Inverting airborne electromagnetic data with machine learning

TL;DR

This paper presents a machine learning approach to rapidly invert 2D airborne electromagnetic data by training neural networks on simulated data, enabling near real-time conductivity mapping over aquifers.

Contribution

The study introduces a neural network-based inversion method that reduces computational time by training on precomputed models, facilitating quick 2D inversion of airborne electromagnetic data.

Findings

Neural network inversion produces realistic 2D conductivity models.

Inversion time is reduced to seconds on a standard laptop.

Method shows promise for near real-time data interpretation.

Abstract

This study focuses on inverting time-domain airborne electromagnetic data in 2D by training a neural-network to understand the relationship between data and conductivity, thereby removing the need for expensive forward modeling during the inversion process. Instead the forward modeling is completed in the training stage, where training models are built before calculating 3D forward modeling training data. The method relies on training data being similar to the field dataset of choice, therefore, the field data was first inverted in 1D to get an idea of the expected conductivity distribution. With this information, $ 10,000 $ training models were built with similar conductivity ranges, and the research shows that this provided enough information for the network to produce realistic 2D inversion models over an aquifer-bearing region in California. Once the training was completed, the actual inversion time took only a matter of seconds on a generic laptop, which means that if future data was collected in this region it could be inverted in near real-time. Better results are expected by increasing the number of training models and eventually the goal is to extend the method to 3D inversion.