Research on Microseismic Magnitude Prediction Method Based on Improved Residual Network and Transfer Learning
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...
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/15/8246 |
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| author | Huaixiu Wang Haomiao Wang |
| author_facet | Huaixiu Wang Haomiao Wang |
| author_sort | Huaixiu Wang |
| collection | DOAJ |
| description | 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 ±0.2 and 94.7% within an error range of ±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. |
| format | Article |
| id | doaj-art-85f52f73d64d4d57b1b2f77216913b39 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-85f52f73d64d4d57b1b2f77216913b392025-08-20T03:35:57ZengMDPI AGApplied Sciences2076-34172025-07-011515824610.3390/app15158246Research on Microseismic Magnitude Prediction Method Based on Improved Residual Network and Transfer LearningHuaixiu Wang0Haomiao Wang1School of Intelligence Science and Technology, Beijing University of Civil Engineering and Architecture, Beijing 102600, ChinaSchool of Intelligence Science and Technology, Beijing University of Civil Engineering and Architecture, Beijing 102600, ChinaTo 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 ±0.2 and 94.7% within an error range of ±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.https://www.mdpi.com/2076-3417/15/15/8246earthquake magnitude predictiontransfer learningResNet18attention mechanism |
| spellingShingle | Huaixiu Wang Haomiao Wang Research on Microseismic Magnitude Prediction Method Based on Improved Residual Network and Transfer Learning Applied Sciences earthquake magnitude prediction transfer learning ResNet18 attention mechanism |
| title | Research on Microseismic Magnitude Prediction Method Based on Improved Residual Network and Transfer Learning |
| title_full | Research on Microseismic Magnitude Prediction Method Based on Improved Residual Network and Transfer Learning |
| title_fullStr | Research on Microseismic Magnitude Prediction Method Based on Improved Residual Network and Transfer Learning |
| title_full_unstemmed | Research on Microseismic Magnitude Prediction Method Based on Improved Residual Network and Transfer Learning |
| title_short | Research on Microseismic Magnitude Prediction Method Based on Improved Residual Network and Transfer Learning |
| title_sort | research on microseismic magnitude prediction method based on improved residual network and transfer learning |
| topic | earthquake magnitude prediction transfer learning ResNet18 attention mechanism |
| url | https://www.mdpi.com/2076-3417/15/15/8246 |
| work_keys_str_mv | AT huaixiuwang researchonmicroseismicmagnitudepredictionmethodbasedonimprovedresidualnetworkandtransferlearning AT haomiaowang researchonmicroseismicmagnitudepredictionmethodbasedonimprovedresidualnetworkandtransferlearning |