TDW FOSS GEO-STACK FOR MINERAL EXPLORATION

TDW FOSS Geo-Stack for Mineral Exploration
Javed Syed BSc. (Sask), MH (Emerson).
Data Analyst , T-Data
Co-Chair OSGeo Ottawa
EnvisionGEOMATICS Conference
2:30 pm Thursday 13 November 2014 Gatineau, Canada.
TDW – Stackable Geospatial Package: Integrating FOSS

Topics
1. Introduction: Plays
2. TDW Architecture: SpatialLite, PostGIS, QGIS, Udig, GrassGIS, Optics,
Polsar Pro, Metavist, Geonetwork.
3. Applications:
- Mineral Exploration
- Detecting Hydrocarbon Seeps
- Detecting Lineaments
- Detecting Rock Alterations
4. Conclusion
5. What’s Next

Plays
I. Major Players, Bit Players
II. Flow Through shares, Sovereign Funds
III. Mineral Plays
IV. Tread is your Friend
V. Geospatial Data

Introduction
Turning Data into Wealth (TDW) presents a stackable system that allows the storage,
visualization, and analyses of geospatial data.
This Free and Open Source Software (FOSS) solution integrates an; extensible
geospatial database, stackable visualization, analysis, and metadata handling tools.
This system can be deployed from a USB flash drive or within a Virtual Machine
environment.
The reason behind creating TDW FOSS Geo-stack was that there are numerous
applications, data formats for vector and raster files collected from open source portals,
which makes it very difficult to view and analyze data.
TDW package addresses data formatting or application issues and improves quality of
data presentation. Data can be collected, described, analyzed and interpreted by using
tools available in TDW FOSS Geostack.

TDW Architecture 1/3

PostgreSQL/PostGIS
An object-relational database system. PostGIS is a FOSS extension for
PostgreSQL that enables the database system to store and process geographic
objects. PostgreSQL/PostGIS is an enterprise wide database management system
SQLite/SpatiaLite
Spatial Lite is a FOSS extension that spatially enables the SQLite relational
database management system to store and process geographic objects.
SQLite/SpatiaLite is a standalone database system. SQLite/SpatiaLite is suitable for
local storage and can be imbedded into other software applications such as web
browsers.
GeoNetwork/Metavist
Is a metadata tool used to record metadata information of data stored in the
database system. Provides powerful metadata editing and searching capabilities; also
contains an embedded data viewer.

Quantum GIS (QGIS)
QGIS is an open source desktop Geographic Information System (GIS) viewer
that provides data viewing, editing, and analysis capabilities. QGIS can also be used as a graphical
user interface to GRASS GIS. QGIS can be connect to PostgreSQL/PostGIS and SQLite/SpatiaLite
which allows access to data within the database. Numerous tools available for Geoprocessing.
Udig
Udig is a Java-based FOSS GIS that provides editing and viewing capabilities of
geospatial data.
Geographic Resources Analysis Support System GIS (GRASS)
Grass provides capabilities to manipulate and process raster and vector data. Grass
contains a comprehensive suite of algorithms to process and analyze remotely sensed data.
Opticks
Opticks software is an expandable remote sensing analysis tool that contains
algorithms for processing multispectral, hyperspectral, and Synthetic Aperture Radar (SAR) data.

TDW Architecture/QGIS 1/9
Quantum GIS (QGIS) runs on Linux, Unix, Mac OSX, Windows or Android. QGIS
supports vector, raster, and database formats. QGIS is licensed under the GNU
Public License. Coding for QGIS began in May 2002. The idea was conceived in
February 2002 when Gary Sherman began looking for a GIS viewer for Linux that was
fast and supported a wide range of data stores. In the beginning Quantum GIS was
established as a project on Source Forge in June 2002. The first code was input into
CVS on Source Forge on Saturday 6 July 2002, and the first, mostly non-functioning
release came on 19 July 2002. The first release supported only Post GIS layers. QGIS
is a cross-platform (Linux, Windows, Mac) FOSS GIS. QGIS Desktop - The classic
QGIS desktop application offers many GIS functions for data viewing, editing, and
analysis. QGIS browser is a fast and easy data viewer for your local, network and
online (WMS) data. QGIS Server is a standard-compliant WMS 1.3 server that can be
easily configured using QGIS Desktop project files. QGIS Client is a web front-end for
web mapping needs based on Open Layers and Geo Ext.

