###### Abstract  
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. seismic signal; mel spectrograms; mel frequency cepstral coefficient; deep learning; stacking 202320233130  
NIFTY 50 time series in the following week. Additionally, [PERSON], [PERSON] et al. and [PERSON], [PERSON] et al. [7; 8] effectively employed a CNN and LSTM to predict the remaining lifespan of mechanical components under different environmental conditions. Machine learning is also widely applied in the field of earthquake prediction. For example, [PERSON], [PERSON] et al. [9] introduced an autoregressive (AR) model to predict the shear stress in fault zones; [PERSON], [PERSON] et al. and [PERSON], [PERSON] et al. [10; 11] predicted earthquake magnitude using ML-based methods; [PERSON], [PERSON] et al. [12] utilized transfer learning to train a convolutional encoder-decoder to predict instantaneous and future fault sliding; [PERSON], [PERSON] et al. [13] used XGBoost, random forest (RF), and a deep neural network (DNN) to predict site amplification factors for different periods; [PERSON], [PERSON] et al. [14] proposed a novel ML-based reservoir prediction method based on virtual sample generation; [PERSON], [PERSON] et al. [15] showed that ML-based phase-picking exhibited good performance in constraining high-precision earthquake catalogs and constructing high-resolution velocity models; and [PERSON], [PERSON] et al. [16] utilized ML for earthquake phase analysis to address the subjective issue of earthquake interpretation. Accurately predicting earthquakes is crucial to mitigating their societal impact. In this study, the goal of predicting the remaining time before the next earthquake occurs based on seismic signals generated in a laboratory setting [17; 18] was considered. Various machine learning (ML) algorithms were employed, including gradient boosting decision trees (GBDT), convolutional neural networks (CNN), long short-term memory (LSTM) networks, and model fusion (stacking). GBDT is an ensemble learning method [19], which builds a series of decision trees by boosting the mispredictions in the training samples, and makes final predictions by taking the weighted average of the predictions from all the decision trees. CNNs are deep learning algorithms that are particularly suited for processing image and signal data [20; 21; 22]. These networks use convolutional layers to extract features from the input data and pooling layers to reduce the dimensionality of the feature maps. LSTM is superior to recurrent neural networks (RNN) and is designed to address the long-term dependency problem in traditional RNN, and has demonstrated great potential for modeling time-series data [23; 24; 25]. Stacking is a powerful model ensembling strategy that can improve the prediction accuracy of machine learning models. Stacking is a very effective technique in various machine learning tasks, especially in cases where the best performance is desired [26; 27; 28].