5.2 Resistivity and induced polarization methods

Electrical Resistivity Surveys

Principles and Factors Affecting Resistivity

Electrical resistivity surveys work by injecting a known current into the ground through electrodes, then measuring the resulting voltage (potential difference) at separate electrodes. From these measurements, you can calculate how strongly the subsurface material resists the flow of electric current.

Resistivity is the intrinsic property that quantifies this resistance, measured in ohm-meters ($\Omega \cdot m$). It's the inverse of electrical conductivity. The apparent resistivity you measure in the field represents a bulk average over the volume of earth the current passes through, not the true resistivity of any single layer.

Several factors control the resistivity of subsurface materials:

• Rock type — Igneous and metamorphic rocks tend to have high resistivities (often $10^3$ to $10^5 \; \Omega \cdot m$), while saturated sedimentary rocks are typically much lower ($1$ to $10^3 \; \Omega \cdot m$).
• Porosity and saturation — In most crustal rocks, current flows primarily through pore fluids rather than through mineral grains. Higher porosity and higher saturation mean more conductive pathways, lowering bulk resistivity. This relationship is formalized by Archie's Law: $\rho = a \, \rho_w \, \phi^{-m} \, S_w^{-n}$, where $\rho_w$ is the fluid resistivity, $\phi$ is porosity, $S_w$ is water saturation, and $a$, $m$, $n$ are empirical constants.
• Fluid chemistry — Saline pore water is far more conductive than fresh water. A sandstone saturated with brine might have a resistivity orders of magnitude lower than the same sandstone filled with fresh water.
• Temperature — Higher temperatures increase ion mobility in pore fluids, which decreases resistivity.
• Clay content — Clay minerals contribute additional surface conductivity through their cation exchange capacity, lowering bulk resistivity independent of pore fluid salinity.

Applications and Detection Capabilities

Because different materials and conditions produce distinct resistivity signatures, these surveys can detect a wide range of subsurface features:

• Groundwater exploration — Freshwater-saturated aquifers typically show moderate resistivity (contrasting with surrounding dry rock or clay), while saline aquifers appear as low-resistivity zones.
• Environmental site assessment — Conductive contaminant plumes (e.g., landfill leachate, saltwater intrusion) show up as low-resistivity anomalies against a higher-resistivity background.
• Geotechnical investigations — Mapping bedrock depth, identifying clay-rich zones, and characterizing soil variability for engineering projects.
• Mineral exploration — Massive sulfide ore bodies (pyrite, chalcopyrite, galena) are highly conductive and produce strong low-resistivity anomalies.

Note that resistivity alone doesn't uniquely identify a material. A low-resistivity anomaly could be clay, saline water, or sulfide mineralization. That ambiguity is one reason IP measurements and integration with other data are so important.

Electrode Configurations

Common Electrode Arrays

A standard resistivity measurement uses four electrodes: two current electrodes (commonly labeled A and B, or C1 and C2) that inject and collect current, and two potential electrodes (M and N, or P1 and P2) that measure the resulting voltage. The geometric factor $K$ depends on the specific arrangement and spacing of these four electrodes, and it converts your voltage and current readings into apparent resistivity:

$\rho_a = K \frac{\Delta V}{I}$

The choice of array affects your depth of investigation, spatial resolution, sensitivity to lateral versus vertical structures, and signal strength. The four most common configurations are the Wenner, Schlumberger, dipole-dipole, and pole-pole arrays.

Characteristics and Applications of Electrode Arrays

Wenner array — All four electrodes are equally spaced at distance $a$. The current electrodes are on the outside, potential electrodes on the inside. The geometric factor is $K = 2\pi a$.

• Strong signal strength (large voltage for a given current), so it's relatively robust against noise.
• Good sensitivity to vertical resistivity changes (horizontal layering).
• Limited sensitivity to lateral variations.
• To increase depth of investigation, you expand the entire array, which means moving all four electrodes for each reading. This makes 2D profiling slower compared to some other arrays.

Schlumberger array — Four collinear electrodes, but the current electrode spacing ($AB$) is much larger than the potential electrode spacing ($MN$). The geometric factor is $K = \pi \frac{(AB/2)^2 - (MN/2)^2}{MN}$, which simplifies to $K \approx \pi \frac{L^2}{MN}$ when $AB \gg MN$.

• Greater depth of investigation than Wenner for the same overall spread, because you expand only the current electrodes while keeping the potential electrodes fixed (until the signal drops too low).
• Efficient for vertical electrical sounding (VES) to determine layered resistivity structure.
• Sensitive to vertical variations; less sensitive to lateral heterogeneity near the potential electrodes.

Dipole-dipole array — Two closely spaced current electrodes (separation $a$) and two closely spaced potential electrodes (also separation $a$), separated by a distance $n \times a$. The geometric factor is $K = \pi n(n+1)(n+2) \, a$.

• Excellent sensitivity to lateral resistivity changes, making it well suited for mapping vertical structures (faults, dikes, lateral boundaries).
• Signal strength drops rapidly as $n$ increases (proportional to $n^{-3}$), so it becomes noisy at large electrode separations.
• Very efficient for multi-channel 2D profiling because you can use a fixed cable with many electrodes and switch between combinations electronically.

Pole-pole array — Only one current electrode and one potential electrode are used in the survey area; the return current electrode and reference potential electrode are placed at effectively infinite distance (typically $>$ 10 times the maximum survey electrode spacing).

