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We present an approach for rapidly estimating full moment tensors of earthquakes and their parameter uncertainties based on short time windows of recorded seismic waveform data by considering deep learning of Bayesian Neural Networks (BNNs). The individual neural networks are trained on synthetic seismic waveform data and corresponding known earthquake moment-tensor parameters. A monitoring volume has been predefined to form a three-dimensional grid of locations and to train a BNN for each grid point. Variational inference on several of these networks allows us to consider several sources of error and how they affect the estimated full moment-tensor parameters and their uncertainties. In particular, we demonstrate how estimated parameter distributions are affected by uncertainties in the earthquake centroid location in space and time as well as in the assumed Earth structure model. We apply our approach as a proof of concept on seismic waveform recordings of aftershocks of the Ridgecrest 2019 earthquake with moment magnitudes ranging from Mw 2.7 to Mw 5.5. Overall, good agreement has been achieved between inferred parameter ensembles and independently estimated parameters using classical methods. Our developed approach is fast and robust, and therefore, suitable for down-stream analyses that need rapid estimates of the source mechanism for a large number of earthquakes.
Crustal seismic attenuation parameters in the western region of the North Anatolian Fault Zone
(2020)
Detailed knowledge of the crustal structure along the North Anatolian Fault Zone can help in understanding past and present tectonic processes in relation to the deformation history. To estimate the frequency-dependent crustal attenuation parameters beneath the western part of the North Anatolian Fault Zone we apply acoustic radiative transfer theory under the assumption of multiple isotropic scattering to generate synthetic seismogram envelopes. The inversion depends on finding an optimal fit between observed and synthetically computed coda wave envelopes in five frequency bands. 2-D lateral variation of intrinsic and scattering attenuation at various frequencies tends to three crustal blocks (i.e., Armutlu-Almacik, Istanbul-Zonguldak and Sakarya Zones) separated by the southern and northern branches of the western part of the North Anatolian Fault Zone. Overall, scattering attenuation appears to be dominant over intrinsic attenuation in the study area at lower frequencies. Relatively low attenuation properties are observed beneath the older Istanbul Zone whereas higher attenuation properties are found for the younger Sakarya Zone. The Armutlu Almacik Zone exhibits more complex lateral variations. Very high attenuation values towards the west characterize the area of the Kuzuluk Basin, a pull-apart basin formed under west-east extension. Our coda-derived moment magnitudes are similar to the local magnitude estimates that were previously calculated for the same earthquakes. For smaller earthquakes (M-L < 2.5), however, the relation between local and moment magnitudes appears to lose its coherency. This may stem from various reasons including the use of seismic data recorded in finite sampling interval, possible biases in local magnitude estimates of earthquake catalogues as well as biases due to wrong assumptions to consider anelastic attenuation terms.
Understanding the association between autonomic nervous system [ANS] function and brain morphology across the lifespan provides important insights into neurovisceral mechanisms underlying health and disease. Resting-state ANS activity, indexed by measures of heart rate [HR] and its variability [HRV] has been associated with brain morphology, particularly cortical thickness [CT]. While findings have been mixed regarding the anatomical distribution and direction of the associations, these inconsistencies may be due to sex and age differences in HR/HRV and CT. Previous studies have been limited by small sample sizes, which impede the assessment of sex differences and aging effects on the association between ANS function and CT. To overcome these limitations, 20 groups worldwide contributed data collected under similar protocols of CT assessment and HR/HRV recording to be pooled in a mega-analysis (N = 1,218 (50.5% female), mean age 36.7 years (range: 12-87)). Findings suggest a decline in HRV as well as CT with increasing age. CT, particularly in the orbitofrontal cortex, explained additional variance in HRV, beyond the effects of aging. This pattern of results may suggest that the decline in HRV with increasing age is related to a decline in orbitofrontal CT. These effects were independent of sex and specific to HRV; with no significant association between CT and HR. Greater CT across the adult lifespan may be vital for the maintenance of healthy cardiac regulation via the ANS-or greater cardiac vagal activity as indirectly reflected in HRV may slow brain atrophy. Findings reveal an important association between CT and cardiac parasympathetic activity with implications for healthy aging and longevity that should be studied further in longitudinal research.