Physics

Multistatic ground-penetrating radar (GPR) signals can be imaged tomographically to produce three-dimensional distributions of image intensities. In the absence of objects of interest, these intensities can be considered to be estimates of clutter. These clutter intensities spatially vary over several orders of magnitude, and vary across different arrays, which makes direct comparison of these raw intensities difficult. However, by gathering statistics on these intensities and their spatial variation, a variety of metrics can be determined. In this study, the clutter distribution is found to fit better to a two-parameter Weibull distribution than Gaussian or lognormal distributions. Based upon the spatial variation of the two Weibull parameters, scale and shape, more information may be gleaned from these data. How well the GPR array is illuminating various parts of the ground, in depth and cross-track, may be determined from the spatial variation of the Weibull scale parameter, which may in turn be used to estimate an effective attenuation coefficient in the soil. The transition in depth from clutter-limited to noise-limited conditions (which is one possible definition of GPR penetration depth) can be estimated from the spatial variation of the Weibull shape parameter. Finally, the underlying clutter distributions also provide an opportunity to standardize image intensities to determine when a statistically significant deviation from background (clutter) has occurred, which is convenient for buried threat detection algorithm development which needs to be robust across multiple different arrays.

Attenuation study of a province is considered as a basic quantity for seismic hazard assessment, ground motion simulation process and source parameter studies. It is already established that the study of two physical processes, first, the seismic sources and second, propagation of the waves, is essential for seismic-hazard mapping, attenuation being one of the properties paying importance to the latter. Here, a computational tool entitled CodaQback is presented. Based on back scattering model, this versatile software is equipped with user friendly graphical user interface. It also allows quick picking of phases for computing coda attenuation parameter. All outputs after each execution step in CodaQback are efficiently exported stepwise into a separate folder in excel and text formats. This CodaQback is checked in real data analysis and there is found to be good matching of computed values with already established ones. It is envisioned that this package will enable user to derive quick and reliable estimation of coda attenuation parameter irrespective of geological and geo-morphological units.

This is a comment on the PRL: Gravitational mass M carried by sound waves by A. Esposito, R. Krichevsky and A. Nicolis, Phys.Rev. Lett. {\bf 122}, 084501 (2019).

Conventional Full Waveform Inversion requires calculating the objective function to be minimized and construction a gradient using the whole property model, when is often the case where geoscientist are only interested in a local region. In this study, we use two strategies to perform local FWI in time domain. One that disregards the interaction of the locally altered model with the exterior (FD injection) and the second that can take these iterations into account (Immersive Boundary Conditions). Numerical tests show the influence of whether or not to set aside these interactions for different accuracy of the exterior model.

On 15 May 2020 an MWW 6.5 earthquake occurred beneath the Monte Cristo Range in the Mina Deflection region of western Nevada. Rapid deployment of eight temporary seismic stations enables detailed analysis of its productive and slowly decaying aftershock sequence (p=0.8) which included ~18,000 autodetected events in 3.5 months. Double-difference, waveform-based relative relocation of 16,714 earthquakes reveals a complex network of faults, many of which cross the inferred 35-km long east-northeast-striking, left-lateral mainshock rupture. Seismicity aligns with left-lateral, right-lateral, and normal mechanism moment tensors of 128 of the largest earthquakes. The mainshock occurred near the middle of the aftershock zone at the intersection of two distinct zones of seismicity. In the western section, numerous subparallel, shallow, north-northeast-striking faults form a broad flower-structure-like fault mesh that coalesces at depth into a near-vertical, left-lateral fault. We infer the near-vertical fault to be a region of significant slip in the mainshock and an eastward extension of the left-lateral Candelaria fault. Near the mainshock hypocenter, seismicity occurs on a northeast-striking, west-dipping structure which extends north from the Eastern Columbus Salt Marsh normal fault. Together, these two intersecting structures bound the Columbus Salt Marsh tectonic basin. East of this intersection and the mainshock hypocenter, seismicity occurs in a narrow, near-vertical, east-northeast-striking fault zone through to its eastern terminus. At the eastern end, the aftershock zone broadens and extends northwest towards the southern extension of the northwest-striking, right-lateral Petrified Springs fault system. The eastern section hosts significantly fewer aftershocks than the western section, but has more moment release.

After the 2004 and 2011 tsunamis came unprecedented to the scientific community the role of probabilistic tsunami hazard assessment (PTHA) in tsunami-prone areas came to the fore. The Makran subduction zone (MSZ) is a hazardous tsunami-prone region; however, due to its low population density, it is not as prominent in literature. In this study, we assess the threat of tsunami hazard posed to the coast of Iran and Pakistan by the MSZ and present a comprehensive PTHA for the entire coast regardless of population density. We accounted for sources of epistemic uncertainties by employing event tree and ensemble modeling. Aleatory variability was also considered through probability density function. Further, we considered the contribution of small to large magnitudes and used our event trees to create a multitude of scenarios as initial conditions. Funwave-TVD was employed to propagate these scenarios. Our results demonstrate that the spread of hazard curves for different locations on the coast is remarkably large, and the probability that a maximum wave will exceed 3 m somewhere along the coast reaches {13.5,25,52,74,91}% for return periods {50,100,250,500,1000} , respectively. Moreover, we found that the exceedance probability could be higher at the west part of Makran for a long return period if we consider it as active as the east part of the MSZ. Finally, we demonstrated that the contribution of aleatory variability is significant, and overlooking it leads to a significant hazard underestimation, particularly for a long return period.

