2D Gravity Data Inversion Using the Active Constraint Balancing Method and the Lanczos Algorithm: A Case Study of the Esfandar Iron Ore Mine

Authors

1 Shahrood University of Technology, Faculty of Mining, Petroleum and Geophysics

2 Faculty of Engineering, University of Malayer

Abstract

Geophysical methods provide indirect measurements of subsurface properties and are essential for identifying and characterizing buried geological targets, including mineral deposits and structural features. Among these, gravity data inversion is a key tool for estimating subsurface density distributions. However, gravity inversion is inherently non-unique, and its linear formulation typically results in an underdetermined and ill-posed problem, necessitating regularization to obtain stable and geologically meaningful solutions.

In this study, the Active Constraint Balancing (ACB) method is applied to automatically determine the optimal regularization parameter for two-dimensional (2D) gravity inversion. The inversion is performed using the Lanczos bidiagonalization (LSQR) algorithm, which efficiently handles large, sparse systems. A dedicated algorithm was developed to compute the regularization parameter based on spatial variability and focusing characteristics, ensuring well-resolved and realistic density models.

The algorithm was first tested on synthetic data to assess its ability to recover focused anomalies and maintain stability, and then applied to real gravity data from the Esfandar Iron Mine in Yazd Province, Iran. Results demonstrate that the ACB-based approach improves model focusing, enhances depth resolution, and produces more reliable inversion outcomes compared with conventional fixed-parameter regularization. Overall, the method provides an effective and automated tool for focused gravity inversion, with significant applications in subsurface characterization and mineral exploration

Keywords

References

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Volume 12, Issue 1
April 2026
Pages 51-60

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