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Relevant bibliographies by topics / Arrival-time picking error

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Academic literature on the topic 'Arrival-time picking error'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Arrival-time picking error"

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von Seggern, D. H. "Direct Empirical Estimation of Arrival-Time Picking Error from Waveform Cross-Correlations." Bulletin of the Seismological Society of America 99, no. 5 (2009): 2749–58. http://dx.doi.org/10.1785/0120080304.

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Abakumov, Ivan, Aurelian Roeser, and Serge A. Shapiro. "Arrival-time picking uncertainty: Theoretical estimations and their application to microseismic data." GEOPHYSICS 85, no. 4 (2020): U65—U76. http://dx.doi.org/10.1190/geo2019-0589.1.

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Traveltime-based methods depend on the accurate determination of the arrival times of seismic waves. They further benefit from information on the uncertainty with which the arrival times are determined. Among other applications, arrival-time uncertainties are used to weight data in inversion algorithms and to define the resolution of reconstructed velocity models. The most physically meaningful approaches for the estimation of arrival-time uncertainties are based on probabilistic formulations. The two approaches for the assessment of the lower bound of arrival-time uncertainties, the Cramér–Ra

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Senkaya, Mustafa, and Hakan Karslı. "A semi-automatic approach to identify first arrival time: the Cross-Correlation Technique." Earth Sciences Research Journal 18, no. 2 (2015): 107–13. http://dx.doi.org/10.15446/esrj.v18n2.35887.

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<p class="MsoNormal" style="line-height: 200%;">The high-quality interpretation of seismic refraction data depends on the accurate and reliable identification of the first arrival times. First arrivals can be identified on a graphic or image by conventional picking, but this process depends on external factors, such as the scale and quality of the imaging data, amplitude ratio, sensitivity of the picking cursor and user experience. Under these considerations, identifying first arrivals in noisy data becomes more complex and unstable. In this study, the Cross-Correlation Technique (CCT),

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Guo, Jian, Li Zhou, and Jie Zhu. "Stochastic Model and Simulation Research for Random-Storage S-Type Manual Order Picking." Key Engineering Materials 467-469 (February 2011): 92–97. http://dx.doi.org/10.4028/www.scientific.net/kem.467-469.92.

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It researches the service efficient problem of logistics distribution center manual order picking, tour length and then. It builds picker route stochastic model under random storage S-type picking condition. It makes model approximation calculation with simulation result under order random arrival frequencies. It conducts well approximation between model and simulation results under the condition of permission error. It is the foundation for improving storage strategy. And it provides basic foundation for researching of picking time distribution function for manual order picking random service

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Zhu, Quanjie, Xiaohui Liu, Xiaoyun Liu, Haitao Chai, and Jianjun Shi. "A Multistep Method for Automatic Determination and Optimization of Microseismic P-Phase Arrival Times in a Coal Mine." Advances in Civil Engineering 2019 (June 19, 2019): 1–15. http://dx.doi.org/10.1155/2019/3780875.

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We present an algorithm for P-phase arrival time automatic determination and optimization for microseismic events of a coal mine in steps. For P-phase detection, we apply four modules: single denoising (band-filter), fast searching (STA/LTA and peak value), accurately picking (AIC), and optimization selection (time difference of arrival time and envelope circle judgment). This method is applied for a precise determination of P-phase arrival time; in addition, an automatic quality estimate is obtained to optimize and select the effective stations and used for source location. Through the analys

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Yamada, Masumi, and Jim Mori. "P-wave picking for earthquake early warning: refinement of a T pd method." Geophysical Journal International 228, no. 1 (2021): 387–95. http://dx.doi.org/10.1093/gji/ggab349.

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SUMMARY Detecting P-wave onsets for online processing is an important component for real-time seismology. As earthquake early warning systems around the world come into operation, the importance of reliable P-wave detection has increased, since the accuracy of the earthquake information depends primarily on the quality of the detection. In addition to the accuracy of arrival time determination, the robustness in the presence of noise and the speed of detection are important factors in the methods used for the earthquake early warning. In this paper, we tried to improve the P-wave detection met

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Durán, Evert L., Ludmila Adam, and Irene C. Wallis. "A robust methodology for time picking and error analysis of ultrasonic waveforms and rock densities in the laboratory." GEOPHYSICS 84, no. 2 (2019): MR85—MR97. http://dx.doi.org/10.1190/geo2018-0331.1.

