Edge-Intelligence-Based Seismic Event Detection Using a Hardware-Efficient Neural Network With Field-Programmable Gate Array
Yadongyang Zhu, Shuguang Zhao, Fudong Zhang, Wei Wei, Fa Zhao
Abstract
This article presents a neural network model based on edge intelligence for seismic event detection. We implemented the model in hardware using a field-programmable gate array (FPGA) to achieve in-situ detection of seismic events at acquisition nodes or edge nodes. We designed and implemented the model, focusing on its suitability for hardware implementation on FPGA, employing an encoder—decoder structure. The encoder incorporates reparameterization and depthwise separable convolutions. During training, a multibranch structure was employed, which was then converted to an equivalent single-branch structure during inference to reduce model complexity and parameters. The features extracted by the encoder were further learned by the bi-directional long short-term memory (Bi-LSTM) network and then fed into the decoder for classification. We evaluated the model using the stanford earthquake data set (STEAD) and observed a 70% reduction in parameters while achieving comparable detection performance to EQTransformer. Furthermore, the model structure is well-suited for hardware implementation on FPGA. Applying this model to edge devices for seismic event detection can effectively minimize redundant data transmission and enable in-situ quality control.