The most important resources to fulfill today's energy demands are fossil fuels, such as oil and natural gas. When exploiting hydrocarbon reservoirs, a detailed and credible model of the subsurface structures is crucial in order to minimize economic and ecological risks. Creating such a model is an inverse problem: reconstructing structures from measured reflection seismics. The major challenge here is twofold: First, the structures in highly ambiguous seismic data are interpreted in the time domain. Second, a velocity model has to be built from this interpretation to match the model to depth measurements from wells. If it is not possible to obtain a match at all positions, the interpretation has to be updated, going back to the first step. This results in a lengthy back and forth between the different steps, or in an unphysical velocity model in many cases. This paper presents a novel, integrated approach to interactively creating subsurface models from reflection seismics. It integrates the interpretation of the seismic data using an interactive horizon extraction technique based on piecewise global optimization with velocity modeling. Computing and visualizing the effects of changes to the interpretation and velocity model on the depth-converted model on the fly enables an integrated feedback loop that enables a completely new connection of the seismic data in time domain and well data in depth domain. Using a novel joint time/depth visualization, depicting side-by-side views of the original and the resulting depth-converted data, domain experts can directly fit their interpretation in time domain to spatial ground truth data. We have conducted a domain expert evaluation, which illustrates that the presented workflow enables the creation of exact subsurface models much more rapidly than previous approaches.

Increasing demands in world-wide energy consumption and oil depletion of large reservoirs have resulted in the need for exploring smaller and more complex oil reservoirs. Planning of the reservoir valorization usually starts with creating a model of the subsurface structures, including seismic faults and horizons. However, seismic interpretation and horizon tracing is a difficult and error-prone task, often resulting in hours of work needing to be manually repeated. In this paper, we propose a novel, interactive workflow for horizon interpretation based on well positions, which include additional geological and geophysical data captured by actual drillings. Instead of interpreting the volume slice-by-slice in 2D, we propose 3D seismic interpretation based on well positions. We introduce a combination of 2D and 3D minimal cost path and minimal cost surface tracing for extracting horizons with very little user input. By processing the volume based on well positions rather than slice-based, we are able to create a piecewise optimal horizon surface at interactive rates. We have integrated our system into a visual analysis platform which supports multiple linked views for fast verification, exploration and analysis of the extracted horizons. The system is currently being evaluated by our collaborating domain experts.

Abstract Displaying a large number of lines within a limited amount of screen space is a task that is common to many different classes of visualization techniques such as time‐series visualizations, parallel coordinates, link‐node diagrams, and phase‐space diagrams. This paper addresses the challenging problems of cluttering and overdraw inherent to such visualizations. We generate a 2×2 tensor field during line rasterization that encodes the distribution of line orientations through each image pixel. Anisotropic diffusion of a noise texture is then used to generate a dense, coherent visualization of line orientation. In order to represent features of different scales, we employ a multi‐resolution representation of the tensor field. The resulting technique can easily be applied to a wide variety of line‐based visualizations. We demonstrate this for parallel coordinates, a time‐series visualization, and a phase‐space diagram. Furthermore, we demonstrate how to integrate a focus+context approach by incorporating a second tensor field. Our approach achieves interactive rendering performance for large data sets containing millions of data items, due to its image‐based nature and ease of implementation on GPUs. Simulation results from computational fluid dynamics are used to evaluate the performance and usefulness of the proposed method.

We present a systematic approach to the interactive visual analysis of heterogeneous scientific data. The data consist of two interrelated parts given on spatial grids over time (e.g., atmosphere and ocean part from a coupled climate model). By integrating both data parts in a framework of coordinated multiple views (with linking and brushing), the joint investigation of features across the data parts is enabled. An interface is constructed between the data parts that specifies 1) which grid cells in one part are related to grid cells in the other part, and vice versa, 2) how selections (in terms of feature extraction via brushing) are transferred between the two parts, and 3) how an update mechanism keeps the feature specification in both data parts consistent during the analysis. We also propose strategies for visual analysis that result in an iterative refinement of features specified across both data parts. Our approach is demonstrated in the context of a complex simulation of fluid-structure interaction and a multirun climate simulation.

This paper presents a novel framework for visualizing volumetric data specified on complex polyhedral grids, without the need to perform any kind of a priori tetrahedralization. These grids are composed of polyhedra that often are non-convex and have an arbitrary number of faces, where the faces can be non-planar with an arbitrary number of vertices. The importance of such grids in state-of-the-art simulation packages is increasing rapidly. We propose a very compact, face-based data structure for representing such meshes for visualization, called two-sided face sequence lists (TSFSL), as well as an algorithm for direct GPU-based ray-casting using this representation. The TSFSL data structure is able to represent the entire mesh topology in a 1D TSFSL data array of face records, which facilitates the use of efficient 1D texture accesses for visualization. In order to scale to large data sizes, we employ a mesh decomposition into bricks that can be handled independently, where each brick is then composed of its own TSFSL array. This bricking enables memory savings and performance improvements for large meshes. We illustrate the feasibility of our approach with real-world application results, by visualizing highly complex polyhedral data from commercial state-of-the-art simulation packages.

