← Back to project catalogue
GP-GE-0N4L249GeologyReady

Deep-Sea Mineral Resource Assessment and Environmental-Baseline Uncertainty

Explore what seabed samples can tell us about minerals and ecological change, and where the evidence stops.

Deep-Sea Mineral Resource Assessment and Environmental-Baseline Uncertainty project visual
GP-GE-0N4L249 · Geology
  • Python 3.12
  • Matplotlib

Software compatibility

Read without installing software

PDF and editable Word explain the study. Optional Python calculations run offline using the standard library. Matplotlib is only needed to rebuild plots. No web framework or paid simulation software is required. Git is optional for ZIP delivery.

Project definition

Problem statement

A seabed sample, a deposit-wide estimate and a certified mineral resource are different things. Targeted sampling, detection limits and incompatible mass bases can change the interpretation.

An ecological change also depends on which baseline and comparison area are used. A descriptive contrast is not automatically a causal mining effect.

Project objectives

  • Compare nodules, ferromanganese crusts and massive sulphides in their geological context.
  • Separate missing samples, observed zeros and below-detection measurements.
  • Examine rock-level and core-level composition summaries.
  • Reconcile published ecological summaries against occurrence records.
  • Show how sampling allocation and baseline selection change results.
  • Explain what the available evidence cannot establish.

Project structure

Project components

01

Literature and geology

Thirty-six annotated references connect mineral formation, sampling, environmental evidence and resource-classification limitations.

02

Measurement checks

Published Samoa data retain detection limits, failed cores, duplicates and concentration units.

03

Sampling design

Exact hypothetical populations demonstrate weighting, bias and variance without claiming regional representativeness.

04

Ecological baselines

Published occurrence records support descriptive comparisons while preserving unresolved source discrepancies.

05

Resource boundaries

Hypothetical inventory calculations distinguish wet mass, dry mass and contained metal. Missing field conversion factors remain missing.

Methodology

Project workflow

  1. 01
    Read the study

    Start with the problem, geological context and literature review.

  2. 02
    Inspect the sources

    Follow the source matrix, data attribution and explicit measurement qualifications.

  3. 03
    Reproduce the results

    Run the read-only check or inspect the supplied tables and figures without installing software.

  4. 04
    Compare assumptions

    Explore the stated sampling and baseline cases while keeping observed and hypothetical quantities separate.

  5. 05
    Discuss limitations

    Explain unresolved discrepancies and propose additional observations that could address them.

Demonstration scenario

The same published post-trial track mean can look different when compared with an earlier or later baseline. The study reproduces those descriptive contrasts and shows why neither is automatically a causal impact estimate.

Engineering

Tools and method

Tools
The project uses Python 3.12, Matplotlib for subject analysis, simulation, and results.
Domain study
Introduction, literature review, theory, methodology, results, discussion, conclusions and further work form the report.
Offline calculations
Python handles measurement summaries, exact finite sampling examples and baseline comparisons. No server or database is needed.
Independent checks
Rational arithmetic checks defined numerical cases. Report tables are compared cell by cell with retained outputs.
Editable documentation
Word documents retain editable equations and linked contents, figure and table lists. Repagination requires updating page fields.

Testing

Evaluation

Evaluation measures

  • Detection limits, missingness and denominator selection
  • Rock weighting compared with core weighting
  • Compatible wet, dry and concentration bases
  • Sampling bias, variance and area weighting
  • Occurrence-to-density reconciliation and baseline sensitivity
  • Numerical reproduction, attribution and interpretation limits

Project boundaries

  • Published source data are attributed. No new expedition or laboratory experiment is claimed.
  • Sampling and inventory teaching cases are explicitly hypothetical.
  • Two published ecological density summaries remain unreconciled and are documented, not silently corrected.
  • No certified resource, reserve, profitability forecast, safe plume limit or environmental approval is provided.
  • No combined mineral and ecological score is used.
  • No information collected.

Included

  1. 0172-page project documentation in PDF and editable Word formats
  2. 02Three-page setup and usage guide in PDF and editable Word
  3. 03Editable 18-slide presentation with source notes and data charts
  4. 0436 annotated references and a source matrix with evidence limitations
  5. 05Seven labelled figures, seven tables and 20 editable display equations
  6. 06Five analytical plots and two attributed source photographs
  7. 07Published mineral data and an attributed ecological occurrence extract
  8. 08Complete Python source and eight reproducible numerical result files
  9. 0994 analysis tests, 32 document, presentation and repository tests

Project record

No information is collected on this page.

Permanent project ID
GP-GE-0N4L249
Catalogued
21 Aug 2026
Completed
07 Sept 2026
Verified
07 Sept 2026
Demonstration
Included in repository

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.