In seismic interpretation, our ability to identify geologic features is directly impacted by the way we visualize data. When we analyze seismic attributes – from coherence highlighting fault edges to spectral decomposition revealing stratigraphic features – the choice of colormaps becomes crucial. These mathematical transformations of seismic data help reveal features that might be overlooked in conventional amplitude displays, but their effectiveness depends heavily on how we display them.
Poor colormap choices can compromise interpretation quality in several ways. They can create artificial boundaries that do not represent real geologic features, leading to misidentified faults or stratigraphic boundaries. They could mask important seismic variations by concentrating perceptual contrast in irrelevant data ranges. Traditional rainbow colormaps are particularly problematic, introducing false patterns that can bias interpretation and lead to missed pay zones or incomplete geologic models.
The main challenge lies in accurately representing small variations in attribute values that often indicate significant geologic features. Rainbow colormaps frequently mask subtle transitions or create artificial boundaries where none exist – a critical issue when interpreting fault damage zones, stratigraphic pinch-outs or facies changes that indicate shifts in the depositional environment.
Scientific colormap design addresses these visualization challenges through perceptually uniform colormaps (figure 1), where equal steps in data values correspond to equal perceived color differences. This approach improves interpretation of complex geologic structures while ensuring accessibility for all interpreters, including those with color vision deficiency (approximately 4-8 percent of interpreters).
The Great South Basin offshore New Zealand serves as our case study for demonstrating these colormaps. With its complex geologic history spanning multiple deformation phases since the Mesozoic and approximately 9 kilometers of sedimentary succession, the basin offers diverse structural and stratigraphic features for testing attribute visualization. The succession, ranging from Cretaceous to recent deposits, includes back-arc deposits, deltaic sequences and extensive polygonal fault systems developed during the Eocene.
We present three main categories of scientific colormaps, each designed for specific attribute visualization needs (figure 1). Sequential color maps like ‘Oslo’ and ‘Lajolla’ use increasing luminance to show progression from low to high values, ideal for coherence or sweetness attributes. Diverging colormaps such as ‘Balance’ help distinguish positive from negative values in curvature attributes. Cyclic colormaps like ‘RomaO’ handle wraparound values in phase or azimuth data without creating artificial breaks. All maintain perceptual uniformity and colorblind accessibility while revealing subtle geological features.
Sequential Attributes
The ‘Oslo’ colormap helps to improve how we visualize coherence and energy attributes, offering key advantages over traditional grayscale displays (figure 2). While grayscale remains a standard choice for edge detection, Oslo’s approach combines luminance with subtle blue hues to improve feature detection. This dual-channel visualization engages both our luminance and color perception systems, revealing fine details that often remain hidden in grayscale displays, as can be seen in the mid-range values.
Figure 2 demonstrates Oslo’s effectiveness, with figure 2b exposing critical discontinuities barely visible in the grayscale display (2a). Small-offset faults appear with remarkable clarity while maintaining the sharp edge definition essential for coherence interpretation. When applied to energy ratio similarity data, Oslo aids in distinguishing subtle stratigraphic features.
For sweetness attributes, commonly used to identify hydrocarbon-bearing sandstone units, the ‘Lajolla’ colormap offers an intuitive visualization approach. Its color scheme mirrors natural lithological variations – high sweetness values appear in yellows and tans reminiscent of sandstone, while lower values take on darker brown characteristics of mudstone. Unlike traditional rainbow colormaps that emphasize only high sweetness values, Lajolla maintains consistent resolution across the full data range, helping identify structural features that could influence reservoir behavior.
The Gray Level Co-occurrence Matrix (GLCM) energy attribute reveals its full potential when displayed using the ‘Roma’ colormap (figure 3). A comparison of rainbow (3a), ‘Viridis’ (3b) and ‘Roma’ (3c) displays show progressively improving feature definition. Roma excels in the central image portion, where depositional features emerge clearly without the artificial boundaries typical of rainbow displays. This clarity extends to subtle textural variations that often indicate depositional environment changes, offering superior facies discrimination. While rainbow colormaps introduce misleading depth-perception effects in yellow-to-red transitions that can mask sub-linear features, Roma maintains consistent visual integrity throughout the display range.
Diverging Attributes
For structural interpretation, curvature attributes present unique challenges. The ‘Balance’ colormap offers advantages over traditional red-white-blue schemes. The key improvement lies in its perceptually uniform progression and carefully designed neutral point, which avoids the artificial emphasis that pure white can create in traditional diverging colormaps.
When visualizing most-positive (k1) and most-negative (k2) curvature, the Balance colormap maintains equal visual weight for anticlinal and synclinal features (figure 4). This balanced design proves effective in areas where complex fault networks create subtle flexures and strain accommodation zones. Balance continues to improve visualization, as with Oslo, in the mid-range values, even when co-rendered with a transparent grayscale for similarity. The improved visualization helps interpreters better understand the relationship between different scales of deformation, leading to more comprehensive structural interpretation.
Cyclic Attributes
Phase and aberrancy attributes, critical for stratigraphic and structural interpretation respectively, require special consideration due to their cyclic nature. The ‘RomaO’ colormap effectively handles wrap-around points (±180 degrees) without creating artificial discontinuities. This improvement can be noticed in areas with subtle stratigraphic onlaps and truncations that might otherwise be obscured by colormap-induced boundaries (figure 5).
For aberrancy attributes, which are highly sensitive to subtle changes in reflector geometry, the RomaO colormap’s balanced color distribution prevents interpreter attention from being artificially drawn to specific azimuth ranges. When displaying these attributes, magnitude can be effectively shown as a translucent overlay, where high magnitude appears transparent and low magnitude as an opaque gray mask. This approach, combined with RomaO’s balanced visualization across all orientations, proves valuable when identifying features that align with known regional geological trends.
Discussion
Integration of these colormaps into seismic attribute analysis workflows is useful for improving visualizations that can better guide the interpreter. For instance, whether looking at faults with the coherence attribute displayed in Oslo, or the sweetness attributes in Lajolla for lithological variations, both new colormaps help improve clarity in mapping the more complex reservoir architectures. This improved visualization capability strengthens the bridge between geophysical data and geological interpretation, enabling more effective communication with drilling and reservoir engineering teams.
The technical barrier to implementing these color maps is minimal. Leading interpretation platforms including Petrel, Kingdom, and OpendTect support direct import of these color palettes through ASCII files or RGB values. For those working in Python environments, these colormaps are readily available through the matplotlib package. All colormaps discussed here are freely accessible, allowing immediate integration into existing workflows.
We encourage interpreters to move beyond traditional rainbow displays, despite their familiarity. While the initial visual shift may require adjustment, the improved geologic insight justifies the change. In an industry where subtle features can make the difference between successful and unsuccessful wells, better visualization tools, including simple changes in colormaps, provide a competitive edge in both exploration and development settings.