scholarly journals Shear Wave Splitting Analysis Beneath Sumatra For-Arc Inferred from Broadband Seismic Network Station

2021 ◽  
Vol 1 (1) ◽  
pp. 11-16
Author(s):  
Lewi Ristiyono ◽  
Chichi Nurhafizah ◽  
Suhenri Purba ◽  
Marhaposan Situmorang

The observation of broadband network seismic had been deployed in Sumatra For-Arc. The waveform data for this study were recorded from January 2014 – December 2016. The earthquake event data were selected with the epicenter of around 950 – 1800 in distance and Magnitude with more than 7 Mw. In this case, we use shear wave splitting to determine an anisotropic structure in Sumatra For-arc. Seismic Anisotropy can perform as a tool to classify and observe anisotropic structures of subsurface deformation processes beneath Sumatra For-Arc. The valid outcomes, in this case, have been gained that they only correspond to the upper layer, which has the delay time duration of 0.5 – 0.8 s is the anisotropic layer located in the Mentawai Island. The fast an anisotropic polarization direction found in Sumatra For-arc are parted into NE-SW direction found on the upper layer.

2020 ◽  
Author(s):  
Götz Bokelmann ◽  
Gerrit Hein

<p>Seismic anisotropy is an important tool for studying geodynamic processes in the Earth, and a common way of constraining it is to analyse shear-wave splitting of seismological body-wave phases,<br>i.p. SKS. Different techniques exist to quantify shear-wave splitting, but they do not always give the same result, raising the question of how stable they are, and whether there are systematic biases. Furthermore, the strength of the splitting ("splitting delay") has generally been more difficult to determine than the other (the "fast orientation").<br>A robust technique for determining shear-wave splitting can be set up<br>based on the splitting intensity method. That technique can in particular also constrain the splitting delay well. Ambient noise can however lead to an underestimation of splitting delay, and it needs to be accounted for, e.g. by a least-squares Wiener filter.<br>We apply that modified splitting intensity method to data from the AlpArray. We have processed 3 years of teleseismic earthquake data for 336 stations of the AlpArray deployment and additional 315 stations of the Italian network to get a potentially broad and more complete image of anisotropic structures in and outside the Alpine region.<br>The technique makes restrictive assumptions, e.g. assuming single-layer anisotropy. Yet, the new constraints, especially the one of the splitting delay are rather useful for understanding the deformation under the mountain belt and around it.</p><p> </p>


2021 ◽  
Vol 873 (1) ◽  
pp. 012101
Author(s):  
Annisa Trisnia Sasmi ◽  
Andri Dian Nugraha ◽  
Muzli Muzli ◽  
Sri Widiyantoro ◽  
Zulfakriza Zulfakriza ◽  
...  

Abstract Shear-wave splitting (SWS), or the propagation of two independent shear waves, can be used as an indicator of seismic anisotropy. In this study, we utilize this concept using aftershock data of the 2018 Lombok earthquake which had been acquired in period of August 4 – September 9, 2018. The goal of this research is to better understand the crack distribution related to the rupture zone of the 2018 Lombok earthquake. After applying instrument correction to the data, the waveform data were then windowed in each P and S arrival time. To determine the SWS parameters, we performed rotation in each horizontal seismogram components. The horizontal components were rotated from azimuth 0° to 180° with an increment of 1°. Cross-correlation coefficient (CCC) was determined for each rotation angle. The polarization direction and the SWS delay time were chosen from the parameters shown in the highest value of CCC.


Author(s):  
Enbo Fan ◽  
Yumei He ◽  
Yinshuang Ai ◽  
Stephen S. Gao ◽  
Kelly H. Liu ◽  
...  

2018 ◽  
Vol 216 (1) ◽  
pp. 535-544 ◽  
Author(s):  
Changhui Ju ◽  
Junmeng Zhao ◽  
Ning Huang ◽  
Qiang Xu ◽  
Hongbing Liu

