scholarly journals Rupture process of the 2010 Mw 7.8 Mentawai tsunami earthquake from joint inversion of near-field hr-GPS and teleseismic body wave recordings constrained by tsunami observations

2014 ◽  
Vol 119 (7) ◽  
pp. 5574-5593 ◽  
Author(s):  
Han Yue ◽  
Thorne Lay ◽  
Luis Rivera ◽  
Yefei Bai ◽  
Yoshiki Yamazaki ◽  
...  
Keyword(s):  
Joint Inversion ◽  
Body Wave ◽  
Near Field ◽  
Rupture Process ◽  
Tsunami Observations ◽  
Tsunami Earthquake

scholarly journals The waveform inversion of mainshock and aftershock data of the 2006 M6.3 Yogyakarta earthquake

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Hijrah Saputra ◽  
Wahyudi Wahyudi ◽  
Iman Suardi ◽  
Ade Anggraini ◽  
Wiwit Suryanto
Keyword(s):  
Joint Inversion ◽  
Body Wave ◽  
Moment Tensor ◽  
Near Field ◽  
Source Parameters ◽  
Waveform Inversion ◽  
Moment Tensor Inversion ◽  
Slip Distribution ◽  
Aftershock Distribution ◽  
Velocity Models

AbstractThis study comprehensively investigates the source mechanisms associated with the mainshock and aftershocks of the Mw = 6.3 Yogyakarta earthquake which occurred on May 27, 2006. The process involved using moment tensor inversion to determine the fault plane parameters and joint inversion which were further applied to understand the spatial and temporal slip distributions during the earthquake. Moreover, coseismal slip distribution was overlaid with the relocated aftershock distribution to determine the stress field variations around the tectonic area. Meanwhile, the moment tensor inversion made use of near-field data and its Green’s function was calculated using the extended reflectivity method while the joint inversion used near-field and teleseismic body wave data which were computed using the Kikuchi and Kanamori methods. These data were filtered through a trial-and-error method using a bandpass filter with frequency pairs and velocity models from several previous studies. Furthermore, the Akaike Bayesian Information Criterion (ABIC) method was applied to obtain more stable inversion results and different fault types were discovered. Strike–slip and dip-normal were recorded for the mainshock and similar types were recorded for the 8th aftershock while the 9th and 16th June were strike slips. However, the fault slip distribution from the joint inversion showed two asperities. The maximum slip was 0.78 m with the first asperity observed at 10 km south/north of the mainshock hypocenter. The source parameters discovered include total seismic moment M0 = 0.4311E + 19 (Nm) or Mw = 6.4 with a depth of 12 km and a duration of 28 s. The slip distribution overlaid with the aftershock distribution showed the tendency of the aftershock to occur around the asperities zone while a normal oblique focus mechanism was found using the joint inversion.

scholarly journals Rupture process of the April 24, 2017, Mw 6.9 Valparaíso earthquake from the joint inversion of teleseismic body waves and near-field data

2018 ◽  
Vol 279 ◽  
pp. 1-14 ◽  
Author(s):  
Javier A. Ruiz ◽  
Eduardo Contreras-Reyes ◽  
Francisco Ortega-Culaciati ◽  
Paula Manríquez
Keyword(s):  
Field Data ◽  
Joint Inversion ◽  
Near Field ◽  
Rupture Process ◽  
Body Waves

scholarly journals Joint inversion of teleseismic, geodetic, and near-field waveform datasets for rupture process of the 2015 Gorkha, Nepal, earthquake

2016 ◽  
Vol 68 (1) ◽  
Author(s):  
Hiroaki Kobayashi ◽  
Kazuki Koketsu ◽  
Hiroe Miyake ◽  
Nobuo Takai ◽  
Michiko Shigefuji ◽  
...  
Keyword(s):  
Joint Inversion ◽  
Near Field ◽  
Rupture Process ◽  
Nepal Earthquake ◽  
2015 Gorkha Nepal Earthquake

scholarly journals The waveform inversion of mainshock and aftershock data of the 2006 M6.3 Yogyakarta earthquake

2021 ◽  
Author(s):  
Hijrah Saputra ◽  
Wahyudi Wahyudi ◽  
Iman Suardi ◽  
Ade Anggraini ◽  
Wiwit Suryanto
Keyword(s):  
Joint Inversion ◽  
Body Wave ◽  
Moment Tensor ◽  
Near Field ◽  
Source Parameters ◽  
Moment Tensor Inversion ◽  
Slip Distribution ◽  
Coseismic Slip ◽  
Aftershock Distribution ◽  
Velocity Models

Abstract This study comprehensively investigates the source mechanisms associated with the mainshock and aftershocks of the Yogyakarta earthquake of magnitude Mw = 6.3 on May 27, 2006. Therefore, this study is to provide a more precise answer to the controversial source mechanism. This study uses moment tensor inversion to obtain fault plane parameters and joint inversion to obtain spatial and temporal slip distributions during an earthquake. The coseismic slip distribution is overlaid with the relocated aftershock distribution to see the stress field variations around the tectonic area of the study. Moment tensor inversion uses near-field data, and joint inversion uses near-field and teleseismic body wave data. The data is filtered by trial and error using a bandpass filter with frequency pairs and velocity models from several previous studies. The green's function for moment tensor inversion calculated using the extended reflectivity method and joint inversion computed using the Kikuchi and Kanamori methods. In this study, we apply the Akaike Bayesian Information Criterion (ABIC) method to obtain more stable inversion results. The results of the mainshock and aftershock moment tensor inversion show different fault types. The mainshock fault types are strike-slip and dip-normal types, while the 8th aftershock is of the same type as the mainshock, while the 9th and 16th June are strike slips. The joint inversion results show two asperities. The maximum slip is 0.78 m, with the first asperity 10 km south of the mainshock and the second asperity 10 km north of the mainshock. The obtained source parameters are total seismic moment M0 = 0.4311E + 19 (Nm) or Mw = 6.4, with a source depth of 12 km and a source duration of 28 seconds. Slip distribution overlay with aftershock distribution shows compatibility. The type of focus mechanism that results from this joint inversion is the oblique.

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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