A python-based application for automated very low frequency-electromagnetic data processing and subsurface interpretation

Abstract

Background: Very Low Frequency Electromagnetic (VLF-EM) method is widely applied in near-surface geophysical investigations for identifying subsurface structures such as fractures, faults, and conductive zones. However, the interpretation of VLF-EM data often requires complex processing steps and specialized software, which may limit efficiency and accessibility for field-based analysis. This study presents the development of a Python-based application designed for automated processing and interpretation of VLF-EM data to support subsurface structure identification. Methods: The application integrates several essential VLF-EM data processing stages, including data input, signal filtering, Fraser and Karous–Hjelt transformations, profile visualization, and subsurface pseudo-section generation. The system was developed using Python programming language and graphical user interface (GUI) components to enable user-friendly interaction and efficient data handling. Field VLF-EM data collected from Neheun area, Aceh Besar, were used to evaluate the performance of the proposed application. The processed data were analyzed to identify subsurface conductive anomalies associated with geological structures. Findings: The results demonstrate that the developed application is capable of producing clear and interpretable VLF-EM profiles and pseudo-sections, allowing effective identification of subsurface conductive zones. Automated processing significantly reduces manual interpretation time while maintaining consistency and reliability of results. The visualization outputs enhance the understanding of subsurface structures and support preliminary geological interpretation. Conclusion: In conclusion, the proposed Python-based application provides an effective and accessible tool for automated VLF-EM data processing and subsurface interpretation. Its flexibility, open-source environment, and integrated visualization features make it suitable for geophysical surveys and educational purposes. Novelty/Originality of this article: The novelty of this study lies in the integration of automated VLF-EM data processing and interpretation within a standalone Python-based application that simplifies analysis while preserving essential geophysical principles.