The permeability of complex porous materials can be obtained via direct flow simulation, which provides the most accurate results, but is very computationally expensive. In particular, the simulation convergence time scales poorly as simulation domains become tighter or more heterogeneous. Semi-analytical models that rely on averaged structural properties (i.e. porosity and tortuosity) have been proposed, but these features only summarize the domain, resulting in limited applicability. On the other hand, data-driven machine learning approaches have shown great promise for building more general models by virtue of accounting for the spatial arrangement of the domains solid boundaries. However, prior approaches building on the Convolutional Neural Network (ConvNet) literature concerning 2D image recognition problems do not scale well to the large 3D domains required to obtain a Representative Elementary Volume (REV). As such, most prior work focused on homogeneous samples, where a small REV entails that that the global nature of fluid flow could be mostly neglected, and accordingly, the memory bottleneck of addressing 3D domains with ConvNets was side-stepped. Therefore, important geometries such as fractures and vuggy domains could not be well-modeled. In this work, we address this limitation with a general multiscale deep learning model that is able to learn from porous media simulation data. By using a coupled set of neural networks that view the domain on different scales, we enable the evaluation of large images in approximately one second on a single Graphics Processing Unit. This model architecture opens up the possibility of modeling domain sizes that would not be feasible using traditional direct simulation tools on a desktop computer.

Stochastic network modeling is often limited by high computational costs to generate a large number of networks enough for meaningful statistical evaluation. In this study, Deep Convolutional Generative Adversarial Networks (DCGANs) were applied to quickly reproduce drainage networks from the already generated network samples without repetitive long modeling of the stochastic network model, Gibb's model. In particular, we developed a novel connectivity-informed method that converts the drainage network images to the directional information of flow on each node of the drainage network, and then transform it into multiple binary layers where the connectivity constraints between nodes in the drainage network are stored. DCGANs trained with three different types of training samples were compared; 1) original drainage network images, 2) their corresponding directional information only, and 3) the connectivity-informed directional information. Comparison of generated images demonstrated that the novel connectivity-informed method outperformed the other two methods by training DCGANs more effectively and better reproducing accurate drainage networks due to its compact representation of the network complexity and connectivity. This work highlights that DCGANs can be applicable for high contrast images common in earth and material sciences where the network, fractures, and other high contrast features are important.

Magmatism and volcanism transfer carbon from the solid Earth into the climate system. This transfer may be modulated by the glacial/interglacial cycling of water between oceans and continental ice sheets, which alters the surface loading of the solid Earth. The consequent volcanic-carbon fluctuations have been proposed as a pacing mechanism for Pleistocene glacial cycles. This mechanism is dependant on the amplitude and lag of the mid-ocean ridge response to sea-level changes. Here we develop and analyse a new model for that response, eliminating some questionable assumptions made in previous work. Our model calculates the carbon flux, accounting for the thermodynamic effect of mantle carbon: reduction of the solidus temperature and a deeper onset of melting. We analyse models forced by idealised, periodic sea level and conclude that fluctuations in melting rate are the prime control on magma and carbon flux. We also discuss a model forced by a reconstruction of eustatic sea level over the past 800 kyr. It indicates that peak-to-trough variations of magma and carbon flux are up to about 20% and 10% of the mean flux, respectively. Peaks in mid-ocean ridge emissions lag peaks in sea-level forcing by less than about 20 kyr and the lag could well be shorter. The amplitude and lag are sensitive to the rate of melt segregation. The lag is much shorter than the time it takes for melt to travel vertically across the melting region.

Here, we provide a reappraisal of potential LLSVPs compositions based on an improved mineralogical model including, for instance, the effects of alumina. We also systematically investigate the effects of six parameters: FeO and Al 2 O 3 content, proportion of CaSiO 3 and bridgmanite (so that the proportion of ferropericlase is implicitly investigated), Fe 3+ / ∑ Fe and temperature contrast between far-field mantle and LLSVPs. From the 81 millions cases studied, only 79000 cases explain the seismic observations. Nevertheless, these successful cases involve a large range of parameters with, for instance, FeO content between 12--25~wt\% and Al 2 O 3 content between 3--17~wt\%. We then apply a principal component analysis (PCA) to these cases and find two robust results: (i) the proportion of ferropericlase should be low ( < 6vol\%); (ii) the formation of Fe 3+ -bearing bridgmanite is much more favored than other iron-bearing phases. Following these results, we identify two end-member compositions, Bm-rich and CaPv-rich, and discuss their characteristics. Finally, we discuss different scenarios for the formation of LLSVPs and propose that investigating the mineral proportion produced by each scenario is the best way to evaluate their relevance. For instance, the solidification of a primitive magma ocean may produce FeO and Al 2 O 3 content similar to those suggested by our analysis. However, the mineral proportion of such reservoirs is not well-constrained and may contain a larger proportion of ferropericlase than what is allowed by our results.