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Sparse regularization inversion method for transient electromagnetic data and high-resolution prospection of

ZhiYong Zhou¹, Ke He², ShiHai Kang³

¹Education Information Technology Center, China West Normal University, Nanchong, 637002, China.

Scientific Reports

|August 25, 2025

Summary

This summary is machine-generated.

This study introduces a new inversion algorithm for Transient Electromagnetic Method (TEM) imaging. It enhances subsurface resolution by using L1-norm regularization to better detect sharp geological boundaries.

Keywords:

Iteratively reweighted least squares L1-norm Sparse regularization inversion Transient electromagnetic method

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Area of Science:

Geophysics
Inversion methods
Electromagnetic methods

Background:

Conventional Transient Electromagnetic Method (TEM) inversion often uses L2-norm regularization, assuming smooth subsurface resistivity.
This assumption limits the ability to resolve sharp electrical interfaces, crucial for detailed geological imaging.
High-resolution subsurface imaging is vital for various applications, including resource exploration and environmental studies.

Purpose of the Study:

To develop and implement an advanced inversion algorithm for TEM data.
To improve the resolution of sharp electrical boundaries in subsurface models.
To overcome the limitations of traditional L2-norm regularization in TEM inversion.

Main Methods:

Implemented an inversion algorithm incorporating L1-norm regularization for promoting sparsity in the resistivity model's spatial gradient.
Utilized the Iteratively Reweighted Least Squares (IRLS) method within a Gauss-Newton (GN) framework to handle L1-norm non-differentiability.
Transformed the L1 regularization problem into a series of weighted L2 subproblems solvable by the GN method.

Main Results:

Demonstrated significantly improved resolution of sharp resistivity transitions in both 1D and 2D synthetic models compared to L2-norm methods.
Successfully applied the algorithm to field TEM data, accurately identifying underground coal mine voids and fault structures.
The L1-norm approach effectively reconstructs abrupt electrical boundaries with sufficient iterations.

Conclusions:

The proposed L1-norm based inversion algorithm offers superior resolution for sharp subsurface interfaces in TEM data.
This method provides a robust and computationally viable alternative to conventional L2-norm approaches for detailed geophysical imaging.
The successful field application confirms the practical utility of the algorithm for detecting geological structures and anomalies.