marine georesources of the southern tyrrhenian sea: critical elements potential assessed by...

V. Funari1, M. Rovere2, F. Gamberi2, M. Marani3, E. Dinelli1, R. Braga1

MARINE GEORESOURCES

OF THE SOUTHERN TYRRHENIAN SEA:

Critical Elements potential assessed by geochemical data

1 BiGeA Dept. (Geology Division) – University of Bologna, Piazza di Porta San Donato 1, Bologna - Italy2 Institute of Marine Sciences – National Research Council, Via Piero Gobetti 101, Bologna - Italy

SESSIONS 26. Energy and earth resources: geology between renewable, sustainable and conventional energy sources

[email protected] GEORESOURCES OF SOUTHERN TYRRHENIAN SEA

1

GEORESOURCES AND RAW MATERIAL SUPPLY

The importance of Critical Elements (CrE)

Main producers of critical raw materials

The highest production of CrE is based in

non-EU countries, China mostly.

The European Commission defined a

list of CrE (EC, 2010; 2014).

They have strategic importance for

emerging new and green-energy

technologies.

SUP

PLY

RIS

K

ECONOMIC IMPORTANCEImage source: Critical raw materials for the EU, European commission. May 2014

Data from http://eur-lex.europa.eu/


2

SEABED EXPLORATION AND EXPLOITATION

Recent findings on marine resources

For many years we have known aboutmanganese nodules littering the deep seafloor, but there is more than manganese tomine at the bottom of the ocean:

• Hein et al. (2009) lay out a mine-site

model for Co-rich seamount areas in the

central Pacific.

• Kato et al. (2011) show that deep-sea

mud constitutes a promising huge

resource of REE.


3


Work in progress

7 exploration licenses for seabed mining have been granted to:• UK Seabed Resources• Government of India• Russian Ministry of Natural Resources• Brazil’s Companhia de Pesquisa de Recursos Minerias• Ocean Mineral Singapore • Germany Federal Institute for Geosciences • Cook Islands Investment Corporation

Source: http://www.isa.org/Initiatives to mine for minerals in deep waters have recently been unveiled in Papua New Guinea...

Is this the beginning of an era of seafloor georesources exploitation?


4


Environmental Issues

“Most were concerned about the unknown effects of the mining on animals and plants, and the extent of the ash plume that occurs when leftover sediment is dumped back on the seabed...”

The EPA's decision-making committee.

The Trans-Tasman Resources (NZ) case

The science around the environmental impactsof deep seafloor is incomplete.

Any disturbance to sediments at the bottom ofthe ocean could harm seafloor communities...


5


Environmental Issues “on land”

From http://www.nasa.gov

Acid mine drainage causes severe environmental problems in the Rio Tinto, Spain.

Northern Territory, Australia: major radioactive component U-238.

Acid drainage and slope instability distress the dismissed mine of Libiola (Sestri Levante).

Iron Mine Tailings, Ishpeming, USA.

From “The Overthinker”

From the Lonely Planet


6


The Critical Elements Potential of Tyrrhenian Seabed

There are a wide variety of data on Italian seabed…

Knowledge on the spatial distribution of resource deposits is virtually unknown…

Reliable resource assessment (Potential ore per unit of seabed surface area)

AIM

Preliminary synthesis with an heterogeneous database (following the Kato’s example)


7

THE STUDY AREA

The Southern Tyrrhenian Sea

40

38

11 13 15 17

0 100km

Southern Tyrrhenian

AREA: ~ 60 000 km2

TOTAL SAMPLES: 130

Tyrrhenian Sea


7

40

38

11 13 15 17

0 100km

Tyrrhenian Sea

PN: Palinuro

MS: Marsili

THE STUDY AREA

The Southern Tyrrhenian Sea

VA: Vavilov

PB: Paola Basin

E-EO: Eolian Arc (Est)

W-EO: Eolian Arc (West)


8

Iron oxy-hydroxides Mn -crusts

Burrows of sulphides

Carbonates

Biogenic sandEmipelagic mud

Material comes from box and gravity corer.Only the near-surface sediments and crusts ( ~1 m)

THE STUDY AREA

The Samples


9

ANALYTICAL METHODS

The heterogeneity of data set and measurements

• 130 marine samples SCATTERED sampling

• Different analytical techniques ICP-MS, XRF, AAS

• Different list of analytes

• Different laboratories Univ. of Bologna, Univ. of Florence, Actlabs

• Different CNR-ISMAR oceanografic cruises from 1997 to 2011

BiGeA (Sezione Geologia)


