diff --git a/README.md b/README.md
index e939c1d..257da44 100644
--- a/README.md
+++ b/README.md
@@ -1,10 +1,10 @@
# Mining Geospatial Analytics
-Applied geospatial data science, spatial machine learning, and reproducible analytics for mineral exploration and mining.
+Applied geospatial data science, spatial machine learning, geochemical compositional analysis, and reproducible analytics for mineral exploration and mining.
This repository presents reproducible technical walkthroughs that connect geospatial reasoning, scientific validation, reusable Python code, automated testing, and clear technical communication.
-The portfolio is developed incrementally. Each public laboratory is intended to demonstrate not only that code runs, but that the underlying spatial analysis is scientifically meaningful, reproducible, and testable.
+The portfolio is developed incrementally. Each public laboratory is intended to demonstrate not only that code runs, but that the underlying analysis is scientifically meaningful, reproducible, and testable.
## Featured work
@@ -114,54 +114,129 @@ sampling density is not underlying spatial signal
The walkthrough demonstrates:
-- Random K-Fold versus Spatial Block Cross-Validation;
-- a fixed local KNN baseline so that validation design, rather than model selection, is the experimental variable;
-- 5 km spatial blocks as the primary validation scenario;
-- 10 km sensitivity analysis;
-- 20 km stress testing;
-- train-validation spatial-separation diagnostics;
-- spatial-group overlap diagnostics;
-- RMSE and MAE as primary performance metrics;
-- limitations of R² when validation folds contain very low target variance.
+- Random K-Fold versus Spatial Block Cross-Validation
+- a fixed local KNN baseline so that validation design, rather than model selection, is the experimental variable
+- 5 km spatial blocks as the primary validation scenario
+- 10 km sensitivity analysis
+- 20 km stress testing
+- train-validation spatial-separation diagnostics
+- spatial-group overlap diagnostics
+- RMSE and MAE as primary performance metrics
+- limitations of R² when validation folds contain very low target variance
For the primary 5 km scenario:
-- Random pooled RMSE: **3.9676**;
-- Spatial Block CV pooled RMSE: **4.2610**;
-- relative RMSE optimism: **6.89%**;
-- Random median test-to-train separation: **1.11 km**;
-- Spatial median test-to-train separation: **2.39 km**;
-- mean 5 km spatial-group overlap: **73.6** for Random K-Fold versus **0.0** for Spatial Block CV.
+- Random pooled RMSE: **3.9676**
+- Spatial Block CV pooled RMSE: **4.2610**
+- relative RMSE optimism: **6.89%**
+- Random median test-to-train separation: **1.11 km**
+- Spatial median test-to-train separation: **2.39 km**
+- mean 5 km spatial-group overlap: **73.6** for Random K-Fold versus **0.0** for Spatial Block CV
The result is experiment-specific and is not presented as a universal correction factor or as evidence that one spatial block size is optimal for other datasets.
+
+**Core lesson:**
+
+```text
+validation geometry changes the prediction problem being evaluated
+```
+
+---
+
+### LAB 04 - Geochemical Compositional Data Analysis
+
+[Open the recruiter-facing notebook](labs/lab04_geochemical_coda/04_geochemical_coda.ipynb)
+
+**Problem:** multivariate geochemical concentrations can be misinterpreted when relative compositional structure is ignored and conventional Euclidean analysis is applied without considering closure and log-ratio geometry.
+
+The walkthrough demonstrates:
+
+- deterministic synthetic geochemical data generation
+- ten-element geochemical subcomposition
+- explicit detection-limit and missing-value semantics
+- constant-sum closure
+- centered log-ratio transformation
+- isometric log-ratio transformation
+- Aitchison distance
+- raw, closed, CLR, and ILR PCA comparison
+- numerical equivalence of non-zero CLR and ILR PCA eigenvalues
+- detection-limit replacement sensitivity
+- explicit geochemical data contracts
+- automated notebook and analytical validation
+
+The controlled dataset contains:
+
+- **1,200** synthetic samples
+- **10** selected elements
+- WGS 84 / UTM Zone 19S (`EPSG:32719`)
+- **1,169** synthetically censored cells
+- **111** missing elemental observations
+- **1,092** complete samples used in the primary multivariate analysis
+
+For `D = 10` compositional variables:
+
+```text
+CLR dimension = 10
+CLR rank = 9
+ILR dimension = 9
+```
+
+The non-zero eigenvalues from CLR and ILR PCA agree numerically, providing an internal validation of the compositional geometry implementation.
+
+Detection-limit sensitivity between `LOD / 2` and `LOD / sqrt(2)` produced:
+
+| Component | Score correlation |
+| --- | ---: |
+| PC1 | 0.983686 |
+| PC2 | 0.981298 |
+| PC3 | 0.954433 |
+
+The absolute change in PC1 explained variance was approximately `0.020898`.
+
+This indicates substantial structural agreement without implying complete invariance to the censoring treatment.
+
+The selected ten elements are explicitly treated as a **subcomposition**. Closure to 100 is a relative normalization step and must not be interpreted as complete whole-rock mass balance.
+
+**Core lesson:**
+
+```text
+geochemical composition requires relative, not purely absolute, geometry
+```
+
## Why this matters for GeoAI
-Spatial errors can silently propagate into features such as:
+Spatial and geochemical errors can silently propagate into downstream predictive features such as:
```text
distance_to_fault_m
distance_to_intrusive_m
distance_to_contact_m
-distance_to_occurrence_m
nearest_anomaly_distance_m
neighbourhood_density
+clr_Cu
+clr_Mo
+clr_Au
+ilr_1
+ilr_2
```
-A machine-learning model may train successfully even when these input features are spatially incorrect.
+A machine-learning model may train successfully even when its spatial validation is optimistic or its geochemical representation is methodologically inappropriate.
-For this reason, the portfolio treats spatial validation as a prerequisite for predictive modelling.
