Journal
APPLIED SCIENCES-BASEL
Volume 13, Issue 22, Pages -Publisher
MDPI
DOI: 10.3390/app132212268
Keywords
seismic signal; mel spectrograms; mel frequency cepstral coefficient; deep learning; stacking
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This study extracts relevant features from seismic signals using mel spectrogram and mel frequency cepstral coefficient (MFCC) and applies a deep learning model with a hierarchical structure for earthquake prediction. The results show significant improvement in predictive performance, and the combination of 1D-CNN and 2D-CNN demonstrates unique advantages in handling time-series problems.
Predicting the remaining time before the next earthquake based on seismic signals generated in a laboratory setting is a challenging research task that is of significant importance for earthquake hazard assessment. In this study, we employed a mel spectrogram and the mel frequency cepstral coefficient (MFCC) to extract relevant features from seismic signals. Furthermore, we proposed a deep learning model with a hierarchical structure. This model combines the characteristics of long short-term memory (LSTM), one-dimensional convolutional neural networks (1D-CNN), and two-dimensional convolutional neural networks (2D-CNN). Additionally, we applied a stacking model fusion strategy, combining gradient boosting trees with deep learning models to achieve optimal performance. We compared the performance of the aforementioned feature extraction methods and related models for earthquake prediction. The results revealed a significant improvement in predictive performance when the mel spectrogram and stacking were introduced. Additionally, we found that the combination of 1D-CNN and 2D-CNN has unique advantages in handling time-series problems.
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