The S-STK/LTK algorithm for arrival time picking of microseismic signals

Dong, Xintong; Li, Yue; Wu, Ning; Tian, Yanan; Yu, Pengjun

doi:10.1088/1742-2140/aab30c

Cited by 12 publications

(2 citation statements)

References 6 publications

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“…Over the past few decades, researchers have proposed numerous well-established methods for first-arrival picking, such as the short/long time average ratio (STA/LTA) [ 7 ], Akaike information criterion (AIC) [ 8 ] and various improved approaches [ 9 , 10 ], wavelet denoising and its improved forms [ 11 , 12 ], PAI-S/K based on skewness and kurtosis [ 13 ], STK/LTK based on the long- and short-time window kurtosis ratio [ 14 ], the Markov optimal decision process [ 15 ], waveform similarity-based approaches [ 16 ], and improved multi-channel cross-correlation [ 17 ]. The STA/LTA offers the advantages of simplicity and computational efficiency in first-arrival picking.…”

Section: Introductionmentioning

confidence: 99%

Using Fuzzy C-Means Clustering to Determine First Arrival of Microseismic Recordings

Zhao,

Chen,

et al. 2024

Sensors

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Accurate and automatic first-arrival picking is one of the most crucial steps in microseismic monitoring. We propose a method based on fuzzy c-means clustering (FCC) to accurately divide microseismic data into useful waveform and noise sections. The microseismic recordings’ polarization linearity, variance, and energy are employed as inputs for the fuzzy clustering algorithm. The FCC produces a membership degree matrix that calculates the membership degree of each feature belonging to each cluster. The data section with the higher membership degree is identified as the useful waveform section, whose first point is determined as the first arrival. The extracted polarization linearity improves the classification performance of the fuzzy clustering algorithm, thereby enhancing the accuracy of first-arrival picking. Comparison tests using synthetic data with different signal-to-noise ratios (SNRs) demonstrate that the proposed method ensures that 94.3% of the first arrivals picked have an error within 2 ms when SNR = −5 dB, surpassing the residual U-Net, Akaike information criterion, and short/long time average ratio approaches. In addition, the proposed method achieves a picking accuracy of over 95% in the real dataset tests without requiring labelled data.

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Section: Introductionmentioning

confidence: 99%

Using Fuzzy C-Means Clustering to Determine First Arrival of Microseismic Recordings

Zhao,

Chen,

et al. 2024

Sensors

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“…A characteristic function is generally constructed from the single receiver recording in single-level algorithms, and the maximum of the characteristic difference is chosen as the arrival time of the microseismic signal. The short-and long-time average ratio method (STA/LTA) [11,12], Akaike information criterion method (AIC) [13], polarization-based method [14], higher-order statistics such as skewness and the kurtosisbased method [15,16], and the time-frequency analysis method [17] are commonly used single-level methods. Hybrid algorithms have also been proposed to achieve more accurate and precise arrival-time results for low signal-to-noise ratio (S/N) events by combining information from one or more individual picking algorithms [18,19].…”

Section: Introductionmentioning

confidence: 99%

Optimization and Quality Assessment of Arrival Time Picking for Downhole Microseismic Events

Leng

Mao

et al. 2022

Sensors

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Arrival-time picking is a critical step in microseismic data processing, and thus the quality control of arrival results is necessary. Conventional picking methods may be inaccurate or inconsistent due to varied signal-to-noise ratios (SNR) and waveform patterns of the events recorded in different time sections. To address this issue, we propose a quality assessment method based on waveform similarity coefficients to evaluate arrival results and also a global optimization algorithm based on iterative cross-correlation to refine arrival times. The recordings after moveout correction are applied to calculate the intra-event and inter-event waveform coefficients for the quality assessment of arrival results. The residual time differences of intra-event and inter-event traces are calculated sequentially using an enhanced iterative cross-correlation method. In addition, the stacked waveform of each event after the intra-event residual time correction is introduced for global optimization to obtain the inter-event residual time discrepancies. We use both synthetic data and field data to validate the proposed method. The results indicate that the proposed method yields more robust and reliable results. The quality assessment of the optimized arrivals is greatly enhanced compared to the adjusted picks obtained from single event-based processing methods.

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Real-time arrival picking of rock microfracture signals based on convolutional-recurrent neural network and its engineering application

Chen

Wang

Zhu

et al. 2024

Journal of Rock Mechanics and Geotechnical Engineering

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The S-STK/LTK algorithm for arrival time picking of microseismic signals

Cited by 12 publications

References 6 publications

Using Fuzzy C-Means Clustering to Determine First Arrival of Microseismic Recordings

Using Fuzzy C-Means Clustering to Determine First Arrival of Microseismic Recordings

Optimization and Quality Assessment of Arrival Time Picking for Downhole Microseismic Events

Real-time arrival picking of rock microfracture signals based on convolutional-recurrent neural network and its engineering application

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