4  HTEM Survey Overview

TipFor Newcomers

You will get: - An intuitive picture of how we can “see” underground without drilling. - A first look at the main aquifer (a buried sand-and-gravel valley). - A sense of what HTEM can and cannot tell us about water.

You do not need to understand every line of code. - You can skim the code blocks and focus on the explanations and figures. - This chapter is about the story of the subsurface; code shows how we generated the maps.

4.1 What You Will Learn in This Chapter

By the end of this chapter, you will be able to:

• Explain, in plain language, how helicopter-borne time-domain EM surveys provide a “picture” of the subsurface.
• Describe the six stratigraphic units (A–F) and why Unit D is the primary aquifer.
• Interpret basic HTEM-derived products: material type grids, spatial coverage maps, and multi-unit resistivity comparisons.
• Understand the main strengths and limitations of HTEM data, and why it must be calibrated with wells in later chapters.

4.2 Seeing Through the Earth

Imagine trying to find water underground without digging a single well. Impossible? Not anymore. Helicopter-borne Time-Domain Electromagnetic (HTEM) surveys use geophysics to “see” through hundreds of meters of earth, revealing the buried architecture of aquifer systems.

This chapter explores the HTEM survey conducted over Champaign County, Illinois—tens of millions of electromagnetic measurements that map resistivity variations across 6 stratigraphic units from surface to bedrock. This is our spatial foundation—the most comprehensive view of aquifer structure ever assembled for this region.

Note🌊 What the Survey Reveals

• Spatial coverage: ~2,300 km² of continuous mapping over the Champaign County region (see the data dictionary for exact extents)
• Depth range: Surface to ~200 m (bedrock and beyond)
• Resolution: ~100 m horizontal, variable vertical (5–20 m)
• Stratigraphic units: 6 layers (A-F) representing different geological formations
• Material types: 105 distinct resistivity-based material classifications
• Primary aquifer: Unit D (12-96m depth, high resistivity sand/gravel)

---

4.3 Part 1: The HTEM Survey Method

4.3.1 How It Works

The Flying Sensor: - Helicopter tows electromagnetic transmitter/receiver loop - Transmitter sends pulsed electromagnetic field into ground - Ground materials induce secondary electromagnetic fields - Receiver measures decay time and amplitude - Resistivity = how fast the field decays

Physics principle: - Clay/silt: Conductive (low resistivity, ~5-35 Ω·m) → Fast decay - Sand/gravel: Resistive (high resistivity, ~50-500 Ω·m) → Slow decay - Bedrock: Very resistive (high resistivity, ~100-1000 Ω·m) → Very slow decay

Why this matters for water: - High resistivity = coarse sediments (sand/gravel) = good aquifer - Low resistivity = fine sediments (clay/silt) = confining layer

Tip🌍 For Hydrologists

HTEM vs. Traditional Methods:

Traditional aquifer mapping: - Drill test boreholes every 1-5 km ($50K-$200K each) - Interpolate between sparse points - Miss critical features between boreholes - Slow (years to complete regional survey)

HTEM mapping: - Continuous spatial coverage (no interpolation gaps) - Survey entire county in weeks - Cost-effective for regional scale ($100-$500/km²) - Non-invasive (no drilling)

Trade-off: HTEM is indirect (measures resistivity, infers lithology) vs. drilling is direct (see actual materials). Best approach: Calibrate HTEM with wells, then extrapolate everywhere.

---

4.4 Part 2: The 6 Stratigraphic Units

The subsurface beneath Champaign County is not a uniform mass of earth—it consists of distinct stratigraphic layers deposited over millions of years. The HTEM survey divides this into 6 units (A through F), each with different geological origins, depths, and water-bearing potential.

The code below initializes our data loader and prepares the HTEM grids for visualization. You don’t need to understand the code details—focus on the table and figures that follow.

import os
import sys
from pathlib import Path
import pandas as pd
import numpy as np
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots

# Setup paths
def find_repo_root(start: Path) -> Path:
    for candidate in [start, *start.parents]:
        if (candidate / "src").exists():
            return candidate
    return start

quarto_project = Path(os.environ.get("QUARTO_PROJECT_DIR", str(Path.cwd())))
project_root = find_repo_root(quarto_project)
if str(project_root) not in sys.path:
    sys.path.append(str(project_root))

from src.data_loaders.htem_loader import HTEMLoader
from src.utils import get_data_path

# Initialize loader
htem_path = get_data_path("htem_root")
loader = HTEMLoader(data_root=htem_path)

print("✓ HTEM data loader initialized")

# The code below loads HTEM data for the main aquifer units
# and prepares maps/plots shown in this chapter.

✓ HTEM data loader initialized

4.4.1 Unit Characteristics

Note📘 Interpreting Stratigraphic Units

What Are They? Stratigraphic units are distinct layers of rock or sediment deposited during different geological periods. The concept dates to Nicolas Steno’s “law of superposition” (1669): deeper layers are older, upper layers are younger.

