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September 16


 



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THE HILL REPORT
A Favorability Map Is a Ranking, Not a Finding
Connor Hill · InsightfulWord · September 16, 2026
There is a real federal program applying machine learning to mineral
assessment, it has been documented publicly since 2022, and what it does is
almost exactly the opposite of what the phrase suggests to a general reader.

The program is a collaboration between the American defense research agency
and the federal geological survey. Its purpose is to compress the time required
to produce a formal mineral resource assessment, which had been running to
roughly two years for a single deposit type using manual methods.

The bottleneck it attacks is not geology. It is paperwork. Decades of
geological maps and reports exist as scanned images and printed documents, and
extracting the information from them into a machine-readable form is a manual
task of enormous scale.

That is what the automation does. Officials describing the work have stated it
plainly: the process for pulling data from maps and documents, compiling it,
and making it analysis-ready has been automated. Pilot assessments demonstrated
a workflow running start to finish in about two and a half days.

What the output is, however, has not changed. A mineral resource assessment is
a probabilistic statement about deposits that have not been found, in areas
where the geology permits them, using statistical models built from deposits
that have been found elsewhere.

It is not a location. It is not a quantity anyone can extract. And the
agencies themselves state that generated assessments still have to pass
scientific standards and peer review before they are adopted, which means the
machine output is an input to a process rather than a conclusion from one.

The gap between that and a map of found deposits is the entire subject, and
the methodology that defines the gap has been published for decades and is not
difficult to follow.

What follows is what the program was built to do, the three parts of a formal
assessment, why the training data necessarily points backward, why a
probability is not a deposit, and what would turn a favorable area into a
discovery.


What the Program Was Built to Do

The motivation is a statutory obligation running into a practical constraint,
and both are on the record.

Legislation passed in 2020 directed the geological survey to assess the
country's critical mineral resources. The agency tracks well over a hundred
commodities, a large number of which meet criticality thresholds on supply-risk
grounds.

Producing a formal assessment for each of those, by the established method, at
roughly two years per deposit type, is arithmetic that does not work. The
program exists to change the arithmetic.

The approach was to move the workflow from a serial, largely manual,
intermittently updated process to a parallel, continuous, machine-assisted one.
The tasks targeted were document and map processing rather than interpretation.

That focus is the informative part. The scarce resource in mineral assessment
is not ideas about where deposits might be; it is the labor of assembling the
underlying observations into a consistent dataset. Geological maps from
different eras use different symbologies, projections, unit names and
conventions, and reconciling them has historically consumed most of the effort.

A competition run in 2022 to develop these capabilities, followed by pilot
assessments and structured development events through 2024, produced the
working pipeline. The published accounts describe extraction and compilation,
not discovery.

The difficulty of that extraction is easy to underrate. A single geological
map encodes unit boundaries, fault traces, dips and strikes, a legend whose
symbols vary by publishing era, and a projection that may predate modern
datums. Converting a scanned sheet into georeferenced features that a model can
use is a problem in computer vision and cartography rather than in earth
science, which is exactly why it was tractable to automate.

The Three Parts of a Formal Assessment

The method that the output feeds into has been standard for decades, is
documented in published short courses, and consists of three distinct
operations.

📌 Fresh Signal Two years to 2.5 days The reduction in the time required to
run a critical mineral resource assessment workflow demonstrated by a joint
program of the U.S. Geological Survey and the Defense Advanced Research
Projects Agency. What was automated is the extraction of geospatial data from
maps and documents and its compilation into analysis-ready form — not the
interpretation. Generated assessments still have to pass the survey's
scientific standards and peer review before adoption. Source: DARPA, USGS
collaborate to accelerate critical mineral assessment, 2025.

Support or oppose: should probabilistic resource assessments be published with
a plain-language statement of what they are not?

Supporters argue that these documents are routinely quoted as though they
located something, that the misreading is predictable and consequential where
public land is involved, and that a short standard preface would cost an agency
nothing. Opponents answer that the methodology is already stated in every
report, that scientific publications should not be written around the
possibility of misquotation, and that a disclaimer would be stripped out by
exactly the people who misquote them. Which is better?

Hit reply — one line is enough.

The first part delineates permissive tracts: geographic areas where the
geology permits deposits of a specified type to exist. Boundaries are drawn
from mapped rock units and extended using geophysical surveys that detect
permissive rocks under cover.

The word permissive is carrying the weight. It means the geology does not rule
the deposit type out. It does not mean anything is there.

The second part builds grade and tonnage models. Frequency distributions of
the grades and tonnages of well-explored deposits of a type serve as
statistical models for the undiscovered ones — on the reasoning that deposits
of the same type tend to resemble one another.

The third part estimates how many undiscovered deposits the tract contains,
expressed as a probability distribution rather than a number. The published
description is explicit that these estimates represent the probability, or
degree of belief, that some fixed but unknown number of undiscovered deposits
exists.

Combining the three produces a probabilistic statement about metal that might
be present in an area. Every word in that sentence is doing necessary work, and
the compressed versions that circulate keep none of them.

Why the Training Data Points Backward

The machine-learning component introduces a specific limitation that is well
described in the technical literature and is structural rather than fixable.

A model that predicts where deposits are must be trained on places where
deposits are known to be. Those are the only labeled positive examples
available.