-
-The current workflow follows:
+For this reason, the portfolio builds predictive modelling only after establishing:
```text
problem
->
spatial reasoning
->
+data semantics
+ ->
reproducible analysis
->
independent validation
->
+compositional reasoning
+ ->
reusable implementation
->
automated testing
@@ -173,12 +248,15 @@ technical interpretation
The current public portfolio includes:
-- 3 recruiter-facing geospatial notebooks
-- reusable CRS and spatial-analysis utilities under `src/geoai/`
+- 5 recruiter-facing analytical notebooks
+- reusable CRS, spatial-analysis, spatial-validation, and compositional-analysis utilities under `src/geoai/`
- automated pytest validation
-- 24 passing automated tests
+- 58 passing automated tests
- persisted synthetic Monte Carlo result artifacts for LAB 02
+- persisted spatial-validation results for LAB 03
+- persisted geochemical CoDA summaries and PCA artifacts for LAB 04
- documented provenance for the LAB 02 geographic context layer
+- explicit geochemical data contracts for LAB 04
- reproducible Conda environment definition
- pre-commit quality controls
- Gitleaks secret scanning
@@ -195,43 +273,74 @@ mining-geospatial-analytics/
| | |-- 01b_cross_zone_crs.ipynb
| | `-- README.md
| |
-| `-- lab02_spatial_eda/
-| |-- 02_spatial_eda.ipynb
-| |-- 02_spatial_eda.py
+| |-- lab02_spatial_eda/
+| | |-- 02_spatial_eda.ipynb
+| | |-- 02_spatial_eda.py
+| | |-- README.md
+| | |-- data/
+| | | `-- context/
+| | | |-- chile_context.geojson
+| | | `-- SOURCE.md
+| | `-- results/
+| | |-- experiment_metadata.json
+| | |-- monte_carlo_by_realization.csv
+| | `-- resolution_summary.csv
+| |
+| |-- lab03_spatial_cross_validation/
+| | |-- 03_spatial_cross_validation.ipynb
+| | |-- 03a_run_spatial_cv_experiment.py
+| | |-- 03b_summarize_cv_results.py
+| | |-- README.md
+| | `-- results/
+| | |-- aggregate_comparison.csv
+| | |-- aggregate_metrics.csv
+| | |-- experiment_metadata.json
+| | |-- fold_results.csv
+| | |-- scenario_diagnostics.csv
+| | |-- strategy_summary.csv
+| | `-- validation_comparison.csv
+| |
+| `-- lab04_geochemical_coda/
+| |-- 04_geochemical_coda.ipynb
+| |-- 04a_generate_synthetic_geochemistry.py
+| |-- 04b_run_coda_analysis.py
+| |-- 04c_lod_sensitivity.py
+| |-- 04d_audit_data_contract.py
+| |-- geochemistry_config.py
+| |-- geochemistry_contract.json
| |-- README.md
| |-- data/
-| | `-- context/
-| | |-- chile_context.geojson
-| | `-- SOURCE.md
+| | `-- synthetic_geochemistry.csv
| `-- results/
-| |-- experiment_metadata.json
-| |-- monte_carlo_by_realization.csv
-| `-- resolution_summary.csv
+| |-- analysis_summary.json
+| |-- correlation_closed.csv
+| |-- correlation_clr.csv
+| |-- correlation_raw.csv
+| |-- data_contract_audit.json
+| |-- lod_sensitivity_summary.json
+| |-- lod_sensitivity_variance.csv
+| |-- pca_explained_variance.csv
+| |-- pca_loadings_closed.csv
+| |-- pca_loadings_clr.csv
+| |-- pca_loadings_ilr.csv
+| `-- pca_loadings_raw.csv
|
-| |
-| `-- lab03_spatial_cross_validation/
-| |-- 03_spatial_cross_validation.ipynb
-| |-- 03a_run_spatial_cv_experiment.py
-| |-- 03b_summarize_cv_results.py
-| |-- README.md
-| `-- results/
-| |-- aggregate_comparison.csv
-| |-- aggregate_metrics.csv
-| |-- experiment_metadata.json
-| |-- fold_results.csv
-| |-- scenario_diagnostics.csv
-| |-- strategy_summary.csv
-| `-- validation_comparison.csv
-||-- src/
+|-- src/
| `-- geoai/
| |-- __init__.py
+| |-- composition.py
| |-- crs.py
-| `-- spatial.py
+| |-- spatial.py
+| `-- validation.py
|
|-- tests/
-| |-- test_crs.py
+| |-- test_composition.py
| |-- test_cross_zone_crs.py
-| `-- test_spatial.py
+| |-- test_crs.py
+| |-- test_lab04_contract.py
+| |-- test_lab04_notebook.py
+| |-- test_spatial.py
+| `-- test_validation.py
|
|-- environment.yml
|-- pyproject.toml
@@ -247,7 +356,7 @@ The portfolio separates four complementary layers:
Markdown + visual evidence
2. DEMONSTRATE
- executable notebooks and results
+ executable notebooks and persisted results
3. ENGINEER
reusable Python components
@@ -279,10 +388,12 @@ conda env create -f environment.yml
conda activate mining-geospatial-analytics
```
-The current geospatial stack includes:
+The analytical stack currently includes:
- NumPy
- Pandas
+- SciPy
+- scikit-learn
- GeoPandas
- Shapely
- PyProj
@@ -304,25 +415,38 @@ python -m pytest -q
Current validated result:
```text
-24 passed
+58 passed
```
-Reusable CRS and spatial-analysis logic is tested independently from the explanatory notebooks.
+Reusable CRS, spatial-analysis, spatial-validation, and compositional-analysis logic is tested independently from the explanatory notebooks.
+
+The public LAB04 notebook additionally retains:
+
+```text
+24 code cells
+24 executed code cells
+24 outputs
+0 notebook error outputs
+```
## Data strategy
-The current walkthroughs use synthetic spatial examples.
+The current analytical experiments use synthetic data.
This approach keeps the experiments:
- reproducible
- redistribution-safe
- independent of proprietary exploration datasets
-- focused on the spatial concept being demonstrated
+- focused on the methodological concept being demonstrated
+
+LAB 02 additionally uses a Natural Earth country boundary only for geographic context. Its source, version, license status, and integrity hashes are documented alongside the local context file. The analytical experiment itself remains synthetic.
-LAB 02 additionally uses a Natural Earth country boundary only for geographic context. Its source, version, license status, and integrity hashes are documented alongside the local context file. The analytical experiment itself remains entirely synthetic.
+LAB 04 uses a synthetic ten-element geochemical subcomposition with explicit units, detection-limit semantics, missing-value semantics, and compositional assumptions documented in a machine-readable data contract.
-Future external datasets will require explicit review of source, license, attribution, CRS, resolution, redistribution rights, and known limitations.
+Synthetic detection limits and concentration values are methodological assumptions and must not be interpreted as certified assay values or as empirical properties of a real mineral deposit.
