Economic Geology

Economic geology is the study of Earth's mineral and energy resources, their formation, distribution, and extraction. It encompasses both metallic and non-metallic resources that have economic value and examines the geological processes that concentrate these materials into mineable deposits.

Classification of Ore Deposits

Magmatic Deposits

Formed during crystallization of magmas:

Cumulate Deposits

Where $C_0$ is initial concentration, $F$ is fraction of liquid remaining, and $D$ is distribution coefficient.

Examples:

• Layered mafic intrusions: PGE, chromite, titanium
• Ultramafic intrusions: Platinum group elements, chromium

Hydrothermal Deposits

Formed from hot, chemically active fluids:

Classification by Temperature

• Magmatic-hydrothermal: >500°C (porphyry Cu-Au-Mo, skarn)
• Epithermal: 50-250°C (gold-silver, mercury)
• Mesothermal: 200-300°C (gold-quartz veins)

Ore Mineral Assemblages

• Sulfides: Pyrite, chalcopyrite, galena, sphalerite
• Oxides: Hematite, magnetite, cassiterite
• Native metals: Gold, silver, platinum

Sedimentary Deposits

Chemical Precipitation

• Banded iron formations: Archean age, Fe-oxide deposits
• Evaporites: Halite, gypsum, potash
• Phosphorites: Phosphate deposits

Mechanical Accumulation

• Placer deposits: Heavy mineral concentrations
• Paleoplacers: Ancient placer deposits
• Residual deposits: Weathering concentration (bauxite, laterite)

Metamorphic Deposits

Formed or modified during metamorphism:

• Skarns: Contact metamorphic deposits
• Greisen: Pneumatolytic alteration
• Graphite: From organic matter

Ore Deposit Models

Porphyry Copper Systems

Characteristics:

• Associated with calc-alkaline magmatism
• Stockwork veining (Q-type quartz)
• Zoning: K-feldspar → biotite → propylitic

Alteration Assemblages

• Potassic: K-feldspar + biotite
• Phyllic: Sericite + pyrite
• Propylitic: Chlorite + epidote + calcite
• Argillic: Kaolinite + montmorillonite

VMS (Volcanogenic Massive Sulfide)

Formed at seafloor spreading centers:

• Cyprus-type: Bimodal volcanic sequence
• Noranda-type: Felsic volcanic environment
• Besshi-type: Siliciclastic environment

SEDEX (Sedimentary Exhalative)

Formed in restricted marine basins:

• Mississippi Valley-type: Dolomite-hosted Pb-Zn
• Sedex-type: Clastic-hosted Pb-Zn-Ag

Exploration Techniques

Geochemical Exploration

Sampling Media

• Stream sediments: Downstream dispersion
• Soils: Direct sampling over deposits
• Till: Glacial transport indicator
• Groundwater: Mobile metal detection

Geophysical Methods

Gravity Methods

For horizontal cylinder of height $h$ and density contrast $\Delta \rho$.

Magnetic Methods

• Magnetic susceptibility: Concentration of magnetic minerals
• Total field intensity: Magnetic anomalies
• Aeromagnetics: Regional coverage

Electrical Methods

• Resistivity: Electrical property contrasts
• Induced polarization: Chargeability effects
• Electromagnetic: Conductive mineral detection

Seismic Methods

• Reflection: Structural controls
• Refraction: Velocity variations

Remote Sensing

• Spectral analysis: Mineral identification
• Structural mapping: Lineament analysis
• Alteration detection: Hydrothermal alteration mapping

Resource Evaluation

Grade-Tonnage Relationships

Cut-off Grade

Reserve Classifications

• Measured: Detailed geological knowledge
• Indicated: Geological continuity shown
• Inferred: Limited geological knowledge

Mining Methods

Surface Mining

• Open pit: Large, near-surface deposits
• Strip mining: Horizontal or gently dipping deposits
• Placer mining: Alluvial deposits

Economics

Underground Mining

• Room and pillar: Extraction with support pillars
• Longwall: Complete extraction in panels
• Block caving: Gravity extraction of broken ore

Support Systems

• Bolting: Rock reinforcement
• Shotcreting: Surface stabilization
• Steel sets: Frame support

Environmental Considerations

Acid Mine Drainage

Prevention Methods

• Alkaline addition: Neutralize acidity
• Oxygen exclusion: Prevent oxidation
• Water management: Control flow paths

Tailings Management

• Dry stacking: Reduced environmental risk
• Paste thickening: Improved stability
• Cemented paste backfill: Underground support

