The 2011 Mw 9.0 Tohoku Earthquake: Comparison of GPS and Strong-Motion Data

Wang, Rongjiang; Parolai, Stefano; Ge, Maorong; Mitsugi, Jin; Walter, Thomas R.; Zschau, Jochen

doi:10.1785/0120110264

Cited by 140 publications

(92 citation statements)

References 42 publications

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“…A constrained least squares method presented in Wang et al (2013) was used to invert for the slip models, in which an a-priori smoothing constraint was added to make the solution stable and equitable. Similar least squares method has been widely used for slip model inversion elsewhere (e.g., Freymueller et al, 1994;Reilinger et al, 2000).…”

Section: Data Handling and Modeling Methodsmentioning

confidence: 99%

The 2015 Gorkha earthquake investigated from radar satellites: slip and stress modeling along the MHT

et al. 2015

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Section: Data Handling and Modeling Methodsmentioning

confidence: 99%

The 2015 Gorkha earthquake investigated from radar satellites: slip and stress modeling along the MHT

et al. 2015

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“…Wang et al (2013) inverted the coseismic displacement calculated from GNSS and strong-motion stations to modulate the earthquake source of the 2011 M w 9.0 Tohoku earthquake. Earthquake source inversion methods have become important in the last years because of their potential for forecasting tsunamis (Melgar and Bock, 2013).…”

Section: Introductionmentioning

confidence: 99%

Calculation of coseismic displacement from lidar data in the 2016 Kumamoto, Japan, earthquake

Moya

Yamazaki

et al. 2017

Nat. Hazards Earth Syst. Sci.

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Abstract. The spatial distribution of the coseismic displacements that occurred along the Futagawa fault during the 2016 Kumamoto earthquake of M w 7.0 was estimated using airborne light detection and ranging (lidar) data. In this study, a pair of digital surface models (DSMs) obtained from the high-density lidar data before and after the mainshock on 16 April 2016 were used. A window matching search approach based on the correlation coefficient between the two DSMs was used to estimate the geodetic displacement in the near-field region. The results showed good agreements with the geodetic displacements calculated from strong-motion acceleration records and coincided with the fault line surveyed by the Geological Survey of Japan. IntroductionOn 14 April 2016, an M w 6.2 earthquake struck Kumamoto Prefecture, Japan, at 21:26 JST. The epicenter was located at the end of the Hinagu fault at a shallow depth. After approximately 28 h (at 01:25 on 16 April 2016), another earthquake of M w 7.0 struck the Futagawa fault, which is near the Hinagu fault. The first event was designated as the foreshock and the second one as the mainshock. Both the events occurred in the town of Mashiki (with a population of approximately 33 000), which is located to the east of Kumamoto City (with a population of approximately 735 000). Many aftershocks followed these events, and as of 6 September, 4 months after the foreshock, the total number of aftershocks (larger than M w 3.5) is 272. This number is the largest among the recent inland (crustal) earthquakes in Japan (Japan Meteorological Agency, 2016). This Kumamoto earthquake sequence triggered secondary effects such as landslides and liquefaction and caused extensive damage to lifeline systems, buildings, bridges, and transportation structures. A total of 8550 buildings, mostly in Kumamoto Prefecture, were seriously damaged or collapsed, and 50 human lives were lost, mostly because of landslides or the collapse of buildings (Cabinet Office of Japan, 2016).Soon after the occurrence of the foreshock, various satellites and airborne remote sensing technologies were employed to monitor crustal movements and various damages . The Japan Aerospace Exploration Agency (JAXA) carried out extensive monitoring of the source area using the PALSAR-2 sensor on board ALOS-2 satellite. Interferometric synthetic aperture radar (InSAR) analysis using a pair of imagery data obtained from PALSAR-2 before (pre-event data) and after (postevent data) the mainshock showed the line-of-sight (LOS) displacements to the satellite direction (Geospatial Information Authority of Japan, 2016). Using the pre-event data (30 November 2015, 7 March 2016 and the co-event data (7 March, 18 April 2016) from PALSAR-2, the authors of this paper calculated the spatial coherence values (International Charter, 2016), which could highlight the extensive landslides and severe damages to buildings along the Futagawa fault line.After the Kumamoto earthquake, government agencies and aerial survey companies in Japan conducted several ...

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“…No obvious multitrends (a few trends present in series constituting the shape of letters V or W) were found. In other techniques, such as GPS, the multitrends mostly originate from postseismic relaxation of the ground (Wang et al 2013;King and Santamaría-Gómez 2016) or may be caused by silent earthquakes or slow-slip events (Holtkamp and Brudzinski 2010;Tanaka and Yabe 2017). In the DORIS time series, we did not find any obvious post-relaxation curve, since no close (less than 30 km) earthquake was reported in the log files.…”

Section: Discussionmentioning

confidence: 64%

Stochastic models in the DORIS position time series: estimates for IDS contribution to ITRF2014

Kłos

Bogusz

Moreaux

2017

J Geod

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This paper focuses on the investigation of the deterministic and stochastic parts of the Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) weekly time series aligned to the newest release of ITRF2014. A set of 90 stations was divided into three groups depending on when the data were collected at an individual station. To reliably describe the DORIS time series, we employed a mathematical model that included the long-term nonlinear signal, linear trend, seasonal oscillations and a stochastic part, all being estimated with maximum likelihood estimation. We proved that the values of the parameters delivered for DORIS data are strictly correlated with the time span of the observations. The quality of the most recent data has significantly improved. Not only did the seasonal amplitudes decrease over the years, but also, and most importantly, the noise level and its type changed significantly. Among several tested models, the power-law process may be chosen as the preferred one for most of the DORIS data. Moreover, the preferred noise model has changed through the years from an autoregressive process to pure power-law noise with few stations characterised by a positive spectral index. For the latest observations, the medians of the velocity errors were equal to 0.3, 0.3 and 0.4 mm/year, respectively, for the North, East and Up components. In the best cases, a velocity uncertainty of DORIS sites of 0.1 mm/year is achievable when the appropriate coloured noise model is taken into consideration.

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The 2011 Mw 9.0 Tohoku Earthquake: Comparison of GPS and Strong-Motion Data

Cited by 140 publications

References 42 publications

The 2015 Gorkha earthquake investigated from radar satellites: slip and stress modeling along the MHT

The 2015 Gorkha earthquake investigated from radar satellites: slip and stress modeling along the MHT

Calculation of coseismic displacement from lidar data in the 2016 Kumamoto, Japan, earthquake

Stochastic models in the DORIS position time series: estimates for IDS contribution to ITRF2014

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