The most consequential decision in any active exploration project is where to put the next drill hole. A typical diamond drill hole in a moderately remote jurisdiction costs $150 to $500 per meter all-in, which means a 500-meter hole is a $75,000 to $250,000 commitment. Across a 5,000-meter program, the cumulative decisions about where to drill — and crucially, where not to drill — drive most of the project's budget and most of its information value. Doing this decision-making well is the highest-leverage thing an exploration manager does.
Traditionally, drill targeting is a synthesis exercise. The exploration geologist integrates the geophysics, the geochemistry, the surface geology, the structural model, and the prior drilling results into a target ranking, presents it to the team, and the team picks the next holes. The synthesis is often excellent — there's no substitute for an experienced exploration geologist's pattern recognition — but it's also opaque, hard to communicate, and inconsistent across geologists. The case for AI-assisted drill targeting is not that it replaces the synthesis, but that it makes it explicit, reproducible, and amenable to systematic improvement.
Two Distinct Problems Inside Drill Targeting
It helps to separate two questions that get blurred in casual discussion. The first is target generation: identifying the broad zones where drilling might be productive. This is the regional-to-district-scale prospectivity problem, where ML methods have been delivering value for several years. The second is target ranking and prioritization: among a set of candidate targets, which should be drilled first, in what sequence, with what hole orientation and depth. This is a different problem, with different methods.
Target generation is well covered in our prospectivity mapping post. Target ranking, by contrast, is about optimization under uncertainty: given a budget and a set of candidate locations with associated probabilities and expected payoffs, what drilling sequence maximizes the information per dollar spent. This is where Bayesian methods, value-of-information analysis, and sequential decision frameworks earn their keep.
The methods that work for one problem don't necessarily work for the other. A random forest prospectivity model can identify ten candidate targets; it cannot tell you which to drill first, with what spacing, in what order. That decision requires reasoning about uncertainty, cost, and the marginal information value of each candidate hole — a different kind of analysis.
Bayesian Optimization for Hole Placement
The core technical idea behind AI-assisted drill ranking is Bayesian optimization, a methodology that comes from the operations research and ML hyperparameter tuning literature and has been adapted to spatial sampling problems. The setup is roughly: you have a function (in our case, mineralization grade or thickness) that's expensive to evaluate at any single point (drill cost), and you want to find the maximum or characterize it efficiently across a region (the target area). The Bayesian optimizer maintains a probabilistic model of the function across space, suggests the next evaluation point that maximizes either the expected improvement or the information gain, evaluates there, updates the model, and iterates.
Applied to drilling, the iterative loop looks like this. You start with prior geological knowledge and any existing drilling. The Bayesian model produces a posterior probability surface of mineralization across the target. You drill a hole, the assay results update the model, and the model suggests the next hole based on where additional information would be most valuable. Over a multi-hole campaign, this produces a drill sequence that's measurably more efficient at characterizing the target than a regular grid or geologist-intuition pattern.
The catch is the same as for all Bayesian methods: the output depends on the prior. A poor prior — overconfident, mis-located, wrong about the deposit-type model — produces poor recommendations. The methodology amplifies the quality of the input geological model rather than substituting for it. Where Bayesian optimization works best is in projects where the geological framework is reasonably well understood and the question is efficient delineation rather than initial discovery.
Value-of-Information Analysis
A related but distinct framework is value-of-information (VOI) analysis. The setup is: each candidate drill hole has a cost, a probability of confirming or refuting some hypothesis (e.g., "this zone hosts economic mineralization"), and an associated value of resolving that uncertainty (e.g., the expected increase in project NPV given a positive result). VOI tells you which holes are worth drilling — those whose information value exceeds their cost — and in what order.
VOI is more strategic and less iterative than Bayesian optimization. It's the right tool for "should we drill this target at all" decisions, particularly on borderline targets where the cost is significant relative to expected payoff. The methodology comes from the decision analysis literature, has been applied to oil and gas exploration for decades, and is now being adapted more systematically to hard-rock targeting.
The practical use case is portfolio-level decisions. A junior with five targets and a budget for three drill programs uses VOI to rank which three to drill first, defer the others, or drop them entirely. The analysis forces explicit reasoning about uncertainty and expected payoff that's often left implicit in geologist-only ranking, and the framework produces decisions that survive board scrutiny better than intuition-driven prioritization.
Sequential Decisions and Adaptive Programs
The third related concept is sequential decision-making — designing a drilling program as a series of decisions rather than a fixed plan. Traditional drilling programs are largely pre-committed: the next 5,000 meters are planned upfront based on initial targets, and execution proceeds through the plan. An adaptive program, by contrast, drills a small initial batch, evaluates results, and re-plans based on what the data shows.
