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Introduction to Environmental Geophysics

Geophysics lets us image the subsurface without digging: every method measures a physical field at the surface and infers what property contrasts below could produce it. The central skill of this module is matching the right method to the right problem, because target depth, target size, and property contrast decide what is detectable.

Learning Objectives

Undergraduate Core: By the end of this module, you will be able to:

  • Match a subsurface target to the physical property contrast a method senses.
  • Explain the trade-off among depth of investigation, resolution, and signal-to-noise ratio.
  • Propose a defensible first-pass survey method and spacing for a stated target.
  • Distinguish measured data, an inverted physical-property model, and a geological interpretation.
Graduate Extension

Analyze method selection as a non-unique decision problem: compare sensitivity, expected data error, prior information, and the value of collecting complementary data.

Practice this module Teach with active-learning slides

Learning Path

Work through the apps in order. Each one opens full screen; the bar at its top shows this path and has Prev and Next buttons.

  1. Depth of Investigation vs. Resolution Demo

    Compare the depth ranges and resolving power of gravity, magnetics, seismic, ERT, EM, and GPR side by side.

  2. Survey Spacing & Noise Simulator Interactive lecture

    See how station spacing and noise decide whether a survey resolves a small target, for four methods at once.

After the path: practice questions.

Why This Matters: Hydrogeophysics

Turning geophysical images into hydrology

A resistivity or velocity image is only a means to an end. The goal in much of modern near-surface work is to watch water move through the subsurface: soil moisture, groundwater, and the weathered "critical zone" where rock becomes soil. Doing that means combining several methods and linking them to hydrological models.

PyHydroGeophysX, developed in Dr. Chen's group, is built for exactly this. It converts hydrological model outputs (MODFLOW, ParFlow) into ERT, seismic, and EM responses, and inverts field data back to water content.

Orientation reading: Binley et al. (2015) and Parsekian et al. (2015) on the References page.

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