Spatial Analysis of Geochemical Diagrams for the Classification of Igneous Rocks Using ArcGIS: A

Prototype for the Central Colorado Assessment Project

By John Horton - Penn State MGIS CandidatePhysical Scientist - US Geological Survey

July 8, 2008

Academic Advisor: Tim White

The Central Colorado Assessment Project (CCAP)

The Front Range urban corridor of Colorado has experienced rapid growth in recent years. Raises concerns of resources and land management

issues faced by governing agencies in the region. Project being conducted by the USGS:

“to provide comprehensive

geoscience data and interpretations

that will allow federal, state, and

local land management entities to

make informed land-use decisions

in central Colorado.”

Updating Geochemical Data

Creation of updated geochemical databases (including rock samples) is essential in interpretations of geology in the study area. Enhances understanding of landslide hazards,

mineral resources potential, and surface - groundwater pollution due to acid mine drainage related to historic mining.

Requires petrologist assigned to project to classify thousands of rock samples in a standardized way to help interpret the geologic setting throughout the area.

Database of classifications will be spatially included with updated digital geology, structural, and other geochemical data (soil/stream sediments) to aid in spatial analysis of the area.

Rock Types (quick background)

Three basic types of rocks on Earth: Igneous: rocks derived from melt.

granite, basalt, gabbro

Two major subtypes:Intrusive – plutonic (deep cooling within upper crust)

Extrusive – volcanic (rapid cooling on surface)

Sedimentary: rocks derived from physical and chemical processes of the earth.

sandstone, limestone, shale

Metamorphic: transformation of existing rock types by heat and/or pressure.

marble (limestone), slate (shale), gneiss (granite)

Igneous Rocks - Significance

Geologically important 95% of upper crust is igneous in origin Provides information about composition of the Earth’s

mantle and processes occurring deep within the Earth. Radiometric dating provides absolute ages of rock

formation or alteration – allows determination of approximate ages of other rock types (sedimentary) for correlating ages of geologic units worldwide.

Characteristics determine setting in which rock formed; important for understanding tectonic processes.

Majority of the World’s and Colorado’s mineral deposits are located within igneous rocks.

Rock Classification

Field criteria - location and structural characteristics present (geologic setting).

Texture – different types of crystallization. Mineralogy – minerals present (thin section) Geochemical characteristics – chemical

analysis of samples. This is the type of classification that will be

used in this project.

Geochemistry of Igneous Rocks

Rock samples are chemically analyzed using different methods to identify various elements: inductively coupled plasma (ICP), x-ray fluorescence (XRF), and instrumental neutron activation analysis (INAA) are the primary ones.

Whole rock concentrations of various elements, compounds, and isotopes can be used to classify the rock by name and also aid in petrogenesis classifications.

Multiple variation diagrams (graphs) have been published over the past 50+ years to classify igneous rocks. The International Union of Geological Sciences (IUGS)

formed a commission in 1970 in order to standardize the various classification schemes.

There are many other published diagrams from the literature that provide more specific classifications (missing from IUGS diagrams) and also petrogenesis classifications based on geochemistry.

A Current Day in the Life of an Igneous Petrologist

Representative rock samples are prepared for analysis, analysis is returned from lab, get table of concentrations for various elements/compounds. Plot values on multiple diagrams for classification – visually

identify classification and record. Very tedious process. Requires using multiple graphing

programs (1 program doesn’t have all the diagrams). Learning multiple interfaces. Merging of multiple output files. Hard to automate (different programs have different

scripting/schemes). Compare rock concentrations to various geochemical rock

databases (GEOROC, PETDB). Also tedious. Results not always useful.

Requires data entry of multiple specific chemical parameters. Multiple matches to sample; classification uncertain.

Question: Is it possible for ArcGIS to automatically return classifications based on point positions in graph space?

Need to classify 5,000+ rock samples within CCAP study area from various sources. The current National Geochemical Database only has 1445 igneous rock samples classified in the study area.

Focusing on igneous rocks for now since they are the most dominant lithology.

Usual classification process is very tedious and infeasible for this large of a sample population.

