A Multitask Fourier Transformer Network for Seismic Source Characterization Estimation From a Single-Station Waveform

This study introduces a novel approach for the estimation of seismic source parameters using a multitask learning network that incorporates a Fourier Transformer architecture. The Fourier Transformer is designed to extract information from both the time and frequency domains, which reduces the time complexity by utilizing a fast Fourier transform (FFT) in place of the traditional attention mechanism in the Transformer encoder. The network consists of a shared encoder for general feature extraction and four task-specific decoders for parameter estimation. The model is both lightweight and accurate, capable of simultaneously estimating magnitude, epicentral distance, p travel time, and depth based on a 30-s single-station waveform. The proposed approach was validated using the Stanford Earthquake dataset (STEAD) and compared with the state-of-the-art techniques. The results show standard deviations of 0.19 for magnitude, 3.77 km for epicentral distance, 0.46 s for p travel time, and 5.77 km for depth, with a lower error and a faster response compared to the existing prediction framework. The code is available at
https://github.com/KG-TSI-Civil/MFTnet
.