(Quasi-)Real-Time Inversion of Airborne Time-Domain Electromagnetic Data via Artificial Neural Network

TL;DR

This paper introduces a neural network-based method for rapid inversion of airborne electromagnetic data, enabling near real-time resistivity modeling that matches traditional inversion accuracy but with significantly reduced computation time.

Contribution

The study presents a novel neural network approach for fast resistivity model retrieval from airborne electromagnetic data, comparable in quality to standard inversion methods.

Findings

Neural network method achieves similar accuracy to traditional inversion.

Significantly reduces computation time for resistivity modeling.

Validated on both synthetic and real datasets.

Abstract

The possibility to have results very quickly after, or even during, the collection of electromagnetic data would be important, not only for quality check purposes, but also for adjusting the location of the proposed flight lines during an airborne time-domain acquisition. This kind of readiness could have a large impact in terms of optimization of the Value of Information of the measurements to be acquired. In addition, the importance of having fast tools for retrieving resistivity models from airborne time-domain data is demonstrated by the fact that Conductivity-Depth Imaging methodologies are still the standard in mineral exploration. In fact, they are extremely computationally efficient, and, at the same time, they preserve a very high lateral resolution. For these reasons, they are often preferred to inversion strategies even if the latter approaches are generally more accurate in terms of proper reconstruction of the depth of the targets and of reliable retrieval of true resistivity values of the subsurface. In this research, we discuss a novel approach, based on neural network techniques, capable of retrieving resistivity models with a quality comparable with the inversion strategy, but in a fraction of the time. We demonstrate the advantages of the proposed novel approach on synthetic and field datasets.