Why Do They Matter? Each unit has different water-bearing properties:

• Depth: Shallow units easier to access but more vulnerable
• Material: Sand/gravel = good aquifer; clay = confining layer
• Thickness: Thicker units store more water

How to Read the Unit Table:

Column	What It Shows	Why It Matters
Typical Depth	Burial depth range	Determines drilling costs, accessibility
Typical Resistivity	Electrical property	High = sand/gravel; Low = clay
Water Significance	Aquifer quality	Guides where to focus analysis

Key Insight: Unit D is the star—shallow enough to access (12-96m), thick enough to store water, high resistivity (sand/gravel), and extensive across the region.

The subsurface is divided into 6 stratigraphic units (A through F), each representing different geological formations:

# Unit descriptions and depth ranges
units_info = pd.DataFrame({
    'Unit': ['A', 'B', 'C', 'D', 'E', 'F'],
    'Name': [
        'Deep Bedrock',
        'Transition Zone',
        'Upper Bedrock',
        'Primary Aquifer (Mahomet)',
        'Clay-rich Quaternary',
        'Mixed Surface Materials'
    ],
    'Typical_Depth_Range_m': [
        '180-194',
        '108-168',
        '124-166',
        '12-96',
        '0-30',
        '0-20'
    ],
    'Typical_Resistivity': [
        '~48.7 Ω·m (bedrock)',
        'Variable (mixed)',
        '~50-200 Ω·m (limestone)',
        '~128.3 Ω·m (sand/gravel)',
        '~10-30 Ω·m (clay/till)',
        '~20-80 Ω·m (mixed)'
    ],
    'Water_Significance': [
        'Low (deep, bedrock)',
        'Moderate (confining?)',
        'Low to moderate',
        '⭐ PRIMARY AQUIFER',
        'Confining layer',
        'Surface drainage'
    ]
})

units_info

	Unit	Name	Typical_Depth_Range_m	Typical_Resistivity	Water_Significance
0	A	Deep Bedrock	180-194	~48.7 Ω·m (bedrock)	Low (deep, bedrock)
1	B	Transition Zone	108-168	Variable (mixed)	Moderate (confining?)
2	C	Upper Bedrock	124-166	~50-200 Ω·m (limestone)	Low to moderate
3	D	Primary Aquifer (Mahomet)	12-96	~128.3 Ω·m (sand/gravel)	⭐ PRIMARY AQUIFER
4	E	Clay-rich Quaternary	0-30	~10-30 Ω·m (clay/till)	Confining layer
5	F	Mixed Surface Materials	0-20	~20-80 Ω·m (mixed)	Surface drainage

Key insight: Unit D is the star of the show—the Mahomet Aquifer, a buried sand and gravel valley that provides most of the region’s groundwater. All subsequent analyses focus heavily on Unit D.

---

4.5 Part 3: 2D Grid Analysis

NoteReading the Code in This Chapter

The code blocks: - Load HTEM data for the six subsurface units. - Build grids and visualizations of underground materials. - Drive the maps and figures you see in the text.

You do not need to follow every line of code. Focus on what the figures reveal about: - Where the main aquifer (Unit D) is, - How thick it is, - And how it relates to surrounding layers.

4.5.1 Loading the 2D Grids

Note📘 Context: Working with HTEM Grids

What Are 2D Grids? HTEM surveys produce gridded data—resistivity values at regular intervals (like pixels in an image). Each “grid file” represents one stratigraphic unit with X,Y coordinates and resistivity/material type values.

Why Load Grids This Way? The code below demonstrates data access patterns:

• Discovery: Find available grid files (which units exist?)
• Loading: Read specific unit data into memory
• Inspection: Check data dimensions and coverage

What You’ll See: The output shows grid file names (like “Unit_D_Preferred_MT.txt”) and summary statistics. This confirms data exists and is readable before detailed analysis.

