# Kriging Estimation Model AI Agent Connect

> Perform Ordinary Kriging to estimate block grades and spatial uncertainty.

## Overview
- **Category:** mathematics
- **Price:** Free
- **Endpoint:** https://edge.vinkius.com/vk_preview_bpI2P6MJOkCMUP4nkuIhdrCAeup3yh2mTroa3jHJ/ai-agent-connect
- **Tags:** kriging, interpolation, spatial-analysis, mining, geology

## Description

This MCP server provides geostatistical tools for resource modeling using Ordinary Kriging. It allows AI agents to calculate block grades, estimation variances, and kriging weights from spatial sample data. Users can validate variogram parameters with `validate_variogram_parameters`, analyze sample density via `get_spatial_correlation_stats`, and retrieve specific sample influences using `get_kriging_weights`. The engine supports anisotropy and search ellipse constraints for precise spatial interpolation.

## Tools

### get_block_estimates
Calculates the estimated grade and uncertainty for a specific set of block discretization points

### get_kriging_weights
Retrieves the specific influence (weights) each sample has on a target estimation point

### get_spatial_correlation_stats
Provides high-level summary statistics regarding the density and distribution of samples within the search space

### validate_variogram_parameters
Ensures the variogram model provided is physically and mathematically sound for kriging

## Prompt Examples

**Prompt:** 
```
Calculate the estimated grade for these block points using the provided sample data and variogram.
```

**Response:** 
```
The estimated grade for the target block is 4.52 g/t with an estimation variance of 0.12.
```

**Prompt:** 
```
What are the weights for the samples near the center point (10, 10, 10)?
```

**Response:** 
```
The sample at (10, 10, 10) has a weight of 0.65, and the sample at (12, 10, 10) has a weight of 0.35.
```

**Prompt:** 
```
Check the spatial correlation statistics for my sample data within a 50m radius.
```

**Response:** 
```
Within the 50m search radius, there are 12 samples with an average distance to target of 24.5m and a coverage density of 0.08.
```

## Frequently Asked Questions

**What is Ordinary Kriging?**
Ordinary Kriging is a geostatistical interpolation method that estimates values at unsampled locations by weighting nearby known samples based on spatial correlation.

**How do I ensure my variogram model is valid?**
You can use the `validate_variogram_parameters` tool to check if your nugget, sill, and range parameters are mathematically sound.

**Can this model handle directional dependencies?**
Yes, the model accounts for anisotropy and uses a search ellipse to define the spatial boundary for sample selection.