Classification and Magnitude Estimation of Global and Local Seismic Events Using Conformer and Low-Rank Adaptation Fine-Tuning

Classifying seismic events and estimating their magnitude are crucial topics in the study of seismic waves. Due to the disparities between global and local geologic features, models exclusively trained on global data may exhibit suboptimal performance in local contexts. To solve this problem, this letter proposes a method to evaluate the effectiveness of the low-rank adaptation (LoRA) technique in seismic wave research using the convolution-augmented transformer (Conformer). We simplified and modified the Conformer model, reducing the number of parameters by more than 169-fold, and applied the LoRA technique to this model. Experimental results using the Stanford Earthquake Dataset (STEAD) and the Korean Peninsula Earthquake Dataset (KPED) from 2017 to 2018 showed that fine-tuning the model with a significantly reduced number of parameters using the proposed method is suitable for research on seismological applications. Our approach achieved over 99.99% accuracy in seismic event classification for both datasets. Additionally, our model demonstrated a 7% decrease in mean absolute error (MAE) on the STEAD dataset and a 48% decrease on the KPED dataset compared to the state-of-the-art model. Furthermore, the results also indicate that the Conformer is suitable for seismic event classification and magnitude estimation. The model's performance in the seismic event classification task decreased by 0.1%, despite reducing the number of retrain parameters by 59 times. Additionally, in the magnitude estimation task, there was an 89-fold decrease in the number of retrain parameters, yet the performance decreased by 1%.