# Integrated fractal clustering and inversion of induced polarization data for concealed gold exploration in Kabudan area NE Iran

## TL;DR

This paper introduces a new method combining fractal clustering and geophysical inversion to locate hidden gold deposits in northeastern Iran, improving exploration accuracy in complex geological settings.

## Contribution

The study proposes an integrated framework using fractal clustering and geophysical inversion for concealed mineral exploration, validated with statistical indices and borehole data.

## Key findings

- The C–P fractal model showed the best clustering performance with the highest Silhouette and Calinski–Harabasz indices.
- 2D inversion of IP and resistivity data confirmed subsurface chargeable structures matching fractal clusters.
- Borehole data validated the presence of high-grade gold-bearing sulfide zones aligned with identified anomalies.

## Abstract

The identification and delineation of concealed mineralized zones in settings with weak or overlapping anomalies remains a critical challenge. Conventional geophysical methods provide limited resolution and reliability in such conditions. To overcome this limitation, this study introduces a systematic framework that integrates fractal clustering and geophysical inversion to enhance the accuracy of mineral exploration. Induced polarization (IP) chargeability data, acquired using a rectangular array at the Kabudan gold prospect in northeastern Iran—an area characterized by the Lack of outcrops and surface indications of mineralization—were analyzed using four well-established fractal models: Concentration–Area (C–A), Concentration–Perimeter (C–P), Concentration–Number (C–N), and Number–Size (N–S). To quantitatively evaluate the performance of each model, four statistical validation indices were employed: Silhouette, Davies–Bouldin, Calinski–Harabasz, and cluster stability. Among these models, The C–P fractal model exhibited the highest clustering quality, with the highest Silhouette index (closest to 1 among the models), the lowest Davies–Bouldin index, the highest Calinski–Harabasz index, and the lowest Silhouette index standard deviation (highest cluster stability). To verify the subsurface continuity of the identified anomalies, Four geoelectrical profiles were acquired over the anomalous zones, and two-dimensional (2D) inversion of the induced polarization (IP) and resistivity data was performed. The data were subsequently modeled, and the corresponding cross-sections were generated to illustrate the subsurface variations. The inverted sections revealed coherent chargeable structures that closely corresponded to the clusters derived from the fractal models. The results were further assessed and validated using borehole data, where the correspondence between a high-grade gold-bearing sulfide zone and the anomalies delineated in the profiles confirmed the reliability and accuracy of the interpretations. Overall, the proposed integration of fractal clustering, geophysical inversion, and statistical validation not only enhances the interpretability of subsurface data under complex geological conditions but also provides a scalable and transferable framework for next-generation mineral exploration.