Rehabilitation

• Soil replacement: Establish vegetation
• Contouring: Restore landforms
• Capping: Isolate contaminated materials

Energy Resources

Fossil Fuels

Coal

• Formation: Organic matter preservation
• Rank: Lignite → sub-bituminous → bituminous → anthracite
• Quality parameters: Ash, sulfur, calorific value

Petroleum Systems

Formation Evaluation

• Source rock: TOC, Rock-Eval pyrolysis
• Reservoir: Porosity, permeability
• Maturation: Vitrinite reflectance

Nuclear Resources

Uranium Deposits

• Unconformity-type: Sandstone-hosted
• Vein-type: Hydrothermal
• Sandstone-type: Roll-front deposits

Thorium Resources

• Monazite: Rare earth phosphate
• Thorite: ThSiO₄
• Bearing minerals: Accessory phases

Critical Minerals

Supply Risk Assessment

InLCA (Indicators for Long-term Criticality Assessment)

Key Minerals

• Rare earth elements: Neodymium, dysprosium (magnets)
• Lithium: Battery technology
• Cobalt: Batteries, superalloys
• Graphite: Battery anodes

Recycling and Substitution

• Urban mining: Recovery from waste
• Substitution: Alternative materials
• Efficiency: Reduced consumption

Global Distribution

Metallogenic Provinces

• Circum-Pacific: Porphyry systems
• Tethyan: Orogenic gold, VMS
• Siberian Traps: Ni-Cu-PGE

Political Considerations

• Resource nationalism: Government control
• Trade restrictions: Supply chain concerns
• Investment security: Political stability

Real-World Application: Porphyry Copper Deposit Evaluation

Porphyry copper deposits represent the world's primary source of copper and are critical for the global economy.

Porphyry System Analysis

# Porphyry copper deposit evaluation
deposit_data = {
    'ore_grade': 0.6,     # % Cu (copper grade)
    'tonnage': 1000,      # million tonnes
    'recovery_rate': 85,  # % (metallurgical recovery)
    'copper_price': 3.5,  # $/lb (copper price)
    'mining_cost': 1.2,   # $/tonne (operating cost)
    'stripping_ratio': 3, # waste/ore ratio
    'mineralization_type': 'porphyry'
}

# Calculate metal content and value
total_copper_metal = deposit_data['tonnage'] * 1e6 * deposit_data['ore_grade'] / 100  # tonnes
total_copper_value = total_copper_metal * 1000 * 2205 * deposit_data['copper_price']  # tonnes to lbs conversion

# Calculate extraction costs
total_ore_tons = deposit_data['tonnage'] * 1e6  # tonnes
total_waste_tons = total_ore_tons * deposit_data['stripping_ratio']
total_mining_cost = (total_ore_tons + total_waste_tons) * deposit_data['mining_cost']

# Calculate net smelter return (NSR)
recovery_tons = total_copper_metal * deposit_data['recovery_rate'] / 100
copper_pounds = recovery_tons * 1000 * 2205  # tonnes to pounds
revenue = copper_pounds * deposit_data['copper_price']
net_revenue = revenue - total_mining_cost

print(f"Deposit evaluation for porphyry copper:")
print(f"  Ore grade: {deposit_data['ore_grade']}% Cu")
print(f"  Tonnage: {deposit_data['tonnage']} million tonnes")
print(f"  Total copper metal: {total_copper_metal:,.0f} tonnes")
print(f"  Total metal value: ${total_copper_value/1e9:.1f} billion")
print(f"  Total mining cost: ${total_mining_cost/1e6:.0f} million")
print(f"  Net revenue: ${net_revenue/1e9:.1f} billion")
print(f"  Average stripping ratio: {deposit_data['stripping_ratio']}:1")

# Economic assessment
if net_revenue > 0:
    economic_status = "Economically viable"
    roi = (net_revenue / total_mining_cost) * 100
    print(f"  Return on investment: {roi:.1f}%")
else:
    economic_status = "Not economically viable"
    
print(f"  Economic status: {economic_status}")

# Environmental considerations
environmental_impact_score = deposit_data['stripping_ratio'] * 10  # Simplified scoring
if environmental_impact_score > 25:
    env_status = "High environmental impact"
elif environmental_impact_score > 15:
    env_status = "Moderate environmental impact"
else:
    env_status = "Low environmental impact"
    
print(f"  Environmental impact: {env_status}")

Exploration Vectoring

Using geochemical and structural indicators to locate additional mineralization.

