Artificial intelligence(AI) is transforming seismic interpretation. Tasks that once took weeks—fault detection, channel mapping, facies classification, geobody extraction—can now be accelerated with AI models that generate consistent, survey-wide results. It’s a major leap forward for geoscience teams looking to reduce manual effort, improve repeatability, and make faster decisions.

With that momentum, a question naturally comes up:

Do we still need seismic attributes in the age of AI?

Some argue that modern deep learning can “learn everything” directly from raw seismic amplitudes. In theory, that sounds plausible. In real projects—where noise, acquisition footprint, varying wavelets, survey stitching, processing differences, and geological complexity are everyday realities—the answer is clear:

Yes, seismic attributes are still essential.

And even better: when combined with AI, seismic attributes make interpretation significantly more powerful.

Seismic attributes remain one of the most effective ways to enhance subsurface features, improve visibility of geological patterns, and deliver richer information to both interpreters and machine learning models. In the AI workflow, attributes act as high-value, fit-for-purpose inputs that help AI models learn faster, generalize better, and produce cleaner, more trustworthy probability volumes.

At SubsurfaceAI, seismic attributes and AI are not separate workflows—they’re designed to work together. Our platform enables users to interactively compute the right attributes for the job and use them directly in AI and machine learning training and prediction. The result is a practical, integrated workflow that improves interpretation quality and accelerates decision-making.

From Human Interpretation to AI Probability Volumes

Traditional seismic interpretation is a pattern-recognition process. Interpreters examine sections and time slices, integrate geological context, and map features based on subtle variations in amplitude, continuity, frequency, and structure. Over time, the industry developed seismic attributes as tools to make those patterns stand out more clearly.

AI-based interpretation builds on that same foundation—but instead of hand-picking every fault or channel, the interpreter trains a model and receives results in the form of probability volumes, such as:

• Fault probability
• Channel probability
• Facies probability
• Geobody probability
• Stratigraphic feature probability

These probability volumes can be generated for both 3D seismic volumes and 2D seismic sections, and they provide an immediate, consistent view of what the model “believes” is present in the data.

But here is the key point:
AI is still interpreting a signal—and the clarity of that signal matters.

That’s exactly where attributes come in.

Attributes Help AI “See” What Matters

Seismic attributes were created to highlight subsurface signatures that can be difficult to detect in raw amplitude data. They bring out discontinuities, enhance textures, reveal subtle stratigraphic architecture, and expose frequency-related features. In short, they help interpreters see more—and they help AI models learn more.

Consider a simple example: a semblance (coherence) slice.

Semblance highlights discontinuities—features that often correspond to faults, fracture corridors, channel edges, or stratigraphic breaks. On a semblance slice, these discontinuities may jump out clearly, even when the original amplitude slice looks ambiguous.

But semblance alone does not tell you why the discontinuity exists. It still requires interpretation:

• Is the discontinuity a fault plane?
• Is it a channel margin?
• Is it stratigraphic truncation?
• Is it acquisition footprint?
• Is it processing artifact?

A trained AI model can learn to make these distinctions—just like an interpreter—when given the right training data and the right inputs. Semblance can be one of those inputs, along with other attributes that provide complementary information.

This is why seismic attributes remain so valuable in AI workflows: attributes enhance separability. They make it easier for models to distinguish between real geological signals and non-geological noise, and between one feature class and another.

Fit-for-Purpose Attributes: The Smart Way to Improve Results

Not all attributes are useful for every problem. And using too many attributes without strategy can actually make results worse.

A modern, best-practice approach is to compute fit-for-purpose attributes—the small set of attribute families that match the target feature. For example:

• Fault and fracture detection benefits from discontinuity and structure-sensitive attributes (e.g., semblance/coherence, curvature, dip variance).
• Channels and stratigraphy benefit from geomorphology-friendly attribute blends (e.g., sweetness, RMS amplitude, spectral decomposition, continuity measures).
• Reservoir properties such as porosity and lithology benefit from attributes that capture elastic sensitivity and stratigraphic consistency—often including pre-stack and partial-stack attributes.

In SubsurfaceAI, this philosophy is built directly into the workflow. Users can compute the attributes they need, evaluate them visually, validate them against known geology, and feed them immediately into AI or ML models—all within the same platform.

Why Seismic Attributes Still Matter in AI Interpretation

Here are three high-impact reasons seismic attributes remain essential—and how they elevate AI interpretation workflows in SubsurfaceAI.

1) Train Better AI Models for Faults, Geobodies, and Channels

AI models learn patterns across large datasets. The better those patterns are represented in the input data, the better the results.

For structural interpretation, attributes provide powerful inputs that help AI detect and map discontinuities more accurately:

• Fault probability volumes become sharper and more continuous.
• Fault edges become better defined.
• False positives from noise and footprint are reduced.
• Fault systems become easier to validate and interpret in context.