# Get available files
available = loader.get_available_files()
grid_files = available['material_types']

print(f"📁 Found {len(grid_files)} material type grid files")
print("\nSample files:")
for f in grid_files[:5]:
    print(f"  • {f}")

📁 Found 14 material type grid files

Sample files:
  • Unit_E_Preferred_MT.csv
  • Unit_D_HiFreqSand_MT.csv
  • Unit_A_LoFreqSand_MT.csv
  • Unit_B_Preferred_MT.csv
  • Unit_A_HiFreqSand_MT.csv

4.5.2 Analyzing Unit D: The Primary Aquifer

Note📘 Interpreting Grid Summary Statistics

What Do These Numbers Mean? The statistics below quantify the HTEM survey’s scope for Unit D.

How to Interpret:

Statistic	What It Tells You	Management Insight
Total grid cells	Data point density	More cells = finer resolution mapping
X/Y range	Geographic extent (meters)	Covers ~40km × 50km region
Z (elevation) range	Vertical span	Shows aquifer burial depth variation

Expected Pattern: Hundreds of thousands of grid cells across tens of kilometers indicates comprehensive regional mapping—far superior to sparse well networks (3 points).

# Load Unit D preferred grid
unit_d_grid = loader.load_material_type_grid('D', 'Preferred')

print(f"\n🗺️ Unit D Grid Summary:")
print(f"  • Total grid cells: {len(unit_d_grid):,}")
print(f"  • X (Easting) range: {unit_d_grid['X'].min():.0f} - {unit_d_grid['X'].max():.0f} m")
print(f"  • Y (Northing) range: {unit_d_grid['Y'].min():.0f} - {unit_d_grid['Y'].max():.0f} m")
print(f"  • Z (Elevation) range: {unit_d_grid['Z'].min():.1f} - {unit_d_grid['Z'].max():.1f} m")

unit_d_grid.head()

🗺️ Unit D Grid Summary:
  • Total grid cells: 4,078,961
  • X (Easting) range: 374550 - 421250 m
  • Y (Northing) range: 4421550 - 4473650 m
  • Z (Elevation) range: 12.0 - 212.0 m

	X	Y	Z	MT_Index
0	416250	4445050	12	1
1	421250	4454950	12	1
2	421250	4455050	12	1
3	421250	4455150	12	1
4	421250	4455250	12	1

4.5.3 Material Type Distribution

What Are Material Types?

Material types are numerical classifications (1-105) that translate continuous resistivity measurements into discrete geological categories. This classification system was developed by combining HTEM geophysical data with borehole lithology logs, creating a standardized way to describe subsurface materials across the entire survey area.

Why Does Material Type Classification Matter?

Material type classification bridges the gap between geophysics and hydrogeology:

• For geophysicists: Provides interpretable categories from resistivity inversions
• For hydrogeologists: Enables prediction of water-bearing capacity without drilling
• For water managers: Supports well-siting decisions and aquifer vulnerability assessments

How to Interpret Material Types

Material Type Range	Description	Resistivity	Aquifer Quality	Water Management Implication
1-4	Clay to silty clay	5-25 Ω·m	Poor (confining)	Protects aquifer from contamination
5-8	Silt to fine sand	25-60 Ω·m	Low to moderate	Limited well yields
9-11	Fine to medium sand	60-120 Ω·m	Good	Typical productive aquifer
11-14	Well-sorted sand/gravel	120-500 Ω·m	Excellent	High-yield well targets
101-105	Bedrock/carbonate	Variable	Variable	May have fracture flow

What Will You See in the Visualization?

The bar chart below shows the frequency of each material type in Unit D. Look for:

• Dominant material types: Which types cover the most area?
• High-quality indicators: Do types 11-14 appear frequently (good news for water supply)?
• Heterogeneity: Is there one dominant type or a mix (affects aquifer behavior)?

# Analyze material types in Unit D
material_counts = unit_d_grid['MT_Index'].value_counts().head(10)

fig = go.Figure(data=[
    go.Bar(
        x=material_counts.values,
        y=material_counts.index.astype(str),
        orientation='h',
        marker=dict(
            color=material_counts.values,
            colorscale='Viridis',
            showscale=True,
            colorbar=dict(title="Count")
        ),
        hovertemplate='Material Type: %{y}<br>Count: %{x:,}<extra></extra>'
    )
])

fig.update_layout(
    title='Top 10 Material Types in Unit D (Primary Aquifer)',
    xaxis_title='Number of Grid Cells',
    yaxis_title='Material Type Code',
    height=500,
    template='plotly_white'
)

fig.show()

(a) Top 10 material types in Unit D (primary aquifer). Higher material type codes generally indicate coarser, more permeable sediments suitable for groundwater extraction.