Your Challenge: Mineral Exploration Analysis

Analyze geochemical data to identify potential mineralization targets and assess their economic potential.

Goal: Evaluate geochemical anomalies and assess their potential for economic mineral deposits.

Exploration Data

import math

# Geochemical exploration data from grid sampling
sample_data = [
    {'id': 'A1', 'x': 0, 'y': 0, 'Cu_ppm': 150, 'Au_ppb': 8, 'Pb_ppm': 45, 'Zn_ppm': 120},
    {'id': 'A2', 'x': 100, 'y': 0, 'Cu_ppm': 220, 'Au_ppb': 12, 'Pb_ppm': 65, 'Zn_ppm': 180},
    {'id': 'A3', 'x': 200, 'y': 0, 'Cu_ppm': 450, 'Au_ppb': 25, 'Pb_ppm': 90, 'Zn_ppm': 320},  # Anomaly center
    {'id': 'A4', 'x': 300, 'y': 0, 'Cu_ppm': 180, 'Au_ppb': 10, 'Pb_ppm': 55, 'Zn_ppm': 150},
    {'id': 'A5', 'x': 400, 'y': 0, 'Cu_ppm': 90, 'Au_ppb': 5, 'Pb_ppm': 35, 'Zn_ppm': 80},
    {'id': 'B1', 'x': 0, 'y': 100, 'Cu_ppm': 130, 'Au_ppb': 6, 'Pb_ppm': 40, 'Zn_ppm': 100},
    {'id': 'B2', 'x': 100, 'y': 100, 'Cu_ppm': 380, 'Au_ppb': 20, 'Pb_ppm': 85, 'Zn_ppm': 280},  # Anomaly
    {'id': 'B3', 'x': 200, 'y': 100, 'Cu_ppm': 650, 'Au_ppb': 45, 'Pb_ppm': 120, 'Zn_ppm': 450},  # Anomaly center
    {'id': 'B4', 'x': 300, 'y': 100, 'Cu_ppm': 280, 'Au_ppb': 15, 'Pb_ppm': 75, 'Zn_ppm': 220},
    {'id': 'B5', 'x': 400, 'y': 100, 'Cu_ppm': 110, 'Au_ppb': 4, 'Pb_ppm': 30, 'Zn_ppm': 70},
    {'id': 'C1', 'x': 0, 'y': 200, 'Cu_ppm': 100, 'Au_ppb': 4, 'Pb_ppm': 25, 'Zn_ppm': 60},
    {'id': 'C2', 'x': 100, 'y': 200, 'Cu_ppm': 250, 'Au_ppb': 14, 'Pb_ppm': 70, 'Zn_ppm': 190},
    {'id': 'C3', 'x': 200, 'y': 200, 'Cu_ppm': 520, 'Au_ppb': 35, 'Pb_ppm': 110, 'Zn_ppm': 380},  # Anomaly
    {'id': 'C4', 'x': 300, 'y': 200, 'Cu_ppm': 220, 'Au_ppb': 12, 'Pb_ppm': 60, 'Zn_ppm': 170},
    {'id': 'C5', 'x': 400, 'y': 200, 'Cu_ppm': 80, 'Au_ppb': 3, 'Pb_ppm': 20, 'Zn_ppm': 50},
]

# Background levels for each element
background_levels = {
    'Cu_ppm': 50,   # ppm (parts per million)
    'Au_ppb': 2,    # ppb (parts per billion)
    'Pb_ppm': 20,   # ppm
    'Zn_ppm': 65    # ppm
}

# Calculate anomaly thresholds (typically 2-3x background)
threshold_multipliers = {'Cu_ppm': 3, 'Au_ppb': 4, 'Pb_ppm': 3, 'Zn_ppm': 2.5}
anomaly_thresholds = {elem: background_levels[elem] * mult 
                       for elem, mult in threshold_multipliers.items()}

# Identify anomalous samples
anomalous_samples = []
for sample in sample_data:
    has_anomaly = False
    for element in ['Cu_ppm', 'Au_ppb', 'Pb_ppm', 'Zn_ppm']:
        if sample[element] > anomaly_thresholds[element]:
            has_anomaly = True
            break
    if has_anomaly:
        anomalous_samples.append(sample)

# Calculate weighted index for each sample (importance: Au > Cu > Zn > Pb)
for sample in sample_data:
    # Normalize each element against background
    cu_norm = sample['Cu_ppm'] / background_levels['Cu_ppm']
    au_norm = sample['Au_ppb'] / background_levels['Au_ppb']
    zn_norm = sample['Zn_ppm'] / background_levels['Zn_ppm']
    pb_norm = sample['Pb_ppm'] / background_levels['Pb_ppm']
    