For stratigraphic interpretation, attributes help highlight the geometry and texture associated with depositional features:

• Channels become more distinct in slices and volumes.
• Thin-bedded and subtle stratigraphic features become easier for models to learn.
• Geobody boundaries become clearer.
• Results become more consistent across varying signal-to-noise conditions.

Just as importantly, attributes help reduce the amount of training effort required. When the input already emphasizes the feature, the model often needs fewer examples to learn effectively.

The outcome: interpreters can train better models faster—and generate probability volumes that are more reliable for decision-making.

2) Use Pre-Stack and Partial-Stack Attributes to Predict Reservoir Properties

When the goal goes beyond mapping geometry—when the real target is rock and fluid behavior—pre-stack information becomes vital.

Reservoir properties such as lithology and porosity are often linked to elastic contrasts that express themselves through angle-dependent behavior. That’s why pre-stack and partial-stack attributes can provide critical predictive power.

With SubsurfaceAI, users can incorporate pre-stack-derived attribute suites to build models that support:

• Lithology discrimination
• Porosity prediction
• Elastic property trends
• Reservoir quality mapping
• Fluid-related anomaly screening (where applicable)

AI and ML models thrive on richer, more diagnostic inputs. Partial stacks and AVO/AVA-based attributes can capture subtle variations that may not be visible on full-stack amplitude alone.

This is a major advantage:
AI can be driven by rock-physics sensitivity—not just image recognition.

When you combine fit-for-purpose pre-stack attributes with a guided machine learning workflow, you create a practical path from seismic response to reservoir properties at scale.

3) Integrate Well Logs and Seismic Attributes to Generate Property Volumes (“Log Volumes”)

The subsurface is always sampled unevenly:

• Wells provide high-resolution truth, but only at a limited number of locations.
• Seismic provides continuous coverage, but with lower vertical resolution and a more complex relationship to reservoir properties.

Machine learning bridges this gap.

By training ML models with well logs as ground truth and seismic attributes as predictors, users can generate property volumes across the reservoir interval—often referred to as “log volumes.” These can include:

• Porosity volumes
• Vshale volumes
• Facies probability volumes
• Net-to-gross proxies
• Stratigraphically constrained property distributions

This workflow is especially valuable when:

• well control is sparse or clustered,
• reservoir quality changes laterally,
• stratigraphic architecture is subtle,
• deterministic methods are too linear for the problem,
• or teams need fast screening volumes to guide decisions.

Because attributes can be sampled at well locations and then predicted across the full seismic grid, they serve as the essential link between sparse and dense data domains.

The outcome: faster reservoir characterization, better property mapping between wells, and improved understanding of the reservoir interval—without sacrificing geological control.

The SubsurfaceAI Advantage: Attributes + AI in One Integrated Workflow

In many environments, attribute computation and AI model building are disconnected workflows. Data moves between applications, quality control gets fragmented, and iteration becomes slow.

SubsurfaceAI takes a different approach: an integrated platform where seismic attributes and AI/ML workflows work together by design.

With SubsurfaceAI, users can:

• interactively compute fit-for-purpose seismic attributes,
• visualize and validate attributes immediately in 2D and 3D,
• generate training datasets efficiently,
• train AI models for faults, channels, facies, and geobodies,
• run predictions and create probability volumes,
• integrate well logs and attributes for reservoir property mapping,
• and iterate quickly—because everything happens within one environment.

This integration reduces workflow friction and improves outcomes. Teams spend less time managing data and more time interpreting results.

Better Interpretability and Stronger QC

One of the biggest barriers to adopting AI interpretation is trust. Interpreters and decision-makers need confidence that results reflect geology—not artifacts.

Attributes help here too.

Because interpreters already understand the behavior of common attributes, they can QA AI outputs more effectively:

• Does fault probability align with coherence discontinuities?
• Do channel probabilities match geomorphologic attribute patterns?
• Do facies probabilities honor known depositional trends and well control?
• Do property volumes behave consistently within stratigraphic intervals?

This makes AI outputs more transparent and actionable—and dramatically improves team confidence in the results.

The Bottom Line

AI is transforming seismic interpretation—but it doesn’t eliminate the need for seismic attributes. Instead, it amplifies their value.

Seismic attributes remain essential because they:

• enhance subsurface features and improve visibility of geology,
• provide fit-for-purpose inputs that help AI learn more effectively,
• improve model performance and generalization,
• support pre-stack sensitivity for reservoir property prediction,
• enable ML integration of wells and seismic for property volumes,
• and strengthen QC and interpretability of AI results.

In the age of AI, seismic attributes are no longer just “nice-to-have” tools. They are a strategic advantage—especially when paired with an integrated platform built for modern interpretation.

With SubsurfaceAI, attributes and AI work together to deliver faster, smarter, and more consistent subsurface understanding—so you can move from data to decisions with confidence.