the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Three-dimensional deformation field analysis of the 2016 Kumamoto Mw 7.1 earthquake
Qingyun Zhang
Jingfa Zhang
Yongsheng Li
Bingqun Li
Quancai Xie
Sanming Luo
Qingzun Ma
Abstract. The Kumamoto earthquake is analyzed mainly with InSAR data combined with strong earthquake and GPS data, using a variety of joint InSAR technology methods and multisource data solution methods and comprehensively considering the normalization and weighting of multisource data. The three-dimensional (3D) deformation field is determined. The results show that the joint solution of multisource data can improve the accuracy of the 3D solution deformation results to a certain extent. From the 3D solution results, the maximum east-west deformation caused by the 2016 Kumamoto earthquake is approximately 2 m; the north-south direction mainly manifests expansion and stretching; the northwestern side subsides vertically, with a maximum subsidence of 2 m; and the southeastern side is uplifted. The horizontal deformation characteristics show that the earthquake is dominated by right-lateral strike-slip; the strike is NE-SW, the dip of the seismogenic fault is nearly vertical, and the Futagawa fault has a few normal fault properties. By analyzing the coseismic 3D deformation field, the seismogenic fault can be better understood, which provides a foundation for studying seismic mechanisms.
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Qingyun Zhang et al.
Interactive discussion
Status: closed
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CC1: 'Comment on nhess-2020-408', Sylvain Barbot, 16 Jan 2021
The paper "Three-dimensional deformation field analysis of the 2016 Kumamoto Mw 7.1 earthquake" by Zhang et al. provides a technical, straightforward methodology to combine either multiple InSAR data or heterogeneous InSAR and geodetic datasets to build a 3-component displacement map for earthquakes. The technique is well known, being used for almost two decades. The paper is technically correct, but its novelty is questionable.
Line 55, azimuthal InSAR is also described in
Barbot, S., Hamiel, Y. and Fialko, Y., 2008. Space geodetic investigation of the coseismic and postseismic deformation due to the 2003 Mw7. 2 Altai earthquake: Implications for the local lithospheric rheology. Journal of Geophysical Research: Solid Earth, 113(B3).
Line 356: I do not see a justification for the vertical fault. Modeling of the deformation indicates north-dipping faults. See
Moore, J.D., Yu, H., Tang, C.H., Wang, T., Barbot, S., Peng, D., Masuti, S., Dauwels, J., Hsu, Y.J., Lambert, V. and Nanjundiah, P., 2017. Imaging the distribution of transient viscosity after the 2016 Mw 7.1 Kumamoto earthquake. Science, 356(6334), pp.163-167.
Citation: https://doi.org/10.5194/nhess-2020-408-CC1 -
CC2: 'Reply on CC1', zhang qingyun, 13 Feb 2021
We appreciate the thorough review of our manuscript and the constructive feedback provided by Sylvain Barbot. In the Supplement linked below, we address each comment and reference the changes in the revised manuscript.
Please also note the supplement to this comment:
- AC1: 'Reply on CC1', zhang qingyun, 15 Feb 2021
-
CC2: 'Reply on CC1', zhang qingyun, 13 Feb 2021
-
RC1: 'Comment on nhess-2020-408', Yunmeng Cao, 24 Feb 2021
The authors used regular InSAR and MAI results from ALOS-2, GPS, and the strong earthquake data to estimate the 3D coseismic deformation field of the 2016 Kumamoto earthquake, and compared with using InSAR results (InSAR + MAI) only, the authors found they can improve the 3D displacements field by fusing external GPS and strong earthquake data, which will be helpful for studying the source models of the earthquake. Technically, the presented methodology has been widely used in the previous researches, so the authors need to weaken their words about the description of âinnovationâ. Several other comments: (1) About equation 7, the authors should clarify how they calculate the weights. Only by modeling the decorrelation noise based on the coherence? But decorrelation noise is not the major error source of InSAR in many cases. (2) About equation 11, did the authors want to fit the variance? To my understand, the obit errors can be modeled in space, but not for their variances. (3) About equation (12), how to estimate the variance of MAI observations? Please clarify it. (4) Line 350-355: âThe study finds that the quality of the 3D deformation field obtained after adding GPS and strong earthquake data constraints is significantly improvedâ. It would be good to present solid evidence about the âsignificant improvementâ.