(b)

Figure 4.1

Interpreting the bar chart:

The distribution of material types tells us about aquifer quality. If the most common types are in the range 9-14 (medium sand to well-sorted gravel), Unit D has excellent water-bearing potential. If lower types (1-5) dominate, the unit would be clay-rich and poor for water supply.

In a healthy aquifer system, you want to see a mix—some high-quality sand/gravel for water production, and some clay for protective confining layers above.

Note💻 For Computer Scientists

Material Type Classification:

HTEM resistivity values are binned into 105 discrete material types using a lookup table: - 1-14: Quaternary sediments (clay → extremely well sorted sands) - 101-105: Carboniferous bedrock and carbonates

Why discrete classes? - Geologists think in lithology (clay, sand, gravel), not resistivity (Ω·m) - Classification enables semantic search (“find all gravel zones”) - Simplifies machine learning targets (classification vs. regression)

Data structure:

# Raw HTEM output
resistivity: float  # Continuous (0-1000 Ω·m)

# After classification
material_type: int  # Discrete (1-105)
material_name: str  # Semantic ("Well sorted sand")

ML consideration: When training models, use resistivity (more information) as feature, but material type as interpretable output.

4.5.4 Spatial Coverage Map

Note📘 What to Look For in Spatial Distribution Maps

Before Viewing the Figure: Spatial distribution maps reveal how aquifer properties vary across the landscape.

What Will You See? A scatter plot where each point represents an HTEM measurement location, colored by material type.

Look For These Patterns:

Visual Pattern	Geological Meaning	Management Implication
Color clusters	Homogeneous zones (e.g., buried sand channel)	Good zones for well fields
Sharp color boundaries	Geological contacts (sand meets clay)	Aquifer boundaries, flow barriers
Gradual transitions	Gradual facies changes	Transmissivity varies smoothly
Coverage gaps	No HTEM data	Need additional surveys or interpolation

Expected for Buried Valley Aquifer: You should see elongated high-resistivity (yellow/light) zone following ancient river valley, surrounded by lower resistivity (blue/dark) clay-rich materials.

# Sample for visualization (full dataset too large)
sample = unit_d_grid.sample(min(5000, len(unit_d_grid)), random_state=42)

fig = px.scatter(
    sample,
    x='X',
    y='Y',
    color='MT_Index',
    hover_data=['Z'],
    title='Unit D Material Type Distribution<br><sub>Spatial coverage of primary aquifer (sampled for performance)</sub>',
    labels={'X': 'Easting (m)', 'Y': 'Northing (m)', 'MT_Index': 'Material Type', 'Z': 'Elevation (m)'},
    height=700,
    color_continuous_scale='Viridis'
)

fig.update_layout(
    xaxis_title='Easting (m, UTM Zone 16N)',
    yaxis_title='Northing (m, UTM Zone 16N)'
)

fig.show()

Figure 4.2: Spatial distribution of material types in Unit D across the study area. Each point represents a grid cell colored by material type, revealing the heterogeneous structure of the primary aquifer.

Reading the spatial map:

Each point represents a location where HTEM measured subsurface properties. The color indicates material type—look for:

• Spatial clustering: Areas where similar colors group together indicate geological features like buried channels or clay lenses
• Gradients: Smooth color transitions suggest gradual geological changes; sharp boundaries indicate distinct geological contacts
• Coverage gaps: Any white areas indicate regions without HTEM data

The rectangular boundary visible in the plot shows the extent of the HTEM survey—884 km² of continuous coverage.

Tip🌍 For Hydrogeologists: Interpreting Material Types

What the material type numbers mean for aquifer quality:

MT Range	Description	Aquifer Potential	Why It Matters
1-4	Clay to silty clay	Poor - aquitard	Confines aquifer, prevents contamination
5-8	Silt to fine sand	Low to moderate	May yield limited water
9-11	Fine to medium sand	Good	Typical productive aquifer
11-14	Well-sorted sand/gravel	Excellent	High yield, target for wells
101-105	Bedrock/carbonate	Variable	May have fracture flow

Reading the bar chart:

• Dominant MT 11-14 → Excellent aquifer zone, prioritize for development
• Dominant MT 1-4 → Clay-rich, acts as confining layer (protects deeper aquifer)
• Mixed types → Heterogeneous; expect variable well yields

Management insight: Map shows spatial clustering—areas with MT 11-14 clusters are optimal well locations.

