3D S-wave velocity imaging of a subsurface disturbed by mining using ambient seismic noise

2019 ◽  
Vol 251 ◽  
pp. 115-127 ◽  
Author(s):  
Rafał Czarny ◽  
Zenon Pilecki ◽  
Nori Nakata ◽  
Elżbieta Pilecka ◽  
Krzysztof Krawiec ◽  
...  
2017 ◽  
Author(s):  
Soumen Koley ◽  
Henk Jan Bulten ◽  
Jo van den Brand ◽  
Maria Bader ◽  
Xander Campman ◽  
...  

2017 ◽  
Vol 53 (3) ◽  
pp. 341-352 ◽  
Author(s):  
S. Ya. Droznina ◽  
N. M. Shapiro ◽  
D. V. Droznin ◽  
S. L. Senyukov ◽  
V. N. Chebrov ◽  
...  

2013 ◽  
Vol 75 ◽  
pp. 26-35 ◽  
Author(s):  
José Badal ◽  
Yun Chen ◽  
Mimoun Chourak ◽  
Jacek Stankiewicz

2014 ◽  
Vol 81 ◽  
pp. 38-52 ◽  
Author(s):  
Yu-Chih Huang ◽  
Huajian Yao ◽  
Francis T. Wu ◽  
Wen-Tzong Liang ◽  
Bor-Shouh Huang ◽  
...  

2019 ◽  
Vol 124 (2) ◽  
pp. 1601-1625 ◽  
Author(s):  
Paul M. Bremner ◽  
Mark P. Panning ◽  
R. M. Russo ◽  
Victor Mocanu ◽  
A. Christian Stanciu ◽  
...  

2021 ◽  
Author(s):  
◽  
Rachel Heckels

<p>Ambient seismic noise is used to examine the spatial and temporal surface wave velocity structures and ambient seismic noise fields in the vicinity of different fault zone environments. This study focuses on two distinct regions of central South Island, New Zealand. The Canterbury Plains is a sedimentary basin with many minor faults, which was considered to have low seismic hazard prior to the 2010 – 2011 Canterbury earthquake sequence. We focus on the time period immediately following the 2010 Darfield earthquake, which ruptured the previously unmapped Greendale Fault. The second region of interest is the central Southern Alps. The locked portion of the Alpine Fault currently poses one of the largest seismic hazards for New Zealand. The wealth of data from both permanent and temporary seismic deployments in these regions make them ideal areas in which to assess the effectiveness of ambient noise for velocity modelling in regions surrounding faults at different stages of their seismic cycles.  Temporal velocity changes are measured following the Mw 7.1 Darfield earthquake of 4 September 2010 in the Canterbury Plains. Nine-component cross-correlations are computed from temporary and permanent seismic stations lying on and surrounding the Greendale Fault. Using the Moving-Window Cross-Spectral method, surface wave velocity changes are calculated for the four months immediately following the earthquake until 10 January 2011, for 0.1 — 1.0 Hz. An average increase in seismic velocity of 0.14 ± 0.04 % is determined throughout the region, providing the first such estimate of postseismic relaxation rates in Canterbury. Depth analyses further showed that velocity changes are confined to the uppermost 5 km of the subsurface and we attribute this to postseismic relaxation via crack-healing of the Greendale Fault and throughout the surrounding region.  Rayleigh and Love wave dispersion is examined throughout the Canterbury region. Multi-component cross-correlation functions are analysed for group and phase dispersion curves. These are inverted using frequency-time analysis for 2-D phase and group velocity maps of Rayleigh and Love waves. A high-velocity zone to the southeast of the region coincides with volcanic rocks of Banks Peninsula. Dispersion curves generated from the surface wave tomography are further inverted for one-dimensional shear velocity profiles. These models show a thin, low-velocity near surface layer consistent with the basin sediments, which thins towards the foothills of the Southern Alps. A near-surface damage zone is identified along the length of the Greendale Fault, with consistent reduced Vs velocities to depth of up to 5 km.  Surface and shear wave velocity maps are computed for the central Southern Alps to image the seismic structure of the region. Tomographic surface maps at periods of 5 – 12 s are produced from dispersion measurements of three-component cross-correlation functions. At periods of 5 – 8 s a strong NE-SW trending velocity contrast highlights the Alpine Fault. One-dimensional shear velocity models, computed from the surface wave maps, are in agreement with previous models produced by other conventional methods. An analysis of surface wave amplitudes through signal-to-noise ratios of cross-correlations reveals strong directional effects. Calculated signal-to-noise ratios are up to eight times higher for surface waves travelling north-west than for waves travelling to the south or east. We attribute this to a combination of more energetic ocean wave signals from the Southern Ocean compared to the Tasman Sea.</p>


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