+
+Future external datasets will require explicit review of source, license, attribution, CRS, measurement units, analytical methods, detection limits, resolution, redistribution rights, and known limitations.
## Current roadmap
@@ -331,7 +455,7 @@ Future external datasets will require explicit review of source, license, attrib
| LAB 01 | Geospatial Python Foundations | Complete |
| LAB 02 | Spatial Exploratory Data Analysis | Complete |
| LAB 03 | Spatial Cross-Validation | Complete |
-| LAB 04 | Geochemical CoDA | Planned |
+| LAB 04 | Geochemical CoDA | Complete |
| LAB 05 | Mineral Prospectivity Mapping | Planned |
| LAB 06 | Explainable GeoAI | Planned |
| LAB 07 | Uncertainty Quantification | Planned |
@@ -339,18 +463,23 @@ Future external datasets will require explicit review of source, license, attrib
| LAB 09 | Deep Learning for GeoAI | Planned |
| LAB 10 | Graph & Multimodal GeoAI | Planned |
-LAB 01, LAB 02, and LAB 03 are completed and validated.
+LAB 01 through LAB 04 are completed and validated.
+
+LAB 03 establishes that validation geometry can materially change estimated predictive performance when observations are spatially structured.
-LAB 03 extends the portfolio from exploratory spatial reasoning into spatial cross-validation, with explicit attention to spatial leakage, train-validation separation, spatial-group overlap, and the limitations of conventional random cross-validation.
+LAB 04 adds the compositional-data foundation needed to represent multivariate geochemistry without treating a constrained subcomposition as ordinary unconstrained Euclidean data.
-LAB 04 will move into geochemical compositional data analysis while preserving the same emphasis on reproducibility, explicit assumptions, and spatially appropriate validation.
+LAB 05 will combine these foundations in a controlled mineral prospectivity mapping experiment, with particular attention to spatial validation, leakage control, baseline comparison, and interpretable geoscientific features.
## Technical principles
- Reproducibility before complexity
- Spatially correct feature engineering
- Explicit CRS and units
+- Explicit data contracts and analytical assumptions
+- Appropriate treatment of compositional information
- Independent validation where appropriate
+- Leakage-aware predictive modelling
- Simple evidence before advanced modelling
- Automated tests for reusable code
- Clear assumptions and limitations
@@ -360,7 +489,7 @@ LAB 04 will move into geochemical compositional data analysis while preserving t
This repository is intended as a curated technical portfolio rather than a production mineral exploration platform.
-Later releases will progressively demonstrate capabilities in spatial EDA, spatial validation, geochemistry, mineral prospectivity modelling, explainability, uncertainty, remote sensing, deep learning, and advanced GeoAI.
+Later releases will progressively demonstrate capabilities in mineral prospectivity modelling, explainability, uncertainty quantification, remote sensing, deep learning, graph methods, and multimodal GeoAI.
The emphasis throughout the portfolio is:
diff --git a/labs/lab04_geochemical_coda/.gitignore b/labs/lab04_geochemical_coda/.gitignore
new file mode 100644
index 0000000..2e59491
--- /dev/null
+++ b/labs/lab04_geochemical_coda/.gitignore
@@ -0,0 +1,5 @@
+__pycache__/
+*.pyc
+
+results/analytical_dataset.csv
+results/lod_sensitivity_scores.csv
diff --git a/labs/lab04_geochemical_coda/04_geochemical_coda.ipynb b/labs/lab04_geochemical_coda/04_geochemical_coda.ipynb
new file mode 100644
index 0000000..e2cda4a
--- /dev/null
+++ b/labs/lab04_geochemical_coda/04_geochemical_coda.ipynb
@@ -0,0 +1,1924 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "34779e1f",
+ "metadata": {},
+ "source": [
+ "# LAB04 - Geochemical Compositional Data Analysis\n",
+ "\n",
+ "This laboratory demonstrates why multivariate geochemical concentration data\n",
+ "should be treated carefully when relative information, closure, censoring, and\n",
+ "log-ratio geometry matter.\n",
+ "\n",
+ "The experiment uses a controlled synthetic geochemical dataset with ten\n",
+ "selected elements. The synthetic latent variables are retained only for\n",
+ "validation and teaching. They are not treated as information that would be\n",
+ "available in a real exploration campaign.\n",
+ "\n",
+ "## Learning objectives\n",
+ "\n",
+ "By the end of this laboratory, the reader should be able to:\n",
+ "\n",
+ "- distinguish raw concentration analysis from compositional analysis;\n",
+ "- explain the effect of closure on correlation structure;\n",
+ "- apply CLR and ILR transformations to strictly positive data;\n",
+ "- understand why a D-part composition has D minus 1 effective dimensions;\n",
+ "- compare conventional PCA with PCA in Aitchison geometry;\n",
+ "- evaluate sensitivity to simple below-LOD replacement rules;\n",
+ "- interpret CLR loadings while avoiding direct elemental interpretation of\n",
+ " basis-dependent ILR coordinates.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7d67d267",
+ "metadata": {},
+ "source": [
+ "## Scientific scope and limitations\n",
+ "\n",
+ "The dataset is fully synthetic. Concentrations follow a synthetic ppm\n",
+ "convention defined by the LAB04 data contract. Detection limits are experimental\n",
+ "assumptions rather than certified laboratory limits.\n",
+ "\n",
+ "The ten selected elements form a **subcomposition**. Closing them to 100 is a\n",
+ "relative normalization used to study compositional geometry. It does not mean\n",
+ "that these ten elements constitute 100 percent of a physical rock sample.\n",
