Inferring Critical Slip-Weakening Distance from Near-Fault Accelerogram of the 2014 Mw 6.2 Ludian Earthquake

To better assess potential earthquake hazards requires a better understanding of fault friction and rupture dynamics. Critical slip-weakening distance (Dc) as one of the key friction parameters, however, is hard to determine on natural faults. For strike-slip earthquakes, we may directly estimate the Dc from Dc″—the double near-fault ground displacement at the time of the peak velocity (Fukuyama and Mikumo, 2007). Yet near-fault observations are very few, and, thus, there were only limited earthquakes with such Dc″ estimation. In 2014, an Mw 6.2 strike-slip event—the Ludian earthquake—occurred in southwest China. The strong-motion station (LLT) that is ∼0.45 km from the fault recorded the earthquake and enabled us to estimate Dc″ from the accelerograms. We inspect the polarity of the accelerometers and compare the integrated velocities with waveforms of nearby broadband stations. We also analyze the particle motion at the LLT station and retrieve the earthquake initiation at the intersection of the conjugated faults. We then apply the baseline correction to the seismograms, recover the ground velocities and displacements, and obtain the value of Dc″=0.1 m at the station. The recovered final displacements are compared with the predicted ground displacements of a finite-fault model. The discrepancy of fault-parallel displacements might imply limited underestimation of Dc″, and the estimated upper limit is 0.3 m. Comparison between the Dc″ and final slip on the fault patch follows the scaling of previous larger earthquakes. Analysis of the near-fault accelerometer data enhances our understanding on the earthquake source of the Ludian earthquake. This case extends the lower magnitude boundary of the Dc″ values obtained from natural faults and opens a window into the friction property in the seismically active region.

Preliminary Report on the 18 May 2020 Ms 5.0 Qiaojia Earthquake, Yunnan, China

The 18 May 2020 Ms 5.0 Qiaojia earthquake occurred in Qiaojia County, Yunnan Province, ∼25 km away from the 3 August 2014 Ms 6.5 Ludian earthquake. This earthquake was well recorded by dense local seismic stations of the Qiaojia array constructed near the Xiaojiang fault zone. The focal mechanism of the mainshock exhibited strike-slip motion with a centroid depth of 8 km. We determined the seismogenic fault of the Qiaojia earthquake using aftershock relocation with local dense seismic arrays. The mainshock is located on a previously unmapped fault. Aftershocks clearly delineated east–west rupture plane, which was not revealed by the regional seismic network due to relatively sparse stations. The length and width of the aftershock zone are ∼5 km and 3 km, respectively. The focal mechanisms of 70 aftershocks with magnitudes ML≥1.0 showed similar focal mechanism with the mainshock. The stress field inverted from focal mechanisms of the aftershocks is consistent with the tectonic stress field. The coseismic and postseismic static coulomb stress changes show that the Ludian earthquake has a negative impact on the Qiaojia earthquake with a value of −0.01 MPa, implying that the Qiaojia earthquake was unlikely statically triggered by the Ludian earthquake. The Qiaojia earthquake sequence was characterized by low b-value and low-decay rate in the aftershock area, indicating high-seismic risk in this region. The dense seismic observation allows us to study the moderate earthquake in detail and provides us with valuable information of near-fault seismicity to analyze earthquake hazard and the potential of large earthquakes in the future.

Temperature Variations in Multiple Air Layers before the Mw 6.2 2014 Ludian Earthquake, Yunnan, China

On 3 August 2014, an Mw 6.2 earthquake occurred in Ludian, Yunnan Province, China (27.245° N 103.427° E). This damaging earthquake caused approximately 400 fatalities, 1800 injuries, and the destruction of at least 12,000 houses. Using air temperature data of the National Center for Environmental Prediction (NCEP) and the tidal force fluctuant analysis (TFFA) method, we derive the temperature variations in multiple air layers between before and after the Ludian earthquake. In the spatial range of 30° × 30° (12°–42° N, 88°–118° E) of China, a thermal anomaly appeared only on or near the epicenter before earthquake, and air was heated from the land, then uplifted by a heat flux, and then cooled and dissipated upon rising. With the approaching earthquake, the duration and range of the thermal anomaly during each tidal cycle was found to increase, and the amplitude of the thermal anomaly varied with the tidal force potential: air temperature was found to rise during the negative phase of the tidal force potential, to reach peak at its trough, and to attenuate when the tidal force potential was rising again. A significance test supports the hypothesis that the thermal anomalies are physically related to Ludian earthquakes rather than being coincidences. Based on these results, we argue that the change of air temperature could reflect the stress changes modulated under the tidal force. Moreover, unlike the thermal infrared remote sensing data, the air temperature data provided by NCEP are not affected by clouds, so it has a clear advantage for monitoring the pre-earthquake temperature variation in cloudy areas.

Seismo-Deformation Anomalies Associated with the M6.1 Ludian Earthquake on August 3, 2014

A time-frequency method retrieving the acceleration changes in the terminal stage of theM6.1 Ludian earthquake in China is discussed in this article. The non-linear, non-stationaryseismo-demformation was obtained by using the Hilbert–Huang transform and followed by aband-pass filter. We found that the temporal evolution of the residual GNSS-derived orientationexhibits a unique disorder-alignment-disorder sequence days before the earthquake whichcorresponds well with the four stages of an earthquake: elastic strain buildup, crack developments,deformation, and the terminal stage of material failure. The disordering orientations are graduallyaligned with a common direction a few days before the terminal stage. This common direction isconsistent with the most compressive axis derived from the seismological method. In addition, theregion of the stress accumulation, as identified by the size of the disordered orientation, isgenerally consistent with the earthquake preparation zones estimated by using numerical models.

An improved method of Newmark analysis for mapping hazards of coseismic landslides

Coseismic landslides can destroy buildings, dislocate roads, sever pipelines, and cause heavy casualties. It is thus important but challenging to accurately map the hazards posed by coseismic landslides. Newmark's method is widely applied to assess the permanent displacement along a potential slide surface and model the coseismic response of slopes. This paper proposes an improved Newmark analysis for mapping the hazards of coseismic landslides by considering the roughness and effect of the size of the potential slide surfaces. This method is verified by data from a case study on the 2014 Mw 6.1 (the United States Geological Survey) Ludian earthquake in Yunnan Province, China. Permanent displacements due to the earthquake ranged from 0 to 122 cm. The predicted displacements were compared with a comprehensive inventory of landslides triggered by the Ludian earthquake to map the spatial variation in the hazards of coseismic landslides using the certainty factor model. The confidence levels of coseismic landslides indicated by the certainty factors ranged from −1 to 0.95. A hazard map of the coseismic landslide was generated based on the spatial distribution of values of the certainty factor. A regression curve relating the predicted displacement and the certainty factor was drawn, and can be applied to predict the hazards of coseismic landslides for any seismic scenario of interest. The area under the curve was used to compare the improved and the conventional Newmark analyses, and revealed the improved performance of the former. This mapping procedure can be used to predict the hazards posed by coseismic landslides, and provide guidelines for decisions regarding the development of infrastructure and post-earthquake reconstruction.