Automatic S-wave picking based on time-frequency analysis for passive seismic applications

Karastathis, V.; Aspiotis, Theodoros; Tselentis, A.; Russill, Nick

doi:10.1190/segam2016-13962843.1

Cited by 6 publications

(3 citation statements)

References 9 publications

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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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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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“…After the microseismic events had been identified, the arrivals of records were picked. Karastathis [14] used time-frequency analysis to obtain a picking method of the first arrival of microseism, and compared with the traditional method, the picking accuracy was improved significantly. Massin [15] used the component energy correlation method to identify microseismic body waves.…”

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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“…First break picking is a fundamental, but important step, in three-component (3C) vertical seismic profiling (VSP) data processing, such as velocity estimation, wave-field separation, and anisotropy estimates [ 1 ]. Any errors and/or misidentifications of these arrival times may have significant effects on the static corrections and velocity inversion [ 2 ], including the structural and lithological investigation employing passive seismic tomography methodologies [ 3 ]. During the last few decades, various techniques have been developed for determining first breaks automatically or semi-automatically [ 4 ], such as automatic methods available that are based on the correlation properties, on some statistical criteria, or on artificial neural networks for both individual and groups of traces [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

A New First Break Picking for Three-Component VSP Data Using Gesture Sensor and Polarization Analysis

Tuo

Shen

et al. 2017

Sensors

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A new first break picking for three-component (3C) vertical seismic profiling (VSP) data is proposed to improve the estimation accuracy of first arrivals, which adopts gesture detection calibration and polarization analysis based on the eigenvalue of the covariance matrix. This study aims at addressing the problem that calibration is required for VSP data using the azimuth and dip angle of geophones, due to the direction of geophones being random when applied in a borehole, which will further lead to the first break picking possibly being unreliable. Initially, a gesture-measuring module is integrated in the seismometer to rapidly obtain high-precision gesture data (including azimuth and dip angle information). Using re-rotating and re-projecting using earlier gesture data, the seismic dataset of each component will be calibrated to the direction that is consistent with the vibrator shot orientation. It will promote the reliability of the original data when making each component waveform calibrated to the same virtual reference component, and the corresponding first break will also be properly adjusted. After achieving 3C data calibration, an automatic first break picking algorithm based on the autoregressive-Akaike information criterion (AR-AIC) is adopted to evaluate the first break. Furthermore, in order to enhance the accuracy of the first break picking, the polarization attributes of 3C VSP recordings is applied to constrain the scanning segment of AR-AIC picker, which uses the maximum eigenvalue calculation of the covariance matrix. The contrast results between pre-calibration and post-calibration using field data show that it can further improve the quality of the 3C VSP waveform, which is favorable to subsequent picking. Compared to the obtained short-term average to long-term average (STA/LTA) and the AR-AIC algorithm, the proposed method, combined with polarization analysis, can significantly reduce the picking error. Applications of actual field experiments have also confirmed that the proposed method may be more suitable for the first break picking of 3C VSP. Test using synthesized 3C seismic data with low SNR indicates that the first break is picked with an error between 0.75 ms and 1.5 ms. Accordingly, the proposed method can reduce the picking error for 3C VSP data.

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Automatic S-wave picking based on time-frequency analysis for passive seismic applications

Cited by 6 publications

References 9 publications

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

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

First-Arrival Picking for Microseismic Monitoring Based on Deep Learning

A New First Break Picking for Three-Component VSP Data Using Gesture Sensor and Polarization Analysis

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