Receiver functions auto-picking method on the basis of deep learning

As a commonly used seismic tool, receiver functions analysis plays significant roles in detecting discontinuous interface of the earth and S wave velocity inversion. However, picking receiver functions needs lots of manpower, which is a barrier for us to obtain the underground structure fast and precisely. In this condition, a fast and efficient method is urgently needed. In this work, we establish a deep learning network to auto-pick receiver functions. Receiver functions from 2000 to 2019 calculated from MDJ and BJT stations, belonging to China Earthquake Administration, are used to test our method, and the results show that the deep learning method is effective in receiver functions auto-picking. Those auto-picked data are used to estimate the crustal structure beneath the two stations. It shows a high degree of consistency compared with manual picked data. Several groups' experiments are carried out to analyze the influence of testing data size, helping us make a conclusion that our method has following advantages as less dependence for size of training set, fully mining useful seismic data and suitable for joint analysis of multi-stations. Once accuracy receiver functions auto-picking models of the seismic stations are built, it will have an enormous potential for auto-picking receiver functions in the future.