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%.

中文翻译：

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使用顺应器和低阶自适应微调对全球和局部地震事件进行分类和震级估计

对地震事件进行分类并估计其震级是地震波研究的关键主题。由于全球和当地地质特征之间的差异，专门根据全球数据训练的模型在当地环境中可能表现出次优的性能。为了解决这个问题，这封信提出了一种使用卷积增强变压器（Conformer）来评估地震波研究中低秩自适应（LoRA）技术有效性的方法。我们对Conformer模型进行了简化和修改，参数数量减少了169倍以上，并将LoRA技术应用于该模型。 2017-2018年斯坦福地震数据集（STEAD）和朝鲜半岛地震数据集（KPED）的实验结果表明，使用该方法对模型进行微调，参数数量显着减少，适合地震学应用研究。我们的方法对两个数据集的地震事件分类准确率达到了 99.99% 以上。此外，与最先进的模型相比，我们的模型在 STEAD 数据集上的平均绝对误差 (MAE) 降低了 7%，在 KPED 数据集上降低了 48%。此外，结果还表明Conformer适用于地震事件分类和震级估计。尽管重新训练参数数量减少了 59 倍，但模型在地震事件分类任务中的性能下降了 0.1%。此外，在幅度估计任务中，重新训练参数的数量减少了89倍，但性能却下降了1%。