Absence of Near-Trench Early Triggering during the 2012 Mw 7.2 Indian Ocean Strike-Slip Earthquake: Evidence from One-Day Aftershocks

Dynamic earthquake triggering is a widely accepted mechanism of earthquake interaction, which plays a vital role in seismic hazard estimation, although its efficacy at regional distances is under debate. The 2012 Mw 7.2 Indian Ocean event is one of the first reported events to produce dynamic stress triggering at regional distances using backprojection (BP) techniques. Alternatively, the coherent radiators in BP images can be interpreted as localized water reverberation phases. We present further evidence against near-trench triggering during this event. We collected 24 hr seismic recordings of two nearby stations located near the trench. We adopted a waveform denoising algorithm and detected 125 aftershocks using two regional seismic stations with a minimum magnitude of ML∼2.7 and completeness magnitude of ML∼3.6, whereas none of these aftershocks occurred near the trench. The absence of immediate (within one day) aftershocks near the trench suggest the absence of dynamic triggering during the offshore mainshock.

The Effect of the Wenchuan and Lushan Earthquakes on the Size Distribution of Earthquakes along the Longmenshan Fault

Changes in the stress state of faults and their surroundings is a highly plausible mechanism explaining earthquake interaction. These stress changes can impact the seismicity rate and the size distribution of earthquakes. However, the effect of large earthquakes on the earthquake size distribution along the Longmenshan fault has not been quantified. We evaluated the levels of the b value for the stable state before and after the large earthquakes on 12 May 2008 (Wenchuan, MS 8.0) and 20 April 2013 (Lushan, MS 7.0) along the Longmenshan fault. We found that after the mainshocks, the size distribution of the subsequent earthquakes shifted toward relatively larger events in the Wenchuan aftershock zone (b value decreased from 1.21 to 0.84), and generally remained invariable in the Lushan aftershock zone (b value remained at 0.76). The time required for the b value to return to stable states after both mainshocks was entirely consistent with the time needed by the aftershock depth images to stop visibly changing. The result of the temporal variation of b values shows decreasing trends for the b value before both large earthquakes. Our results are available for assessing the potential seismic risk of the Longmenshan fault as a reference.

DynTriPy: A Python Package for Detecting Dynamic Earthquake Triggering Signals

Long-term and large-scale observations of dynamic earthquake triggering are urgently needed to understand the mechanism of earthquake interaction and assess seismic hazards. We developed a robust Python package termed DynTriPy to automatically detect dynamic triggering signals by distinguishing anomalous seismicity after the arrival of remote earthquakes. This package is an efficient implementation of the high-frequency power integral ratio algorithm, which is suitable for processing big data independent of earthquake catalogs or subjective judgments and can suppress the influence of noise and variations in the background seismicity. Finally, a confidence level of dynamic triggering (0–1) is statistically yielded. DynTriPy is designed to process data from multiple stations in parallel, taking advantage of rapidly expanding seismic arrays to monitor triggering on a global scale. Various data formats are supported, such as Seismic Analysis Code, mini Standard for Exchange of Earthquake Data (miniSEED), and SEED. To tune parameters more conveniently, we build a function to generate a database that stores power integrals in different time and frequency segments. All calculation functions possess a high-level parallel architecture, thoroughly capitalizing on available computational resources. We output and store the results of each function for continuous operation in the event of an unexpected interruption. The deployment of DynTriPy to data centers for real-time monitoring and investigating the sudden activation of any signal within a certain frequency scope has broad application prospects.

Exploring possible causative mechanisms for earthquakes triggered by hydraulic fracturing: examples from the Montney Basin, BC, Canada.

<p>The Dawson-Septimus area near the towns of Dawson Creek and Fort St. John, British Columbia, Canada has experienced a drastic increase in seismicity in the last ~ 6 years, from no earthquakes reported by Natural Resources Canada (NRCan) prior to 2013 to a total of ~ 200 cataloged events in 2013 – 2019. The increase follows the extensive horizontal drilling and multistage hydraulic fracturing activity that started to extract shale gas from the unconventional siltstone resource of the Montney Formation. In addition to hydraulic fracturing, ongoing wastewater disposal in the permeable sandstones and carbonates located stratigraphically above and below the Montney formation may also be contributing to elevated seismicity in the region. Earthquakes occur in close spatial and temporal proximity to hydraulic fracturing wells, at distances up to ~ 10 km. The expected diffusion time scales in the low-diffusivity siltstone rock units and the temporal and spatial scale of seismic activity beg questions about the possible processes controlling the location and timing of earthquakes.</p><p> </p><p>Here, we investigate the causative mechanisms for two of the largest events in the Montney Basin, British Columbia: the August 2015 M4.6 earthquake near Fort St. John, and the November 2018 M4.5 earthquake near Dawson Creek. Both events are thought to have occurred within the crystalline basement, ~2 km below the injected shale units (Montney formation).  We use a finite-element 3D poroelastic model to calculate the coupled evolution of elastic stress and pore pressure due to injection at several hydraulic fracturing stages. Initially, we consider a simple layered model with differing hydraulic parameters based on lithology. Subsequently, also considering the seismicity distribution for each sequence, we introduce hypothetic hydraulic conduits connecting the injection intervals with the crystalline basement, where the respective mainshock occurred. We test a range of permeability values (10<sup>-15</sup> m<sup>2</sup>– 10<sup>-12</sup> m<sup>2</sup>) commonly implemented for fault zones.</p><p> </p><p>Our results show that, for both cases, the poroelastic stress perturbation may be not sufficient to trigger events in the basement. Instead, a scenario with a high-permeability (10<sup>-13</sup> m<sup>2</sup>– 10<sup>-12</sup> m<sup>2</sup>) conduits connecting the Montney formation to the fault responsible for the mainshock could better explain the relationship between the hydraulic stimulation and the timing of the two M > 4 earthquakes. For the 2018 M4.5 event, aftershock distribution can be mainly attributed to earthquake-earthquake interaction via static Coulomb stress transfer from the mainshock slip. In addition to the modeling of single well/event sequences, future work will include the long-term poroelastic effect due to multiple disposal wells located in the region.</p>

Dynamic Responses of Liquid Storage Tanks Caused by Wind and Earthquake in Special Environment

Based on potential flow theory and arbitrary Lagrangian–Eulerian method, shell–liquid and shell–wind interactions are solved respectively. Considering the nonlinearity of tank material and liquid sloshing, a refined 3-D wind–shell–liquid interaction calculation model for liquid storage tanks is established. A comparative study of dynamic responses of liquid storage tanks under wind, earthquake, and wind and earthquake is carried out, and the influences of wind speed and wind interference effect on dynamic responses of liquid storage tank are discussed. The results show that when the wind is strong, the dynamic responses of the liquid storage tank under wind load alone are likely to be larger than that under earthquake, and the dynamic responses under wind–earthquake interaction are obviously larger than that under wind and earthquake alone. The maximum responses of the tank wall under wind and earthquake are located in the unfilled area at the upper part of the tank and the filled area at the lower part of the tank respectively, while the location of maximum responses of the tank wall under wind–earthquake interaction is related to the relative magnitude of the wind and earthquake. Wind speed has a great influence on the responses of liquid storage tanks, when the wind speed increases to a certain extent, the storage tank is prone to damage. Wind interference effect has a significant effect on liquid storage tanks and wind fields. For liquid storage tanks in special environments, wind and earthquake effects should be considered reasonably, and wind interference effects cannot be ignored.