What you will learn
- What exploration data looks like, and why it ends up scattered
- How drill holes, assays and surveys are stored and checked
- How maps and sections are built from the stored data
- What a target ranking needs beyond a pretty map
- Where data management ends and geological judgement begins
What this project is
Exploration generates many kinds of data: drill hole locations and depths, assay results from samples, geophysical surveys, surface mapping, and historical records from earlier explorers. Left in spreadsheets and separate files, that data cannot be queried as a whole, and a geologist planning the next hole cannot be sure they have seen everything. A platform stores the data in a structured way, with checks that catch errors early, so the picture of the ground is built from all the evidence, not just the file someone remembered.
Data quality is the point. A drill hole with the wrong coordinates, an assay with the wrong sample depth, or a survey with an unrecorded datum can mislead a whole program. The platform should validate data on entry: coordinates within the project area, depths that do not exceed hole length, assays linked to the right samples. Errors found at entry are cheap; errors found after a hole is drilled on the wrong target are expensive.
Core features
- Drill hole database with collar locations, surveys, lithology and assay results linked by hole and depth
- Sample tracking from field collection through laboratory results
- Geophysical and surface data storage with coordinate systems and datums recorded
- Validation checks on entry, flagging impossible depths, out-of-area coordinates and missing links
- Map and section views that draw from the same database, so every view agrees
- Target management, recording why each target was chosen and what each hole found
How it is built
- Start with the existing data: audit what exists, in what formats, and with what coordinate systems
- Build the drill hole and assay database with validation, and load historical data with errors flagged, not silently fixed
- Add sample tracking and survey storage, then map and section views
- Add target management so drilling decisions leave a record
- Pilot on one project, comparing the platform's picture of the ground with the old scattered files
Technology considerations
Coordinate systems are a classic trap. Data collected over decades may use different datums, and a hole plotted in the wrong datum can be hundreds of metres from its true position. The platform must record the datum for every dataset and convert consistently. Assay data needs similar care: detection limits, units and laboratory methods differ, and comparing results across laboratories without recording those differences produces false patterns.
The database should be built for querying, not just storage. A geologist should be able to ask for all holes near a target, all assays above a grade in a depth range, or all samples awaiting laboratory results, without exporting to a spreadsheet and rebuilding the query by hand. Versioning matters too: when an assay is corrected, the old value should remain visible, so past decisions can be understood.
For the first version, load one project's drill holes and assays with validation turned on, and fix the errors the validator finds before loading history from other projects. Geologists judge the platform on whether they can ask a question, such as all holes near a target, and get an answer they trust. A platform that does that for one project is a better foundation than a platform loaded with every project at once, full of unflagged errors.
Challenges to plan for
- Historical data with unknown datums or missing sample links
- Assay results that arrive in different units or with different detection limits
- Drill holes that were never surveyed properly, leaving their true path uncertain
- Targets chosen on one dataset while another dataset, filed elsewhere, contradicts them
Who it is for, and its limits
This suits an exploration company, a junior miner or a geological consultancy managing drill programs. It fits projects where drilling is expensive and data accumulates over years. It does not fit a single short program with a handful of holes, and it is not a resource estimation system: it organises the evidence, it does not calculate reserves.
An exploration platform does not find ore. It stops good ground being missed because data was scattered, and stops bad holes being drilled because data was wrong. Costs vary with project count and historical data volume, so confirm with a quote after a data audit.
Frequently asked questions
Why does datum matter so much?
Because the same coordinates mean different places under different datums. A hole plotted under the wrong datum can miss its target entirely, and the error is invisible on a map that looks correct.
How are assay errors caught?
By validation on entry: depths within hole length, samples linked to holes, units recorded, and detection limits noted. Suspicious values are flagged for checking, not automatically accepted.
Can historical data be loaded?
Yes, but with its problems visible. Missing links and uncertain datums are flagged rather than hidden, so geologists know which old data to trust and which to verify.
Does the platform rank targets?
It records the evidence for each target and what drilling found, which supports ranking. The ranking judgement itself stays with geologists, who weigh evidence the database cannot weigh.
What happens when an assay is corrected?
The correction is recorded with the old value kept visible. That way, decisions made on the old value can still be understood, and the current picture always uses the corrected value.