QGIS supports WMS versions
1.3.0 (and lower) with
GetCapabilities, GetMap,
GetFeatureInfo, layer
transparency, and provides a
metadata browser for the service

To add a WMS layer from the
menu, choose Layer then Add
WMS Layer. In the Add Layer(s)
from a Server pop-up box click
the ‘New’ button, and then in the
Create a new WMS connection
pop-up add a name for your
service

After adding the the WMS service to
the list of available WMS services. To
add a layer select the WMS service
from the Add Layer(s) from a Server or
Database and click ‘Connect’. This will
show you a list of the layers being
served from the WMS service or
database.

In this screen shot the Bedrock
Lithostratigraphy and the
superficial lithostratigraphy
geology layers are joined to
create a ‘Lithostratigraphy layer’.
.

You may right click on
any layer in the layer
list to get at the
metadata for that layer
and the service that
serves it.

Here we have OneGeology
shapefile of British 1:625,000
Bedrock Lithology units.
.

Here we have 1:625,000 Bedrock
subsurface Lithology Units

Here we have selected features
from the OneGeology shape file
service of British 1:625,000
detailing Surface Geology.
T

TDW Architecture
Components are chosen based on:
1. Maturity level
2. Capabilities
3. High degree of interoperability.
We believe that this combination of properties allow for a Geo-stack that has
an acceptable level of usability, ability to do complex analyses, and ability to
import and export different data formats between components as needed.
Interoperability is especially important since a component may not have the
capability to do a specific analysis and it becomes necessary to export the
data into another component for further processing.

TDW Applications for
Mineral Exploration
1. Detecting Hydrocarbon Seeps
2. Detecting Lineaments
3. Detecting Mineral Alteration

Hydrocarbon Seeps 1/20
INTRODUCTION:
Detection of subsurface Petroleum reservoir accumulations using RSI techniques had its beginning in Hydrocarbon
seeps.
Hydrocarbon seeps are direct indicators of subsurface Petroleum accumulations.
Remote Sensing Imagery (RSI) use is one of the key Tools for understanding the surface
Hydrocarbon Seeps and detecting subsurface accumulation potential, genesis and quality without drilling.
The detection process involves the integration of Geospatial data from a variety of sources and formats.
Through Seismic techniques we can determine porous and impermeable rocks where there is a potential for
Hydrocarbon accumulation.
Biomarker distribution as part of Geochemistry can be used to infer characteristics of the source rock that generated
the hydrocarbon such as the relative amount of oil and gas-prone, kerogen type, the age of the source rock, the
environment of organic matter deposition.
Geobotanical features can be used to map surface expressions, enhanced porosity and permeability in oil and gas
reservoirs.
Most of the above noted tools are labor intensive requiring specialize skills, under sampling in the case of geology and
only looking at the subsurface in the case of geophysics, hence expensive with inflation and recessionary times RSI
becomes a viable option for grassroots Petroleum exploration.
To provide solution to these challenges TDW presents a Stackable Geospatial package that can store, analyze
vector and or raster data.

Hydrocarbon seeps are the result of vertical movement of light hydrocarbons from the reservoir to the surface
through networks of fractures, faults, and bedding planes that provide permeable routes within the overlying
strata.
Hydrocarbon seeps express themselves on the surface in an array of alterations and anomalies, in the
overlying sediments.
Hydrocarbon seeps occur in a geographic location where liquid or gaseous Hydrocarbon seeps to the Earth's
surface, generally under low pressure or flow.
The Hydrocarbon seeps may escape along fractures and fissures in the rock, or directly from oil-bearing outcrop.
Hydrocarbon seeps generally occur above either terrestrial or offshore geological accumulation structures.
Hydrocarbon accumulation tends to be in porous sedimentary rock or stratigraphy capped my impermeable
layers.
Hydrocarbons seeps through faults or fractures causing soil halos or anomalies manifested in soil brightness or
vegetation stress.