---

4.6 Part 4: 3D Material Classification

4.6.1 The Third Dimension

Note📘 Why 3D Matters for Aquifer Analysis

What Is the Difference?

• 2D grids: Show horizontal patterns at specific depths (like floor plans)
• 3D cubes: Show full vertical structure (like architectural cross-sections)

Why Does 3D Matter?

Vertical structure controls critical aquifer properties:

• Confinement: Clay layers above aquifer prevent contamination
• Connectivity: Continuous sand layers allow horizontal flow
• Well design: Knowing layer depths guides screen placement

How to Use 3D Data:

1. Identify confining layers: Protect aquifer from surface pollution
2. Map aquifer thickness: Thicker = more storage capacity
3. Detect discontinuities: Clay lenses may compartmentalize flow

Management Insight: 2D analysis might show “good aquifer material” but miss thin clay layer that limits recharge or creates confining conditions.

While 2D grids show horizontal patterns, 3D material cubes reveal vertical layering—critical for understanding confining layers and aquifer connectivity.

# Load 3D material type data for Unit D
try:
    unit_d_3d = loader.load_3d_material_cube('D')

    print(f"\n📦 Unit D 3D Cube:")
    print(f"  • Total voxels: {len(unit_d_3d):,}")
    print(f"  • Depth range: {unit_d_3d['Z'].min():.1f} to {unit_d_3d['Z'].max():.1f} m")
    print(f"  • Unique material types: {unit_d_3d['MT_Index'].nunique()}")

    unit_d_3d.head()
except Exception as e:
    print(f"Note: 3D material cube not available ({e})")
    print("Proceeding with 2D analysis only")
    unit_d_3d = None

📦 Unit D 3D Cube:
  • Total voxels: 4,078,961
  • Depth range: 12.0 to 212.0 m
  • Unique material types: 13

4.6.2 Vertical Profiles and Aquifer Thickness

NoteUnderstanding Aquifer Thickness

What Is Aquifer Thickness?

Aquifer thickness is the vertical distance from the top of the water-bearing formation to its bottom—essentially how “deep” the sand/gravel layer is. This measurement is critical because thicker aquifers generally store more water and can sustain higher pumping rates.

Brief History: Early hydrogeologists (1900s-1950s) estimated thickness by drilling scattered boreholes and interpolating between them—expensive and error-prone. HTEM revolutionized this by providing continuous 3D measurements across entire regions.

Why Does Thickness Matter?

Aquifer thickness controls: - Storage capacity: Thicker = more water stored - Well yield: Thicker zones can support higher pumping rates - Sustainability: Thick aquifers recover faster from drought - Well design: Determines optimal screen length and depth

How to Interpret Thickness Values:

Thickness Range	Classification	Well Potential	Management Strategy
>30 m (>100 ft)	Very thick	Excellent—high-capacity wells	Priority development zone
15-30 m (50-100 ft)	Thick	Good—moderate capacity wells	Standard development
5-15 m (15-50 ft)	Moderate	Fair—low capacity wells	Limited development
<5 m (<15 ft)	Thin	Poor—may not sustain wells	Avoid or use for monitoring only

What Will You See?

The histogram below shows the distribution of aquifer thickness values across Unit D. Look for: - Median value: The typical thickness (50th percentile) - Distribution shape: Is thickness uniform or highly variable? - Thick zones: Are there areas >30m suitable for high-capacity wells? - Thin zones: Where might well development be challenging?

Typical Pattern for Buried Valley Aquifers:

The Mahomet Aquifer (Unit D) is a buried river valley, so expect: - Valley center: Thickest (30-80m)—ancient river carved deep channel - Valley edges: Moderate (10-30m)—transition zones - Outside valley: Thin or absent (<5m)—bedrock high areas

if unit_d_3d is not None and len(unit_d_3d) > 0:
    # Calculate aquifer thickness from 3D data
    thickness = unit_d_3d.groupby(['X', 'Y'])['Z'].agg(['min', 'max']).reset_index()
    thickness['Thickness_m'] = thickness['max'] - thickness['min']

    fig = px.histogram(
        thickness,
        x='Thickness_m',
        nbins=50,
        title='Unit D Aquifer Thickness Distribution',
        labels={'Thickness_m': 'Thickness (m)', 'count': 'Frequency'},
        height=400
    )

    fig.add_vline(
        x=thickness['Thickness_m'].median(),
        line_dash="dash",
        line_color="red",
        annotation_text=f"Median: {thickness['Thickness_m'].median():.1f} m"
    )

    fig.show()

    print(f"\n📏 Aquifer Thickness Statistics:")
    print(f"  • Mean: {thickness['Thickness_m'].mean():.1f} m")
    print(f"  • Median: {thickness['Thickness_m'].median():.1f} m")
    print(f"  • Range: {thickness['Thickness_m'].min():.1f} - {thickness['Thickness_m'].max():.1f} m")
else:
    print("3D thickness analysis requires material cube data")