2021 ◽  
Author(s):  
◽  
Kenny Graham

<p>This thesis involves the study of crustal seismic anisotropy through shear wave splitting. For the past three decades, shear wave splitting (SWS) measurements from crustal earthquakes have been utilized as a technique to characterize seismic anisotropic structures and to infer in situ crustal properties such as the state of the stress and fracture geometry and density. However, the potential of this technique is yet to be realized in part because measurements on local earthquakes are often controlled and/or affected by physical mechanisms and processes which lead to variations in measurements and make interpretation difficult. Many studies have suggested a variety of physical mechanisms that control and/or affect SWS measurements, but few studies have quantitatively tested these suggestions. This thesis seeks to fill this gap by investigating what controls crustal shear-wave splitting (SWS) measurements using empirical and numerical simulation approaches, with the ultimate aim of improving SWS interpretation. For our empirical approach, we used two case studies to investigate what physical processes control seismic anisotropy in the crust at different scales and tectonic settings. In the numerical simulation test, we simulate the propagation of seismic waves in a variety of scenarios.  We begin by measuring crustal anisotropy via SWS analysis around central New Zealand, where clusters of closely-spaced earthquakes have occurred. We used over 40,000 crustal earthquakes across 36 stations spanning close to 5.5 years between 2013 and 2018 to generate the largest catalog of high-quality SWS measurements (~102,000) around the Marlborough and Wellington region. The size of our SWS catalog allowed us to perform a detailed systematic analysis to investigate the processes that control crustal anisotropy and we also investigated the spatial and temporal variation of the anisotropic structure around the region. We observed a significant spatial variation of SWS measurements in Central New Zealand. We found that the crustal anisotropy around Central New Zealand is confined to the upper few kilometers of the crust, and is controlled by either one mechanism or a combination of more than one (such as structural, tectonic stresses, and gravitational stresses). The high correspondence between the orientation of the maximum horizontal compressive stress calculated from gravitational potential energy from topography and average fast polarization orientation around the Kaikōura region suggests that gravitationally induced stresses control the crustal anisotropy in the Kaikōura region. We suggest that examining the effect of gravitational stresses on crustal seismic anisotropy should not be neglected in future studies. We also observed no significant temporal changes in the state of anisotropy over the 5.5 year period despite the occurrence of significant seismicity.   For the second empirical study, we characterized the anisotropic structure of a fault approaching failure (the Alpine Fault of New Zealand). We performed detailed SWS analysis on local earthquakes that were recorded on a dense array of 159 three-component seismometers with inter-station spacing about 30 m around the Whataroa Valley, New Zealand. The SWS analysis of data from this dense deployment enabled us to map the spatial characteristics of the anisotropic structure and also to investigate the mechanisms that control anisotropy in the Whataroa valley in the vicinity of the Alpine Fault. We observed that the orientation of the fast direction is parallel to the strike of the Alpine Fault trace and the orientations of the regional and borehole foliation planes. We also observed that there was no significant spatial variation of the anisotropic structure as we move across the Alpine Fault trace from the hanging wall to the footwall. We inferred that the geological structures, such as the Alpine Fault fabric and foliations within the valley, are the main mechanisms that control the anisotropic structure in the Whataroa valley.    For our numerical simulation approach, we simulate waveforms propagating through an anisotropic media (using both 1-D and 3-D techniques). We simulate a variety of scenarios, to investigate how some of the suggested physical mechanisms affect SWS measurements. We considered (1) the effect on seismic waves caused by scatterers along the waves' propagation path, (2) the effect of the earthquake source mechanism, (3) the effect of incidence angle of the incoming shear wave. We observed that some of these mechanisms (such as the incidence angle of the incoming shear wave and scatterers) significantly affect SWS measurement while others such as earthquake source mechanisms have less effect on SWS measurements. We also observed that the effect of most of these physical mechanisms depends on the wavelength of the propagating shear wave relative to the size of the features. There is a significant effect on SWS measurements if the size of the physical mechanism (such as scatterers) is comparable to the wavelength of the incoming shear wave. With a larger wavelength, the wave treats the feature as a homogeneous medium.</p>


2014 ◽  
Vol 119 (6) ◽  
pp. 4923-4937 ◽  
Author(s):  
Garrett Ito ◽  
Robert Dunn ◽  
Aibing Li ◽  
Cecily J. Wolfe ◽  
Alejandro Gallego ◽  
...  

Author(s):  
Cristo Ramirez ◽  
Andrew Nyblade ◽  
Michael E Wysession ◽  
Martin Pratt ◽  
Fenitra Andriampenomanana ◽  
...  

2015 ◽  
Vol 120 (12) ◽  
pp. 8404-8421 ◽  
Author(s):  
F. A. Darbyshire ◽  
I. D. Bastow ◽  
A. M. Forte ◽  
T. E. Hobbs ◽  
A. Calvel ◽  
...  

2011 ◽  
Vol 304 (1-2) ◽  
pp. 147-157 ◽  
Author(s):  
Yonghua Li ◽  
Qingju Wu ◽  
Fengxue Zhang ◽  
Qiangqiang Feng ◽  
Ruiqing Zhang

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