+ "\n",
+ "Simple replacement by LOD divided by 2 is used as the primary pedagogical\n",
+ "baseline. LOD divided by the square root of 2 is used for sensitivity analysis.\n",
+ "Neither rule should be treated as a universally optimal solution for censored\n",
+ "geochemical data.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "6be43cf3",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-07T14:37:02.488740Z",
+ "iopub.status.busy": "2026-10-07T14:37:02.488235Z",
+ "iopub.status.idle": "2026-10-07T14:37:03.354331Z",
+ "shell.execute_reply": "2026-10-07T14:37:03.353576Z"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "LAB_DIR = /mnt/c/GeoAI/geoai-mineral-labs/labs/lab04_geochemical_coda\n",
+ "RAW_ROWS = 1200\n",
+ "ANALYTICAL_ROWS = 1092\n",
+ "ELEMENTS = 10\n",
+ "UNIT = ppm\n"
+ ]
+ }
+ ],
+ "source": [
+ "from __future__ import annotations\n",
+ "\n",
+ "import json\n",
+ "from pathlib import Path\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "\n",
+ "\n",
+ "def resolve_lab_dir() -> Path:\n",
+ " cwd = Path.cwd().resolve()\n",
+ " if (cwd / \"geochemistry_contract.json\").exists():\n",
+ " return cwd\n",
+ "\n",
+ " candidate = cwd / \"labs\" / \"lab04_geochemical_coda\"\n",
+ " if (candidate / \"geochemistry_contract.json\").exists():\n",
+ " return candidate\n",
+ "\n",
+ " raise FileNotFoundError(\"LAB04 directory could not be resolved.\")\n",
+ "\n",
+ "\n",
+ "LAB_DIR = resolve_lab_dir()\n",
+ "DATA_DIR = LAB_DIR / \"data\"\n",
+ "RESULTS_DIR = LAB_DIR / \"results\"\n",
+ "\n",
+ "with (LAB_DIR / \"geochemistry_contract.json\").open(\n",
+ " encoding=\"utf-8\"\n",
+ ") as handle:\n",
+ " CONTRACT = json.load(handle)\n",
+ "\n",
+ "ELEMENTS = CONTRACT[\"elements\"]\n",
+ "LOD = pd.Series(CONTRACT[\"detection_limits\"], dtype=float)\n",
+ "\n",
+ "raw = pd.read_csv(DATA_DIR / \"synthetic_geochemistry.csv\")\n",
+ "analytical = pd.read_csv(RESULTS_DIR / \"analytical_dataset.csv\")\n",
+ "explained = pd.read_csv(RESULTS_DIR / \"pca_explained_variance.csv\")\n",
+ "raw_corr = pd.read_csv(RESULTS_DIR / \"correlation_raw.csv\", index_col=0)\n",
+ "closed_corr = pd.read_csv(\n",
+ " RESULTS_DIR / \"correlation_closed.csv\", index_col=0\n",
+ ")\n",
+ "clr_corr = pd.read_csv(RESULTS_DIR / \"correlation_clr.csv\", index_col=0)\n",
+ "raw_loadings = pd.read_csv(\n",
+ " RESULTS_DIR / \"pca_loadings_raw.csv\", index_col=0\n",
+ ")\n",
+ "clr_loadings = pd.read_csv(\n",
+ " RESULTS_DIR / \"pca_loadings_clr.csv\", index_col=0\n",
+ ")\n",
+ "ilr_loadings = pd.read_csv(\n",
+ " RESULTS_DIR / \"pca_loadings_ilr.csv\", index_col=0\n",
+ ")\n",
+ "lod_variance = pd.read_csv(\n",
+ " RESULTS_DIR / \"lod_sensitivity_variance.csv\"\n",
+ ")\n",
+ "lod_scores = pd.read_csv(\n",
+ " RESULTS_DIR / \"lod_sensitivity_scores.csv\"\n",
+ ")\n",
+ "\n",
+ "with (RESULTS_DIR / \"analysis_summary.json\").open(\n",
+ " encoding=\"utf-8\"\n",
+ ") as handle:\n",
+ " ANALYSIS_SUMMARY = json.load(handle)\n",
+ "\n",
+ "with (RESULTS_DIR / \"lod_sensitivity_summary.json\").open(\n",
+ " encoding=\"utf-8\"\n",
+ ") as handle:\n",
+ " LOD_SUMMARY = json.load(handle)\n",
+ "\n",
+ "with (RESULTS_DIR / \"data_contract_audit.json\").open(\n",
+ " encoding=\"utf-8\"\n",
+ ") as handle:\n",
+ " AUDIT_SUMMARY = json.load(handle)\n",
+ "\n",
+ "print(\"LAB_DIR =\", LAB_DIR)\n",
+ "print(\"RAW_ROWS =\", len(raw))\n",
+ "print(\"ANALYTICAL_ROWS =\", len(analytical))\n",
+ "print(\"ELEMENTS =\", len(ELEMENTS))\n",
+ "print(\"UNIT =\", CONTRACT[\"unit\"])\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c0488131",
+ "metadata": {},
+ "source": [
+ "## 1. Data contract and experiment design\n",
+ "\n",
+ "The contract is the single source of truth for element ordering, synthetic\n",
+ "units, detection limits, closure semantics, replacement policies, CRS, and\n",
+ "latent-variable restrictions. This prevents analytical scripts from silently\n",
+ "using different assumptions.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "20d0af7c",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-07T14:37:03.355527Z",
+ "iopub.status.busy": "2026-10-07T14:37:03.355385Z",
+ "iopub.status.idle": "2026-10-07T14:37:03.363786Z",
+ "shell.execute_reply": "2026-10-07T14:37:03.363070Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " element | \n",
+ " unit | \n",
+ " detection_limit | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | 0 | \n",
+ " Cu | \n",
+ " ppm | \n",
+ " 30.0000 | \n",
+ "
\n",
+ " \n",
+ " | 1 | \n",
+ " Mo | \n",
+ " ppm | \n",
+ " 3.5000 | \n",
+ "
\n",
+ " \n",
+ " | 2 | \n",
+ " Au | \n",
+ " ppm | \n",
+ " 0.0105 | \n",
+ "
\n",
+ " \n",
+ " | 3 | \n",
+ " As | \n",
+ " ppm | \n",
+ " 4.8000 | \n",
+ "
\n",
+ " \n",
+ " | 4 | \n",
+ " Sb | \n",
+ " ppm | \n",
+ " 1.2000 | \n",
+ "
\n",
+ " \n",
+ " | 5 | \n",
+ " Fe | \n",
+ " ppm | \n",
+ " 3000.0000 | \n",
+ "
\n",
+ " \n",
+ " | 6 | \n",
+ " Mg | \n",
+ " ppm | \n",
+ " 1700.0000 | \n",
+ "
\n",
+ " \n",
+ " | 7 | \n",
+ " Ca | \n",
+ " ppm | \n",
+ " 1500.0000 | \n",
+ "
\n",
+ " \n",
+ " | 8 | \n",
+ " Al | \n",
+ " ppm | \n",
+ " 2500.0000 | \n",
+ "
\n",
+ " \n",
+ " | 9 | \n",
+ " K | \n",
+ " ppm | \n",
+ " 1150.0000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "class_order = [\"background\", \"halo\", \"anomalous\"]\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(8, 7))\n",
+ "for label in class_order:\n",
+ " subset = raw.loc[raw[\"anomaly_class\"] == label]\n",
+ " ax.scatter(\n",
+ " subset[\"easting\"],\n",
+ " subset[\"northing\"],\n",
+ " s=16,\n",
+ " alpha=0.65,\n",
+ " label=label,\n",
