Fixed window joint Euler deconvolution for depth estimation of magnetic and gravity data in the Shavaz region

Area of Science:

• Geophysics
• Mineral Exploration
• Computational Methods

Background:

• Traditional Euler deconvolution methods analyze magnetic and gravity data independently, leading to limitations in depth estimation accuracy.
• Accurate subsurface depth estimation is crucial for identifying mineralized bodies and structural discontinuities in geological terrains.

Purpose of the Study:

• To develop and validate an advanced joint Euler deconvolution algorithm for integrated magnetic and gravity data analysis.
• To enhance subsurface depth estimation accuracy using an adaptive fixed window size in the Shavaz region.
• To demonstrate the practical reliability of the integrated geophysical methodology for mineral exploration.

Main Methods:

• Simultaneously solving Euler's equations for both magnetic and gravity potential fields.
• Employing an adaptive fixed window size strategy for enhanced depth estimation.
• Validating the algorithm using synthetic modeling and applying it to field data from the Shavaz region.

Main Results:

• The joint Euler deconvolution algorithm significantly improves the accuracy of subsurface depth estimation compared to traditional methods.
• Application to Shavaz region data successfully revealed the spatial distribution of iron mineralization, correlating with known geological structures and drilling results.
• The adaptive windowing strategy enhanced anomaly boundary delineation and depth solution precision.

Conclusions:

• The proposed integrated geophysical methodology offers a robust and efficient tool for mineral exploration in geologically complex terrains.
• This advanced algorithm effectively mitigates interpretational ambiguity and reduces exploration risk.
• The study confirms the practical reliability and accuracy of the joint Euler deconvolution for subsurface analysis.