Hydrocarbon seeps 4/20
Methodology:
1. Literature review,
2. Download Geologic data,
3. Determine Area of Interest (AOI),
4. Download Landsat imagery from Glovis,
5. Process Image Restoration,
6. Run Image Enhancement,
7. Calculate Band Ratios,
8. Apply Spectral Angle Mapper (SAM) Algorithm,
9. Do a Spectral Plot,
10 . Run Average AOI signature plot,
11. Load processed image into a TDW viewer to digitize location of the H-C seeps,
12. Extrapolate solitary and clustered pixels across the image that have spectral
signatures similar to the target pixels or spectral library.

Image Restoration -Geometric distortion corrections, errors due to instruments aboard
the Satellite
Atmospheric correction - The solar radiation travelling from the sun to the Earth and
from the Earth to the sensor interacts with the atmosphere, through absorption and
diffusion processes. The objective of atmospheric correction is to retrieve the surface
reflectance from remotely sensed imagery by removing these atmospheric effects.
Band Ratio transformations of the remotely sensed data is applied to reduce the effects
of environment. Band Ratios also provide subtle spectral reflectance or colour differences
between surface materials that are often difficult to detect in a standard image. Band
Ratios also minimize the effect of shadowing.

The Spectral Angle Mapper computes a spectral angle between each pixel spectrum
and each target spectrum. The smaller the spectral angle, the more similar the pixel
and target spectra. This spectral angle will be relatively insensitive to changes in pixel
illumination.
Spectral analysis compares hydrocarbon pixel spectra with a reference spectrum
known as targets. Target spectra can be derived from spectral libraries or from areas
of interest within an image, or individual pixels within a spectral image.
Image Enhancement :
Contrast Stretch, DN values plotted against the frequency, lower values
assigned black (0), upper values are assigned white (255).
Edge Enhancement, Mathematical techniques applied to manipulate the image
so boundaries are revealed.
False Colour Composite, assign specific colour to each spectral band then
combine the band to produce a full colour image.

Case Study1:
Lake Maracaibo, Venezuela Lat/Long 9° 48′ 57″ N, 71° 33′ 24″ W
Series of hydrocarbon spills were observed
from Dec 2002 to Feb 2003.
Hu et al. (2003) identified specific spill areas
from multiple MODIS satellite imagery.

Using Opticks FOSS and Landsat ETM+
imagery (Path = 7 Row = 53, acquired on
January 20, 2003)
Applied Spectral Angle Mapper (SAM)
Algorithm.
Calculate Band ratio TM3/TM5.
ETM+ 7 contrast stretch (equalization)
Spectral plot - Reflectance y axis, Band
Number x axis, of area INSIDE
Hydrocarbon spill (ETM 1,2,3,4,5,7).
Spectral plot, Reflectance y axis, Band
number x axis, area OUTSIDE
Hydrocarbon spill (ETM
1,2,3,4,5,7).
TM3/TM5 average AOI Signature OUTSIDE of
oil spill
TM3/TM5 average AOI Signature INSIDE of
oil spill

Lake Maracaibo, Venezuela. Landsat Band RatioTM3/TM5 showing Hydrocarbon spills.

Lake Maracaibo, Venezuela. TM3/TM5 Band Ratio close up (AOI) of specific
Hydrocarbon spill.

Lake Maracaibo, Venezuela. ETM7 contrast stretch (equalization). Shows location of
Hydrocarbon spill area .

Lake Maracaibo,Venezuela. Spectral plot , Reflectance y axis, Band Number x axis, of
area INSIDE Hydrocarbon spill (ETM 1,2,3,4,5,7).

Lake Maracaibo, Venezuela. Spectral plot, Reflectance y axis, Band numbers x axis, of
area OUTSIDE Hydrocarbon Spill (ETM 1,2,3,4,5,7).

