Geochemical Data Modelling Overview

Modelling the Spatial Distribution of Geochemical Data

• Geochemical data modelling involves representing the spatial distribution of chemical elements to understand underlying processes and trends.
• This is crucial for various applications like mineral exploration, environmental monitoring, and public health.
• Geochemical maps are similar to physical maps but represent the distribution of chemical elements.

Why Model Geochemical Data?

• To understand the relative importance of different geological processes.
• To document geochemical background and variations.
• To discover mineral deposits.
• To identify and quantify human impact on element distributions.
• For land use planning and environmental management decisions, including setting cleanup levels for contaminated areas.
• To understand the relationship between chemical elements and the occurrence of endemic diseases.

Considerations for Modeling Geochemical Data

• Geographical Information Systems (GIS) have become the primary tool for geochemical mapping, but special techniques are still needed to fully utilize the information contained within geochemical data.
• Choosing an appropriate map scale is vital. The size of the area mapped and the map's purpose determine the scale.
• Too much information can overwhelm the map, while too little may not be sufficient.
• A4 size (210 mm × 297 mm) is generally the maximum size for a geochemical map.
• It's crucial to avoid cluttering a geochemical map with unnecessary background information, such as detailed geological maps.
• The focus should be on the spatial distribution of the chemical elements themselves.

Placing Geochemical Data on Maps

• Several methods exist to display geochemical data on maps:

Direct Value Display

• Disadvantages:
  • Difficult to read with many samples.
  • Outliers are not easily identified.
• Advantages:
  • Directly reflects the analytical results.
  • Useful for tracing contours.

Arbitrarily Chosen Class Boundaries

• Disadvantages:
  • No relation to data structure.
  • Outliers are not defined.
• Advantages:
  • Classes are easily understood.
  • Create a seemingly logical visual representation.

Mean and Standard Deviation (SD) based Classes

• Disadvantages:
  • Requires data transformation, potentially resulting in negative values.
  • Class boundaries are highly influenced by outliers.
• Advantages:
  • May relate to data structure if outliers are removed before calculation.
  • Provides a statistically based approach to data classification.

Symbol Size Proportionate to Analytical Value (Bubble Plots)

• Disadvantages:
  • Scaling can be challenging.
  • High values dominate the visual map.
• Advantages:
  • Effective visual representation if scaling is performed correctly.
  • Allows for visual differentiation based on value magnitude.

Percentiles and Symbols

• Disadvantages:
  • Careful symbol selection is essential.
  • Outlier definition is limited to the uppermost data percentiles.
• Advantages:
  • Direct relation to data structure when classes are evenly spread.
  • Well-suited for color mapping.

Exploratory Data Analysis (EDA) approach (Boxplot and EDA Symbols)

• Disadvantages:

  • Maps are difficult to interpret at first glance.
  • High values have minimal visual impact.

• Advantages:

  • Data structure is directly displayed, showcasing geochemical processes.
  • Lower outliers are readily detected.
  • Doesn't assume a normal distribution of data.

• It's important to choose the appropriate method for visualizing geochemical data based on the data characteristics and the intended purpose of the map.

This quiz explores the significance of modelling the spatial distribution of geochemical data. It covers applications in mineral exploration, environmental monitoring, and public health, highlighting the importance of understanding geological processes and human impacts. Learn how GIS enhances the analysis of geochemical distributions and informs management decisions.