The shift from manual sectional modeling to implicit 3D modeling has been one of the largest methodology changes in exploration geology over the past two decades. The transition began with the introduction of Leapfrog Geo in the late 2000s, accelerated as Mira Geoscience's Geoscience ANALYST and Seequent's GOCAD-Mining-Suite matured, and now defines how most modern projects construct their 3D geological models. AI and ML methods are now layering on top of this established implicit modeling foundation, not replacing it.
The distinction matters. Implicit modeling itself — the use of radial basis functions, signed distance fields, and related mathematical techniques to interpolate geological surfaces from drillhole intercepts — is not new ML in the modern sense. It's a mathematical technique that became commercially practical in the 2000s and has matured steadily since. ML adds new capabilities on top: better automated handling of constraints, integration of geophysical data into the structural model, automated classification of geological features from logging data, and increasingly, generative methods for filling in geology between sparse data points. The result is a more capable modeling workflow without abandoning the validated foundation.
Where Implicit Modeling Earned Its Place
Before implicit modeling, 3D geological modeling for exploration and resource projects was largely a sectional workflow. A modeler would interpret cross-sections through the drillhole data, then triangulate the section interpretations into 3D surfaces. The work was slow, the output reflected each modeler's interpretive choices in ways that were hard to standardize, and updating the model when new drill data arrived was a substantial re-do.
Implicit modeling changed this by inverting the workflow. Instead of starting with sections and building surfaces, the implicit approach starts with the raw drillhole intercepts (e.g., contacts between different lithologies), interpolates a continuous scalar field that honors those intercepts, and extracts isosurfaces at zero crossings to produce the modeled volumes. The result is a 3D model that updates automatically when new drillholes are added, that produces a defensible mathematical interpolation rather than an interpretive drawing, and that scales to large datasets without the labor-intensive section drafting.
The trade-off is that implicit models can produce geologically unreasonable interpolations in regions with sparse data, and they don't naturally honor structural features (faults, unconformities) without explicit constraint construction. These limitations were the focus of two decades of methodology improvement and remain the focus of current development.
Where ML Methods Are Adding Value Now
Three categories of ML augmentation have demonstrated clear value in production geological modeling. First, automated lithology classification from drill imagery and downhole geophysics — discussed in our automated core logging post — produces structured lithology logs that flow directly into implicit modeling as more consistent input data. The model isn't doing ML; the inputs are.
Second, ML-assisted structural feature extraction from geophysical data. CNNs trained on labeled geophysical-survey datasets can identify lineaments, fault traces, and structural domains from magnetic, gravity, or EM data more consistently than manual interpretation. The output becomes input to the structural model as suggested fault locations, dip estimates, and zone boundaries that the modeler reviews and incorporates. The CNN doesn't replace the structural geologist; it accelerates the first-pass interpretation that the structural geologist refines.
Third, integration of geological constraints with geophysical inversion. The classical mode of geophysical inversion produces a 3D physical-property model (susceptibility, density, conductivity) from surface measurements; converting that to a geological model is a separate interpretive step. Joint inversion methods — sometimes wrapped in ML frameworks for constraint enforcement — produce inversions that are simultaneously consistent with geophysical data and with geological prior knowledge. The result is a more integrated 3D model in fewer steps. SimPEG, the open-source geophysical inversion framework, has been extending its capabilities in this direction; commercial inversion software is doing the same.
Generative Methods and the Sparse-Data Problem
An emerging area worth tracking is generative methods for filling in geology between sparse data. The fundamental problem in 3D geological modeling is that drilling is sparse — even a heavily drilled deposit has direct observations on a tiny fraction of the modeled volume — and the model has to extrapolate from those observations to fill the rest. Classical implicit modeling does this with smooth mathematical interpolation; generative methods do it by sampling from learned distributions of geological structure.
The methodology comes from the broader ML field of generative models — variational autoencoders, GANs, diffusion models — adapted to spatial geological data. The output is not a single model but a set of plausible models, each consistent with the data, that together represent the uncertainty in regions away from drilling. The advantage is that the uncertainty is structurally interpretable: not just "this region has more variance" but "here are five different reasonable geological interpretations of this region."
Adoption is early. The methods are present in the research literature, in some specialist software, and in academic-industry collaborations, but they're not yet standard in commercial implicit modeling platforms. For projects where the sparse-data uncertainty is a critical question — early-stage deposits, deep extensions of known mineralization, regional projects with limited drilling — these methods are worth tracking and will likely become more accessible over the next several years.