This makes the model a formalization of the resemblance between unexplored
ground and ground where discoveries were already made. It is genuinely useful —
resemblance is real information, and the established assessment method rests on
the same premise. It also means the model is structurally incapable of
identifying a deposit type nobody has found yet.

The negative examples are worse. There is no reliable register of places
confirmed to contain nothing, because confirming absence requires drilling that
was never funded. Practitioners construct negative samples by various
conventions, each of which imports an assumption about what non-prospective
ground looks like.

The imbalance is extreme. Known deposits are vanishingly rare relative to the
area being classified, which is a recognized problem in this literature with
its own methods and its own failure modes — a model can achieve excellent
accuracy by predicting that nothing is anywhere.

And the training locations themselves carry a sampling bias. Discoveries
cluster where exploration happened, which followed roads, terrain, land access
and historical accident as much as geology. A model trained on that pattern
learns exploration history alongside ore genesis, and cannot separate them.

None of this makes the technique unsound. It makes it a tool for prioritizing
where to spend exploration money, which is what its developers say it is.

A Probability Is Not a Deposit

The step from an assessment to a quantity of metal involves several
conversions, each of which loses something.


Context — what a resource assessment implies about any particular company

A probabilistic estimate of undiscovered deposits in a region is not a
statement about any firm's assets. It does not establish that anything will be
found, that a discovery would be economic, that the ground is available, or
that a company holding claims nearby has any relationship to the assessed
material. Assessments are inputs to public land-use and policy decisions, and
are published for that purpose. Nothing here is a comment on any specific
company, sector or security, and none of it is a recommendation.

The assessment output is a distribution over the number of undiscovered
deposits, combined with distributions over their possible grades and tonnages.
Multiplying through gives a distribution over contained metal, not a figure.

That distribution has a long tail and a substantial probability mass at or
near zero for many tracts. Quoting its mean, or worse its upper percentile, as
the resource is a misuse that the methodology documentation specifically warns
against.

The grade and tonnage models describe premining figures at the lowest cutoff
grade — the total metal the deposit contained before anyone touched it,
including material that would never be economic to extract. They are
deliberately not economic estimates.

There is no economic filter anywhere in the three parts. The method is
designed to describe what the geology could hold, precisely so that the
economic question can be asked separately against whatever prices and costs
prevail when it is asked.

And the assessments are for undiscovered deposits by construction. Material
already identified is excluded from them, because the purpose is to estimate
what remains to be found.

The unit of the estimate is also worth noticing. The method counts deposits,
not tons, and a deposit in this framework is defined by the model type it
belongs to. A tract credited with a handful of undiscovered deposits of a
low-tonnage type and one credited with the same number of a high-tonnage type
describe very different quantities behind an identical-looking number.

What Turns a Tract Into a Discovery

The distance between a favorable area and a mine is measured in years and in
drill meters, and each stage is documented.

Reconnaissance comes first: geological mapping, geochemical sampling of soils
and stream sediments, and geophysical surveys over the favorable ground. This
narrows an area measured in thousands of square kilometers to targets measured
in hundreds of hectares.

Drilling is the only step that establishes whether anything is present. A
target becomes a discovery when holes intersect mineralization of a grade and
width that justifies continuing, and the great majority of drilled targets do
not.

Delineation follows: enough drilling on a close enough spacing to establish
continuity and estimate tonnage and grade with defined confidence, which is
what permits classification under securities disclosure rules.

Studies come next — metallurgical test work establishing recovery, then
engineering and economic studies at successive levels of precision, each of
which can and frequently does terminate the project.

Permitting and financing run alongside and after, and each has its own
attrition.

The base rate across that sequence is the number worth carrying. The
industry's own figures for the proportion of identified prospects that become
operating mines are very small, and the proportion of favorable ground that
ever hosts a drill hole is smaller still.

The composite point is that a machine-assisted assessment is a faster way of
producing a document that says where it is worth looking, that the document has
always said that, and that the interval between saying it and knowing anything
runs through drilling nobody has yet paid for.


The bill, not the debate

The federal program applying machine learning to mineral assessment automated
the extraction of data from old maps and documents, compressing a two-year
workflow to about two and a half days. What it produces is a probabilistic
estimate of undiscovered deposits in areas where geology permits them, with no
economic filter and no located ore — and it still goes through peer review.
When a map is described to you as having found something, is the word
permissive anywhere in the description?

Connor Hill reads every reply.

Sources checked • Defense Advanced Research Projects Agency — critical mineral
assessments with AI support, program page —
[link removed]

<[link removed]>
• Defense Advanced Research Projects Agency — USGS and DARPA collaborate to
accelerate critical mineral assessment —
[link removed]
<[link removed]> • U.S. Geological
Survey — winners of the artificial intelligence competition to aid critical
minerals assessments —
[link removed]

<[link removed]>
• U.S. Geological Survey — short course introduction to quantitative mineral
resource assessments —[link removed]
<[link removed]> • U.S. Geological Survey —
basic concepts in three-part quantitative assessments of undiscovered mineral
resources —[link removed]
<[link removed]> • U.S. Geological Survey — Global
Mineral Resource Assessment Project —
[link removed]
<[link removed]> Connor Hill · InsightfulWord



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