An automated method is needed to facilitate the classification process. The petrologist can then focus efforts on interpretation and collaboration with other scientists on project.

Answer: YES!

ArcGIS can certainly be used to help automate the process.

A spatial join will return classifications (point in polygon spatial analysis using geoprocessing tools) and a Python script can be written to repeat the process through all the diagrams.

Overview: Using ArcGIS to ‘automate’ the classification process.

Remove geographic portion of GIS by not defining a projection. Space will be entirely numeric.

Treat diagrams as ‘maps’ and create polygons of classifications from them (each diagram is a separate shapefile). Scan complex diagrams (curved lines) and spatially reference image to

numeric space using diagram’s chemical parameters. OR provided coordinates of polygons (boundaries) by petrologist.

Plot each rock sample’s chemical concentrations for the appropriate diagram as XY points.

Perform a spatial join of points in polygons to return classification attributes from polygon and assign it to rock sample.

Output a table with all classification results from all the diagrams (shapefiles)to petrologist for further analysis. Creation of an igneous rock classification geodatabase for CCAP project.

Will be able to query on rock name/tectonic setting. Example: show me all the samples classified as gabbro within the study area;

instead of show me all samples with 48-52% silica oxide, 5-9% magnesium oxide and greater than 8% calcium oxide.

Step 1 - Digitizing the Diagrams

Scan complex classification diagrams (curved lines) and spatially register image to numeric graph space in Arc.

Step 1 - Digitizing the Diagrams

Scan complex classification diagrams (curved lines) and spatially register image to numeric graph space in Arc.

Create polygon shapefile and name it according to name of classification diagram.

Add field for classifications (name of field same as name of diagram; since final table output will have attributes from multiple diagrams).

Digitize polygons from diagram and attribute the classifications.

Digitizing – Alternate Method

Diagrams with straight lines or very complex structure. Given coordinates of polygons (boundaries)

by petrologist, so a point file can be generated and then polygons are digitized by snapping to these points.

Plot Type Reference

SiO2 vs K2O binary Peccerillo and Taylor 1976

Sr vs Rb binary DeWitt, adapt of Ewart 1982

SiO2 vs K2O binary Ewart 1982, modif. Pec. & Taylor 1976

Ti/100 vs V binary Shervais 1982

SiO2 vs Na2O+K2O binary Cox at al. 1979, revised by Wilson 1989

SiO2 vs Na2O+K2O binary Cox at al. 1979

Fe2O3+MgO vs Al2O3/SiO2 binary Bhatia 1983

Fe2O3+MgO vs TiO2 binary Bhatia 1983

Zr/10 vs Sc vs Th ternary Bhatia 1986

Sc vs Th vs La ternary Bhatia 1986

Y vs Zr/4 vs 2Nb ternary Meschede 1986

P2O5*10 vs MnO*10 vs TiO2 ternary Mullen 1983

SiO2/Al2O3 vs Fe2O3/K2O binary Herron 1988

K2O/Na2O vs SiO2/Al2O3 binary Rosert 1986

SiO2 vs K2O/Na2O binary Rosert 1986

4Fe-2Mg-3Ca vs 12CCO2-4Al-8(Na+K) binary DeWitt 1994 (IF1 vs IF2)

Ta/Yb vs Th/Yb binary Pearce 1982

Ta vs Th vs Hf/3 ternary Wood 1980

Nb/16 vs Th vs Hf/3 ternary Wood 1980

Partial List of Diagrams(50+ when finished)

Ta*3 vs Hf vs Rb/30 ternary Harris 1986

Nb*3/16 vs Zr/40 vs Rb/30 ternary Harris 1986

Ta vs Th vs Hf/3 ternary Pearce 1996 mod of Wood

Nb/16 vs Th vs Hf/3 ternary Pearce 1996 mod of Wood

Nb/Y vs Zr/Ti binary Winchester and Floyd 1977 + Pearce 1996

Y*3 vs Zr vs Ti/100 ternary Pearce and Cann 1973 - mod 1996

Nb/8 vs La/10 vs Y/15 ternary Cabanis? and Lecolle 1989

Ta*2 vs Th vs Tb*3 ternary Cabanis? and Thieblenont? 1988

MgO vs Al2O3 vs FeO + TiO2 ternary Jensen 1976

Y*3 vs Zr vs Ti/100 ternary Pearce and Cann 1973

Zr vs Ti binary Pharoah?