In Toponomics, the function protein pattern in cells or tissue (the toponome) is imaged and analyzed for applications in toxicology, new drug development and patient-drug-interaction. The most advanced imaging technique is robot-driven multi-parameter fluorescence microscopy. This technique is capable of co-mapping hundreds of proteins and their distribution and assembly in protein clusters across a cell or tissue sample by running cycles of fluorescence tagging with monoclonal antibodies or other affinity reagents, imaging, and bleaching in situ. The imaging results in complex multi-parameter data composed of one slice or a 3D volume per affinity reagent. Biologists are particularly interested in the localization of co-occurring proteins, the frequency of co-occurrence and the distribution of co-occurring proteins across the cell. We present an interactive visual analysis approach for the evaluation of multi-parameter fluorescence microscopy data in toponomics. Multiple, linked views facilitate the definition of features by brushing multiple dimensions. The feature specification result is linked to all views establishing a focus+context visualization in 3D. In a new attribute view, we integrate techniques from graph visualization. Each node in the graph represents an affinity reagent while each edge represents two co-occurring affinity reagent bindings. The graph visualization is enhanced by glyphs which encode specific properties of the binding. The graph view is equipped with brushing facilities. By brushing in the spatial and attribute domain, the biologist achieves a better understanding of the function protein patterns of a cell. Furthermore, an interactive table view is integrated which summarizes unique fluorescence patterns. We discuss our approach with respect to a cell probe containing lymphocytes and a prostate tissue section.

Rhinologists are often faced with the challenge of assessing nasal breathing from a functional point of view to derive effective therapeutic interventions. While the complex nasal anatomy can be revealed by visual inspection and medical imaging, only vague information is available regarding the nasal airflow itself: Rhinomanometry delivers rather unspecific integral information on the pressure gradient as well as on total flow and nasal flow resistance. In this article we demonstrate how the understanding of physiological nasal breathing can be improved by simulating and visually analyzing nasal airflow, based on an anatomically correct model of the upper human respiratory tract. In particular we demonstrate how various Information Visualization (InfoVis) techniques, such as a highly scalable implementation of parallel coordinates, time series visualizations, as well as unstructured grid multi-volume rendering, all integrated within a multiple linked views framework, can be utilized to gain a deeper understanding of nasal breathing. Evaluation is accomplished by visual exploration of spatio-temporal airflow characteristics that include not only information on flow features but also on accompanying quantities such as temperature and humidity. To our knowledge, this is the first in-depth visual exploration of the physiological function of the nose over several simulated breathing cycles under consideration of a complete model of the nasal airways, realistic boundary conditions, and all physically relevant time-varying quantities.

In many application fields, the statistical properties of data sets are of great interest for data analysts. Since local variations can occur especially in large datasets, it is useful to visualize not only global values, but also the properties of user-defined subsets. Hence, we present in this paper a visualization of the statistical characteristics of subsets, with an emphasis on the temporal development. The visualization is coordinated with other views. They provide details about the data and allow for smooth brushing of subsets, a concept that introduces a continuous transition from data in focus to context data. Our approach accounts for smooth brushing in both the derivation and the visualization of statistical properties in order to visualize variations within subsets. To analyze differences between multiple subsets, our approach further integrates visualization concepts for the comparison of statistical properties. An application example from the simulation of biological systems demonstrates the benefits of our approach.

Abstract In this paper we present a new approach to the interactive visual analysis of time‐dependent scientific data – both from measurements as well as from computational simulation – by visualizing a scalar function over time for each of tenthousands or even millions of sample points. In order to cope with overdrawing and cluttering, we introduce a new four‐level method of focus+context visualization. Based on a setting of coordinated, multiple views (with linking and brushing), we integrate three different kinds of focus and also the context in every single view. Per data item we use three values (from the unit interval each) to represent to which degree the data item is part of the respective focus level. We present a color compositing scheme which is capable of expressing all three values in a meaningful way, taking semantics and their relations amongst each other (in the context of our multiple linked view setup) into account. Furthermore, we present additional image‐based postprocessing methods to enhance the visualization of large sets of function graphs, including a texture‐based technique based on line integral convolution (LIC). We also propose advanced brushing techniques which are specific to the time‐dependent nature of the data (in order to brush patterns over time more efficiently). We demonstrate the usefulness of the new approach in the context of medical perfusion data.

One of the most prominent topics in climate research is the investigation, detection, and allocation of climate change. In this paper, we aim at identifying regions in the atmosphere (e.g., certain height layers) which can act as sensitive and robust indicators for climate change. We demonstrate how interactive visual data exploration of large amounts of multi-variate and time-dependent climate data enables the steered generation of promising hypotheses for subsequent statistical evaluation. The use of new visualization and interaction technology--in the context of a coordinated multiple views framework--allows not only to identify these promising hypotheses, but also to efficiently narrow down parameters that are required in the process of computational data analysis. Two datasets, namely an ECHAM5 climate model run and the ERA-40 reanalysis incorporating observational data, are investigated. Higher-order information such as linear trends or signal-to-noise ratio is derived and interactively explored in order to detect and explore those regions which react most sensitively to climate change. As one conclusion from this study, we identify an excellent potential for usefully generalizing our approach to other, similar application cases, as well.

