Deep learning for quality control of receiver functions - Gong, Chang; Chen, Ling; Xiao, Zhuowei; Wang, Xu (Frontiers in Earth Science, 2022)
Receiver function has been routinely used for studying the structures of the discontinuities in the crust and upper mantle. The manual quality control of receiver functions, which plays a key role in high-quality data selection and accurate structural imaging, has recently been challenged by today's booming data volumes. Traditional automatic quality control methods usually require tuning hyperparameters and fail to generalize to data of low signal-to-noise ratio. Recently, deep learning has been increasingly used to deal with high volumes of seismic data. In this study, we develop and compare four different deep learning network designs with manual and traditional quality control methods using 19854 receiver functions from the broadband seismic station AK.SAW in Alaska. Our results show that a combination of convolutional and long-short memory layers achieves the best performance of 90.13% accuracy. Compared with the traditional automatic method, our model retrieves ~4 times more reliable receiver functions from relatively small earthquakes with magnitudes between 5.0 and 5.5. The average waveforms and H-κ stacking results of these receiver functions are comparable to those obtained by manual quality control from earthquakes with magnitudes larger than 5.5, which further demonstrates the validity of our method and indicates its potential for making use of smaller earthquakes in receiver function analysis.