10

PUCTUAL ANALYSES

The CrE abundance

40

38

11 13 15 17

0 100km

Mean concentration (mg/kg ), by location:

Vavilov: 535 ƩCrE 116 ƩREE

Marsili: 815 ƩCrE 126 ƩREE

Paola Basin: 671 ƩCrE 227 ƩREE

Palinuro: 1218 ƩCrE 87 ƩREE

Eolian (West): 709 ƩCrE 111 ƩREE

Eolian (Est): 420 ƩCrE 127 ƩREE

CrE: Ce, Co, Cr, Dy, Er, Eu, Ga, Gd, Ho, La, Lu Nb, Nd, Pr, Sb, Sc, Sm, Tb, Tm, Y, Yb(Mg not included)


11

PUNCTUAL ANALYSES

The distribution of some CrE

Samples are grouped by the main oxides

Co in Mn-rich and Fe-rich

Cr in Mn-rich and Ca-Mg rich

Ce in Al-Si rich


12

PUNCTUAL ANALYSES

Hypotheses for the origin of CrE

Hydrothermal and hydrogenous zones are from Hein et al. (1994)

Hydrothermal

Hydrogenous


13

(n=50)

(n=80)

Boxplot of some CrE, by inferred origin. Concentrations are very similar between hydrothermal and hydrogenous zone.

PUNCTUAL ANALYSES

Hypotheses for the origin of CrE


14

INTERPOLATION OF PUNCTUAL ANALYSES

Geostatistic and assumptions

Kriging (KR) and Inverse distance to a power (IDW) predict unknown values using known values and

a model variogram (the latter only for KR).

Both methods are suitable for geostatistical interpolation

IWD exact interpolator KR smoothing interpolator

However, homogeneous and dense sampling indispensable for accurate interpolation.

Assumptions: Data are normally distributed Variogram (KR): visible trend Linear model + nugget effect

no trend nugget effect


15


ƩREE interpolated map

SETTING

Lag direction omni-dir.

Max Lag Dist.: 1.1° (~111 km)

Num. of Lags: 26

Data search

Max n. samples 15

Min n. samples 10

R of search 0.5° (~55 km)