Lake Maracaibo, Venezuela. TM3/TM5 contrast stretch (Linear 2-98 percentile).

Lake Maracaibo, Venezuela: TM3/TM5 average AOI Signature OUTSIDE of Oil Spill.

Lake Maracaibo,Venezuela: TM3/TM5 average AOI Signature INSIDE of Oil Spill.

Case Study2:
Preeceville Saskatchewan, Hydrocarbon seeps (Lat/Long: 51.95, -102.666667)
East Central Sask, North West Flank of
Western Canadian Sedimentary Basin.
Nordic Oil Company detected Hydrocarbon
seeps and subsequently drilled the seeps
in Preeceville Sask.
Township Twp 40 Range 4 and 5 W2.
Location Diagram Courtesy Sask Energy
and Mines
Using Opticks FOSS and Landsat ETM+
Imagery (Path = 35 Row = 24, acquired on
September 19, 2001).
Applied SAM Algorithm.
Calculate Band Ratios TM3/TM5.
.

Hydrocarbon Seep Preeceville, SK. Courtesy of Nordic Oil.

Preeceville, SK (Landsat, RGB = 742).

Preeceville, SK (SAM parameters = 4819, 1559)

Preeceville, SK (SAM results). Able to pick up other water bodies and isolate them from
Land and Vegetation.

Preeceville, SK (Band Ratio TM3/TM5). Also picked up water bodies and separate them
from Land and Vegetation.

CONCLUSION:
The two case studies demonstrate that Opticks a FOSS RSI analysis tool and using
Landsat imagery available free from Glovis as part of TDW Geo-stack was able to
calculate Band Ratios, process Spectral Angle Mapper (SAM) Algorithm, do a spectral
plot and calculate average signature plot.
In the case of Lake Maracaibo SAM algorithm did not pick up other Hydrocarbon spills
pixels but the Band ratio TM3/TM5 showed good results and was able to isolate
Hydrocarbon spill from water. ETM7 contrast stretch (equalization) shows location of
Hydrocarbon spill. The TM3/TM5 average AOI Signature outside of oil spill (green grid
lines) values 4.4. TM3/TM5 average AOI Signature inside of oil spill (red grid lines) value
5.4 show that values are higher inside the spill.
For Preeceville, SK case study the assumption was made that Hydrocarbon seeps tend
to occur in water bodies. The calculation of Band ratio TM3/TM5 and running SAM
algorithm using pixel value 4819, 1559 was able to isolate water bodies from Land and
Vegetation; more analysis need to be done to discern oil slicks.

Lineaments
A Lineament is a linear feature in a
landscape which is an expression of an
underlying geo structure such as fault.
Lineament will comprise a fault-aligned
valley, a series of fault or fold-aligned hills,
a straight coastline a combination of these
features.
Fracture zones, shear zones, and igneous
intrusions such as dykes can also give rise
to Lineaments.
Lineaments are usually seen in Radar or
DEM Imagery.

Lineament Methodology 2/2
Lineament study is the identification and
characterization of structural expression
that plays an important role in Mineral,
exploration, and detecting structures
include faults, folds, synclines and
anticlines, circular patterns or trends.
Radar or DEM data is used to Map the
Lineaments that may control ore prospects
or deposits. Lineament intersections are
possible Mineral target areas.
A contrast stretch or a Gaussian
stretching can improve the display of the
the Lineament on a radar image.

DEM Vs Radar Image 2/2
Lineament comparison between
Digital Elevation Model (DEM) and
Radarsat Image.

Rock Alteration
Rock alteration (RA) occurs when primary
Minerals are replaced by the secondary
Minerals. RA occur due to changes in
temperature, pressure, or chemical
conditions or any combination of these.
Hydrothermal alteration is a change in the
mineralogy as a result of interaction of the
rock with hydrothermal fluids.

