Automatic imaging of earthquake rupture processes by iterative deconvolution and stacking of high‐rate GPS and strong motion seismograms. Issue 7 (10th July 2014)

• Record Type: Journal Article
• Authors: Zhang, Yong; Wang, Rongjiang; Zschau, Jochen; Chen, Yun‐tai; Parolai, Stefano; Dahm, Torsten
• Is Part Of: Journal of geophysical research. Volume 119:Issue 7(2014:Jul.)
• Page Start: 5633
• Page End: 5650
• Publication Date: 2014-07-10
• Subjects: Geomagnetism -- Periodicals; Geochemistry -- Periodicals; Geophysics -- Periodicals; Earth sciences -- Periodicals; 551.1
• DOI: 10.1002/2013JB010469
• Languages: English
• ISSNs: 2169-9313

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>By combining the complementary advantages of conventional network inversion and backprojection methods, we have developed an iterative deconvolution and stacking (IDS) approach for imaging earthquake rupture processes with near‐field complete waveform data. This new approach does not need any manual adjustment of the physical (empirical) constraints, such as restricting the rupture time and duration, and smoothing the spatiotemporal slip distribution. Therefore, it has the ability to image complex multiple ruptures automatically. The advantages of the IDS method over traditional linear or nonlinear optimization algorithms are demonstrated by the case studies of the 2008 Wenchuan and 2011 Tohoku earthquakes. For such large earthquakes, the IDS method is considerably more stable and efficient than previous inversion methods. Additionally, the robustness of this method is demonstrated by comprehensive synthetic tests, indicating its potential contribution to tsunami and earthquake early warning and rapid response systems. It is also shown that the IDS method can be used for teleseismic waveform inversions. For the two major earthquakes discussed here, the IDS method can provide, without tuning any physical or empirical constraints, teleseismic rupture models consistent with those derived from the near‐field GPS and strong motion data.</p> </abstract>