TDW FOSS GEO-STACK FOR MINERAL EXPLORATION

TDW – Stackable Geospatial Package: Integrating FOSS

TDW FOSS Geo-Stack for Mineral Exploration

Javed Syed BSc. (Sask), MH (Emerson).Data Analyst , T-Data

Co-Chair OSGeo Ottawa

EnvisionGEOMATICS Conference2:30 pm Thursday 13 November 2014 Gatineau, Canada.


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/PostGISAn 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/SpatiaLiteSpatial 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/MetavistIs 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.

UdigUdig 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.

OpticksOpticks 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’.

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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.

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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.

TDW – Stackable Geospatial Package: Integrating FOSST

TDW Architecture


Components are chosen based on:

1. Maturity level2. Capabilities3. 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 forMineral Exploration

1. Detecting Hydrocarbon Seeps

2. Detecting Lineaments

3. Detecting Mineral Alteration


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 1/20



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 spillTM3/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.

TDW – Stackable Geospatial Package: Integrating Open Source Software

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.

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


TDW – Stackable Geospatial Package: Integrating Open Source Software

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.

References

What’s Next?

Looking for partners to apply TDW FOSS Geo-Stack to their own Projects.



Thank youQuestions?

Javed [email protected]

mailto:[email protected]

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.

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TDW FOSS GEO-STACK FOR MINERAL EXPLORATION

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