Datos de investigación

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)

Cometto, Pablo Marcelo Icon

Otros responsables

Fecha

Publicador:

Consejo Nacional de Investigaciones Científicas y Técnicas

Fecha de depósito:

27/05/2026

Fecha de creación:

13/5/2026

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.

Condiciones de uso

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Licencia Creative Commons

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Archivos

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netcdf_sbai_025_lowpass_fir.nc
Netcdf input file
48.86 MB
classification_kmeans_lowpass2y_SS.nc
Classification output file
120.97 KB
welch_fir_lowpass_2y.nc
FFT processed output file
2.16 MB
canvas.gpkg
ROI input file
58.27 MB
FS-WEL-2-inf-S_readme.docx
Tutorial
8.49 KB
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