Example of the convolutional neural network (CNN) approach from an area in the Black Hill norite
Example of the convolutional neural network (CNN) approach from an area in the Black Hill norite (Australia), a mafic intrusion of Ordovician age, known for the impact of remanent magnetization on the magnetic anomaly expression and analyzed before with classic approaches. Left: Total magnetic intensity (TMI) map. Center: Zoom of the TMI map to the region of the anomaly studied. Right: Data map prediction from an optimally inferred source body with a magnetic declination of 225 degrees and inclination of 25 degrees estimated from the trained CNN. This estimate compares well with estimates from classic magnetic anomaly interpretation approaches. Credit: Nurindrawati and Sun [2020], Figure 8
Source: Journal of Geophysical Research: Solid Earth

Interpretation of (aero)magnetic anomaly maps typically involves a series of steps after optimization of raw flight data. Nurindrawati and Sun [2020] explore the performance of machine learning methods for this kind of problem. They test various sets of convolutional neural networks (CNN) and train the two optimal CNN designs (one for declination and one for inclination) to predict declination and inclination directly from input total field maps. No reduction to pole or similar data pretreatment is required. Classic magnetic anomaly interpretation is rather often critically impacted by constraints on the depth and shape of the anomaly source body.

Importantly, the authors illustrate that their CNN approach is less influenced by source body shape, under the proviso the center of the source body is reasonably placed, for example as inferred from existing depth estimate methods. The CNN approach is anticipated to develop into a welcome addition to the existing tool kit for magnetic anomaly interpretation.

Citation: Nurindrawati, F., Sun, J. [2020]. Predicting magnetization directions using convolutional neural networks. Journal of Geophysical Research: Solid Earth, 125, e2020JB019675. https://doi.org/10.1029/2020JB019675

—Mark J. Dekkers, Associate Editor, JGR: Solid Earth

Text © 2020. The authors. CC BY-NC-ND 3.0
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