E2 · Publication Volume 24

Geological Observation Entities

lithology, alteration, structure, mineralisation and weathering

Learning objectives

  • Explain why lithology, alteration, structure, mineralisation and weathering require explicit semantic modelling.
  • Design identities, relations and constraints that preserve geological observation entities across exchange.
  • Separate hard release gates from diagnostic metrics and interpretation choices.
  • Produce a normalised observation package with explicit supports and interpretation links from synthetic evidence.

The lesson is complete only when the learner can defend both the model and the release decision. A neat schema without evidence, tests or declared limitations is an unverified design. The assessed artefact must make assumptions visible and distinguish source assertions from derived conclusions.

Decision context

A schema should answer who or what made the observation, where its support lies, which concept was assigned, how confident the assignment is and whether the claim supersedes another claim. Forcing all geological meaning into one “rock code” discards relationships needed for reinterpretation.

Start with a decision record: name the intended use, the evidence required, the consequence of error, the accepted uncertainty and the role authorised to accept residual risk. Then ask whether the proposed model can answer the decision question without relying on filename conventions, row order, undocumented defaults or someone’s memory. This prevents technology selection from concealing a missing semantic requirement.

The same record may be fit for one use and unfit for another. A rapid exploratory view can tolerate conditions that a released exchange package cannot. Fitness is therefore stated against a use, contract version and quality gate rather than attached permanently to the data.

Core concept

A geological log is a set of observation claims, not a direct copy of the subsurface. Lithology, alteration, structure, mineralisation and weathering may overlap, use different supports and change with new evidence. Modelling them as separate observation families preserves both co-occurrence and interpretive uncertainty.

The working scope is lithology, alteration, structure, mineralisation and weathering. For each item in that scope, distinguish the thing itself, the label used by a source, the claim made about it and the record that carries the claim. Identity is not a display name; a value is not its unit; an observation is not a model; current is not the same as valid. These distinctions create explicit places for correction, uncertainty and competing interpretations.

A good semantic design can be explained as a set of sentences before it is encoded. Each sentence identifies a subject, a property or relationship, an object or result, and the context under which the claim holds. Physical tables and files are then projections of those sentences, not their source of meaning.

Semantic model

Use an observation record with feature of interest, property family, assigned concept, geometry or interval support, method, observer role, event time, confidence and evidence references. Separate descriptive observations from genetic interpretations. Permit multiple non-exclusive observations over the same support and link competing interpretations without overwriting them.

Test every proposed record against seven questions: What has identity? What type is it? Which property or relationship is asserted? Which spatial and temporal context applies? Which state or qualifier modifies the assertion? Which evidence supports it? Which version and activity produced the stored representation? Missing answers become explicit contract gaps.

Normalisation is used to separate independent facts, not to maximise the number of tables. A compact nested object can be semantically sound if the same identities, constraints and provenance remain explicit. Conversely, a highly normalised database can still be ambiguous when relationships and units exist only in documentation or application code.

Constraints and invariants

| Invariant | Executable or review test | | --- | --- | | Observation family is explicit | Do not infer alteration or weathering from a generic code column. | | Support is bounded | Require valid geometry or from–to interval with declared boundary convention. | | Concept assignment is versioned | Store the concept-scheme version used at observation time. | | Interpretations remain traceable | A later reinterpretation links to, rather than deletes, the earlier claim. |

An invariant is a condition that must remain true across storage, export, correction and reprocessing. Implement it as close to the authoritative boundary as practical and repeat the check at exchange boundaries. Record rule identifier, version, severity, evaluated scope, observed value and outcome so a failure can be reproduced.

Hard gates protect identity, semantic validity, required provenance and authorised use. Diagnostic checks reveal unusual values or patterns but require interpretation. Never convert a diagnostic threshold into deletion or correction without a reviewed rule and preserved source evidence.

Quantitative reasoning

For interval observations, calculate coverage C_i = L_o / L_h, where L_o is the union length covered by valid observations and L_h is the intended logged length. Report overlap by observation family rather than treating every overlap as an error: lithology overlaps may be invalid while lithology–alteration co-occurrence is expected.

Every reported ratio states its numerator, denominator, exclusions and evaluation time. Stratify results by source, entity type, contract version or processing run where aggregation could hide a local failure. Counts accompany percentages so a seemingly large change based on a tiny denominator remains visible.

Precision is part of meaning. Do not add decimal places merely because a storage type permits them, and do not round identity, interval or coordinate fields without a declared tolerance and test. Quantitative summaries support a release decision; they do not replace semantic review.

Evidence and uncertainty

Confidence may describe concept assignment, boundary position or observation quality; these are different uncertainties. A sharp contact with uncertain lithology is not equivalent to a certain lithology with an uncertain boundary. Store the dimension being qualified and the basis of the estimate.

Build an evidence packet containing preserved source reference, acquisition or assertion context, applicable method, validation results, reviewer decision and links to derivatives. Classify uncertainty as observational, semantic, structural, parametric or policy-related where that distinction changes treatment. “Unknown” is a valid state when the evidence cannot justify a stronger claim.