    # Calculate weighted index (Au most important)
    sample['weighted_index'] = au_norm * 1.5 + cu_norm * 1.0 + zn_norm * 0.5 + pb_norm * 0.3

# Rank samples by weighted index
ranked_samples = sorted(sample_data, key=lambda x: x['weighted_index'], reverse=True)

# Calculate distance of anomalies from center (for vectoring)
if anomalous_samples:
    center_x = sum(s['x'] for s in anomalous_samples) / len(anomalous_samples)
    center_y = sum(s['y'] for s in anomalous_samples) / len(anomalous_samples)
    
    for sample in sample_data:
        sample['distance_from_center'] = math.sqrt((sample['x'] - center_x)**2 + (sample['y'] - center_y)**2)

Analyze the geochemical data to assess the potential for economic mineralization.

Hint:

• Look for coherent multi-element anomalies
• Consider element ratios that indicate specific deposit types
• Evaluate the spatial distribution of anomalies for vectoring
• Calculate potential resource sizes and grades

# TODO: Calculate exploration targets and potential deposit type
primary_anomaly_center = ""  # ID of sample with highest weighted index
multi_element_anomalies = []  # List of samples with multiple element anomalies
deposit_type = ""  # Type of deposit based on element associations
resource_estimate = 0  # Million tonnes (estimated resource)
economic_potential = ""  # Viability assessment

# Identify multi-element anomalies
for sample in anomalous_samples:
    anomalous_elements = []
    for element in ['Cu_ppm', 'Au_ppb', 'Pb_ppm', 'Zn_ppm']:
        if sample[element] > anomaly_thresholds[element]:
            anomalous_elements.append(element)
    if len(anomalous_elements) >= 2:  # Multi-element anomaly
        multi_element_anomalies.append(sample['id'])

# Assess deposit type based on element ratios
# Cu/Au ratios can indicate deposit type
high_cu_au_ratio_samples = []
for sample in sample_data:
    if sample['Au_ppb'] > 0:
        cu_au_ratio = sample['Cu_ppm'] / sample['Au_ppb']
        if cu_au_ratio > 15:
            high_cu_au_ratio_samples.append(sample['id'])

if len(high_cu_au_ratio_samples) > 0:
    deposit_type = "Porphyry-type (Cu-rich)"
elif len(multi_element_anomalies) > 0:
    deposit_type = "Polymetallic (Cu-Au-Pb-Zn)"
else:
    deposit_type = "Unknown"

# Calculate potential tonnage (simplified approach)
# Volume estimation based on anomaly size
if len(anomalous_samples) > 0:
    # Assume 20m vertical extent and rectangular prism
    anomaly_area = 0.02 * (len(anomalous_samples) * 100)  # km² (2% of grid area per anomaly)
    thickness = 50  # meters (assumed)
    density = 2.7  # g/cm³ = 2700 kg/m³
    resource_estimate = (anomaly_area * 1e6 * thickness * density) / 1e9  # billion tonnes

# Economic assessment
if ranked_samples[0]['weighted_index'] > 10:
    economic_potential = "High - warrants detailed exploration"
elif ranked_samples[0]['weighted_index'] > 5:
    economic_potential = "Moderate - consider additional sampling"
else:
    economic_potential = "Low - unlikely to be economic"

# Print results
print(f"Primary anomaly center: {primary_anomaly_center}")
print(f"Multi-element anomalies: {multi_element_anomalies}")
print(f"Deposit type: {deposit_type}")
print(f"Resource estimate: {resource_estimate:.2f} billion tonnes")
print(f"Economic potential: {economic_potential}")
print(f"Top ranked sample: {ranked_samples[0]['id']} (index: {ranked_samples[0]['weighted_index']:.2f})")

# Next exploration steps
next_steps = []
if len(multi_element_anomalies) > 2:
    next_steps.append("Detailed mapping of anomaly extent")
    next_steps.append("Soil or stream sediment sampling to expand anomaly")
if deposit_type == "Porphyry-type":
    next_steps.append("Structural analysis for porphyry targeting")
    next_steps.append("Geophysical survey for alteration mapping")
    
print(f"Recommended next steps: {next_steps}")

What additional exploration techniques would you recommend based on the geochemical data and spatial distribution of anomalies?