A Method for Online Detection of the Operating Status of Geophones in the Microseismic System Using Data Augmentation and Spatiotemporal Neural Networks

Abstract

The microseismic monitoring system relies on multiple geophones to detect seismic events, while other methods to determine the operation status of geophones mainly rely on manual inspection of each geophone or comparison of significant changes in observation data, resulting in lower efficiency and accuracy. To address these limitations, we propose an innovative online detection method based on geophone spatiotemporal correlation and data augmentation to continuously monitor geophone status. The proposed extracts time–frequency and energy distribution features from observation data by applying a 230-Hz low-pass filter to preserve the main frequency band and decompose multiple frequency bands. To enhance the dataset, we use Monte Carlo method to generate additional samples of energy distribution features and extend the time–frequency features from 96 samples to 1000 samples using a generative adversarial network (GAN) model. In addition, a dualstream spatiotemporal network model is established for detecting geophone states, which utilizes the spatiotemporal correlation between geophones to improve detection accuracy. The accuracy of the model on the simulated dataset is 98.67%, with an F1-score of 0.9834. Using bootstrap to estimate the performance of the model on real datasets, the average accuracy is 98.99%, with a 95% confidence interval of [0.9688, 1.0000]. The experimental results verify that this method can detect geophone anomalies online, reducing the need for manual intervention. In addition to microseismic monitoring, our method also has potential applications in the detection and maintenance of operational sensors.