Research on Seismic Signal Recognition and Prejudgment Based on Deep Learning Model CNN

Traditional seismic signal detection algorithms mainly use micro-seismic signal sensors to capture seismic signal waves. In this way, the corresponding parameters and specific information of the seismic source can be obtained. However, this method has its potential disadvantages, mainly due to the harmonic noise under the micro-sensor collected during the earthquake being too large. Moreover, the sensor's monitoring environment in the automatic signal transmission is often very harsh. Therefore, the signal-to-noise ratio of the acquired data information is extremely low, which causes the problem that microseismic events are difficult to identify. This paper uses an improved deep learning convolutional neural network model to innovate the traditional seismic signal recognition process algorithm to deal with the above problems. Focus on improving the accuracy and reliability of seismic signal identification using sensors by optimizing the processes and steps of seismic signal identification and judgment.