scholarly journals P-wave arrival time inversion by using the τ-pmethod: Application to the Mt. Vesuvius Volcano, southern Italy

1997 ◽  
Vol 24 (5) ◽  
pp. 515-518 ◽  
Author(s):  
Raffaella De Matteis ◽  
Aldo Zollo ◽  
Jean Virieux
Keyword(s):  
Arrival Time ◽  
Southern Italy ◽  
P Wave ◽  
Time Inversion ◽  
Vesuvius Volcano ◽  
Wave Arrival

Joint microseismic event location and anisotropic velocity inversion with the cross double-difference method using downhole microseismic data

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. KS63-KS73
Author(s):  
Yangyang Ma ◽  
Congcong Yuan ◽  
Jie Zhang
Keyword(s):  
Arrival Time ◽  
Velocity Model ◽  
P Wave ◽  
Double Difference ◽  
S Wave ◽  
Arrival Times ◽  
Monitoring Well ◽  
The Cross ◽  
Microseismic Event ◽  
Wave Arrival

We have applied the cross double-difference (CDD) method to simultaneously determine the microseismic event locations and five Thomsen parameters in vertically layered transversely isotropic media using data from a single vertical monitoring well. Different from the double-difference (DD) method, the CDD method uses the cross-traveltime difference between the S-wave arrival time of one event and the P-wave arrival time of another event. The CDD method can improve the accuracy of the absolute locations and maintain the accuracy of the relative locations because it contains more absolute information than the DD method. We calculate the arrival times of the qP, qSV, and SH waves with a horizontal slowness shooting algorithm. The sensitivities of the arrival times with respect to the five Thomsen parameters are derived using the slowness components. The derivations are analytical, without any weak anisotropic approximation. The input data include the cross-differential traveltimes and absolute arrival times, providing better constraints on the anisotropic parameters and event locations. The synthetic example indicates that the method can produce better event locations and anisotropic velocity model. We apply this method to the field data set acquired from a single vertical monitoring well during a hydraulic fracturing process. We further validate the anisotropic velocity model and microseismic event locations by comparing the modeled and observed waveforms. The observed S-wave splitting also supports the inverted anisotropic results.

Feasibility of estimating vertical transverse isotropy from microseismic data recorded by surface monitoring arrays

Geophysics ◽  
2011 ◽  
Vol 76 (6) ◽  
pp. WC117-WC126 ◽  
Author(s):  
Davide Gei ◽  
Leo Eisner ◽  
Peter Suhadolc
Keyword(s):  
Vertical Velocity ◽  
Field Data ◽  
P Wave ◽  
Time Inversion ◽  
Source Depth ◽  
Effective Anisotropy ◽  
Arrival Times ◽  
Microseismic Data ◽  
Surface Monitoring ◽  
Wave Arrival

Microseismic data recorded by surface monitoring arrays can be used to estimate the effective anisotropy of the overburden and reservoir. In this study we used the inversion of picked P-wave arrival times to estimate the Thomsen parameter [Formula: see text] and the anellipticity coefficient [Formula: see text]. This inversion employs an analytic equation of P-wave traveltimes as a function of offset in homogeneous, transversely isotropic media with a vertical axis of symmetry. We considered a star-like distribution of receivers and, for this geometry, we analyzed the sensitivity of the inversion method to picking noise and to uncertainties in the P-wave vertical velocity and source depth. Long offsets, as well as a high number of receivers per line, improve the estimation of [Formula: see text] and [Formula: see text] from noisy arrival times. However, if we do not use the correct value of the P-wave vertical velocity or source depth, the long-offset may increase the inaccuracy in the estimation of the anisotropic parameters. Such inaccuracy cannot be detected from time residuals. We also applied this inversion to field data acquired during the hydraulic fracturing of a gas shale reservoir and compared the results with the anisotropic parameters estimated from synthetic arrival times computed for an isotropic layered medium. The effective anisotropy from the inversion of the field data cannot be explained by layering only and is partially due to the intrinsic anisotropy of the reservoir and/or overburden. This study emphasizes the importance of using accurate values of the vertical velocity and source depth in the P-wave arrival time inversion for estimating anisotropic parameters from passive seismic data.

scholarly journals P-wave picking for earthquake early warning: Refinement of a T pd method

2021 ◽  
Author(s):  
Masumi Yamada ◽  
Jim Mori
Keyword(s):  
Real Time ◽  
Early Warning ◽  
Arrival Time ◽  
Early Warning Systems ◽  
Earthquake Early Warning ◽  
P Wave ◽  
Warning Systems ◽  
Wave Detection ◽  
Earthquake Early Warning Systems ◽  
Wave Arrival

