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The near-surface seismic structure (to a depth of about 1000 m), particularly the shear wave velocity (VS), can strongly affect the propagation of seismic waves and, therefore, must be accurately calibrated for ground motion simulations and seismic hazard assessment. The VS of the top (<300 m) crust is often well characterized from borehole studies, geotechnical measurements, and water and oil wells, while the velocities of the material deeper than about 1000 m are typically determined by tomography studies. However, in depth ranges lacking information on shallow lithological stratification, typically rock sites outside the sedimentary basins, the material parameters between these two regions are typically poorly characterized due to resolution limits of seismic tomography. When the alluded geological constraints are not available, models, such as the Southern California Earthquake Center (SCEC) Community Velocity Models (CVMs), default to regional tomographic estimates that do not resolve the uppermost VS values, and therefore deliver unrealistically high shallow VS estimates. The SCEC Unified Community Velocity Model (UCVM) software includes a method to incorporate the near-surface earth structure by applying a generic overlay based on measurements of time-averaged VS in top 30 m (VS30) to taper the upper part of the model to merge with tomography at a depth of 350 m, which can be applied to any of the velocity models accessible through UCVM. However, our 3-D simulations of the 2014 Mw 5.1 La Habra earthquake in the Los Angeles area using the CVM-S4.26.M01 model significantly underpredict low-frequency (<1 Hz) ground motions at sites where the material properties in the top 350 m are significantly modified by the generic overlay (‘taper’). On the other hand, extending the VS30-based taper of the shallow velocities down to a depth of about 1000 m improves the fit between our synthetics and seismic data at those sites, without compromising the fit at well-constrained sites. We explore various tapering depths, demonstrating increasing amplification as the tapering depth increases, and the model with 1000 m tapering depth yields overall favourable results. Effects of varying anelastic attenuation are small compared to effects of velocity tapering and do not significantly bias the estimated tapering depth. Although a uniform tapering depth is adopted in the models, we observe some spatial variabilities that may further improve our method.

 

We have simulated 0–5 Hz deterministic wave propagation for a suite of 17 models of the 2014 Mw 5.1 La Habra, CA, earthquake with the Southern California Earthquake Center Community Velocity Model Version S4.26-M01 using a finite-fault source. Strong motion data at 259 sites within a 148 km × 140 km area are used to validate our simulations. Our simulations quantify the effects of statistical distributions of small-scale crustal heterogeneities (SSHs), frequency-dependent attenuation Q(f), surface topography and near-surface low-velocity material (via a 1-D approximation) on the resulting ground motion synthetics. The shear wave quality factor QS(f) is parametrized as QS, 0 and QS, 0fγ for frequencies less than and higher than 1 Hz, respectively. We find the most favourable fit to data for models using ratios of QS, 0 to shear wave velocity VS of 0.075–1.0 and γ values less than 0.6, with the best-fitting amplitude drop-off for the higher frequencies obtained for γ values of 0.2–0.4. Models including topography and a realistic near-surface weathering layer tend to increase peak velocities at mountain peaks and ridges, with a corresponding decrease behind the peaks and ridges in the direction of wave propagation. We find a clear negative correlation between the effects on peak ground velocity amplification and duration lengthening, suggesting that topography redistributes seismic energy from the large-amplitude first arrivals to the adjacent coda waves. A weathering layer with realistic near-surface low velocities is found to enhance the amplification at mountain peaks and ridges, and may partly explain the underprediction of the effects of topography on ground motions found in models. Our models including topography tend to improve the fit to data, as compared to models with a flat free surface, while our distributions of SSHs with constraints from borehole data fail to significantly improve the fit. Accuracy of the velocity model, particularly the near-surface low velocities, as well as the source description, controls the resolution with which the anelastic attenuation can be determined. Our results demonstrate that it is feasible to use fully deterministic physics-based simulations to estimate ground motions for seismic hazard analysis up to 5 Hz. Here, the effects of, and trade-offs with, near-surface low-velocity material, topography, SSHs and Q(f) become increasingly important as frequencies increase towards 5 Hz, and should be included in the calculations. Future improvement in community velocity models, wider access to computational resources, more efficient numerical codes and guidance from this study are bound to further constrain the ground motion models, leading to more accurate seismic hazard analysis.

 

Robots are entering various domains of human societies, potentially unfolding more opportunities for people to perceive robots as social agents. We expect that having robots in proximity would create unique social learning situations where humans spontaneously observe and imitate robots’ behaviors. At times, these occurrences of humans’ imitating robot behaviors may result in a spread of unsafe or unethical behaviors among humans. For responsible robot designing, therefore, we argue that it is essential to understand physical and psychological triggers of social learning in robot design. Grounded in the existing literature of social learning and the uncanny valley theories, we discuss the human-likeness of robot appearance and affective responses associated with robot appearance as likely factors that either facilitate or deter social learning. We propose practical considerations for social learning and robot design. 

Metazoans host complex communities of microorganisms that include dinoflagellates, fungi, bacteria, archaea and viruses. Interactions among members of these complex assemblages allow hosts to adjust their physiology and metabolism to cope with environmental variation and occupy different habitats. Here, using reciprocal transplantation across depths, we studied adaptive divergence in the corals Orbicella annularis and O. franksi , two young species with contrasting vertical distribution in the Caribbean. When transplanted from deep to shallow, O. franksi experienced fast photoacclimation and low mortality, and maintained a consistent bacterial community. By contrast, O. annularis experienced high mortality and limited photoacclimation when transplanted from shallow to deep. The photophysiological collapse of O. annularis in the deep environment was associated with an increased microbiome variability and reduction of some bacterial taxa. Differences in the symbiotic algal community were more pronounced between coral species than between depths. Our study suggests that these sibling species are adapted to distinctive light environments partially driven by the algae photoacclimation capacity and the microbiome robustness, highlighting the importance of niche specialization in symbiotic corals for the maintenance of species diversity. Our findings have implications for the management of these threatened Caribbean corals and the effectiveness of coral reef restoration efforts.