scholarly journals Estimates of Source Parameters of Two Large Aftershocks of the 1999 Chi-Chi, Taiwan, Earthquake in the Chia-Yi Area

2002 ◽  
Vol 13 (3) ◽  
pp. 299 ◽  
Author(s):  
Ming-Wey Huang ◽  
Jeen-Hwa Wang ◽  
Ruey-Der Hwang ◽  
Kou-Cheng Chen
Keyword(s):  
Source Parameters ◽  
Taiwan Earthquake

scholarly journals Re-Examining Source Parameters of the 2012 Wutai, Taiwan Earthquake

2013 ◽  
Vol 24 (5) ◽  
pp. 827 ◽  
Author(s):  
Chi-Hsuan Chen ◽  
Hsin-Hua Huang ◽  
Wei-An Chao ◽  
Yih-Min Wu ◽  
Chien-Hsin Chang
Keyword(s):  
Source Parameters ◽  
Taiwan Earthquake

Reexamining the source parameters of the 2010 ML 6.4 JiaSian (Taiwan) earthquake using the inversion of teleseismic P-waves

2012 ◽  
Vol 48 ◽  
pp. 24-30 ◽  
Author(s):  
Ruey-Der Hwang ◽  
Tzu-Wei Lin ◽  
Chia-Chang Wu ◽  
Wen-Yen Chang ◽  
Jo-Pan Chang
Keyword(s):  
Source Parameters ◽  
P Waves ◽  
Taiwan Earthquake

scholarly journals Heterogeneous distribution of the dynamic source parameters of the 1999 Chi-Chi, Taiwan, earthquake

2003 ◽  
Vol 108 (B5) ◽  
Author(s):  
Wenbo Zhang ◽  
Tomotaka Iwata ◽  
Kojiro Irikura ◽  
Haruko Sekiguchi ◽  
Michel Bouchon
Keyword(s):  
Source Parameters ◽  
Heterogeneous Distribution ◽  
Dynamic Source ◽  
Taiwan Earthquake

Integrated Search for Taiwan Earthquake Precursors (iSTEP)

2016 ◽  
Vol 136 (5) ◽  
pp. 214-220 ◽  
Author(s):  
Jann-Yenq Liu ◽  
Yi-Ben Tsai ◽  
Chieh-Hung Chen ◽  
Yuh-Ing Chen ◽  
Horng-Yuan Yen
Keyword(s):  
Earthquake Precursors ◽  
Taiwan Earthquake

DEVELOPMENT OF A GEOLOGICALLY-INFORMED FAULT SOURCE PARAMETERS DATABASE AND UPDATES OF THE FAULT SOURCE MODEL FOR THE U.S. NATIONAL SEISMIC HAZARD MODEL

2020 ◽  
Author(s):  
Alexandra E. Hatem ◽  
◽  
Ryan D. Gold ◽  
Richard W. Briggs ◽  
Edward H. Field ◽  
...  
Keyword(s):  
Seismic Hazard ◽  
Source Parameters ◽  
Hazard Model ◽  
Source Model ◽  
Fault Source ◽  
The U.S

scholarly journals Imaging the subsurface using induced seismicity and ambient noise: 3-D tomographic Monte Carlo joint inversion of earthquake body wave traveltimes and surface wave dispersion

2020 ◽  
Vol 222 (3) ◽  
pp. 1639-1655
Author(s):  
Xin Zhang ◽  
Corinna Roy ◽  
Andrew Curtis ◽  
Andy Nowacki ◽  
Brian Baptie
Keyword(s):  
Surface Wave ◽  
Ambient Noise ◽  
Velocity Structure ◽  
Joint Inversion ◽  
Body Wave ◽  
Source Parameters ◽  
Wave Dispersion ◽  
Inversion Method ◽  
Surface Wave Dispersion ◽  
Wave Data

SUMMARY Seismic body wave traveltime tomography and surface wave dispersion tomography have been used widely to characterize earthquakes and to study the subsurface structure of the Earth. Since these types of problem are often significantly non-linear and have non-unique solutions, Markov chain Monte Carlo methods have been used to find probabilistic solutions. Body and surface wave data are usually inverted separately to produce independent velocity models. However, body wave tomography is generally sensitive to structure around the subvolume in which earthquakes occur and produces limited resolution in the shallower Earth, whereas surface wave tomography is often sensitive to shallower structure. To better estimate subsurface properties, we therefore jointly invert for the seismic velocity structure and earthquake locations using body and surface wave data simultaneously. We apply the new joint inversion method to a mining site in the United Kingdom at which induced seismicity occurred and was recorded on a small local network of stations, and where ambient noise recordings are available from the same stations. The ambient noise is processed to obtain inter-receiver surface wave dispersion measurements which are inverted jointly with body wave arrival times from local earthquakes. The results show that by using both types of data, the earthquake source parameters and the velocity structure can be better constrained than in independent inversions. To further understand and interpret the results, we conduct synthetic tests to compare the results from body wave inversion and joint inversion. The results show that trade-offs between source parameters and velocities appear to bias results if only body wave data are used, but this issue is largely resolved by using the joint inversion method. Thus the use of ambient seismic noise and our fully non-linear inversion provides a valuable, improved method to image the subsurface velocity and seismicity.

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