An active learning framework for microseismic event detection

Induced microseismic monitoring has gained increased interest recently, to support various subsurface activities, including geothermal exploration and oil and gas production. To accurately detect and locate origins of microseismisity, deep learning-based methods have become popular due to their high accuracy when trained on large well-labelled datasets. However, though a huge amount of publicly available seismic measurements is available, laballed data to train models is very scarce, since labelling is time consuming and requires very specialist knowledge. Building on our prior work on active learning for time-series data, we propose an active learning method that cleverly picks only a small number of samples to query and stops when the proposed stopping criterion is met. We demonstrate that the proposed approach can save up to 83% of labelling effort even when transferred to a well with different sensing equipment from those used to build the training set.