A seismic tomography, gravity, and flexure study of the crust and upper mantle structure across the Hawaiian Ridge: 2. Ka'ena

The Hawaiian Ridge, a classic intraplate volcanic chain in the Central Pacific Ocean, has long attracted researchers due to its origin, eruption patterns, and impact on lithospheric deformation. Thought to arise from pressure-release melting within a mantle plume, its mass-induced deformation of Earth's surface depends on load distribution and lithospheric properties, including elastic thickness (Te). To investigate these features, a marine geophysical campaign was carried out across the Hawaiian Ridge in 2018. Westward of the island of O'ahu, a seismic tomographic image, validated by gravity data, reveals a large mass of volcanic material emplaced on the oceanic crust, flanked by an apron of volcaniclastic material filling the moat created by plate flexure. The ridge adds ∼7 km of material to pre-existing ∼6-km-thick oceanic crust. A high-velocity and high-density core resides within the volcanic edifice, draped by alternating lava flows and mass wasting material. Beneath the edifice, upper mantle velocities are slightly higher than that of the surrounding mantle, and there is no evidence of extensive magmatic underplating of the crust. There is ∼3.5 km of downward deflection of the sediment-crust and crust-mantle boundaries due to flexure in response to the volcanic load. At Ka'ena Ridge, the volcanic edifice's height and cross-sectional area are no more than half as large as those determined at Hawai'i Island. Together, these studies confirm that volcanic loads to the west of Hawai'i are largely compensated by flexure. Comparisons to the Emperor Seamount Chain confirm the Hawaiian Ridge's relatively stronger lithospheric rigidity

A seismic tomography, gravity, and flexure study of the crust and upper mantle structure of the Hawaiian Ridge: 1

The Hawaiian Ridge has long been a focus site for studying lithospheric flexure due to intraplate volcano loading, but crucial load and flexure details remain unclear. We address this problem using wide-angle seismic refraction and reflection data acquired along a ∼535-km-long profile that intersects the ridge between the islands of Maui and Hawai'i and crosses 80–95 Myr-old lithosphere. A tomographic image constructed using travel time data of several seismic phases reveals broad flexure of Pacific oceanic crust extending up to ∼200–250 km either side of the Hawaiian Ridge, and vertically up to ∼6–7 km. The P-wave velocity structure, verified by gravity modeling, reveals that the west flank of Hawaii is comprised of extrusive lavas overlain by volcanoclastic sediments and a carbonate platform. In contrast, the Hāna Ridge, southeast of Maui, contains a high-velocity core consistent with mafic or ultramafic intrusive rocks. Magmatic underplating along the seismic line is not evident. Reflectors at the top and bottom of the pre-existing oceanic crust suggest a ∼4.5–6 km crustal thickness. Simple three-dimensional flexure modeling with an elastic plate thickness, Te, of 26.7 km shows that the depths to the reflectors beneath the western flank of Hawai'i can be explained by volcano loading in which Maui and the older islands in the ridge contribute ∼43% to the flexure and the island of Hawai'i ∼51%. Previous studies, however, revealed a higher Te beneath the eastern flank of Hawai'i suggesting that isostatic compensation may not yet be complete at the youngest end of the ridge

Molecular Model of Dynamic Social Network Based on E-mail communication

In this work we consider an application of physically inspired sociodynamical model to the modelling of the evolution of email-based social network. Contrary to the standard approach of sociodynamics, which assumes expressing of system dynamics with heuristically defined simple rules, we postulate the inference of these rules from the real data and their application within a dynamic molecular model. We present how to embed the n-dimensional social space in Euclidean one. Then, inspired by the Lennard-Jones potential, we define a data-driven social potential function and apply the resultant force to a real e-mail communication network in a course of a molecular simulation, with network nodes taking on the role of interacting particles. We discuss all steps of the modelling process, from data preparation, through embedding and the molecular simulation itself, to transformation from the embedding space back to a graph structure. The conclusions, drawn from examining the resultant networks in stable, minimum-energy states, emphasize the role of the embedding process projecting the non–metric social graph into the Euclidean space, the significance of the unavoidable loss of information connected with this procedure and the resultant preservation of global rather than local properties of the initial network. We also argue applicability of our method to some classes of problems, while also signalling the areas which require further research in order to expand this applicability domain

The sensitivity of seismic refraction velocity models to survey geometry errors, assessed using Monte Carlo analysis

Seismic refraction models should routinely be reported with their associated uncertainty. Tomographic solutions are widespread, but estimating uncertainties in these via Monte Carlo simulation places great demands on computer resource, hence this task is often omitted. By considering the Plus-Minus method of seismic refraction interpretation, we use Monte Carlo simulations to evaluate the uncertainty in seismic refraction results and determine the sources of uncertainty that are most impactful on the reliability of the output model. Our analysis considers the impact of survey mislocation (i.e., geophones misplaced from a planned position) and interpretational problems (i.e., misidentification of first-break picks and uncertainty in identifying crossover distances) on the overall uncertainty in inferred unit thicknesses and seismic velocities. These are considered for synthetic data with varying subsurface velocity structure, and for field data collected at a shallow (< 50 m) bedrock site in north Wales (UK). Analysis of synthetic data shows that the impact of the aforementioned errors on thickness estimates is ∼1000 times that on velocity estimates. Of all permutations tested, the most significant impact on thickness uncertainty was the accuracy of first-break picks, with the variance in target thickness estimates increasing roughly exponentially with first-break pick uncertainty. It is therefore prudent to minimise such uncertainty through appropriate survey practice (e.g., maximising source energy, taking multiple shots for stacking) and to properly define the resultant uncertainty in unit thickness and velocity estimates. The simplicity of the Plus-Minus method makes it an effective tool for highlighting the errors that would impact more sophisticated interpretation approaches, such as tomography or Full Waveform Inversion. The results from such analysis can be directly applied in straightforward environmental or engineering investigations and can be used to inform more advanced refraction methods. As such, the practice we highlight should be considered for any refraction interpretation

Organic aerosol formation downwind from the Deepwater Horizon oil spill.

