Langping Zhang

Multi-Methods Combined Analysis of Future Earthquake Potential

Prior to an earthquake, natural seismicity is correlated across multiple spatial and temporal scales. Many studies have indicated that an earthquake is hard to accurately predict by a single time-dependent precursory method. In this study, we attempt to combine four earthquake prediction methods, i.e. the Pattern Informatics (PI), Load/Unload Response Ratio (LURR), State Vector (SV), and Accelerating Moment Release (AMR) to estimate future earthquake potential. The PI technique is founded on the premise that the change in the seismicity rate is a proxy for the change in the underlying stress. We first use the PI method to quantify localized changes surrounding the epicenters of large earthquakes to objectively quantify the anomalous areas (hot spots) of the upcoming events. Next, we delineate the seismic hazard regions by integrating with regional active fault zones and small earthquake activities. Then, we further evaluate the earthquake potential in the seismic hazard regions using the LURR, SV and AMR methods. Retrospective tests of this new approach on the large earthquakes (Mxa0>xa06.5) which have occurred in western China over the last 3xa0years show that the LURR and SV time series usually climb to an anomalously high peak months to years prior to occurrence of a large earthquake. And, the asymptote time, tc, “predicted” by the AMR method correspond to the time of the actual events. The results may suggest that the multi-methods combined approach can be a useful tool to provide stronger constraints on forecasts of the time and location of future large events.

Analysis of the Far-Field Co-seismic and Post-seismic Responses Caused by the 2011 MW 9.0 Tohoku-Oki Earthquake

AbstractnWe analyzed the far-field co-seismic response of the MW 9.0 Tohoku-Oki earthquake, which occurred on March 11th 2011 at the Japan Trench plate boundary. Our analysis indicates that the far-field co-seismic displacement was very sensitive to the magnitude of this event, and that a significant co-seismic surface displacement from earthquakes in the Japan Trench region can be observed in Eurasia only for events of MWxa0≥xa08.0. We also analyzed the temporal characteristics of the near-field post-seismic deformation caused by the afterslip and the viscoelastic relaxation following the Japan earthquake. Next, we performed a simulation to analyze the influence of the two post-seismic effects previously mentioned on the far-field post-seismic crustal deformation. The simulation results help explain the post-seismic crustal deformation observed on the Chinese mainland 1.5xa0years after the event. Fitting results revealed that after the MW 9.0 Tohoku-Oki earthquake, the afterslip decayed exponentially, and may eventually disappear after 4xa0years. The far-field post-seismic displacement in Eurasia caused by the viscoelastic relaxation following this earthquake will reach the same magnitude as the co-seismic displacement in approximately 10xa0years. In addition, the co- and post-seismic Coulomb stress on several NE-trending faults in the northeastern and northern regions of the Chinese mainland were significantly enhanced because of the MW 9.0 earthquake, especially on the Yilan-Yitong and the Dunhua-Mishan faults (the northern section of the Tan-Lu fault zone) as well as the Yalujiang and the Fuyu-Zhaodong faults.

Failure Potential Evaluation in Engineering Experiments Using Load/Unload Response Ratio Method

The Load/Unload Response Ratio (LURR) method is proposed for prediction of the failure of brittle heterogeneous materials. Application of the method typically involves evaluating the external load on materials or structures, differentiating between loading and unloading periods, determining the failure response during both periods from data input, and calculating the ratio between the two response rates. According to the method, the LURR time series usually climbs to an anomalously high peak prior to the macro-fracture. To show the validity of the approach in engineering practice, we applied it to the loading and unloading experimental data associated with a two-floor concrete-brick structure. Results show that the LURR time series of the two floors consists of the damage evolution of the structure: they are at low level for most of the time, and reach the maxima prior to the final fracture. We then attempt to combine the LURR values with damage variable (D) to provide the health assessment of the structure. The relationship between LURR and D, defined as a function of Weibull stochastic distribution, is set up to provide more detailed underlying physical means to study damage evolution of the structure. The fact that the damage evolution of the structure correlates well with the variation of LURR time series may suggest that the LURR approach can be severed as a useful tool to provide the health assessment to big scale structures or ancient buildings.

New Progress in LURR-Integrating with the Dimensional Method

The evolution laws of LURR (Loading–Unloading Response Ratio) before strong earthquakes, especially the peak point of LURR, are described in this paper. The results of four methods (experimental, numerical simulation, seismic data analysis and with damage mechanics analysis) lead to a consistent conclusion—the evolution laws of LURR before strong earthquakes are that, at the early stage of the seismic cycle, LURR will fluctuate around 1 and in the late stage, it rises swiftly and to its peak point. At some time after this peak point, a catastrophic event or events occur. These do not occur at the peak point, but lag behind. The lag time which is denoted by T2 depends on the magnitude M of the upcoming earthquake among other factors. In order to consider the influence of geophysical parameters in a specific region such as

LURR's Twenty Years and its Perspective

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Coulomb stress evolution over the past 200 years and seismic hazard along the Xianshuihe fault zone of Sichuan, China

Ea and J(t), where

LURR and the San Simeon M 6.5 Earthquake in 2003 and the Seismic Tendency in CA

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