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The relationships between elastic wave speeds and rock physical properties are commonly studied through laboratory experiments. However, the estimation of elastic wave arrival times and the associated errors are not reported consistently. Changes in experimental conditions such as pressures or fluid substitution vary the arrival time and shape of the recorded ultrasonic signals. Crosscorrelation techniques are a simple way of estimating time differences between waveforms for a range of experimental conditions. However, in the presence of elastic wave attenuation, which results in the change of

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Jia, Bao-xin, Ao Sun, Yi-shan Pan, Hao Chen, and Feng-pu Liu. "Accurate Method for Picking Up the First Arrival Time of Microseismic Signals Based on Entropy Theory." Advances in Civil Engineering 2019 (March 18, 2019): 1–17. http://dx.doi.org/10.1155/2019/6074781.

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The entropy method describes the change in the signal state from a macroscopic angle and weakens the interference generated by random noise. From the angle of chaos, which describes the first arrival of microseismic signals, it predicts the trend of noise entropy based on the grey theory. The results indicate that the entropy method is sensitive to amplitude, frequency, and seismic phase changes. Compared with traditional methods, the entropy method has better noise immunity. In terms of the influence of the pickup parameter on the resulting error, the entropy method demonstrated the best resu

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Liao, Wu-Yu, En-Jui Lee, Dawei Mu, Po Chen, and Ruey-Juin Rau. "ARRU Phase Picker: Attention Recurrent-Residual U-Net for Picking Seismic P- and S-Phase Arrivals." Seismological Research Letters 92, no. 4 (2021): 2410–28. http://dx.doi.org/10.1785/0220200382.

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Abstract Seismograms are convolution results between seismic sources and the media that seismic waves propagate through, and, therefore, the primary observations for studying seismic source parameters and the Earth interior. The routine earthquake location and travel-time tomography rely on accurate seismic phase picks (e.g., P and S arrivals). As data increase, reliable automated seismic phase-picking methods are needed to analyze data and provide timely earthquake information. However, most traditional autopickers suffer from low signal-to-noise ratio and usually require additional efforts t

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Zhang, Fengqiao, Lotfollah Pahlavan, and Yuguang Yang. "Evaluation of acoustic emission source localization accuracy in concrete structures." Structural Health Monitoring 19, no. 6 (2020): 2063–74. http://dx.doi.org/10.1177/1475921720915625.

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Acoustic emission source localization is a promising monitoring technique for concrete structures. However, the accuracy of acoustic emission source localization is influenced by many factors, such as the presence of cracks, which are commonly observed in existing reinforced concrete structures. In this article, the acoustic emission source localization is evaluated using a numerical model with a total number of 11,827,200 independent simulated tests. In this work, the investigated influential factors include the presence of cracks, arrival time picking error, and senor layout. The accuracy of

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Dissertations / Theses on the topic "Arrival-time picking error"

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Gilliland, Ellen. "An Assessment of Hypocenter Errors Associated with the Seismic Monitoring of Induced Hydro-fracturing in Hydrocarbon Reservoirs." Thesis, Virginia Tech, 2009. http://hdl.handle.net/10919/45325.

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Expanding the standard, single-well recording geometry used to monitor seismicity during hydro-fracture treatments could provide more accurate hypocenter locations and seismic velocities, improving general reservoir characterization. However, for the real, two-well data set obtained for this project, only S-wave picks were available, and testing resulted in anomalous hypocenter location behavior. This study uses a hypocenter location algorithm and both real and synthetic data sets to investigate how the accuracy of the velocity model, starting hypocenter location, recording geometry, and arr

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Conference papers on the topic "Arrival-time picking error"

1

Abakumov, Ivan, and Serge Shapiro. "Arrival-time picking error of microseismic events." In SEG Technical Program Expanded Abstracts 2019. Society of Exploration Geophysicists, 2019. http://dx.doi.org/10.1190/segam2019-3216192.1.

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