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

Introduction

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.

MLP: Identification of the P-wave arrival

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: Picking P-wave arrival

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

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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)