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Adaptive ensemble spatial analysis.

Alvaro F Egaña¹, María Jesús Valenzuela¹, Mohammad Maleki²

¹Advanced Laboratory for Geostatistical Supercomputing - ALGES, Advanced Mining Technology Center - AMTC, Universidad de Chile, Santiago, Chile.

Scientific Reports

|July 22, 2025

Summary

This study introduces a novel, data-driven spatial interpolation method for geosciences. It provides accurate estimates without requiring complex geostatistical expertise, performing comparably to traditional methods.

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Area of Science:

Geosciences
Spatial Analysis
Geostatistics

Background:

Spatial interpolation is crucial in geosciences for estimating values at unsampled locations.
Traditional geostatistical methods require significant expertise in statistical modeling and characterization.
This complexity limits the broader application of these techniques in spatial and spatio-temporal data analysis.

Purpose of the Study:

To present a novel, data-driven spatial interpolation technique.
To offer a methodology accessible to a general geoscientific audience, reducing reliance on classical geostatistical expertise.
To reinterpret ensemble spatial interpolation as a generative Bayesian model.

Main Methods:

Extension of a previously proposed ensemble spatial interpolation model.
Development of a data-driven methodology minimizing the need for variographic analysis.
Reinterpretation of the algorithm as a generative Bayesian model.

Main Results:

The proposed model demonstrates good performance in capturing spatial aspects, even in non-stationary cases and with limited data.
Validation experiments show error levels comparable to traditional geostatistics (Ordinary Kriging) in suitable synthetic scenarios.
The technique proves effective in demanding and challenging spatial interpolation scenarios.

Conclusions:

The novel spatial interpolation technique offers a more accessible and data-driven approach for geoscientific applications.
It achieves comparable accuracy to traditional methods while simplifying the process.
Future work includes improving local spatial characterization and extending the method to 3D spatial studies.