Joint inversion of P-wave receiver function and surface wave dispersion data based on deep learning.

The use of P-wave receiver function and surface wave dispersion data is crucial in exploring the structure of the Earth's crust and upper mantle. Typically, to address the ambiguity resulting from using a single type of dataset for inversion, these two types of seismic data, which have different sensitivities to shear wave velocity structure, are jointly inverted to achieve a detailed velocity structure. However, methods that rely on a linearized iterative joint inversion approach depend on the initial model selection, while non-linear joint inversion frameworks based on model parameter space search are computationally intensive. To address these challenges, this study suggests employing a deep learning strategy for the joint inversion of P-wave receiver function and surface wave dispersion data. Two distinct neural networks are developed to extract features from the P-wave receiver function and surface wave dispersion data, and different loss functions are tested to train the proposed neural network. The proposed method has been applied to actual seismic data from South China, and the results are comparable to those obtained by jointly inverting body wave first travel-time, P-wave receiver function, and surface wave dispersion data.