Convolution Neural Networks Applied to the Interpretation of Lineaments in Aeromagnetic Data

Parameter estimation in aeromagnetics is an important tool for geological interpretation. Due to aeromagnetic data being highly prevalent around the world, it can often be used to assist in understanding the geology of an area as a whole, or for locating potential areas of further investigation for mineral exploration. Methods that automatically provide information such as the location and depth to the source of anomalies are useful to the interpretation, particularly in areas where a large number of anomalies exist. Unfortunately, many of the current methods rely on high-order derivatives, and are therefore susceptible to noise in the data. Convolution neural networks (CNNs) are a subset of machine learning methods that are well-suited to image processing tasks, and have been shown to be effective at interpreting other geophysical data, such as seismic sections. Following several similar successful approaches, we have developed a CNN methodology for estimating the location and depth of lineament-type anomalies in aeromagnetic maps. To train the CNN model, we utilized a synthetic aeromagnetic data modeler to vary relevant physical parameters, and developed a representative dataset of approximately 1.4 million images. These were then used for training classification CNNs, with each class representing a small range of depth values. We first applied the model to a series of difficult synthetic datasets with varying amounts of noise, comparing the results against the tilt-depth method. We then applied the CNN model to a dataset from north-eastern Ontario, Canada, that contained a dyke with known depth, which was correctly estimated. This method is shown to be robust to noise, and can easily be applied to new datasets using the trained model, which has been made publicly available.