###### Abstract  
To achieve more precise and effective microseismic magnitude estimation, a classification model based on transfer learning with an improved deep residual network is proposed for predicting microseismic magnitudes. Initially, microseismic waveform images are preprocessed through cropping and blurring before being used as inputs to the model. Subsequently, the microseismic waveform image dataset is divided into training, testing, and validation sets. By leveraging the pretrained ResNet18 model weights from ImageNet, a transfer learning strategy is implemented, involving the retraining of all layers from scratch. Following this, the CBAM is introduced for model optimization, resulting in a new network model. Finally, this model is utilized in seismic magnitude classification research to enable microseismic magnitude prediction. The model is validated and compared with other commonly used neural network models. The experiment uses microseismic waveform data and images of magnitudes 0-3 from the Stanford Earthquake Dataset (STEAD) as training samples. The results indicate that the model achieves an accuracy of 87% within an error range of \(\pm\)0.2 and 94.7% within an error range of \(\pm\)0.3. This model demonstrates enhanced stability and reliability, effectively addressing the issue of missing data labels. It validates that using ResNet transfer learning combined with an attention mechanism yields higher accuracy in microseismic magnitude prediction, as well as confirming the effectiveness of the CBAM. 29 June 202519 July 2025  
earthquake recognition [4], the determination of earthquake source mechanisms [5], and earthquake magnitude prediction. As a result, machine learning has been widely adopted in earthquake data processing, model selection and development, and result analysis. With the advancement of seismology and machine learning, researchers have made the following progress:  
In China, [PERSON] [6] used a convolutional neural network (CNN) with seismic acceleration information as the model input for training and testing. This method achieved an accuracy of 92.3%; however, overfitting occurred during training. Additionally, earthquake acceleration time records contain only limited magnitude information, and there is a problem of missing original label data. To address overfitting caused by large datasets, [PERSON] [7] constructed a CNN magnitude prediction model with 3 s waveform input. By transforming the prediction problem into a classification problem and testing the model with new samples in 2019, it was concluded that the accuracy within a magnitude error range of \(\pm\)0.3 was satisfactory. Nevertheless, this method did not analyze the effects of noise or waveform data beyond the 3 s window. [PERSON] [8] also utilized waveforms for magnitude prediction, selecting wave characteristic parameters such as amplitude and period as inputs to construct a Support Vector Machine (SVM)-based magnitude prediction model. Compared with traditional methods, this approach yielded a smaller prediction error (approximately 0.295), improved the prediction of microearthquakes, reduced the influence of epicentral distance, and demonstrated reliability across different events. [PERSON] [9] employed a Generalized Regression Neural Network (GRNN) for earthquake magnitude prediction, using seven parameters including the cumulative earthquake frequency and released energy as inputs. Through principal component analysis and a particle swarm algorithm, an optimized model was developed. This model effectively reduced the dimensionality, enhancing the prediction accuracy and computational efficiency, with an average error of approximately 5.17% between the predicted and actual values--outperforming both backpropagation (BP) neural networks and standard GRNNs. [PERSON] [10] designed a regressor combining a convolutional neural network (CNN) and a Recurrent Neural Network (RNN) to predict earthquake magnitudes using waveform front-end and amplitude information.