+ " )\n",
+ "\n",
+ "ax.set_title(\"Synthetic geochemical sampling and anomaly classes\")\n",
+ "ax.set_xlabel(\"Easting, EPSG:32719\")\n",
+ "ax.set_ylabel(\"Northing, EPSG:32719\")\n",
+ "ax.legend(title=\"Anomaly class\")\n",
+ "ax.ticklabel_format(style=\"plain\", axis=\"both\")\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "64401601",
+ "metadata": {},
+ "source": [
+ "## 3. Detection limits, censoring, and missing observations\n",
+ "\n",
+ "A value encoded as zero represents a synthetic below-LOD observation. Missing\n",
+ "values remain separate as NaN. Keeping these mechanisms distinct is essential:\n",
+ "censoring contains partial information about concentration magnitude, whereas a\n",
+ "missing observation does not.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "9d5272e1",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-07T14:37:03.548673Z",
+ "iopub.status.busy": "2026-10-07T14:37:03.548522Z",
+ "iopub.status.idle": "2026-10-07T14:37:03.556857Z",
+ "shell.execute_reply": "2026-10-07T14:37:03.556137Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " censored_count | \n",
+ " censored_rate | \n",
+ " missing_count | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Cu | \n",
+ " 157 | \n",
+ " 0.130833 | \n",
+ " 7 | \n",
+ "
\n",
+ " \n",
+ " | Mo | \n",
+ " 175 | \n",
+ " 0.145833 | \n",
+ " 16 | \n",
+ "
\n",
+ " \n",
+ " | Au | \n",
+ " 183 | \n",
+ " 0.152500 | \n",
+ " 9 | \n",
+ "
\n",
+ " \n",
+ " | As | \n",
+ " 143 | \n",
+ " 0.119167 | \n",
+ " 15 | \n",
+ "
\n",
+ " \n",
+ " | Sb | \n",
+ " 190 | \n",
+ " 0.158333 | \n",
+ " 7 | \n",
+ "
\n",
+ " \n",
+ " | Fe | \n",
+ " 43 | \n",
+ " 0.035833 | \n",
+ " 12 | \n",
+ "
\n",
+ " \n",
+ " | Mg | \n",
+ " 65 | \n",
+ " 0.054167 | \n",
+ " 12 | \n",
+ "
\n",
+ " \n",
+ " | Ca | \n",
+ " 61 | \n",
+ " 0.050833 | \n",
+ " 11 | \n",
+ "
\n",
+ " \n",
+ " | Al | \n",
+ " 85 | \n",
+ " 0.070833 | \n",
+ " 12 | \n",
+ "
\n",
+ " \n",
+ " | K | \n",
+ " 67 | \n",
+ " 0.055833 | \n",
+ " 10 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(figsize=(9, 5))\n",
+ "ax.bar(censor_table.index, 100 * censor_table[\"censored_rate\"])\n",
+ "ax.set_title(\"Synthetic censoring rate by element\")\n",
+ "ax.set_xlabel(\"Element\")\n",
+ "ax.set_ylabel(\"Below-LOD observations, percent\")\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "38d9a69e",
+ "metadata": {},
+ "source": [
+ "The complete-case analytical dataset contains 1,092 samples because 108 rows\n",
+ "contain at least one missing observed concentration. Below-LOD values are\n",
+ "replaced before the log-ratio transforms. Missing values are not imputed in this\n",
+ "laboratory.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "id": "04fc9fa7",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-07T14:37:03.643488Z",
+ "iopub.status.busy": "2026-10-07T14:37:03.643357Z",
+ "iopub.status.idle": "2026-10-07T14:37:03.647887Z",
+ "shell.execute_reply": "2026-10-07T14:37:03.647185Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "input_rows 1200\n",
+ "complete_rows 1092\n",
+ "excluded_missing_rows 108\n",
+ "missing_cells 111\n",
+ "censored_cells 1169\n",
+ "Name: Data quality summary, dtype: int64"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.Series(\n",
+ " {\n",
+ " \"input_rows\": ANALYSIS_SUMMARY[\"input_rows\"],\n",
+ " \"complete_rows\": ANALYSIS_SUMMARY[\"complete_rows\"],\n",
+ " \"excluded_missing_rows\": ANALYSIS_SUMMARY[\n",
+ " \"excluded_missing_rows\"\n",
+ " ],\n",
+ " \"missing_cells\": AUDIT_SUMMARY[\"missing_cells\"],\n",
+ " \"censored_cells\": AUDIT_SUMMARY[\"censored_cells\"],\n",
+ " },\n",
+ " name=\"Data quality summary\",\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "988f923e",
+ "metadata": {},
+ "source": [
+ "## 4. Raw concentrations and relative closure\n",
+ "\n",
+ "Raw concentrations are conventional measurements on their original numerical\n",
+ "scales. For the baseline PCA they are standardized because the elements occupy\n",
+ "very different numerical ranges.\n",
+ "\n",
+ "Closure rescales every complete sample so that the selected ten-element\n",
+ "subcomposition sums to 100. Closure imposes a constant-sum constraint and can\n",
+ "alter ordinary correlation patterns. This is one reason that standard\n",
+ "Euclidean analysis of closed compositions can be misleading.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "id": "62e7b16f",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-07T14:37:03.649239Z",
+ "iopub.status.busy": "2026-10-07T14:37:03.649111Z",
+ "iopub.status.idle": "2026-10-07T14:37:03.654514Z",
+ "shell.execute_reply": "2026-10-07T14:37:03.653827Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "minimum_closed_sum 100.0\n",
+ "maximum_closed_sum 100.0\n",
+ "mean_closed_sum 100.0\n",
+ "Name: Closure diagnostic, dtype: float64"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "closed_columns = [f\"{element}_closed\" for element in ELEMENTS]\n",
+ "closed_sums = analytical[closed_columns].sum(axis=1)\n",
+ "\n",
+ "pd.Series(\n",
+ " {\n",
+ " \"minimum_closed_sum\": closed_sums.min(),\n",
+ " \"maximum_closed_sum\": closed_sums.max(),\n",
+ " \"mean_closed_sum\": closed_sums.mean(),\n",
+ " },\n",
+ " name=\"Closure diagnostic\",\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0346e999",
+ "metadata": {},
+ "source": [
+ "## 5. Correlation structure before and after log-ratio transformation\n",
+ "\n",
+ "Correlation is not used here as a score of methodological superiority. The goal\n",
+ "is to demonstrate that the representation changes the dependency structure.\n",
