Microseismic moment tensor inversion based on ResNet model

This paper proposed a moment tensor regression prediction technology based on ResNet for microseismic events. Taking the great advantages of deep networks in classification and regression tasks, it can realize the great potential of fast and accurate inversion of microseismic moment tensors after the network trained. This ResNet-based moment tensor prediction technology, whose input is raw recordings, does not require the extraction of data features in advance. First, we tested the network using synthetic data and performed a quantitative assessment of the errors. The results demonstrate that the network exhibits high accuracy and efficiency during the prediction phase. Next, we tested the network using real microseismic data and compared the results with those from traditional inversion methods. The error in the results was relatively small compared to traditional methods. However, the network operates more efficiently without requiring manual intervention, making it highly valuable for near-real-time monitoring applications. • A novel moment tensor inversion network based on ResNet is proposed. • The input of the model is three component microseismic waveforms. • Both synthetic and real data experiments demonstrate that the network exhibits high efficiency and accuracy.