Nb/Y vs Zr/TiO2 binary Winchester and Floyd 1977

4Si-11(Na+K)-2(Fe+Ti) vs 6Ca+2Mg+1Al binary R1 vs R2 ????

4Si-11(Na+K)-2(Fe+Ti) vs 6Ca+2Mg+1Al binary R1 vs R2 ????

4Si-11(Na+K)-2(Fe+Ti) vs 6Ca+2Mg+1Al binary R1 vs R2 ????

4Si-11(Na+K)-2(Fe+Ti) vs 6Ca+2Mg+1Al binary R1 vs R2 ???? Alkalinity Numeric

Yb+Ta vs Rb binary Pearce et al. 1984

Yb vs Ta binary Pearce et al. 1984

Y+Nb vs Rb binary Pearce et al. 1984

Y vs Nb binary Pearce et al. 1984

SiO2 vs A/CNK binary ?????? Peraluminous

SiO2 vs A/CNK binary ?????? Peraluminous

(FeO+.89Fe2O3)/(FeO+.89Fe2O3+MgO) vs SiO2 binary ?????? Iron Oxides

K2O/K2O+Na2O vs SiO2 binary ?????? Potassium Oxides

Step 2 - Scripting

A Petrologist will have a table of multiple rock samples each with chemical concentrations from the results of analyses. Need to classify all the samples using multiple diagrams.

I will create a Python script to batch process all the samples through all the diagrams.

Use of ArcGIS by non-GIS scientists. USGS has an enterprise license for ArcGIS - installation and

use is free; hence no new software expense. The Python script utilizes the geoprocessing tools of Arc

without prompting the user for commands. Possibly create a custom igneous classification tool using

ArcObjects.

Initial Problems/Issues

Some classification diagrams have log values (must be re-plotted into normal arithmetic scales).

Some diagrams are ternary (use 3 variables). These must be repositioned using only 2 variables.

Some diagrams have uneven X and Y axis ranges. One set of values must be multiplied/divided by a constant in order to spatially register the image in numeric space.

Limited scripting experience. Many new samples are not in digital format. Relying

on others to receive digital tables of the data for our use.

Expected results

Creation of an igneous rock classification feature class within a geodatabase for the CCAP study area. Addition of thousands of previously unclassified samples. New spatial queries on rock samples by different classifications can now be analyzed (instead of relying on chemical values only).

Creation of maps and spatial analyses of new information combined with other GIS layers as directed by CCAP project chiefs.

Publication of the ArcGIS igneous rock classification process as a USGS Open File Report (public) so anyone in the world can use it for other projects.

Illustrate that this GIS-based classification process can be extended to other rock types or to other classification problems in other disciplines.

Project Timeline May: Digitized a few diagrams and developed a prototype script.

Tested on small sample population. A few bugs, but worked correctly.

June/July: Continue to digitize diagrams as they are received. Refine script to ensure correct output for petrologist.

August: Finalize diagram list and all diagram polygons. Continue testing script on more sample suites from petrologist; edit as necessary. Finalize script and give to petrologist for testing. Verify results with previously classified samples.

Sept/Oct: Receive and incorporate feedback from petrologist into finalized versions of script and diagram shapefiles. Get approval on results and begin work with petrologist to classify all samples within the CCAP study area. Incorporate or provide output for use with other spatial databases in project (geology, structure, geochemistry). Analyses as directed by project chiefs.

Nov/Dec: Present results at a USGS talk and a presently unknown conference. Publish results with USGS publications.

Thank You!

Questions?

jhorton@usgs.gov

Central Colorado Assessment Project website:http://minerals.cr.usgs.gov/projects/colorado_assessment/index.html

USGS - Central Mineral Resources Team website:http://minerals.cr.usgs.gov/