• Deepest penetration for a given electrode separation and the simplest geometric factor: $K = 2\pi a$.
• Lowest spatial resolution of the common arrays.
• Practically difficult because the remote electrodes must be very far away, and long wire runs pick up noise. Rarely used in practice except in specialized situations.

Induced Polarization for Exploration

Principles and Measurements

Induced polarization (IP) measures the ability of subsurface materials to store electrical charge temporarily. When you inject current into the ground, charge accumulates at interfaces between mineral grains and pore fluid. After you shut off the current, this stored charge dissipates, producing a decaying secondary voltage. That decay is the IP effect.

Two physical mechanisms produce IP responses:

• Electrode (metallic) polarization — Occurs at the interface between electronically conducting minerals (sulfides, graphite, some oxides) and ionically conducting pore fluid. Charge transfer across this interface is slow, causing strong polarization. This is the dominant mechanism in mineral exploration targets.
• Membrane polarization — Occurs in clay-rich sediments and rocks where narrow pore throats and charged clay surfaces restrict ion flow, creating zones of charge buildup. This produces a weaker but still measurable IP effect.

IP can be measured in two ways:

1. Time-domain IP — You inject a current pulse, switch it off, and measure the decay of the secondary voltage over time. Chargeability ($M$) is defined as the area under the decay curve normalized by the primary voltage:

$M = \frac{1}{V_p} \int_{t_1}^{t_2} V_s(t) \, dt$

Chargeability is reported in milliseconds (ms). Typical values range from a few ms for barren rock to tens or hundreds of ms for disseminated sulfides.

1. Frequency-domain IP — You measure apparent resistivity at two or more frequencies. Because polarizable materials show frequency-dependent resistivity, the percent frequency effect (PFE) or metal factor quantifies the IP response:

$PFE = \frac{\rho_{DC} - \rho_{AC}}{\rho_{AC}} \times 100\%$

where $\rho_{DC}$ is the low-frequency resistivity and $\rho_{AC}$ is the high-frequency resistivity.

Applications in Mineral Exploration

IP is one of the most effective geophysical methods for detecting disseminated sulfide mineralization, which resistivity alone often misses. A rock with only 2–5% disseminated sulfide grains may not produce a significant resistivity anomaly, but it can generate a strong chargeability anomaly because each grain acts as a small capacitor.

Key exploration targets for IP surveys:

• Porphyry copper deposits — Disseminated chalcopyrite and pyrite in large, low-grade ore bodies produce broad, high-chargeability anomalies.
• Disseminated gold deposits — Gold itself isn't polarizable, but it's commonly associated with pyrite and arsenopyrite, which are. IP effectively maps the sulfide halo around gold mineralization.
• Volcanogenic massive sulfide (VMS) deposits — Both the massive sulfide core and the surrounding disseminated sulfide alteration zone produce IP anomalies.

A practical caution: graphite and barren pyrite also produce strong IP responses. High chargeability doesn't automatically mean economic mineralization. You need to interpret IP results alongside resistivity data, geological context, and geochemistry.

Interpreting Resistivity and Induced Polarization Data

Data Representation and Inversion

Raw field measurements give you apparent resistivity and apparent chargeability values, which are weighted averages influenced by everything the current passes through. Converting these into a model of the true subsurface property distribution requires several steps:

1. Pseudosection plotting — Measured values are plotted at a position midway between the electrode array and at a depth proportional to the electrode separation. This creates a 2D cross-section that gives a rough visual impression of subsurface structure, but it is not a true depth section. Pseudosections distort the shapes and positions of anomalies.

2. Data quality control — Before inversion, noisy or erroneous data points (from electrode contact problems, cultural interference, etc.) are identified and removed.

3. Inversion — An algorithm iteratively adjusts a gridded subsurface model until the synthetic data it predicts matches the observed data within an acceptable misfit. Common approaches include:

   • Least-squares (smooth) inversion — Produces a smooth model that minimizes both data misfit and model roughness. Good for broad anomalies but tends to smear sharp boundaries.
   • Robust (L1-norm) inversion — Allows sharper contrasts in the model, better for resolving discrete boundaries like faults or contacts.

4. Model assessment — Check the data misfit (RMS error), inspect for inversion artifacts, and evaluate whether the model features are required by the data or are artifacts of the regularization.

The output is a 2D section or 3D volume of true resistivity and chargeability, which you can then interpret geologically.

Integration and Interpretation

Interpreting resistivity and IP models means linking electrical property variations to geology. Some general associations:

• Low resistivity + low chargeability — Clay, saline groundwater, or weathered rock.
• Low resistivity + high chargeability — Sulfide mineralization (the classic exploration target).
• High resistivity + low chargeability — Fresh, competent rock with low porosity; or dry, clean sand/gravel.
• High resistivity + high chargeability — Less common; can indicate disseminated sulfides in a resistive host rock, or sometimes graphitic schist.

These are guidelines, not rules. The same electrical signature can have different geological causes in different settings, which is why integration with other data is essential:

• Borehole logs provide ground-truth measurements of lithology, mineralization, and physical properties at specific locations, allowing you to calibrate your geophysical models.
• Seismic reflection/refraction data reveal structural and stratigraphic geometry that helps constrain the shapes of resistivity anomalies.
• Geological and geochemical mapping provides the regional framework for understanding what's geologically plausible.
• Magnetic and gravity data can help distinguish between different sources of similar resistivity anomalies (e.g., a magnetic high coinciding with a chargeability high strengthens the case for sulfide mineralization over graphite).

Combining resistivity and IP with complementary datasets reduces ambiguity and leads to more reliable geological interpretations.