Automatic Event Identification From Tectonic Earthquakes with Modified Akaike Information Criterion (mAIC)

Suhendi, Cahli; Sudibyo, Maria R.P.; Erlangga, I.F.; Arbad, Arliandy Pratama

doi:10.1088/1755-1315/258/1/012037

Cited by 3 publications

(1 citation statement)

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“…Accurate and efficient first-arrival picking of microseismic monitoring is the premise for improving the performance of microseismic time-difference source location. Recently, many picking methods have been proposed, such as the Short-Term Average to Long-Term Average (STA/LTA) [8], Akaike information criterion (AIC) [9], and correlation method [10].…”

Section: Introductionmentioning

confidence: 99%

First-Arrival Picking for Microseismic Monitoring Based on Deep Learning

Guo

2021

International Journal of Geophysics

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In microseismic monitoring, achieving an accurate and efficient first-arrival picking is crucial for improving the accuracy and efficiency of microseismic time-difference source location. In the era of big data, the traditional first-arrival picking method cannot meet the real-time processing requirements of microseismic monitoring process. Using the advanced idea of deep learning-based end-to-end classification and the prominent feature extraction advantages of a fully convolution neural network, this paper proposes a first-arrival picking method of effective signals for microseismic monitoring based on UNet++ network, which can significantly improve the accuracy and efficiency of first-arrival picking. In this paper, we first introduced the methodology of the UNet++-based picking method. And then, the performance of the proposed method is verified by the experiments with finite-difference forward modeling simulated signals and actual microseismic records under different signal-to-noise ratios, and finally, comparative experiments are performed using the U-Net-based first-arrival picking algorithm and the Short-Term Average to Long-Term Average (STA/LTA) algorithm. The results show that compared to the U-Net network, the proposed method can obviously improve the first-arrival picking accuracy of the low signal-to-noise ratio microseismic signals, achieving significantly higher accuracy and efficiency than the STA/LTA algorithm, which is famous for its high efficiency in traditional algorithms.

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Section: Introductionmentioning

confidence: 99%