+ "CLR correlations describe associations among log-ratios relative to each\n",
+ "sample's geometric mean and should therefore be interpreted differently from\n",
+ "raw concentration correlations.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "id": "a857462d",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-07T14:37:03.655583Z",
+ "iopub.status.busy": "2026-10-07T14:37:03.655465Z",
+ "iopub.status.idle": "2026-10-07T14:37:03.788168Z",
+ "shell.execute_reply": "2026-10-07T14:37:03.787432Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def plot_correlation(matrix: pd.DataFrame, title: str) -> None:\n",
+ " fig, ax = plt.subplots(figsize=(8, 7))\n",
+ " image = ax.imshow(matrix.to_numpy(dtype=float), vmin=-1, vmax=1)\n",
+ " positions = np.arange(len(matrix.columns))\n",
+ " ax.set_xticks(positions, matrix.columns, rotation=45, ha=\"right\")\n",
+ " ax.set_yticks(positions, matrix.index)\n",
+ " ax.set_title(title)\n",
+ " fig.colorbar(image, ax=ax, label=\"Pearson correlation\")\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "plot_correlation(raw_corr, \"Raw concentration correlation matrix\")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "id": "0a881616",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-07T14:37:03.789408Z",
+ "iopub.status.busy": "2026-10-07T14:37:03.789276Z",
+ "iopub.status.idle": "2026-10-07T14:37:03.908481Z",
+ "shell.execute_reply": "2026-10-07T14:37:03.907885Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_correlation(\n",
+ " closed_corr,\n",
+ " \"Closed subcomposition correlation matrix\",\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "id": "a4591e48",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-07T14:37:03.909864Z",
+ "iopub.status.busy": "2026-10-07T14:37:03.909731Z",
+ "iopub.status.idle": "2026-10-07T14:37:04.033394Z",
+ "shell.execute_reply": "2026-10-07T14:37:04.032684Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_correlation(clr_corr, \"CLR correlation matrix\")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "664a8a42",
+ "metadata": {},
+ "source": [
+ "Selected examples show how interpretation changes with representation. In this\n",
+ "synthetic experiment, Cu and Mo remain positively associated, while several\n",
+ "lithogenic relationships change magnitude after log-ratio transformation.\n",
+ "These differences are expected consequences of working in relative rather than\n",
+ "absolute geometry.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "id": "99795998",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-07T14:37:04.034763Z",
+ "iopub.status.busy": "2026-10-07T14:37:04.034505Z",
+ "iopub.status.idle": "2026-10-07T14:37:04.041127Z",
+ "shell.execute_reply": "2026-10-07T14:37:04.040164Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " raw | \n",
+ " closed | \n",
+ " clr | \n",
+ "
\n",
+ " \n",
+ " | pair | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | Cu-Mo | \n",
+ " 0.708 | \n",
+ " 0.689 | \n",
+ " 0.211 | \n",
+ "
\n",
+ " \n",
+ " | Au-As | \n",
+ " 0.556 | \n",
+ " 0.552 | \n",
+ " 0.001 | \n",
+ "
\n",
+ " \n",
+ " | Au-Sb | \n",
+ " 0.441 | \n",
+ " 0.448 | \n",
+ " -0.046 | \n",
+ "
\n",
+ " \n",
+ " | Fe-Mg | \n",
+ " 0.544 | \n",
+ " 0.353 | \n",
+ " 0.512 | \n",
+ "
\n",
+ " \n",
+ " | Al-K | \n",
+ " 0.545 | \n",
+ " 0.469 | \n",
+ " 0.462 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | representation | \n",
+ " closed | \n",
+ " clr | \n",
+ " ilr | \n",
+ " raw | \n",
+ "
\n",
+ " \n",
+ " | component | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ " | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | CLOSED_PC1 | \n",
+ " 31.045325 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | CLOSED_PC2 | \n",
+ " 24.772728 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | CLOSED_PC3 | \n",
+ " 13.573497 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | CLR_PC1 | \n",
+ " NaN | \n",
+ " 22.559798 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | CLR_PC2 | \n",
+ " NaN | \n",
+ " 18.721529 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | CLR_PC3 | \n",
+ " NaN | \n",
+ " 15.131037 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | ILR_PC1 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " 22.559798 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | ILR_PC2 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " 18.721529 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | ILR_PC3 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " 15.131037 | \n",
+ " NaN | \n",
+ "
\n",
+ " \n",
+ " | RAW_PC1 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " 30.475678 | \n",
+ "
\n",
+ " \n",
+ " | RAW_PC2 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " 20.795641 | \n",
+ "
\n",
+ " \n",
+ " | RAW_PC3 | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " NaN | \n",
+ " 15.655987 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "representations = [\"raw\", \"closed\", \"clr\", \"ilr\"]\n",
+ "pc1 = explained.loc[\n",
+ " explained[\"component\"].str.endswith(\"PC1\"),\n",
+ " [\"representation\", \"explained_variance_ratio\"],\n",
+ "].set_index(\"representation\").loc[representations]\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(8, 5))\n",
+ "ax.bar(\n",
+ " pc1.index,\n",
+ " 100 * pc1[\"explained_variance_ratio\"],\n",
+ ")\n",
+ "ax.set_title(\"PC1 explained variance by representation\")\n",
+ "ax.set_xlabel(\"Representation\")\n",
+ "ax.set_ylabel(\"Explained variance, percent\")\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0ae706dc",
+ "metadata": {},
+ "source": [
+ "## 8. PCA score spaces\n",
+ "\n",