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EC1: 'Comment on nhess-2020-408', Mahdi Motagh, 26 Feb 2021
The paper has already been reviewed by two experts . Both find that the paper is technically correct, but question its novelty. Therefore, we cannot proceed with the publication of the article.
Citation: https://doi.org/10.5194/nhess-2020-408-EC1
Interactive discussion
Status: closed
-
CC1: 'Comment on nhess-2020-408', Sylvain Barbot, 16 Jan 2021
The paper "Three-dimensional deformation field analysis of the 2016 Kumamoto Mw 7.1 earthquake" by Zhang et al. provides a technical, straightforward methodology to combine either multiple InSAR data or heterogeneous InSAR and geodetic datasets to build a 3-component displacement map for earthquakes. The technique is well known, being used for almost two decades. The paper is technically correct, but its novelty is questionable.
Line 55, azimuthal InSAR is also described in
Barbot, S., Hamiel, Y. and Fialko, Y., 2008. Space geodetic investigation of the coseismic and postseismic deformation due to the 2003 Mw7. 2 Altai earthquake: Implications for the local lithospheric rheology. Journal of Geophysical Research: Solid Earth, 113(B3).
Line 356: I do not see a justification for the vertical fault. Modeling of the deformation indicates north-dipping faults. See
Moore, J.D., Yu, H., Tang, C.H., Wang, T., Barbot, S., Peng, D., Masuti, S., Dauwels, J., Hsu, Y.J., Lambert, V. and Nanjundiah, P., 2017. Imaging the distribution of transient viscosity after the 2016 Mw 7.1 Kumamoto earthquake. Science, 356(6334), pp.163-167.
Citation: https://doi.org/10.5194/nhess-2020-408-CC1 -
CC2: 'Reply on CC1', zhang qingyun, 13 Feb 2021
We appreciate the thorough review of our manuscript and the constructive feedback provided by Sylvain Barbot. In the Supplement linked below, we address each comment and reference the changes in the revised manuscript.
Please also note the supplement to this comment:
- AC1: 'Reply on CC1', zhang qingyun, 15 Feb 2021
-
CC2: 'Reply on CC1', zhang qingyun, 13 Feb 2021
-
RC1: 'Comment on nhess-2020-408', Yunmeng Cao, 24 Feb 2021
The authors used regular InSAR and MAI results from ALOS-2, GPS, and the strong earthquake data to estimate the 3D coseismic deformation field of the 2016 Kumamoto earthquake, and compared with using InSAR results (InSAR + MAI) only, the authors found they can improve the 3D displacements field by fusing external GPS and strong earthquake data, which will be helpful for studying the source models of the earthquake. Technically, the presented methodology has been widely used in the previous researches, so the authors need to weaken their words about the description of âinnovationâ. Several other comments: (1) About equation 7, the authors should clarify how they calculate the weights. Only by modeling the decorrelation noise based on the coherence? But decorrelation noise is not the major error source of InSAR in many cases. (2) About equation 11, did the authors want to fit the variance? To my understand, the obit errors can be modeled in space, but not for their variances. (3) About equation (12), how to estimate the variance of MAI observations? Please clarify it. (4) Line 350-355: âThe study finds that the quality of the 3D deformation field obtained after adding GPS and strong earthquake data constraints is significantly improvedâ. It would be good to present solid evidence about the âsignificant improvementâ.
-
EC1: 'Comment on nhess-2020-408', Mahdi Motagh, 26 Feb 2021
The paper has already been reviewed by two experts . Both find that the paper is technically correct, but question its novelty. Therefore, we cannot proceed with the publication of the article.
Citation: https://doi.org/10.5194/nhess-2020-408-EC1
Qingyun Zhang et al.
Qingyun Zhang et al.
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