This paper proposes a fast quality control strategy for P-wave receiver functions based on AlexNet and wiggle plots.Receiver functions are essential tools in seismology,particularly for analyzing seismic wave propagat...This paper proposes a fast quality control strategy for P-wave receiver functions based on AlexNet and wiggle plots.Receiver functions are essential tools in seismology,particularly for analyzing seismic wave propagation and subsurface structures,such as the crust and upper mantle.However,the quality control of receiver functions is often a tedious,time-consuming process.In this study,we transform the time series classification problem of receiver function quality control problem into an image classification task by plotting receiver functions as wiggle diagrams and using the deep learning model AlexNet for binary classification to distinguish between“good”and“bad”receiver functions.The model achieved an accuracy of 92.55%on the testing set and demonstrated strong generalization performance with an accuracy of 89.23%on receiver functions of another seismic network(Sichuan Provincial Permanent Seismic Network).While maintaining strong performance,the model is capable of processing approximately 32 receiver function wiggle plots per second on an NVIDIA GeForce RTX 4050.The results show that the proposed feature mapping strategy significantly improves the efficiency and accuracy of receiver function quality control,making it a valuable tool for practical applications.Future work will focus on expanding the dataset and optimizing model performance for broader seismic data applications.展开更多
基金supported by the National Natural Science Foundation of China(No.42174071)the National Key Research and Development Program of China(No.2022YFF0800601)Sichuan Key Research and Development Program(No.2023YFS0433)。
文摘This paper proposes a fast quality control strategy for P-wave receiver functions based on AlexNet and wiggle plots.Receiver functions are essential tools in seismology,particularly for analyzing seismic wave propagation and subsurface structures,such as the crust and upper mantle.However,the quality control of receiver functions is often a tedious,time-consuming process.In this study,we transform the time series classification problem of receiver function quality control problem into an image classification task by plotting receiver functions as wiggle diagrams and using the deep learning model AlexNet for binary classification to distinguish between“good”and“bad”receiver functions.The model achieved an accuracy of 92.55%on the testing set and demonstrated strong generalization performance with an accuracy of 89.23%on receiver functions of another seismic network(Sichuan Provincial Permanent Seismic Network).While maintaining strong performance,the model is capable of processing approximately 32 receiver function wiggle plots per second on an NVIDIA GeForce RTX 4050.The results show that the proposed feature mapping strategy significantly improves the efficiency and accuracy of receiver function quality control,making it a valuable tool for practical applications.Future work will focus on expanding the dataset and optimizing model performance for broader seismic data applications.
