NetCDF Territorial Classification and Python Colab Scripts Based on StandardScaler-Normalized Welch Power Spectrum SbAI Signals Between 0 and 0.5 Cycles per Year in Southern South America (Argentina, Chile, and Uruguay). (FS-WEL-2-inf-S)

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Fecha
Publicador:
Consejo Nacional de Investigaciones Científicas y Técnicas
Fecha de depósito:
27/05/2026
Fecha de creación:
13/5/2026
Clasificación temática
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Resumen
This notebook implements a full pipeline for spectral analysis and clustering of FIR-filtered SbAI data:
- Loads Welch spectra per pixel after FIR lowpass filtering (cutoff ≥2 years).
- Applies RobustScaler + StandardScaler preprocessing to mitigate outliers and normalize features.
- Performs KMeans++ clustering (n_clusters=5, n_init=10).
- Generates classification maps with geographic boundaries from a geopackage.
- Produces cluster centroids across all frequencies and representative pixels closest to centroids.
- Provides autonomous visualization scripts to replicate maps and spectral plots without recalculating clustering.
Información Técnica
The workflow consists of:
1. Mounting Google Drive and setting the working directory (`base_dir`).
2. Loading FIR-filtered dataset (`welch_fir_lowpass_2y.nc`).
3. Stacking Welch spectra into pixel × frequency matrix.
4. Applying scaling:
- RobustScaler to reduce influence of outliers.
- StandardScaler to center and normalize features.
5. Running KMeans++ clustering (5 clusters, n_init=10):
- Computing metrics: explained variance (R²), silhouette index, relative reconstruction error.
- Saving classification results (`classification_kmeans_lowpass2y_SS.nc`).
6. Plotting:
- Geographic cluster map with `canvas.gpkg`.
- Spectral plots of cluster centroids.
- Spectral plots of representative pixels.
7. Autonomous visualization:
- Scripts to reproduce maps and spectral plots directly from saved NetCDFs.
- No need to rerun clustering.
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