Self‐Aware Joint Inversion of Multidisciplinary Geophysical Data in Mineral Exploration Using Hyperparameters Self‐Adjustment. A Preliminary Study

This paper introduces a novel methodology for subsurface characterization in mineral exploration, based on the simultaneous joint inversion of seismic and geoelectrical data. By combining complementary information provided by multidisciplinary geophysical data, the joint inversion yields a more accurate and consistent representation of subsurface properties. Furthermore, the joint inversion algorithm is empowered by dynamic hyperparameter self-adjustment. A self-adaptive mechanism monitors the evolution of the cost function and optimization performance, automatically tuning hyper-parameters, like the learning rate and the regularization operator, to enhance convergence toward an optimal (global) solution. For the purposes of this preliminary study, the method is tested on synthetic 2D geophysical scenarios featuring resistivity and seismic-velocity anomalies representative of potential mineral targets. Results show the effectiveness of the approach in accurately identifying these subsurface anomalies. Finally, we show that this joint inversion technique holds significant promise for mineral exploration, particularly in detecting geological features such as ore bodies and mineralized zones, which can manifest as contrasts in seismic velocity and resistivity.