+ "The anomaly class is shown only after PCA has been computed. It acts as a\n",
+ "synthetic diagnostic overlay and is not included among the PCA input features.\n",
+ "This separation avoids target leakage in the unsupervised analysis.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "8e3f801d",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-07T14:37:04.124514Z",
+ "iopub.status.busy": "2026-10-07T14:37:04.124393Z",
+ "iopub.status.idle": "2026-10-07T14:37:04.219431Z",
+ "shell.execute_reply": "2026-10-07T14:37:04.218805Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def plot_scores(\n",
+ " frame: pd.DataFrame,\n",
+ " x_column: str,\n",
+ " y_column: str,\n",
+ " title: str,\n",
+ ") -> None:\n",
+ " fig, ax = plt.subplots(figsize=(8, 6))\n",
+ " for label in class_order:\n",
+ " subset = frame.loc[frame[\"anomaly_class\"] == label]\n",
+ " ax.scatter(\n",
+ " subset[x_column],\n",
+ " subset[y_column],\n",
+ " s=18,\n",
+ " alpha=0.65,\n",
+ " label=label,\n",
+ " )\n",
+ " ax.axhline(0.0, linewidth=0.8)\n",
+ " ax.axvline(0.0, linewidth=0.8)\n",
+ " ax.set_title(title)\n",
+ " ax.set_xlabel(x_column)\n",
+ " ax.set_ylabel(y_column)\n",
+ " ax.legend(title=\"Anomaly class\")\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "plot_scores(\n",
+ " analytical,\n",
+ " \"RAW_PC1\",\n",
+ " \"RAW_PC2\",\n",
+ " \"Standardized raw PCA scores\",\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 17,
+ "id": "f11f6dbe",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-07T14:37:04.220576Z",
+ "iopub.status.busy": "2026-10-07T14:37:04.220445Z",
+ "iopub.status.idle": "2026-10-07T14:37:04.330235Z",
+ "shell.execute_reply": "2026-10-07T14:37:04.329461Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_scores(\n",
+ " analytical,\n",
+ " \"CLR_PC1\",\n",
+ " \"CLR_PC2\",\n",
+ " \"CLR PCA scores in Aitchison geometry\",\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "8e4509cb",
+ "metadata": {},
+ "source": [
+ "## 9. Element-level PCA loadings\n",
+ "\n",
+ "Raw and CLR loadings can be displayed against named elements. PCA signs are\n",
+ "arbitrary: reversing every loading and score on one component represents the\n",
+ "same solution. Interpretation therefore focuses on relative groups and\n",
+ "contrasts rather than the absolute sign alone.\n",
+ "\n",
+ "ILR loadings are basis-coordinate loadings and are intentionally not used for\n",
+ "direct element-level geological interpretation.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 18,
+ "id": "4cb9c970",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-07T14:37:04.331503Z",
+ "iopub.status.busy": "2026-10-07T14:37:04.331370Z",
+ "iopub.status.idle": "2026-10-07T14:37:04.401738Z",
+ "shell.execute_reply": "2026-10-07T14:37:04.400783Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def plot_loadings(\n",
+ " loadings: pd.DataFrame,\n",
+ " component: str,\n",
+ " title: str,\n",
+ ") -> None:\n",
+ " values = loadings[component].sort_values()\n",
+ " fig, ax = plt.subplots(figsize=(8, 5))\n",
+ " ax.barh(values.index, values.to_numpy())\n",
+ " ax.axvline(0.0, linewidth=0.8)\n",
+ " ax.set_title(title)\n",
+ " ax.set_xlabel(\"PCA loading\")\n",
+ " ax.set_ylabel(\"Element\")\n",
+ " plt.tight_layout()\n",
+ " plt.show()\n",
+ "\n",
+ "\n",
+ "plot_loadings(\n",
+ " raw_loadings,\n",
+ " \"RAW_PC1\",\n",
+ " \"Raw PCA PC1 loadings\",\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "id": "121add47",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-07T14:37:04.402918Z",
+ "iopub.status.busy": "2026-10-07T14:37:04.402790Z",
+ "iopub.status.idle": "2026-10-07T14:37:04.508511Z",
+ "shell.execute_reply": "2026-10-07T14:37:04.507795Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_loadings(\n",
+ " clr_loadings,\n",
+ " \"CLR_PC1\",\n",
+ " \"CLR PCA PC1 loadings\",\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 20,
+ "id": "66d75728",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-07T14:37:04.509870Z",
+ "iopub.status.busy": "2026-10-07T14:37:04.509722Z",
+ "iopub.status.idle": "2026-10-07T14:37:04.579914Z",
+ "shell.execute_reply": "2026-10-07T14:37:04.579257Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_loadings(\n",
+ " clr_loadings,\n",
+ " \"CLR_PC2\",\n",
+ " \"CLR PCA PC2 loadings\",\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ae00c150",
+ "metadata": {},
+ "source": [
+ "In this controlled experiment, CLR PC1 contrasts a Cu-Mo-Au-rich signature\n",
+ "against several lithogenic components, while CLR PC2 expresses a strong\n",
+ "Al-K versus Fe-Mg contrast. These patterns are consistent with the latent\n",
+ "structure intentionally embedded in the synthetic generator, but they should\n",
+ "not be generalized to real deposits without independent geological evidence.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "3bcf0aa5",
+ "metadata": {},
+ "source": [
+ "## 10. Sensitivity to below-LOD replacement\n",
+ "\n",
+ "The primary analysis uses LOD divided by 2. A second run uses LOD divided by the\n",
+ "square root of 2. The first three CLR PCA score vectors remain highly\n",
+ "correlated, but explained-variance ratios change enough to justify reporting\n",
+ "the sensitivity analysis explicitly.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "id": "4ef4fb96",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-07T14:37:04.581224Z",
+ "iopub.status.busy": "2026-10-07T14:37:04.580967Z",
+ "iopub.status.idle": "2026-10-07T14:37:04.585197Z",
+ "shell.execute_reply": "2026-10-07T14:37:04.584511Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "common_samples 1092.000000\n",