Rock Alteration Methodology
The VNIR wavelength region is useful for
mapping gossans, rich in iron, Mn, Cr and
weathered sulphide regolith.
SWIR wavelength are largely related to
cations typically, Al, Fe, Mg. SWIR is
useful for mapping alteration minerals,
carbonates, and regolith.
The TIR region spectrum is useful for
characterizing mineral groups such as
silicates: quartz, feldspars, and pyroxenes
and carbonates.

Recommendations
To further refine the methodology for hydrocarbon detection a spectral signature
Library of Lake Macaibo Hydrocarbon spills needs to be created. This Library is then
applied to the Landsat imagery of Preeceville to compare targets pixels against AOI
pixels to see if there's a match.
Then do further analyses using Opticks, for information extraction such as; Supervised
and Unsupervised classification, Density Slicing, Principal Component Analysis (PCA) on
collection of reflectance spectra measured from Hydrocarbon spills or seeps from Lake
Macaraibo and Preeceville.
Other spectral image analysis methods to consider are:
Whole pixel analysis methods which attempts to determine whether one or more target
hydrocarbons are abundant within each pixel in a multispectral image on the basis of
the spectral similarity between the comparative pixel and target spectra. Whole-pixel
scale tools include standard supervised classifiers such as Minimum Distance or
Maximum Likelihood, Spectral Feature Fitting, Derivative Spectroscopy.
TDW – Stackable Geospatial Package: Integrating Open Source Software

Conclusion -1
Complex Remote Sensing and GIS analyses is possible using TDW FOSS Geo-stack.
TDW as a GIS tool allows for the analysis of large vector or raster datasets and the
combination of these datasets, such as Satellite imagery, Geological, Geophysical or
Geochemical data.
To get the true picture of the Hydrocarbon seeps, spills, Minerals source Geological,
Geomorphic, Structural, Gravity, Magnetic, Seismic, Well data needs to be
superimposed and integrated for analysis and finally ground verification is required.
Since TDW Geo-stack is stackable and extendable, one can add any open-source or
closed-commercial software allowing the user the freedom to choose the best tool
for the job.

Conclusion - 2
The two Hydrocarbon Seeps case studies demonstrate the versatile utility of TDW
FOSS Geo-stack for detecting Hydrocarbon Seeps in both marine and terrestrial
environment.
The second objective was to showcase the feasibility, and focus on the capability of
TDW FOSS Geo-stack for Hard Rock Mineral Exploration.
The methods used and results obtained from the case studies are preliminary, one
should do their own due diligence when exploring for Minerals using TDW FOSS
Geo-Stack.

Advantages of TDW FOSS Geo-Stack
1. Relatively low cost and fast
- utilize free data and FOSS
- simple classification scheme
2. Take advantage of crowd sourcing (volunteers)
3. Excellent educational tool

References
Hu, C., F.E. Müller-Karger, C. Taylor, D. Myhre, B. Murch, A.L. Odriozola,
and G. Godoy (2003), MODIS detects oil spills in Lake Maracaibo,
Venezuela, Eos Trans. AGU, 84(33), 313.
Alam, Syed (2012). Turning Data into Wealth (TDW) Geospatial Stackable
Package. Proceeding 33rd Canadian Symposium on Remote Sensing /
33ième Symposium Canadien de télédétection June 11-14 juin 2012,
Ottawa, Canada.
Syed, J. (2010). Space-Based Prospecting: Remote Sensing Helps Find
Gold Deposits. Geospatial Today. 23 (6): 16-19.
TDW – Stackable Geospatial Package: Integrating Open Source Software

What’s Next?
Looking for partners to apply TDW FOSS Geo-Stack to
their own Projects.

Thank you
Questions?
Javed Syed
motivism@yahoo.com

Resume
• Javed has a BSc in Geology from University of Saskatchewan and a Master
Herbology Diploma from Emerson College Montreal.
• Javed is Self Taught Data Analyst, focusing on application of Free and
Open Source Software (FOSS) to Mineral Exploration, who believes
collecting, analyzing, interpreting Data is more lucrative then exploiting
commodities.
TDW11Nov2014
Envision_Geomatic

TDW FOSS GEO-STACK FOR MINERAL EXPLORATION

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