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Efficient P-Wave Arrival Identification Using Multi-Layer Perceptron Neural Networks

This research explores the application of Multi-Layer Perceptron Neural Networks in accurately and swiftly detecting P-wave arrivals, crucial for earthquake location and velocity structure analysis. Previous studies on Artificial Neural Networks inform the methodology, with emphasis on noise reduction and P-wave identification. The network architecture, training results, and accuracy rates are presented, providing insights into the identification process and the strategy improvement using the Akaike Information Criteria picker. Practical application and conclusions highlight the algorithm’s efficiency in P-wave arrival picking, demonstrating higher accuracy compared to traditional methods.

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Efficient P-Wave Arrival Identification Using Multi-Layer Perceptron Neural Networks

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  1. Application of Multi-Layer Perceptron (MLP) Neural Networks in Identification and Picking P-wave arrival Haijiang Zhang Department of Geology and Geophysics ECE 539 Project Presentation

  2. P-wave arrival: characterized by a rapid change in the amplitude and/or the arrival of high-frequency energy. Quickly detecting and accurately picking the first-arrival of a P wave is of great importance in locating earthquakes and characterizing velocity structure. The prior study of ANN on seismic phase picking -Input (1) The absolute seismic data (Dai et al. 1997) (2) Different attributes such as planarity, polarization, etc. (Wang et al., 1997) -Output (1) Noise: 0 1 (2) P-wave arrival: 1 0 -Picking rule (1) A characteristic function is constructed from the ANN outputs. (2) P-wave arrival is chosen as some characteristic point. Introduction

  3. Configuration -30 inputs: 20th sample corresponding to P-wave arrival -2 outputs: corresponding to the noise and P-wave arrival -1 hidden layer: 5 nodes -Learning rate: 0.1, Momentum: 0.8 Results -Training set: including 18 P-wave arrival and noise segments -Classification rate: 94.5% -Testing set: including 58 P-wave arrival and noise segments -Classification rate: 82% MLP: Identification of the P-wave arrival

  4. The characteristic function The onset is chosen as a point whose value is greater than a threshold. But it is difficult to choose such a point!!! The first, the maximum, the middle?? Long term, mid-term and short term to improve the picking accuracy (Zhao et al., 1999) My strategy Use Akaike Information Criteria (AIC) picker to pick the onset MLP: Picking P-wave arrival

  5. Practical Application and conclusions • Application -The algorithm is tested on some seismograms from SAFOD. -90% P-wave arrivals are detected and picked. • Conclusions -It cannot discard spikes or glitches. -It is not very sensitive to S/N ratio -Comparing with former methods, this algorithm can pick the P-wave arrival more accurately (within 15ms)

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