The ground velocity pulses generated by rupture directivity effects in the near-fault region can cause a large amount of damage to structures. Proper estimation of the period of such velocity pulses is of particular importance in characterizing near-fault seismic hazard and mitigating potential damage. We propose a simple equation to determine the pulse period as a function of the site location with respect to the fault rupture (defined by the hypocentral distance hypD, the closest distance to the rupture area clsD, and the length of the rupture area that breaks toward the site D) and some basic rupture properties (average rupture speed and average rise time). Our equation is first validated from a dataset of synthetic velocity time histories, deploying simulations of various strike-slip extended ruptures in a homogeneous medium. The analysis of the synthetic dataset confirms that the pulse period does not depend on the whole rupture area, but only on the parameter D. It also reveals that the pulse period is not sensitive to the level of slip heterogeneity on the fault plane. Our model is tested next on a real dataset build from the Next Generation Attenuation-West2 Project database, compiling 110 observations of velocity pulse periods from 10 strike-slip events and 6 non-strike-slip events. The standard deviation of the natural logarithm residuals between observations and predictions is ∼0:5. Furthermore, the correlation coefficient between observations and predictions equals ∼0:8, indicating that despite its simplicity, our model explains fairly well the spatial variability of the pulse periods.

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The extraction of body waves from passive seismic recordings has great potential for monitoring and imaging applications. The low environmental impact, low cost, and high accessibility of passive techniques makes them especially attractive as replacement or complementary techniques to active-source exploration. There still, however, remain many challenges with body-wave extraction, mainly the strong dependence on local seismic sources necessary to create high-frequency body-wave energy. Here, we present the Marathon dataset collected in September 2018, which consists of 30 days of continuous recordings from a dense surface array of 1020 single vertical-component geophones deployed over a mineral exploration block. First, we use a cross-correlation beamforming technique to evaluate the wavefield each minute and discover that the local highway and railroad traffic are the primary sources of high-frequency body-wave energy. Next, we demonstrate how selective stacking of cross-correlation functions during periods where vehicles and trains are passing near the array reveals strong body-wave arrivals. Based on source station geometry and the estimated geologic structure, we interpret these arrivals as virtual refractions due to their high velocity and linear moveout. Finally, we demonstrate how the apparent velocity of these arrivals along the array contains information about the local geologic structure, mainly the major dipping layer. Although vehicle sources illuminating array in a narrow azimuth may not seem ideal for passive reflection imaging, we expect this case will be commonly encountered and should serve as a good dataset for the development of new techniques in this domain.

This article studies the effects of the soil data and exposure data of residential building inventories, as well as their spatial resolution, on seismic damage and loss estimates for a given earthquake scenario. Our aim is to investigate how beneficial it would be to acquire higher resolution inventories at the cost of additional effort and resources. Seismic damage computations are used to evaluate the relative influence of varying spatial resolution on a given damage model, where other parameters were held constant. We use soil characterization maps and building exposure inventories, provided at different scales from different sources: the European database, a national dataset at the municipality scale, and local field investigations. Soil characteristics are used to evaluate site effects and to assign amplification factors to the strong motion applied to the exposed areas. Exposure datasets are used to assign vulnerability indices to sets of buildings, from which a damage distribution is produced (based on the applied seismic intensity). The different spatial resolutions are benchmarked in a case-study area which is subject to moderate-to-average seismicity levels (Luchon valley in the Pyrénées, France). It was found that the proportion of heavily damaged buildings is underestimated when using the European soil map and the European building database, while the more refined databases (national/regional vs. local maps) result in similar estimates for moderate earthquake scenarios. Finally, we highlight the importance of pooling open access data from different sources, but caution the challenges of combining different datasets, especially depending on the type of application that is pursued (e.g., for risk mitigation or rapid response tools).