Realistic synthetic data can be useful for data augmentation when training deep learning models to improve seismological detection and classification performance. In recent years, various deep learning techniques have been successfully applied in modern seismology. Due to the performance of deep learning depends on a sufficient volume of data, the data augmentation technique as a data-space solution is widely utilized. In this paper, we propose a Generative Adversarial Networks (GANs) based model that uses conditional knowledge to generate high-quality seismic waveforms. Unlike the existing method of generating samples directly from noise, the proposed method generates synthetic samples based on the statistical characteristics of real seismic waveforms in embedding space. Moreover, a content loss is added to relate high-level features extracted by a pre-trained model to the objective function to enhance the quality of the synthetic data. The classification accuracy is increased from 96.84% to 97.92% after mixing a certain amount of synthetic seismic waveforms, and results of the quality of seismic characteristics derived from the representative experiment show that the proposed model provides an effective structure for generating high-quality synthetic seismic waveforms. Thus, the proposed model is experimentally validated as a promising approach to realistic high-quality seismic waveform data augmentation.

Recently, most research has been conducted to minimize damage from earthquakes by establishing an early warning system through the analysis of short seismic waves. In particular, deep learning is widely used as it allows to learn complex patterns for earthquake detection from seismic data without complex physical knowledge. In this letter, we propose an improved ConvNetQuake for earthquake event classification by adding learnable features related to the maximum amplitude of the seismic waveform. Since the maximum amplitude is a major factor representing the characteristics of an earthquake, we presented a deep learning structure that can apply this factor in the process of determining whether an earthquake occurs. In the proposed structure, the maximum amplitude is transformed into a feature learned through multi-layer perceptron (MLP) and then concatenates with features extracted through a convolutional neural network (CNN). On the STEAD dataset, the proposed method significantly increases the performance for an earthquake event classification than the previous SOTA (state-of-the-art method) by only adding a few parameters.