📏 Aquifer Thickness Statistics:
  • Mean: 40.8 m
  • Median: 40.0 m
  • Range: 0.0 - 200.0 m

---

4.7 Part 5: Multi-Unit Comparison

4.7.1 Resistivity Across All Units

Note📘 Framework for Multi-Unit Comparison

What Are We Comparing? Comparing resistivity across all 6 units reveals the vertical architecture of the aquifer system.

Why Compare Units?

• Validation: Does Unit D really have higher resistivity than others?
• Stratigraphy: Do units stack in expected order (clay over sand over bedrock)?
• Aquifer protection: Is there a low-resistivity confining layer above Unit D?

How to Interpret the Comparison:

The bar chart will show mean/median resistivity for each unit. Expected pattern for healthy aquifer system:

1. Unit D tallest bar: Primary aquifer (highest resistivity)
2. Unit E shortest bar: Confining clay layer (lowest resistivity)
3. Units A-C moderate: Bedrock (variable but generally high)

Red Flag: If Unit D does NOT have highest resistivity, it may not be the primary aquifer, or classification needs revision.

What Is Resistivity Class?

Resistivity class is a simplified grouping of resistivity values into 15 bins (1-15), where higher numbers indicate higher resistivity (more resistive materials). This coarser classification complements the 105 material types by providing a simpler way to compare large-scale patterns across different stratigraphic units.

Brief History: Resistivity classification dates back to early groundwater geophysics in the 1930s, when pioneers like Conrad Schlumberger used surface electrical methods to locate water. The binning approach balances detail with interpretability.

Why Does Multi-Unit Comparison Matter?

Comparing resistivity across all 6 units reveals the vertical architecture of the aquifer system:

• Identifies confining layers: Low resistivity units (E) protect high resistivity aquifers (D)
• Reveals connectivity: Do units have similar resistivity (potential hydraulic connection)?
• Validates geology: Does the resistivity pattern match expected stratigraphy?

How to Interpret the Unit Comparison

Expected Pattern for a Productive Aquifer System:

Unit	Expected Resistivity	What It Means	If Different, Consider…
Unit D	Highest (8-12)	Primary sand/gravel aquifer	If low: Clay-rich, poor aquifer
Unit E	Lowest (2-5)	Clay confining layer	If high: Aquifer unprotected
Units A-C	Moderate to high (5-10)	Bedrock (variable)	Depends on lithology
Unit F	Variable (3-8)	Mixed surface materials	Depends on local geology

What Will You See?

The visualization shows mean and median resistivity class for each unit. Look for:

• Unit D standing out: Should have highest values (best aquifer)
• Unit E being lowest: Indicates protective clay layer
• Mean vs. median differences: Large gaps indicate heterogeneity

# Load and compare all 6 units
units = ['A', 'B', 'C', 'D', 'E', 'F']
resistivity_by_unit = []

for unit in units:
    try:
        grid = loader.load_resistivity_class_grid(unit)
        # Column name in the resistivity classification grids is typically "Resist_Class"
        # (see the data dictionary). Fall back gracefully if a different name is used.
        col_name = None
        for candidate in ['Resist_Class', 'Resistivity Class']:
            if candidate in grid.columns:
                col_name = candidate
                break

        if grid is not None and col_name is not None:
            resistivity_by_unit.append({
                'Unit': unit,
                'Mean_Resistivity_Class': grid[col_name].mean(),
                'Median_Resistivity_Class': grid[col_name].median(),
                'Count': len(grid)
            })
    except Exception as e:
        print(f"⚠️ Warning: Skipping Unit {unit} - {e}")
        continue

resistivity_df = pd.DataFrame(resistivity_by_unit)

if len(resistivity_df) > 0:
    fig = go.Figure()

    fig.add_trace(go.Bar(
        x=resistivity_df['Unit'],
        y=resistivity_df['Mean_Resistivity_Class'],
        name='Mean',
        marker_color='steelblue'
    ))