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

42

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

4212 13 14 15 16

IDW - ƩREE

Sicily

60708090100110120130140150160170180190200210220230240250260270280290300310

mg/kg


16


ƩREE interpolated map

KR - ƩREE

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

42

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

4212 13 14 15 16

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

42

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1

Lag Distance

0

1000

2000

3000

4000

5000

6000

7000

8000

9000

10000

11000

Va

rio

gra

m

Direction: 0.0 Tolerance: 90.0Column BQ: REE

632

235

57

91

6693

131116

63

67

106

45

120

79125

366

336

381

187

138

380

488182

159

SETTING



Num. of Lags: 26

Data search

Max n. samples 15

Min n. samples 10


Nugget effect: error variance= 3330Linear model: slope= 4890

aniso=1

Sicily

60708090100110120130140150160170180190200210220230240250260270280290300310

mg/kg

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

42


17


Gallium interpolated map

KR - Ga

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

42

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

4212 13 14 15 16

SETTING



Num. of Lags: 29

Data search

Max n. samples 15

Min n. samples 10


Nugget effect: error variance= 74.5Linear model: slope= 106

aniso=1

Sicily

mg/kg

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1

Lag Distance

0

100

200

300

400

500

600

Var

iogr

am

Direction: 0.0 Tolerance: 90.0Column W: Ga

358110

118

39

7127

4849

18

27

7 1

2

5

24

45

95 203

230

222

142

55

73

193

4349

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

42


18


Cobalt interpolated map

KR - Co

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

42

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

4212 13 14 15 16

SETTING



Num. of Lags: 27

Data search

Max n. samples 15

Min n. samples 10


Nugget effect: error variance= 6877Linear model: slope= -

aniso= -

Sicily

mg/kg

101520253035404550556065707580859095100105110115120125130

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1

Lag Distance

0

5000

10000

15000

20000

25000

30000

Var

iogr

am

Direction: 0.0 Tolerance: 90.0Column O: Co

602194

67

8349

46

40

56

49

21

7 3 25

66

158

363

240

348

5297

481

64

113

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

42


19


Chromium interpolated map

KR - Cr

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

42

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

4212 13 14 15 16

SETTING



Num. of Lags: 27

Data search

Max n. samples 15

Min n. samples 10



aniso= -

Sicily

mg/kg

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1

Lag Distance

0

5000

10000

15000

20000

25000

30000

Var

iogr

am

Direction: 0.0 Tolerance: 90.0Column O: Co

602194

67

8349

46

40

56

49

21

7 3 25

66

158

363

240

348

5297

481

64

113

10

20

30

40

50

60

70

80

90

100

110

120

130

140

150

160

170

180

190


20


3D interpolated map of ƩREE

*1-m thick mining area**wet metric tonnes based on density of 1.95 g/cm3

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

42

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

4212 13 14 15 16

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

42

Sicily

Paola Basin

HYPOTHETICAL MINE-SITE

Volume*: ~1100 km3

Depth: <2000 m

Total tonnage**: ~ 2150 t

Grade: ~ 0.03 wt.% TREO

Potential recovery: ~ 0.7 t TREO


21

CONCLUSION AND OUTLOOK

• First attempt for the evaluation of CrE in S-Tyrrhenian Sea and their spatial distribution

• Numbers seem unfavourable but for example:

high critical elements content in Palinuro Ridge zone

high Ce in Al-Si-rich deposits

about 0.7 t TREO potential recovery in the Paola Basin, considering a

relatively small volume

• The need of an homogeneous analytical protocol when exploring marine areas for more accurate map of interpolation


THANK YOU FOR YOUR ATTENTION

Environmentally responsible collection of marine georesources presents a complex challenge for Sciences and scientists. ?

From Nautilus Minerals Inc., modified.


SUPPLEMENTARY MATERIAL


PUNCTUAL ANALYSES:Correlations with major elements

7006005004003002001000

50

40

30

20

10

0

Cr (mg/kg)

Ca

O (

wt.

%)

Scatterplot of CaO vs Cr

9008007006005004003002001000

80

70

60

50

40

30

20

10

0

Co (mg/kg)

Fe2

O3

(w

t.%

)

Scatterplot of Fe2O3% vs Co

180160140120100806040200

90

80

70

60

50

40

30

20

10

0

Ce (mg/kg)

SiO

2 (

wt.

%)

Scatterplot of SiO2 vs Ce

4003002001000

80

70

60

50

40

30

20

10

0

REE (mg/kg)

Fe2

O3

(w

t.%

)

Scatterplot of Fe2O3% vs REE


PUNCTUAL ANALYSES:The distribution of some CrE

Samples are grouped by the main oxides

Ce in Al-Si rich and Alkali-rich

Co in Mn-rich and Fe-rich

Cr in Mn-rich and Ca-Mg rich

Samples are grouped by location

REE and Nb higher in PB and E-EO

Co higher in EO and PB

Cr higher in W-EO

W higher in PN

Concentration means

Concentration means

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

42

0.60.811.21.41.61.822.22.42.62.833.23.43.63.844.24.44.64.855.25.45.65.86


INTERPOLATION OF PUNCTUAL ANALYSES:Magnesium oxide interpolated map

KR - MgO

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

42

11 11.5 12 12.5 13 13.5 14 14.5 15 15.5 16 16.5 17

37

37.5

38

38.5

39

39.5

40

40.5

41

41.5

4212 13 14 15 16

SETTING



Num. of Lags: 26

Data search

Max n. samples 15

Min n. samples 10



aniso= -

Sicily

wt.%

0.60.811.21.41.61.822.22.42.62.833.23.43.63.844.24.44.64.855.25.45.65.86

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.1

Lag Distance

0

2

4

6

8

10

12

14

16

18

20

Va

rio

gra

m

Direction: 0.0 Tolerance: 90.0Column AI: MgO

632

235

57

91

66

93

131

116

63

67

106

45

79

125

160

366

336

381

187138

380

488

182

159


INTERPOLATION OF PUNCTUAL ANALYSES:3D interpolated map: smaller database

Cr

INTERPOLATED AREA: ~1800 km2

HYPOTHETICAL MINE-SITE:Volume*: ~40 km3

Depth: <1000 mTotal tonnage**: ~78 t

*1-m thick mining area**wet metric tonnes based

on density of 1.95 g/cm3.


INTERPOLATION OF PUNCTUAL ANALYSES:3D interpolated map: smaller database

Ce

INTERPOLATED AREA: ~1800 km2

HYPOTHETICAL MINE-SITE:Volume*: ~40 km3

Depth: <1000 mTotal tonnage**: ~78 t

*1-m thick mining area**wet metric tonnes based

on density of 1.95 g/cm3.

marine georesources of the southern tyrrhenian sea: critical elements potential assessed by...

Documents