Generation and Evaluation of Synthetic Low‐Magnitude Earthquake Data Using Auxiliary Classifier GAN

Abstract Low‐magnitude earthquakes occur far more frequently than major quakes and often go unnoticed by the public. These tremors rarely cause any damage, yet they play an important role in advancing our understanding of Earth's seismicity. Accurate detection of low‐magnitude earthquakes is crucial to develop complete earthquake catalogs and improve seismic hazard forecasting models. However, conventional detection algorithms such as the short‐time‐average/long‐time‐average (STA/LTA) method struggle to identify these events because of their inherently low signal‐to‐noise ratio (SNR). Additionally, lack of labeled waveforms for low‐magnitude earthquakes further complicates the training of effective deep‐learning models. In this study, we use an Auxiliary Classifier Generative Adversarial Network (AC‐GAN) to produce synthetic yet realistic three‐component waveforms of low‐magnitude earthquakes. The AC‐GAN is trained on fixed‐length (60‐s) waveform segments conditioned by predefined SNR classes. All selected events have magnitudes lower than 3 and are categorized into 10 distinct SNR classes. Our results indicate that the AC‐GAN model generates realistic three‐component waveforms that effectively capture essential characteristics of real seismic signals. To evaluate the quality of these synthetic waveforms, we employ both quantitative and qualitative assessments. Quantitative analysis using Pearson's correlation coefficient yield relatively low correlations (ranging from 0.01 to 0.04); however, correlation values noticeably improve as SNR increases. Qualitatively, a user‐based visual inspection experiment demonstrate remarkable similarities in general seismic features between the synthetic and authentic waveforms. We also test their effectiveness for data augmentation in binary deep‐learning classifier designed for detecting low‐magnitude earthquakes. Our result show improved classification performance with the addition of synthetic data.