Summary Detecting P-wave onsets for on-line 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 method designed for real-time processing of continuous waveforms. We used the new Tpd method, and proposed a refinement algorithm to determine the P-wave arrival time. Applying the refinement process substantially decreases the errors of the P-wave arrival time. Using 606 strong motion records of the 2011 Tohoku earthquake sequence to test the refinement methods, the median of the error was decreased from 0.15 s to 0.04 s. Only three P-wave arrivals were missed by the best threshold. Our results show that the Tpd method provides better accuracy for estimating the P-wave arrival time compared to the STA/LTA method. The Tpd method also shows better performance in detecting the P-wave arrivals of the target earthquakes in the presence of noise and coda of previous earthquakes. The Tpd method can be computed quickly so it would be suitable for the implementation in earthquake early warning systems.

scholarly journals Preliminary Results of Automatic P-Wave Regional Earthquake Arrival Time Picking using Machine Learning with Kurtosis and Skewness as The Input Parameters

2021 ◽  
Vol 873 (1) ◽  
pp. 012061
Author(s):  
Y H Lumban Gaol ◽  
R K Lobo ◽  
S S Angkasa ◽  
A Abdullah ◽  
I Madrinovella ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Arrival Time ◽  
Moving Average ◽  
P Wave ◽  
Manual Inspection ◽  
Artificial Neural ◽  
Waveform Amplitude ◽  
Wave Arrival

Abstract The traditional method in determining first arrival time of earthquake dataset is time consuming process due to waveform manual inspection. Additional waveform attributes can help determine events detection. One of the widely used attribute is The Short Term Averaging/Long Term Averaging (STA/LTA) which is simply division moving average of waveform amplitude between short time and longer time. Alternatively, waveform attribute can also be built using kurtosis and skewness. The kurtosis attribute is defined as sharpness of data distribution. By definition, the maximum signal should be at or close to the P wave arrival. The skewness is typically used to show normal distribution of the data. The uniqueness of this method is that it has an ability to determine whether the first P wave arrival has positive of negative number. The skewness calculation is similar to kurtosis but it uses the power of 3 instead of 4. With the objective of generating efficient scheme to pick first time arrival, we explore use artificial neural network and a combination of kurtosis and skewness attributes. We use a collection of magnitude events with moment magnitude larger than 3 located close to Moluccas island, Indonesia. We collected all events information from the Indonesian Agency of Meteorology, Climatology and Geophysics. The process is started with manually pick all P wave arrivals using manual inspection. Next, we trained the artificial neural network with attributes numbers as inputs and arrival time we manually picked as the output. In total we used 100 regional events that has clear P wave phases. Then, we validated the results until reaching 0.99 accuracy. In the last step, we tested the once trained procedures on new waveforms contained events. Current result shows an average of 0.4s different between manual pick and trained picked from machine learning. The accuracy can be improved by applying a band pass 0.1-2 Hz filtering with an average of 0.2s.

scholarly journals Development of an Algorithm for P-wave Arrival Time determination Using Amoving Window Function

2015 ◽  
Vol 25 (1) ◽  
pp. 103-113
Author(s):  
Kyung-Soo Lee ◽  
Seong-Ha Cho ◽  
Chang-Soo Lee ◽  
Young-Chul Choi ◽  
Bo-Sun Yoo
Keyword(s):  
Arrival Time ◽  
P Wave ◽  
Window Function ◽  
Time Determination ◽  
Wave Arrival

Automatic algorithm for triaxial hodogram source location in downhole acoustic emission measurement

Geophysics ◽  
1989 ◽  
Vol 54 (4) ◽  
pp. 508-513 ◽  
Author(s):  
K. Nagano ◽  
H. Niitsuma ◽  
N. Chubachi
Keyword(s):  
Acoustic Emission ◽  
Arrival Time ◽  
Field Measurements ◽  
Source Location ◽  
P Wave ◽  
Likelihood Method ◽  
Emission Measurement ◽  
Wave Direction ◽  
S Wave ◽  
Wave Arrival

An automatic acoustic emission (AE) source location algorithm has been developed for downhole AE measurement of subsurface cracks by using the triaxial hodogram method. The P-wave arrival time is detected by analyzing crosscorrelation coefficients among three components of AE signal energy; the P-wave direction is determined by the method of least squares. For detection of S-wave arrival time, a maximum‐likelihood method analyzes a distribution of instantaneous values of the SH-wave component amplitude. This algorithm can locate an AE source as accurately as human analysis. For field measurements, it takes less than 4 s to locate an AE source using a 16-bit personal computer with a program in C language. Automatic AE source location by the triaxial hodogram method has been realized with this algorithm.

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