Contradictory evidence remains available. The model may select one current assertion, but the reason, competing assertion and effective time are retained. This makes later reinterpretation possible without pretending the earlier evidence never existed.

Interfaces and storage

Exchange observations as long-form records when possible: one row or object per claim and support, with identifiers for subject and concept. A wide export may be convenient for analysis, but its derivation must state how overlapping claims, absent values and multi-valued properties were flattened.

Design an interface from the logical contract outward. Specify identifiers, types, cardinalities, units, value states, coordinate and time references, version negotiation, validation behaviour and structured errors before choosing a serialisation. The physical representation then declares its mapping to those logical elements.

Storage optimisation may partition, compress, index or cache data, but it must not change identity or silently remove context. A derived representation points to immutable inputs and a processing manifest. A cache carries freshness and contract-version information and is never treated as the only evidence copy.

Governance and access

Govern concept schemes, logging procedures and reinterpretation status independently. A vocabulary change does not by itself alter historical observations; migration requires a reviewed mapping and records whether the old concept is exact, narrower, broader or merely related to the new concept.

Governance is expressed through named roles, review states and versioned decisions, not through references to a particular organisation. Define who may propose, validate, approve, supersede and withdraw each governed resource. The audit trail records the role and event while avoiding unnecessary personal data.

Apply least-necessary access to source evidence and derivatives. Access controls must not erase identifiers, lineage or quality metadata needed to understand an authorised release. When policy is unresolved, quarantine the output with a precise reason and escalation route.

Integration checkpoint

Overlapping geological observations remain distinct evidence claims
Overlapping geological observations remain distinct evidence claims

The diagram summarises the control flow for this lesson. Read it from source evidence through semantic structure and validation to a decision-ready artefact. Each arrow should correspond to a declared relationship or transformation; each boundary should have a contract; each released node should have an identity, version and provenance pointer.

Integrate the lesson by adding a normalised observation package with explicit supports and interpretation links to the evolving synthetic data package. Verify that earlier artefacts still resolve and that the new model does not overwrite observations, identifiers, values or versions introduced in previous lessons. Record every changed assumption.

Synthetic worked example

In a synthetic hole, 40–52 m is logged as a mafic lithology, 43–49 m as moderate alteration and 47.2 m as a fracture observation. The learner stores three claims with distinct supports and property families. A later interpretation proposes a mineralised domain but links it to the observations instead of relabelling them.

Work the example in four passes:

  1. Preserve the received records and write the intended decision without correcting anything.
  2. Identify entities, claims, context, uncertainties and policy constraints; mark every unresolved item.
  3. Apply the versioned rules, create derivatives and record the exact transformation plus validation evidence.
  4. Issue an accept, reject or quarantine decision and show how an independent reviewer can reproduce it.

Because the example is entirely synthetic, its values demonstrate method only. The important result is the chain from received evidence to justified decision. If a required fact is absent, the worked solution records the gap rather than manufacturing a plausible value.

Practice task

Transform a synthetic wide logging sheet into observation entities. Preserve overlapping families, mark support type, attach concept identifiers and confidence dimensions, then produce a derived wide view with an explicit flattening rule and a report of information that cannot be represented in that view.

Use the following acceptance criteria:

  • All required identifiers and references resolve to declared types.
  • Every transformation preserves the received evidence and records its derivation.
  • Invalid, unknown and inapplicable states remain distinct and machine-testable.
  • The output identifies the contract, vocabulary and processing versions used.
  • A second reader can reproduce the validation result without private knowledge.

Submit the source snapshot, authored contract or model, validation output, derivative, manifest and a short decision record. A screenshot alone is insufficient because it cannot demonstrate the exact input, version or rule execution.

Common failure modes

  • All geology is compressed into one mutually exclusive code.
  • Boundary confidence and concept confidence share one ambiguous field.
  • A reinterpretation overwrites the original logged observation.
  • A wide export silently discards multi-valued observations.

These failures share a pattern: convenient representation is mistaken for verified meaning. Diagnose the earliest boundary at which an assumption became implicit. Correct by restoring source evidence, making the assumption a versioned field or rule, rerunning dependent transformations and superseding—not overwriting—the affected release.

Do not repair a failure by adding an undocumented default. A blocked result with a specific missing dependency is safer and more reusable than a complete-looking result whose meaning cannot be reconstructed.

Review questions

  1. Why can alteration overlap lithology without being an interval error?
  2. Which uncertainty dimension does a confidence value qualify?
  3. How should a reinterpretation relate to the original log?
  4. What information can a wide view conceal?

For each answer, identify the governing invariant, the evidence needed to evaluate it and the appropriate release behaviour when the invariant fails. A strong answer distinguishes scientific uncertainty from missing semantics and distinguishes a recoverable warning from a hard contract violation.

Sources and further reading