MSc thesis GRSG conference 2017 - University of Twente ... · 3k' vwxghqw &hflold &rqwuhudv...

$: MSc thesis GRSG conference 2017 - University of Twente ... · 3k' vwxghqw &hflold &rqwuhudv +hopkrow] ,qvwlwxwh )uhlehuj iru5hvrxufh7hfkqrorj\ 3djh ,7& )dfxow\ ri *hr ,qirupdwlrq$
PhD. student: Cecilia Contreras I Helmholtz Institute Freiberg for Resource Technology I www.hzdr.de/hif

Mapping Epithermal Alteration Mineralogy with High Spatial Resolution Hyperspectral

Imaging of Rock Samples

Lisbon, December 15th, 2017

GRSG 28th International Annual Conference “Applied Geological Remote Sensing”

Cecilia Contreras

Co-authors: Dr. Chris HeckerDr. Freek van der Meer

PhD. student: Cecilia Contreras

Helmholtz Institute Freiberg for Resource TechnologyPage 2

ITC Faculty of Geo-Information Science and Earth Observation

Differentiation betweenhypogene and supergene

composition

crystallinity

mineral associations and patterns

hypogene: primary minerals i.e.: hydrothermal alterations

supergene: secondary minerals i.e.: weathering processes

Introduction

Hypogene and supergene alterations (Modified from White & Hedenquist, 1995)




To quantitatively study hypogene mineral assemblages to automatically classify the alteration zones of an epithermal alteration system

spectral and chemical differences mineral abundances

SWIR colour composite of sample260

hyperspectral images

Objective and Approach




(A) Location of the Rodalquilar Au deposit, Cabo de Gata volcanic field, south-eastern Spain. (B) Central part of the field (Arribas et al., 1995)

Cross-section of the Rodalquilar caldera showing the distribution of the alteration zones modified from White & Hedenquist (1995)

Study area: Rodalquilar Caldera Complex, Almeria, Spain




Compilation of the 22 SWIR color composite images from Rodalquilar samples

288 spectral bandswavelength range: 1.0µm – 2.5µm

pixel size: 0.4mm

Specim Hyperspectral SWIR camera

Image acquisition

Visual analysis Validation Endmember

extraction Unmixing Quantification

Image acquisition: 22 Hyperspectral SWIR images




Sample 260 Wavelength map between 1200 – 2400nm. Depth stretching 0 – 1470

Image acquisition



Visual analysis : Wavelength maps




supergene alunite hypogene alunite

Image acquisition



Visual analysis: Alunite spectral and chemical differences




Supergene

Hypogene

Supergene

Hypogene

ASD spectra

Image acquisition




Spectra from images




Supergene

Hypogene

Spectra from images

Supergene

Hypogene

Image acquisition



ASD spectra





Supergene

Hypogene

Supergene

Hypogene

Image acquisition



Spectra from images

Wavelengths maps, range (1460-1495nm) Supergene - Hypogene

ASD spectra





Supergene HypogeneNa 13 3402.33K 120*103 50*103

Sr 97.33 533.79Y 1.03 3.31Zr <0.26 <0.26

Na/K <0.01 0.07

LowLow High

Inductively Coupled Plasma Optical Emission Spectroscopy

(ICP-OES) Thermogravimetric Analysis (TGA)

Image acquisition



Validation: Geochemical analysis




Total of 20 endmembers

Endmember extraction: Wavelength maps

Image acquisition



Offset and hull-removed spectra of alunite and kaolinite endmembers.




Color composites to evaluate spatial distribution of the minerals

ISMA sample260 Alunite long

Alunite short-BSilicification

ISMA sample213 Alunite short-AJarosite overall

Montmorillonite

ISMA sample208Alunite short-BJarosite 1936Silicification

Unmixing: Iterative Spectral Mixture Analysis (ISMA)

Image acquisition







Alunite short-BSilicification


Alunite short-AAlunite short-B


i.e.: alunite zonation

Image acquisition







Alunite short-AAlunite short-B

Alunite longAlunite short-AAlunite short-B

Alunite long Alunite short-A

Alunite short-B

Image acquisition



i.e.: alunite zonation





Mineral Abundances per Alteration Zones (Arribas et al 1995)

Vuggy silica Interm. argillic NonAdvance argillic SupergeneAlteration zones

Quantification: Iterative Spectral Mixture Analysis (ISMA)

Image acquisition






• Hyperspectral imaging is an emerging technique for mineral mapping(detection mineral mixtures and spatial patterns)

• Spectra and geochemical analysis offer a good tool to differentiate between supergene andhypogene samples

• Alunite minerals in the Rodalquilar show spectral variations and geochemical analysis provethat they are also different in their chemistry

• Exploring automatic algorithms for the endmember extraction

• Applying the proposed methodology on a larger dataset to precisely develop the automaticclassification

Conclusions

Future work

• For a complete evaluation of alteration mineralogies, LWIR data would be useful



Thank you for your attention!


Gracias!

PhD. Student: Isabel Cecilia ContrerasHIF-Exploration division

E-Mail: [email protected]

MSc thesis GRSG conference 2017 - University of Twente ... · 3k' vwxghqw &hflold &rqwuhudv...

Documents

Transcript of MSc thesis GRSG conference 2017 - University of Twente ... · 3k' vwxghqw &hflold &rqwuhudv...