Scientific Background

This section explains the core electromagnetic and inversion concepts behind pyCSAMT workflows. The library is practical by design, but its outputs are only meaningful when the physical assumptions are understood — these pages cover the response functions, field-method differences, distortion effects, inversion ideas, and time-domain concepts that appear throughout the rest of the documentation.

Use this section when you need to understand why a workflow asks for a particular component, error floor, dimensionality, correction, or diagnostic plot.

Concept Pages

CSAMT, AMT, and MT overview

How CSAMT, AMT, MT, CSEM, and TDEM relate: source types, frequency-domain assumptions, and the survey-design consequences.

CSAMT, AMT, and MT Overview

Impedance tensor

Field coupling, tensor components, apparent resistivity and phase, dimensionality indicators, and rotation — the language of EDI data.

Impedance Tensor

Static shift

Near-surface galvanic distortion: symptoms, correction strategies, and the uncertainty that remains after correction.

Static Shift

Inversion concepts

Forward models, misfit and RMS, regularization, roughness, and how to read inversion diagnostics before trusting a model.

Inversion Concepts

TDEM basics

Transient EM diffusion, time gates, transmitter/receiver geometry, and how TDEM data align with frequency-domain results.

TDEM Basics

Recommended Reading Paths

For MT, AMT, or CSAMT impedance workflows:

1. Start with CSAMT, AMT, and MT Overview.

2. Read Impedance Tensor.

3. Read Static Shift if apparent resistivity curves are shifted between nearby stations or if near-surface heterogeneity is expected.

4. Read Inversion Concepts before running Occam2D, ModEM, or MARE2DEM.

For TDEM workflows:

1. Start with TDEM Basics.

2. Read Inversion Concepts to understand how transient data become an inversion data vector.

3. Read CSAMT, AMT, and MT Overview if TDEM products are being compared with frequency-domain EM results.

For model-backend decisions:

1. Read CSAMT, AMT, and MT Overview to understand method assumptions.

2. Read Inversion Concepts to understand regularization and dimensionality.

3. Continue to Choosing A Model Backend.

Core Quantities

Many pyCSAMT workflows revolve around a small set of physical quantities.

Quantity	Typical notation	Why it matters
Resistivity	\(\rho\)	The main interpreted property in most EM inversions.
Conductivity	\(\sigma = 1 / \rho\)	Controls EM diffusion and current concentration.
Angular frequency	\(\omega = 2 \pi f\)	Links harmonic fields, impedance, skin depth, and phase.
Impedance tensor	\(\mathbf{Z}\)	Relates horizontal electric and magnetic fields in MT, AMT, and CSAMT style workflows.
Apparent resistivity	\(\rho_a\)	A frequency-dependent response estimate, not a direct layer resistivity.
Phase	\(\phi\)	Measures phase lag between field components and helps identify conductive or resistive structure.
RMS misfit	\(RMS\)	Summarizes data fit relative to assigned uncertainties.

Relationship To Other Sections

Theory pages connect directly to the applied documentation:

• Data Formats explains how field data enter pyCSAMT.

• Classical model integrations explains how physical assumptions map to external modelling backends.

• Pipeline System explains how repeated processing and inversion workflows are organized.

• Tutorials gives worked examples that apply these concepts.