scholarly journals Visual Analytics of Stratigraphic Correlation for Multi-Attribute Well-Logging Data Exploration

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 98122-98135
Author(s):  
Yuhua Liu ◽  
Chen Shi ◽  
Qifan Wu ◽  
Rumin Zhang ◽  
Zhiguang Zhou
2010 ◽  
Vol 49 (2) ◽  
Author(s):  
E. Coconi-Morales ◽  
G. Ronquillo-Jarillo ◽  
J. O. Campos-Enríquez

Determinación de los límites locales de una columna estratigráfica (por ejemplo relacionados con ambientes de depósito) representan en particular una gran contribución al análisis y caracterización de yacimientos petroleros. En este marco general, las Transformadas de Ondícula, continua y discreta, son aplicadas a datos de registros geofísicos de pozos de un área productora de aceite en el Golfo de México, con el propósito de encontrar periodicidades o ciclos y correlacionarlos con las características litológicas y estratigráficas de los ambientes asociados. Un análisis multiescala de registros geofísicos de pozos (rayos gama, resistividad y potencial espontáneo) fue realizado basado en la transformada de ondicular. En particular los coeficientes ondiculares fueron determinados. El análisis de los escalogramas-espectrogramas permitió obtener pseudolongitudes de onda características para cada escala (frecuencias). Las pseudolongitudes de onda fueron asociadas con posibles periodicidades o periodos deposicionales (ciclos climáticos de Milankovitch) del área de estudio. El caso presentado muestra que el análisis ondicular es una técnica complementaria de gran ayuda para la caracterización de yacimientos, particularmente en la localización de secuencias estratigráficas y de las facies asociadas.


Author(s):  
Jacob L. Cybulski ◽  
Susan Keller ◽  
Dilal Saundage

Visual Analytics (VA) is an approach to data analysis by means of visual manipulation of data representation, which relies on innate human abilities of perception and cognition. Even though current visual toolkits in the Business Analytics (BA) domain have improved the effectiveness of data exploration, analysis and reporting, their features are often not intuitive, and can be confusing and difficult to use. Moreover, visualizations generated from these toolkits are mostly accessible to specialist users. Thus, there is a need for analytic environments that support data exploration, interpretation and communication of insight that do not add to the cognitive load of the analyst and their non-technical clients. In this conceptual paper, we explore the potential of primary metaphors, which arise out of human lived and sensory-motor experiences, in the design of immersive visual analytics environments. Primary metaphors provide ideas for representation of time, space, quantity, similarity, actions and team work. Using examples developed in our own work, we also explain how to combine such metaphors to create complex and cognitively acceptable visual metaphors, such as 3D data terrains that approximate our intuition of reality and create opportunities for data to be viewed, navigated, explored, touched, changed, discussed, reported and described to others, individually or collaboratively.


2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yuan Zhu ◽  
Sami Demiroluk ◽  
Kaan Ozbay ◽  
Kun Xie ◽  
Hong Yang ◽  
...  

Traffic crashes are one of the biggest issues which constitute a threat to lives of the motorists and disrupt operations of the transportation system. To reduce the number of crashes and alleviate their impacts, it is necessary to scrutinize the factors contributing to the risk of traffic crashes. Lately, visual analytics tools become very popular for data exploration and obtaining insights from the data. In this paper, a new web-based data visualization tool called Safety Analysis Visualization and Evaluation Tool (SAVE-T) was introduced. This tool enables users to interactively create queries and visually explore the results. By utilizing an online crash database, it offers various innovative functionalities for analysis and visualization of the crash data such as custom query development module and a subway-like map for easily visualizing the accident on the roadway segments. This tool provides an effective and efficient way to transportation agencies and professionals for traffic safety analyses and visualizations.


Author(s):  
Gennady Andrienko ◽  
Natalia Andrienko ◽  
Fabian Patterson ◽  
Siming Chen ◽  
Robert Weibel ◽  
...  

AbstractVisual analytics science develops principles and methods for efficient human–computer collaboration in solving complex problems. Visual and interactive techniques are used to create conditions in which human analysts can effectively utilize their unique capabilities: the power of seeing, interpreting, linking, and reasoning. Visual analytics research deals with various types of data and analysis tasks from numerous application domains. A prominent research topic is analysis of spatiotemporal data, which may describe events occurring at different spatial locations, changes of attribute values associated with places or spatial objects, or movements of people, vehicles, or other objects. Such kinds of data are abundant in urban applications. Movement data are a quintessential type of spatiotemporal data because they can be considered from multiple perspectives as trajectories, as spatial events, and as changes of space-related attribute values. By example of movement data, we demonstrate the utilization of visual analytics techniques and approaches in data exploration and analysis.


2018 ◽  
Vol 18 (2) ◽  
pp. 251-267 ◽  
Author(s):  
Zhe Cui ◽  
Sriram Karthik Badam ◽  
M Adil Yalçin ◽  
Niklas Elmqvist

Effective data analysis ideally requires the analyst to have high expertise as well as high knowledge of the data. Even with such familiarity, manually pursuing all potential hypotheses and exploring all possible views is impractical. We present DataSite, a proactive visual analytics system where the burden of selecting and executing appropriate computations is shared by an automatic server-side computation engine. Salient features identified by these automatic background processes are surfaced as notifications in a feed timeline. DataSite effectively turns data analysis into a conversation between analyst and computer, thereby reducing the cognitive load and domain knowledge requirements. We validate the system with a user study comparing it to a recent visualization recommendation system, yielding significant improvement, particularly for complex analyses that existing analytics systems do not support well.


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