The Implicit Modeling Platforms Today
Leapfrog Geo, from Seequent (now part of Bentley Systems), remains the dominant commercial implicit modeling platform. The recent versions have added substantial constraint-handling capability, integration with the Leapfrog Edge resource estimation module, and increasingly sophisticated handling of structural complexity. Pricing is per-seat and meaningful, but the platform's market position is strong enough that most resource teams of any scale have at least one Leapfrog seat.
Geoscience ANALYST, from Mira Geoscience, takes a different approach: it's a free viewer with paid add-ons for specific functionality, and it integrates more tightly with geophysical inversion workflows (UBC-GIF's inversion codes, SimPEG) than Leapfrog does. For projects where the geophysical-geological integration is central, ANALYST is often the better fit; for projects where the focus is on the resource model itself, Leapfrog typically wins.
GOCAD-SKUA, from Aspen Technology (formerly Paradigm), serves a more oil-and-gas-aligned market and is less common in hard-rock exploration. Open-source options — GemPy, primarily — have matured to the point of being useful for academic and research work and for projects that need extreme methodology transparency, but the workflow integration and user-experience gap relative to commercial platforms remains substantial.
For most exploration and resource projects in 2026, the realistic platform choice is Leapfrog Geo, with Geoscience ANALYST as a complementary tool when geophysical-geological integration is central. GemPy is worth knowing about for specific use cases but isn't a routine production platform.
Hybrid Workflows: Where the Productivity Gain Lives
The most valuable pattern in modern 3D modeling is hybrid: classical implicit modeling for the geological framework, ML-assisted methods for specific sub-problems where they outperform the classical approach. A typical workflow might use Leapfrog Geo to produce the primary lithology and structural model, layer in CNN-derived structural interpretations from regional geophysics, validate the model against ML-clustered drillhole data, and iterate as new data arrives.
The productivity gain from this hybrid pattern is real but distributed. No single step shows a 10x improvement; instead, multiple steps each show 20-50% improvements that compound across the modeling cycle. A project that takes three months to produce a 3D model in a traditional workflow can compress to two months in a hybrid workflow, and the resulting model integrates more datasets more consistently.
The cost is in the integration work. Connecting the ML outputs to the implicit modeling platform is platform-specific and often requires custom scripting. Leapfrog has improved its API and integration story substantially in recent releases, but the gap between "ML output exists" and "ML output flows seamlessly into the implicit model" still requires meaningful integration work for most teams.
The Practical Failure Modes
Three failure modes show up regularly when teams try to integrate ML into 3D modeling without sufficient methodology discipline. First, ML-generated structural interpretations that the structural geologist disagrees with — and that get included anyway because "the model said so." The model is wrong sometimes, and the structural geologist's interpretation backed by field observation is often more reliable than a CNN's interpretation of geophysics. Treating the ML output as authoritative rather than as one input is a recurring source of bad models.
Second, classification models trained on data from one deposit applied to a fundamentally different deposit. A CNN trained on porphyry copper drill imagery will not classify orogenic gold core sensibly. The training data has to match the application context, and this constraint is easy to overlook when an off-the-shelf model is available.
Third, model complexity that exceeds what the data supports. A high-flexibility ML method given sparse data will produce a model that fits the data closely while extrapolating poorly. Cross-validation can detect this; the cure is methodology simplification or additional data, not more complex models.
What's Coming Next
The active research areas in 3D geological modeling that are likely to show up in commercial workflows over the next several years are: better uncertainty quantification through generative methods, tighter integration of geophysical inversion with implicit modeling through joint optimization, more sophisticated automatic constraint handling for structural complexity, and faster compute through GPU acceleration of the underlying interpolation methods.
None of these will replace the implicit modeling foundation that's been established over the past two decades. They'll layer on top, adding capability and reducing labor. The fundamental workflow — drill data in, structured 3D model out, updated as new data arrives — is stable for the foreseeable future.
A Practical Approach
For a project that needs a current 3D model and hasn't kept up with recent capabilities, the right first step is to ensure the basic implicit modeling workflow is in place and producing clean output. Layer in ML augmentation only where the basic workflow is already working — automated lithology logging if drilling is active, ML-assisted geophysical interpretation if regional geophysics is rich, constraint-based joint inversion if both geophysics and geology are central to the deposit type.
For a project with a mature modeling workflow already, the highest-leverage next step is usually better integration of existing tools rather than adoption of new methodology. Reducing the manual work of moving data between platforms produces compounding gains; chasing the latest ML method usually doesn't.
For consulting support on modernizing your 3D modeling workflow without disrupting the established methodology, our free workflow audit covers modeling and data integration workflows, or contact us for a deeper conversation.