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

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.

Geophysics ◽  
1996 ◽  
Vol 61 (5) ◽  
pp. 1453-1466 ◽  
Author(s):  
Hirokazu Moriya ◽  
Hiroaki Niitsuma

We have developed a signal processing technique for three‐component microseismic data that allows the precise determination of P‐wave arrival times. The method is based on a time‐frequency representation of the signal that allows the evaluation of the 3-D particle motion from seismic waves in both time and frequency domains. A spectral matrix is constructed using the time‐frequency distributions. A crosscorrelation coefficient for the three‐component signal is derived through eigenvalue analysis of the spectral matrix. The P‐wave arrival time is determined through a statistical test of hypotheses using the crosscorrelation coefficient. This signal processing method is evaluated using a synthetic signal and it is compared to the local stationary autoregressive method for determining P‐wave arrival times. We also show that the proposed method is capable of determining the arrival time of a synthetic P‐wave to within 1 ms (five points in the discrete time series) in the presence of a signal‐to‐noise ratio of −5dB. The method can detect the arrival time of different frequency components of the P‐wave, which is a possibility for the evaluation of velocity dispersion of the seismic wave. We demonstrate the feasibility of the method further by applying it to microseismic data from a geothermal field.


1997 ◽  
Vol 40 (5) ◽  
Author(s):  
G. Patanè ◽  
C. Centamore ◽  
S. La Delfa

This paper analyses twelve etnean earthquakes which occurred at various depths and recorded at least by eleven stations. The seismic stations span a wide part of the volcanic edifice; therefore each set of direct P-wave arrival times at these stations can be considered appropriate for tracing isochronal curves. Using this simple methodology and the results obtained by previous studies the authors make a reconstruction of the geometry of the bodies inside the crust beneath Mt. Etna. These bodies are interpreted as a set of cooled magmatic masses, delimited by low-velocity discontinuities which can be considered, at present, the major feeding systems of the volcano.


Geophysics ◽  
2018 ◽  
Vol 83 (2) ◽  
pp. KS1-KS10 ◽  
Author(s):  
Zhishuai Zhang ◽  
James W. Rector ◽  
Michael J. Nava

We have studied microseismic data acquired from a geophone array deployed in the horizontal section of a well drilled in the Marcellus Shale near Susquehanna County, Pennsylvania. Head waves were used to improve event location accuracy as a substitution for the traditional P-wave polarization method. We identified that resonances due to poor geophone-to-borehole coupling hinder arrival-time picking and contaminate the microseismic data spectrum. The traditional method had substantially greater uncertainty in our data due to the large uncertainty in P-wave polarization direction estimation. We also identified the existence of prominent head waves in some of the data. These head waves are refractions from the interface between the Marcellus Shale and the underlying Onondaga Formation. The source location accuracy of the microseismic events can be significantly improved by using the P-, S-wave direct arrival times and the head wave arrival times. Based on the improvement, we have developed a new acquisition geometry and strategy that uses head waves to improve event location accuracy and reduce acquisition cost in situations such as the one encountered in our study.


Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. KS63-KS73
Author(s):  
Yangyang Ma ◽  
Congcong Yuan ◽  
Jie Zhang

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.


2020 ◽  
Author(s):  
Alessandro Caruso ◽  
Aldo Zollo ◽  
Simona Colombelli ◽  
Luca Elia ◽  
Grazia De Landro

<p>For network-based Earthquake Early Warning Systems (EEWS), the real-time earthquake location is crucial for a correct estimation of event location/magnitude and therefore, for a reliable prediction of the potential expected shaking at the target sites in terms of predicted maximum ground shaking. Different approaches have been recently proposed for the real-time location which mainly use absolute (or differential) P-wave travel times at a set of minimum available stations or measurement of the initial P-wave arrival time (Elarms, Presto, Horiuchi), polarization (Eiserman and Bock) or amplitude and time (Yamada). In this work, we propose a new method which is able to exploit the continuous, real-time information available from both time, amplitude and polarization of initial P-wave signals acquired by dense three component arrays deployed in the source zones. The methodology we propose is an evolutionary and Bayesian probabilistic technique that combines three different observed parameters: 1) the differential arrival times of P-waves (which are computed using a 1D velocity model for the estimation of the theoretical arrival times); 2) the differential P-wave amplitudes in terms of P-wave peak velocity) [reference]  (which are computed using an existing P-peak motion prediction equation) and 3) the real-time estimation of back-azimuthal direction, measured shortly after the P-wave arrival. These three parameters are measured in real-time and are used as prior and conditional information to estimate the posterior probability of the event location parameters, e.g. the hypocenter coordinates and the origin time. The method is evolutive, since it updates the location parameters as new data are acquired by more and more distant stations as the P-wavefront propagates across the network. The output is a multi-dimensional Probability Density Function (PDF), which contains the complete information about the maximum likelihood parameter estimation with their uncertainty. The method is computationally efficient and optimized for running in real-time applications, where the earthquake location has to be retrieved in a very short time window (around 1 sec) after data acquisition. We tested the proposed strategy on a sequence of 29 earthquakes of the 2016-2017 central Italy seismic sequence acquired by the RAN (Rete Accelerometrica Nazionale) network with a magnitude range of 4.2-6.5. For the testing phase, we also simulated non-optimal conditions in terms of source-to-receiver geometry. Specifically, we tested the method  by ssimulating the case of “offshore” earthquakes recorded by a coastal network and in the case of a linear “barrier-type” geometry of the network. Our approach turned out to be suitable to work in condition of a sparse network, with a limited number of nodes and poor azimuthal coverage. In most of the cases, reliable location errors, less than 10 km, are achieved within few seconds from the first recorded P wave. As compared to other classical location techniques (i.e RTLOC in PRESTo) our approach shows an improvement of the solutions, especially for the first instants (2 seconds after the first P-wave arrival at network) when a poor number of stations (less than 4) is available.</p>


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