    fig.add_trace(go.Scatter(
        x=resistivity_df['Unit'],
        y=resistivity_df['Median_Resistivity_Class'],
        name='Median',
        mode='markers',
        marker=dict(size=12, color='red', symbol='diamond')
    ))

    fig.update_layout(
        title='Resistivity Class by Stratigraphic Unit<br><sub>Higher values indicate coarser materials (better aquifer)</sub>',
        xaxis_title='Stratigraphic Unit',
        yaxis_title='Resistivity Class',
        height=500,
        template='plotly_white',
        hovermode='x unified'
    )

    fig.show()

    resistivity_df

Interpretation: - Unit D should have highest resistivity (sand/gravel aquifer) - Unit E should have lowest resistivity (clay-rich confining layer) - Units A-C (bedrock) should have moderate to high resistivity

---

4.8 Part 6: Key Findings and Insights

NoteUnderstanding HTEM Survey Statistics

What Are These Metrics?

The summary table below quantifies the HTEM survey’s scope and detail. Each metric reveals different aspects of the data’s power and limitations.

Why Do These Numbers Matter?

These statistics determine: - Spatial resolution: Can we detect small-scale features (contamination plumes, thin clay layers)? - Temporal coverage: Do we have a single snapshot or time-series monitoring? - Classification detail: Can we distinguish fine differences in aquifer quality? - Vertical detail: Can we map confining layers and aquifer boundaries?

How to Interpret Each Metric:

Metric	What It Measures	Why It Matters	Good vs. Poor Values
Grid Cells	Data point density	More cells = finer spatial detail	Good: >100K cells; Poor: <10K
Spatial Coverage	Survey area extent	Larger = more comprehensive	Regional: >500 km²; Local: <50 km²
Depth Range	Vertical span measured	Captures full aquifer system?	Good: Surface to bedrock; Poor: Shallow only
Material Types	Classification detail	Can distinguish aquifer quality?	Detailed: >50 types; Coarse: <10
Stratigraphic Units	Vertical layering detail	Reveals confining layers?	Good: 4-6 units; Poor: 1-2

What Will You See?

The table presents 6 key metrics. Look for: - Scale: Is this a regional survey (100s of km²) or site-specific (<10 km²)? - Resolution: Hundreds of thousands of grid cells indicate high-resolution mapping - Complexity: Many material types and units suggest geological heterogeneity - Depth: Does the survey reach the full depth of the aquifer system?

Expected Pattern for a High-Quality Regional HTEM Survey:

• Coverage: >500 km² (regional scale)
• Grid cells: >100,000 (fine resolution)
• Depth range: Surface to bedrock (complete vertical profile)
• Material types: 50-150 (detailed classification)
• Units: 4-6 (captures main stratigraphic layers)

# Generate summary statistics
summary = pd.DataFrame({
    'Metric': [
        'Total HTEM Grid Cells',
        'Spatial Coverage',
        'Primary Aquifer (Unit D) Coverage',
        'Depth to Primary Aquifer',
        'Material Classification Types',
        'Stratigraphic Units Mapped'
    ],
    'Value': [
        f"{len(unit_d_grid):,} cells",
        "884 km² (44 km × 52 km)",
        f"{len(unit_d_grid):,} grid cells",
        "12-96 m depth range",
        "105 distinct types",
        "6 units (A through F)"
    ]
})

summary

	Metric	Value
0	Total HTEM Grid Cells	4,078,961 cells
1	Spatial Coverage	884 km² (44 km × 52 km)
2	Primary Aquifer (Unit D) Coverage	4,078,961 grid cells
3	Depth to Primary Aquifer	12-96 m depth range
4	Material Classification Types	105 distinct types
5	Stratigraphic Units Mapped	6 units (A through F)

Interpreting These Results:

• Grid cells (hundreds of thousands): High-resolution survey—can detect features at ~100m scale
• Spatial coverage (884 km²): Regional scale—covers entire county, not just individual well fields
• Depth range (12-96m): Captures full primary aquifer, from shallow to deep zones
• Material types (105): Very detailed classification—can distinguish subtle lithology differences
• Units (6): Complete stratigraphic framework—from surface to deep bedrock

Quality Assessment: This is an exceptional HTEM dataset by any standard. Most regional surveys have <50 km² coverage and <20 material types. The 884 km² coverage with 105 material types provides unprecedented aquifer characterization.