The case for adaptive programs is mathematical: information from early holes substantially changes the optimal placement of later holes. A fixed plan that doesn't incorporate this information leaves expected value on the table. The case against adaptive programs is logistical: drill rig contracts, project schedules, contractor coordination, and seasonal access windows often make adaptive re-planning expensive in practice. The right balance varies by project, jurisdiction, and rig type.
AI tooling makes the analytical side of adaptive planning more tractable. After each batch of assay returns, an updated Bayesian model produces a refreshed target ranking and updated hole-by-hole recommendations. Whether the team acts on those recommendations or maintains the original plan is a project-management decision, but having the analysis available shifts the conversation from "what does our gut say about the new data" to "what does the explicit model say, and do we agree with it."
The Real-World Constraints AI Doesn't Capture
The honest limitation of these methods is that the real drilling decision is constrained by factors the optimization doesn't see well. Surface access — roads, drill pad construction, environmental permits — often dictates where holes can be drilled, regardless of where the optimal targeting suggests. Rig type, hole depth, and angle constraints further narrow the feasible set. A model that recommends the geometrically perfect hole on a slope that no drill rig can reach is providing a target the team cannot execute.
The realistic implementation incorporates these constraints into the optimization explicitly: candidate hole locations are filtered against access maps, slope thresholds, environmental restrictions, and rig capability. The AI works inside the feasible set rather than recommending outside it. Doing this well requires the AI workflow to be tightly integrated with the project's GIS data and execution planning, not living in a separate analytical world.
The second class of constraint is interpretive: the team has reasons to want a particular hole drilled — to test a specific structural hypothesis, to extend an interpreted continuity, to follow up a specific geochemical anomaly — that may not be obvious to the model. The right pattern is to use AI ranking as one input to the targeting discussion, alongside geologist intuition and project-specific strategic priorities, rather than as a binding recommendation.
What a Useful Implementation Looks Like
A practical AI-assisted targeting setup for a working junior has a few components. A spatial database of all existing drilling, with assay results and geological logs structured for query. A model — Bayesian or random-forest-based — that ingests the drilling, surface data, and geological framework to produce a probability surface. A target ranking that respects access and execution constraints. A workflow that re-runs the ranking after each batch of assay returns and produces a one-page update on whether the optimal next holes have changed.
The setup is not exotic. It's a few hundred lines of Python code, a sensible database, and integration with the project's existing GIS. The benefit is reproducibility: the same data and the same model produce the same ranking, and disagreements with geologist intuition are documentable rather than hand-wavy. Over a multi-program project, this consistency compounds — the project ends up with a defensible record of why each hole was drilled, which is exactly the documentation that resource consultants and external technical reviewers look for.
Cost and Adoption Realities
Building this capability in-house for a project that's actively drilling runs roughly 4 to 8 weeks of dedicated work by someone with both geological and Python skills. The result is a project-specific tooling stack that's reusable across the project's life and partially portable to other projects in the same shop. The total cost is on the order of $30,000 to $80,000 in consulting time, depending on the complexity of the existing data and the team's capacity to absorb the methodology.
Contracting the capability externally — using a firm that builds the model and hands over rankings — runs in similar ranges, with the difference being that the in-house path leaves the team owning the tooling at the end and the contracted path leaves them dependent on the provider for ongoing updates. For a project with a multi-year drilling horizon, in-house typically pays back faster. For a one-off program, contracted is the right choice.
The smallest entry point is a one-time VOI analysis on a portfolio of candidate targets, without building the full Bayesian re-planning infrastructure. This is a one-to-two-week piece of consulting work, costs $10,000 to $25,000, and produces a defensible target ranking that survives board scrutiny. For a junior facing a "which of these three targets do we drill first" decision, this is often the right starting point.
What This Does Not Replace
An AI-assisted drill targeting workflow does not replace the exploration geologist's hypothesis generation. The model evaluates targets within a hypothesis framework; the framework itself — what deposit type, what structural setting, what controls on mineralization — comes from the geologist. The cleanest division of labor is: the geologist owns the hypothesis and the deposit-type model, the AI owns the ranking and prioritization given the hypothesis, and the team revisits the hypothesis when the data tells them they should.
The model also doesn't tell you when to stop drilling. The decision to terminate a program or move to a new target requires judgment about cumulative information value, market timing, and project strategy that no automated tool captures well. AI ranks the next-best hole conditional on continuing; the decision to continue at all is a strategic one.
A Reasonable Place to Start
If your project is actively drilling and you've never used systematic targeting analysis, the highest-leverage starting point is the one-time VOI analysis described above. The cost is contained, the deliverable is concrete, and the exercise teaches the team what kind of decisions the methodology is best suited for. Going from there to full Bayesian re-planning is a bigger commitment but a natural next step once the initial value is demonstrated.
For an outside view on whether AI-assisted targeting would meaningfully change your next drill program, our free workflow audit covers exploration data and drilling workflows, or contact us to discuss what a pilot VOI study would look like.