Applied geostatistics involves the practical application of statistical techniques to spatial datasets. Unlike traditional geostatistics, which focuses on theory and models, applied geostatistics emphasizes solving real-world problems through data-driven approaches. It accounts for spatial continuity, variability, and relationships to predict unknown values at unsampled locations or design optimal sampling strategies.
Key elements include:
- Spatial Data Analysis: Understanding the patterns and distribution of data.
- Interpolation and Estimation: Using methods like kriging to predict values at unsampled locations.
- Quantifying Uncertainty: Assessing the reliability of predictions and decisions.
Core Principles of Applied Geostatistics
- Spatial Variability
Spatial data often exhibits variability, where values change depending on their location. Understanding this variability is crucial for accurate modeling and prediction. - Spatial Correlation
Applied geostatistics leverages the principle that closer data points are often more similar than those farther apart. This relationship is captured and modeled through tools like the semivariogram. - Estimation Techniques
Key estimation methods include:- Ordinary Kriging: Provides optimal, unbiased predictions by modeling spatial relationships.
- Simple Kriging: Assumes a known mean for the dataset.
- Universal Kriging: Incorporates trends or external variables into predictions.
- Optimal Sampling
Designing cost-effective sampling strategies ensures accurate data collection while minimizing redundancy.