Important🎯 Critical Insights from HTEM Survey

4.8.1 1. Comprehensive Spatial Coverage

Achievement: 884 km² of continuous aquifer mapping—no other method provides this density.

Implication: Can identify aquifer boundaries, thickness variations, and material heterogeneity everywhere, not just at sparse well locations.

4.8.2 2. Unit D is the Primary Aquifer

Evidence: - Consistent high-resistivity signature (sand/gravel) - Extensive spatial footprint - Appropriate depth (12-96 m) for pumping

Implication: Focus water resource management on Unit D. Other units are either too deep (A-C) or poor quality (E-F).

4.8.3 3. Material Heterogeneity is Significant

Evidence: 105 material types identified, not uniform sand

Implication: Aquifer transmissivity varies spatially. Some zones excellent (sorted sand), others marginal (silty sand). Cannot assume uniform properties.

4.8.4 4. 3D Structure Reveals Confinement

Evidence: Vertical resistivity variations show clay layers within/above aquifer

Implication: Aquifer may be semi-confined in some areas, affecting: - Recharge rates (slower through clay) - Pumping impacts (confined aquifers respond differently) - Vulnerability to contamination (clay provides protection)

4.8.5 5. Integration with Wells is Required

Challenge: HTEM is indirect (resistivity ≠ permeability)

Solution: Calibrate HTEM interpretations with well measurements: - Pump test transmissivity vs. HTEM resistivity - Water quality vs. HTEM lithology - Water level response vs. HTEM confinement

Next steps: Parts 2-3 will perform this integration.

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4.9 Integration Roadmap

This HTEM foundation enables subsequent analyses:

Part 2: Spatial Patterns - Aquifer material mapping and quality assessment - Resistivity distribution analysis - Cross-section visualization of stratigraphic units - Aquifer vulnerability mapping

Part 3: Temporal Dynamics - Understanding how aquifer structure controls water level response - Memory and persistence related to confinement - Thermal response analysis linked to material properties

Part 4: Data Fusion Insights - HTEM-Groundwater fusion (structure controls response) - Correlate HTEM resistivity with aquifer transmissivity - Recharge rate estimation using aquifer properties - Network connectivity mapping

Part 5: Predictive Operations - Material classification ML for drilling decisions - Well placement optimization using aquifer quality maps - Contamination risk assessment using confinement layers - MAR site selection based on HTEM structure

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4.10 Dependencies & Outputs

• Data source: data_paths.grids_2d and data_paths.grids_3d (config key)
• Loader: src.data_loaders.HTEMLoader
• Outputs: Inline interactive figures, optional exports to outputs/phase-1/htem/
• Key files: Material type grids, resistivity classifications for all 6 units

To explore HTEM data programmatically:

from src.data_loaders import HTEMLoader
from src.utils import get_data_path

htem_root = get_data_path("htem_root")
loader = HTEMLoader(data_root=htem_root)

# Load any unit
unit_d = loader.load_material_type_grid('D', 'Preferred')

# Get all available files
files = loader.get_available_files()

4.11 Summary

HTEM airborne geophysical survey provides unprecedented subsurface characterization:

✅ 4.74 GB processed grids – hundreds of thousands of gridded resistivity points across 6 stratigraphic units (see the Complete Data Dictionary for exact counts)

✅ Dense coverage – ~100 m horizontal resolution across the full Champaign County study area

✅ Unit D identified – Primary aquifer with a large fraction of medium-to-high quality material (well-sorted sands and gravels; detailed statistics are developed in the Resistivity Distribution chapter)

✅ 14 key sedimentary material types – From clay (low-permeability confining layers) to well-sorted sand/gravel (high-yield aquifer zones)

✅ 3D structure - Vertical profiles enabling cross-section and volumetric analysis

Key Insight: HTEM bridges the gap between sparse well data and continuous subsurface mapping, enabling spatially-distributed aquifer characterization impossible with wells alone.

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4.12 Related Chapters

• Subsurface 3D Model - Full 3D visualization
• Cross-Section Visualization - Stratigraphic architecture
• Resistivity Distribution - Resistivity-K calibration

4.13 Reflection Questions

• In your own words, how does an HTEM survey “see” differences between clay-rich layers and sand/gravel aquifer materials?
• Looking at the material type groupings, which ranges would you treat as high-priority targets for new wells, and which would you treat as protective confining layers?
• How might misinterpreting resistivity (for example, confusing clay-rich low-resistivity zones with poor data) impact later modeling or management decisions?