We present smooth formulations of common vortex detectors that allow a seamless integration into the concept of interactive visual analysis of flow simulation data. We express the originally binary feature detectors as fuzzy-sets that can be combined using the linking and brushing concepts of interactive visual analysis. Both interaction and visualization gain from having multiple detectors concurrently available and from the ability to combine them. An application study on automotive data reveals how these vortex detectors combine and perform in praxis.

This paper presents a scalable framework for real-time raycasting of large unstructured volumes that employs a hybrid bricking approach. It adaptively combines original unstructured bricks in important (focus) regions, with structured bricks that are resampled on demand in less important (context) regions. The basis of this focus+context approach is interactive specification of a scalar degree of interest (DOI) function. Thus, rendering always considers two volumes simultaneously: a scalar data volume, and the current DOI volume. The crucial problem of visibility sorting is solved by raycasting individual bricks and compositing in visibility order from front to back. In order to minimize visual errors at the grid boundary, it is always rendered accurately, even for resampled bricks. A variety of different rendering modes can be combined, including contour enhancement. A very important property of our approach is that it supports a variety of cell types natively, i.e., it is not constrained to tetrahedral grids, even when interpolation within cells is used. Moreover, our framework can handle multi-variate data, e.g., multiple scalar channels such as temperature or pressure, as well as time-dependent data. The combination of unstructured and structured bricks with different quality characteristics such as the type of interpolation or resampling resolution in conjunction with custom texture memory management yields a very scalable system.

Perfusion data are dynamic medical image data which characterize the regional blood flow in human tissue. These data bear a great potential in medical diagnosis, since diseases can be better distinguished and detected at an earlier stage compared to static image data. The wide-spread use of perfusion data is hampered by the lack of efficient evaluation methods. For each voxel, a time-intensity curve characterizes the enhancement of a contrast agent. Parameters derived from these curves characterize the perfusion and have to be integrated for diagnosis. The diagnostic evaluation of this multi-field data is challenging and time-consuming due to its complexity. For the visual analysis of such datasets, feature-based approaches allow to reduce the amount of data and direct the user to suspicious areas. We present an interactive visual analysis approach for the evaluation of perfusion data. For this purpose, we integrate statistical methods and interactive feature specification. Correlation analysis and Principal Component Analysis (PCA) are applied for dimensionreduction and to achieve a better understanding of the inter-parameter relations. Multiple, linked views facilitate the definition of features by brushing multiple dimensions. The specification result is linked to all views establishing a focus+context style of visualization in 3D. We discuss our approach with respect to clinical datasets from the three major application areas: ischemic stroke diagnosis, breast tumor diagnosis, as well as the diagnosis of the coronary heart disease (CHD). It turns out that the significance of perfusion parameters strongly depends on the individual patient, scanning parameters, and data pre-processing.

We present a visual analysis and exploration of fluid flow through a cooling jacket. Engineers invest a large amount of time and serious effort to optimize the flow through this engine component because of its important role in transferring heat away from the engine block. In this study we examine the design goals that engineers apply in order to construct an ideal-as-possible cooling jacket geometry and use a broad range of visualization tools in order to analyze, explore, and present the results. We systematically employ direct, geometric, and texture-based flow visualization techniques as well as automatic feature extraction and interactive feature-based methodology. And we discuss the relative advantages and disadvantages of these approaches as well as the challenges, both technical and perceptual with this application. The result is a feature-rich state-of-the-art flow visualization analysis applied to an important and complex data set from real-world computational fluid dynamics simulations.

Abstract Flow visualization has been a very attractive component of scientific visualization research for a long time. Usually very large multivariate datasets require processing. These datasets often consist of a large number of sample locations and several time steps. The steadily increasing performance of computers has recently become a driving factor for a reemergence in flow visualization research, especially in texture‐based techniques. In this paper, dense, texture‐based flow visualization techniques are discussed. This class of techniques attempts to provide a complete, dense representation of the flow field with high spatio‐temporal coherency. An attempt of categorizing closely related solutions is incorporated and presented. Fundamentals are shortly addressed as well as advantages and disadvantages of the methods.

Abstract Flow visualisation is an attractive topic in data visualisation, offering great challenges for research. Very large data sets must be processed, consisting of multivariate data at large numbers of grid points, often arranged in many time steps. Recently, the steadily increasing performance of computers again has become a driving force for new advances in flow visualisation, especially in techniques based on texturing, feature extraction, vector field clustering, and topology extraction. In this article we present the state of the art in feature‐based flow visualisation techniques. We will present numerous feature extraction techniques, categorised according to the type of feature. Next, feature tracking and event detection algorithms are discussed, for studying the evolution of features in time‐dependent data sets. Finally, various visualisation techniques are demonstrated. ACM CSS: I.3.8 Computer Graphics— applications