A large fraction of atmospheric aerosols are derived from organic compounds with various volatilities. A National Oceanic and Atmospheric Administration (NOAA) WP-3D research aircraft made airborne measurements of the gaseous and aerosol composition of air over the Deepwater Horizon (DWH) oil spill in the Gulf of Mexico that occurred from April to August 2010. A narrow plume of hydrocarbons was observed downwind of DWH that is attributed to the evaporation of fresh oil on the sea surface. A much wider plume with high concentrations of organic aerosol (&gt;25 micrograms per cubic meter) was attributed to the formation of secondary organic aerosol (SOA) from unmeasured, less volatile hydrocarbons that were emitted from a wider area around DWH. These observations provide direct and compelling evidence for the importance of formation of SOA from less volatile hydrocarbons

Suicide ideation of individuals in online social networks

Suicide explains the largest number of death tolls among Japanese adolescents in their twenties and thirties. Suicide is also a major cause of death for adolescents in many other countries. Although social isolation has been implicated to influence the tendency to suicidal behavior, the impact of social isolation on suicide in the context of explicit social networks of individuals is scarcely explored. To address this question, we examined a large data set obtained from a social networking service dominant in Japan. The social network is composed of a set of friendship ties between pairs of users created by mutual endorsement. We carried out the logistic regression to identify users' characteristics, both related and unrelated to social networks, which contribute to suicide ideation. We defined suicide ideation of a user as the membership to at least one active user-defined community related to suicide. We found that the number of communities to which a user belongs to, the intransitivity (i.e., paucity of triangles including the user), and the fraction of suicidal neighbors in the social network, contributed the most to suicide ideation in this order. Other characteristics including the age and gender contributed little to suicide ideation. We also found qualitatively the same results for depressive symptoms.Comment: 4 figures, 9 table

Atmospheric emissions from the deepwater Horizon spill constrain air-water partitioning, hydrocarbon fate, and leak rate

The fate of deepwater releases of gas and oil mixtures is initially determined by solubility and volatility of individual hydrocarbon species; these attributes determine partitioning between air and water. Quantifying this partitioning is necessary to constrain simulations of gas and oil transport, to predict marine bioavailability of different fractions of the gas-oil mixture, and to develop a comprehensive picture of the fate of leaked hydrocarbons in the marine environment. Analysis of airborne atmospheric data shows massive amounts (∼258,000 kg/day) of hydrocarbons evaporating promptly from the Deepwater Horizon spill; these data collected during two research flights constrain air-water partitioning, thus bioavailability and fate, of the leaked fluid. This analysis quantifies the fraction of surfacing hydrocarbons that dissolves in the water column (∼33% by mass), the fraction that does not dissolve, and the fraction that evaporates promptly after surfacing (∼14% by mass). We do not quantify the leaked fraction lacking a surface expression; therefore, calculation of atmospheric mass fluxes provides a lower limit to the total hydrocarbon leak rate of 32,600 to 47,700 barrels of fluid per day, depending on reservoir fluid composition information. This study demonstrates a new approach for rapid-response airborne assessment of future oil spills. Copyright 2011 by the American Geophysical Union

Ordinary Percolation with Discontinuous Transitions

Percolation on a one-dimensional lattice and fractals such as the Sierpinski gasket is typically considered to be trivial because they percolate only at full bond density. By dressing up such lattices with small-world bonds, a novel percolation transition with explosive cluster growth can emerge at a nontrivial critical point. There, the usual order parameter, describing the probability of any node to be part of the largest cluster, jumps instantly to a finite value. Here, we provide a simple example of this transition in form of a small-world network consisting of a one-dimensional lattice combined with a hierarchy of long-range bonds that reveals many features of the transition in a mathematically rigorous manner.Comment: RevTex, 5 pages, 4 eps-figs, and Mathematica Notebook as Supplement included. Final version, with several corrections and improvements. For related work, see http://www.physics.emory.edu/faculty/boettcher

Semi-Markov Graph Dynamics

In this paper, we outline a model of graph (or network) dynamics based on two ingredients. The first ingredient is a Markov chain on the space of possible graphs. The second ingredient is a semi-Markov counting process of renewal type. The model consists in subordinating the Markov chain to the semi-Markov counting process. In simple words, this means that the chain transitions occur at random time instants called epochs. The model is quite rich and its possible connections with algebraic geometry are briefly discussed. Moreover, for the sake of simplicity, we focus on the space of undirected graphs with a fixed number of nodes. However, in an example, we present an interbank market model where it is meaningful to use directed graphs or even weighted graphs.Comment: 25 pages, 4 figures, submitted to PLoS-ON