+ "pc1_score_correlation 0.983686\n",
+ "pc2_score_correlation 0.981298\n",
+ "pc3_score_correlation 0.954433\n",
+ "pc1_variance_difference 0.020898\n",
+ "Name: LOD sensitivity summary, dtype: float64"
+ ]
+ },
+ "execution_count": 21,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "pd.Series(\n",
+ " {\n",
+ " \"common_samples\": LOD_SUMMARY[\"common_samples\"],\n",
+ " \"pc1_score_correlation\": LOD_SUMMARY[\n",
+ " \"pc1_score_correlation\"\n",
+ " ],\n",
+ " \"pc2_score_correlation\": LOD_SUMMARY[\n",
+ " \"pc2_score_correlation\"\n",
+ " ],\n",
+ " \"pc3_score_correlation\": LOD_SUMMARY[\n",
+ " \"pc3_score_correlation\"\n",
+ " ],\n",
+ " \"pc1_variance_difference\": LOD_SUMMARY[\n",
+ " \"pc1_explained_variance_absolute_difference\"\n",
+ " ],\n",
+ " },\n",
+ " name=\"LOD sensitivity summary\",\n",
+ ")\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "id": "25306c20",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-07T14:37:04.586195Z",
+ "iopub.status.busy": "2026-10-07T14:37:04.586076Z",
+ "iopub.status.idle": "2026-10-07T14:37:04.665277Z",
+ "shell.execute_reply": "2026-10-07T14:37:04.664507Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "variance_plot = lod_variance.loc[\n",
+ " lod_variance[\"component\"].isin([\"PC1\", \"PC2\", \"PC3\"])\n",
+ "].copy()\n",
+ "variance_plot[\"percent\"] = 100 * variance_plot[\n",
+ " \"explained_variance_ratio\"\n",
+ "]\n",
+ "\n",
+ "components = [\"PC1\", \"PC2\", \"PC3\"]\n",
+ "x = np.arange(len(components))\n",
+ "width = 0.38\n",
+ "\n",
+ "half = variance_plot.loc[\n",
+ " variance_plot[\"replacement_rule\"] == \"lod_half\"\n",
+ "].set_index(\"component\").loc[components, \"percent\"]\n",
+ "sqrt2 = variance_plot.loc[\n",
+ " variance_plot[\"replacement_rule\"] == \"lod_sqrt2\"\n",
+ "].set_index(\"component\").loc[components, \"percent\"]\n",
+ "\n",
+ "fig, ax = plt.subplots(figsize=(8, 5))\n",
+ "ax.bar(x - width / 2, half, width, label=\"LOD divided by 2\")\n",
+ "ax.bar(\n",
+ " x + width / 2,\n",
+ " sqrt2,\n",
+ " width,\n",
+ " label=\"LOD divided by square root of 2\",\n",
+ ")\n",
+ "ax.set_xticks(x, components)\n",
+ "ax.set_title(\"CLR PCA sensitivity to below-LOD replacement\")\n",
+ "ax.set_xlabel(\"Principal component\")\n",
+ "ax.set_ylabel(\"Explained variance, percent\")\n",
+ "ax.legend()\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "id": "3b131ed2",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-07T14:37:04.666575Z",
+ "iopub.status.busy": "2026-10-07T14:37:04.666444Z",
+ "iopub.status.idle": "2026-10-07T14:37:04.748134Z",
+ "shell.execute_reply": "2026-10-07T14:37:04.747391Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(figsize=(7, 6))\n",
+ "ax.scatter(\n",
+ " lod_scores[\"PC1_lod_half\"],\n",
+ " lod_scores[\"PC1_lod_sqrt2\"],\n",
+ " s=16,\n",
+ " alpha=0.6,\n",
+ ")\n",
+ "ax.set_title(\"PC1 score sensitivity to below-LOD replacement\")\n",
+ "ax.set_xlabel(\"PC1 score, LOD divided by 2\")\n",
+ "ax.set_ylabel(\"PC1 score, LOD divided by square root of 2\")\n",
+ "plt.tight_layout()\n",
+ "plt.show()\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7fe590b5",
+ "metadata": {},
+ "source": [
+ "The score correlations support structural stability of the leading PCA patterns\n",
+ "under the two simple replacement rules. However, the explained variance changes\n",
+ "by about two percentage points for PC1. The appropriate conclusion is therefore\n",
+ "**partial robustness**, not complete insensitivity to censoring treatment.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "dce0a26c",
+ "metadata": {},
+ "source": [
+ "## 11. Ground-truth diagnostics without leakage\n",
+ "\n",
+ "Synthetic latent variables allow us to verify whether the analysis responds to\n",
+ "the processes used to generate the data. This is a simulation-only diagnostic.\n",
+ "A real exploration dataset would not provide latent mineralization,\n",
+ "pathfinder, or alteration fields.\n",
+ "\n",
+ "Future predictive models must therefore keep these columns outside the feature\n",
+ "matrix unless the explicit objective is simulation validation.\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "id": "13cb2590",
+ "metadata": {
+ "execution": {
+ "iopub.execute_input": "2026-10-07T14:37:04.749288Z",
+ "iopub.status.busy": "2026-10-07T14:37:04.749158Z",
+ "iopub.status.idle": "2026-10-07T14:37:04.755483Z",
+ "shell.execute_reply": "2026-10-07T14:37:04.754835Z"
+ }
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " | \n",
+ " CLR_PC1 | \n",
+ " CLR_PC2 | \n",
+ " CLR_PC3 | \n",
+ " latent_mineralization | \n",
+ " latent_pathfinder | \n",
+ " latent_alteration | \n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " | CLR_PC1 | \n",
+ " 1.000 | \n",
+ " -0.000 | \n",
+ " 0.000 | \n",
+ " 0.796 | \n",
+ " 0.527 | \n",
+ " 0.699 | \n",
+ "
\n",
+ " \n",
+ " | CLR_PC2 | \n",
+ " -0.000 | \n",
+ " 1.000 | \n",
+ " -0.000 | \n",
+ " -0.042 | \n",
+ " -0.116 | \n",
+ " -0.054 | \n",
+ "
\n",
+ " \n",
+ " | CLR_PC3 | \n",
+ " 0.000 | \n",
+ " -0.000 | \n",
+ " 1.000 | \n",
+ " -0.126 | \n",
+ " 0.159 | \n",
+ " -0.078 | \n",
+ "
\n",
+ " \n",
+ " | latent_mineralization | \n",
+ " 0.796 | \n",
+ " -0.042 | \n",
+ " -0.126 | \n",
+ " 1.000 | \n",
+ " 0.561 | \n",
+ " 0.872 | \n",
+ "
\n",
+ " \n",
+ " | latent_pathfinder | \n",
+ " 0.527 | \n",
+ " -0.116 | \n",
+ " 0.159 | \n",
+ " 0.561 | \n",
+ " 1.000 | \n",
+ " 0.593 | \n",
+ "
\n",
+ " \n",
+ " | latent_alteration | \n",
+ " 0.699 | \n",
+ " -0.054 | \n",
+ " -0.078 | \n",
+ " 0.872 | \n",
+ " 0.593 | \n",
+ " 1.000 | \n",
+ "
\n",
+ " \n",
+ "
\n",
+ "