Geochemical data analysis aims to determine the chemical composition of a sample, including the identification and quantification of chemical elements and compounds.

Gas chromatography-mass spectrometry (GC-MS) is commonly used for analyzing organic compounds, while XRF, ICP-MS, and AAS are used for analyzing inorganic elements.

Data normalization aims to make the data more comparable by removing systematic errors and scaling the data to a common range.

Principal component analysis (PCA) is a multivariate statistical method that is commonly used to identify patterns and relationships in geochemical data.

Geochemical modeling aims to predict the behavior of chemical elements and compounds in the environment, including their transport, fate, and interactions with other components.

Biological data is not typically considered a type of geochemical data, as it focuses on living organisms rather than the chemical composition of materials.

QA/QC procedures aim to ensure the accuracy and reliability of the geochemical data by identifying and correcting errors, documenting the procedures used in the analysis, and ensuring that the data is compliant with regulatory requirements.

Topographic maps are not typically considered a type of geochemical map, as they focus on the physical features of the Earth's surface rather than the chemical composition of materials.

Data interpretation aims to draw conclusions about the chemical processes that have occurred in a sample, including the formation of minerals, the weathering of rocks, and the transport and fate of chemical elements and compounds.

Biological databases are not typically considered a type of geochemical database, as they focus on living organisms rather than the chemical composition of materials.

Data visualization aims to make the data more accessible and understandable, identify patterns and relationships in the data, and communicate the results of the analysis to others.

Microsoft Excel is not typically considered a type of geochemical data analysis software, as it lacks the specialized features and capabilities of dedicated geochemical software packages.

Geochemical data validation aims to ensure that the data is accurate and reliable by identifying and correcting errors, documenting the procedures used in the analysis, and ensuring that the data is compliant with regulatory requirements.

Data interpretation is not a technique but rather a process of drawing conclusions from the data.

Geochemical data quality control aims to ensure that the data is accurate and reliable by identifying and correcting errors, documenting the procedures used in the analysis, and ensuring that the data is compliant with regulatory requirements.