about the site may provide a more severe test. Overall, all techniques show some skill in that larger estimated uncertainties are generally associated with larger observed errors, although they are sometimes poorly calibrated (i.e. too small or too large in magnitude). No technique uniformly performs best. For powerful formal uncertainty propagation approaches such as optimal estimation, the results illustrate some of the difficulties in appropriate population of the covariance matrices required by the technique. When the data sets are confronted by a situation strongly counter to the retrieval forward model (e.g. potentially mixed land–water surfaces or aerosol optical properties outside the family of assumptions), some algorithms fail to provide a retrieval, while others do but with a quantitatively unreliable uncertainty estimate. The discussion suggests paths forward for the refinement of these techniques.

scholarly journals Petrophysically and geologically guided multi-physics inversion using a dynamic Gaussian mixture model

2020 ◽  
Vol 224 (1) ◽  
pp. 40-68 ◽  
Author(s):  
Thibaut Astic ◽  
Lindsey J Heagy ◽  
Douglas W Oldenburg
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Quantitative Information ◽  
Geophysical Data ◽  
Magnetic Data ◽  
Data Sets ◽  
Data Set ◽  
Geophysical Surveys ◽  
Geological Information

SUMMARY In a previous paper, we introduced a framework for carrying out petrophysically and geologically guided geophysical inversions. In that framework, petrophysical and geological information is modelled with a Gaussian mixture model (GMM). In the inversion, the GMM serves as a prior for the geophysical model. The formulation and applications were confined to problems in which a single physical property model was sought, and a single geophysical data set was available. In this paper, we extend that framework to jointly invert multiple geophysical data sets that depend on multiple physical properties. The petrophysical and geological information is used to couple geophysical surveys that, otherwise, rely on independent physics. This requires advancements in two areas. First, an extension from a univariate to a multivariate analysis of the petrophysical data, and their inclusion within the inverse problem, is necessary. Secondly, we address the practical issues of simultaneously inverting data from multiple surveys and finding a solution that acceptably reproduces each one, along with the petrophysical and geological information. To illustrate the efficacy of our approach and the advantages of carrying out multi-physics inversions coupled with petrophysical and geological information, we invert synthetic gravity and magnetic data associated with a kimberlite deposit. The kimberlite pipe contains two distinct facies embedded in a host rock. Inverting the data sets individually, even with petrophysical information, leads to a binary geological model: background or undetermined kimberlite. A multi-physics inversion, with petrophysical information, differentiates between the two main kimberlite facies of the pipe. Through this example, we also highlight the capabilities of our framework to work with interpretive geological assumptions when minimal quantitative information is available. In those cases, the dynamic updates of the GMM allow us to perform multi-physics inversions by learning a petrophysical model.

Application of Rough Set-Based Information Analysis to Questionnaire Data

2014 ◽  
Vol 18 (6) ◽  
pp. 953-961 ◽  
Author(s):  
Naoto Yamaguchi ◽  
◽  
Mao Wu ◽  
Michinori Nakata ◽  
Hiroshi Sakai ◽  
...  
Keyword(s):  
Missing Values ◽  
Question Answering ◽  
Software Tool ◽  
Data Sets ◽  
Rule Generation ◽  
Questionnaire Data ◽  
Information Analysis ◽  
Data Set ◽  
Machine Leaning ◽  
Student Questionnaire

This article reports an application ofRough Nondeterministic Information Analysis (RNIA)to two data sets. One is the Mushroom data set in the UCI machine leaning repository, and the other is a student questionnaire data set. Even though these data sets include many missing values, we obtained some interesting rules by using ourgetRNIAsoftware tool. This software is powered by theNIS-Apriorialgorithm, and we apply rule generation and question-answering functionalities to data sets with nondeterministic values.

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