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Tectonic mélanges, characterized by conditions reflective of modern subduction fault zones, preserve mineral veins formed through mass transfer, a mechanism influencing the slip behavior of subduction megathrusts. In this study, we apply secondary ion mass spectrometry quartz‐calcite oxygen isotope thermometry and clumped isotope thermometry to examine the temperatures of vein formations in six mélange units in the Cretaceous Shimanto belt and one mélange in the Kodiak accretionary prism. Calcite in the veins exhibits δ¹³C_PDBvalues ranging from −17.2‰ to −6.8‰, indicative of a carbon source mixing with sedimentary carbonate and organic matter. δ¹⁸O_SMOWvalues of calcite range from +11.1‰ to +17.2‰; quartz yields δ¹⁸O_SMOWvalues of +14.9‰ to +21.7‰. Oxygen isotopic signatures in minerals reveal that most vein‐forming fluids are significantly affected by rock buffering, while some retain isotopic compositions of seawater and meteoric water. Temperature estimates, derived from both thermometers, fall within the range of 100–250°C. Notably, vein temperatures remain constant across diverse vein types and mélange units with distinct maximum temperatures. The combined temperature records and fluid isotopic compositions imply vein formations at shallower depths linked to the incorporation of seawater, meteoric water, and fluid released from early dehydration reactions. At greater depths, vein formations are associated with fluid released from clay dehydration and long‐distance fluid flow. Reduced vein formations between 250 and 350°C may correlate with a shift to fluid‐unsaturated conditions resulting from clay hydration reactions. Our study highlights potential mechanical and hydraulic variations within the thermal conditions of 100–350°C along the plate boundary driven by fluid‐mineral interactions.

 

Transfer learning on graphs drawn from varied distributions (domains) is in great demand across many applications. Emerging methods attempt to learn domain-invariant representations using graph neural networks (GNNs), yet the empirical performances vary and the theoretical foundation is limited. This paper aims at designing theory-grounded algorithms for graph domain adaptation (GDA). (i) As the first attempt, we derive a model-based GDA bound closely related to two GNN spectral properties: spectral smoothness (SS) and maximum frequency response (MFR). This is achieved by cross-pollinating between the OT-based (optimal transport) DA and graph filter theories. (ii) Inspired by the theoretical results, we propose algorithms regularizing spectral properties of SS and MFR to improve GNN transferability. We further extend the GDA theory into the more challenging scenario of conditional shift, where spectral regularization still applies. (iii) More importantly, our analyses of the theory reveal which regularization would improve performance of what transfer learning scenario, (iv) with numerical agreement with extensive real-world experiments: SS and MFR regularizations bring more benefits to the scenarios of node transfer and link transfer, respectively. In a nutshell, our study paves the way toward explicitly constructing and training GNNs that can capture more transferable representations across graph domains. Codes are released at https://github.com/Shen-Lab/GDA-SpecReg. 

Estimating interseismic deformation in subduction fault zones can offer insights into the frequency and magnitude of megathrust earthquakes. Diffusive mass transfer is a significant mechanism of strain during interseismic periods along the plate interface, observed through the pervasive scaly fabrics and mineral veins in tectonic mélanges of ancient accretionary prisms. The dissolution of fluid‐mobile elements (e.g., Si and Large‐Ion Lithophile Elements) along scaly folia and subsequent reprecipitation as veins lead to the enrichment of fluid‐immobile elements (e.g., Ti and High Field Strength Elements) in scaly fabrics. The kinetics of dissolution‐precipitation is temperature‐dependent, suggesting depth‐dependent mass transfer along subduction interfaces. Here, we evaluate the magnitudes of volume strain in a suite of mélange samples that span peak metamorphic temperatures of 130–340°C. Micro‐chemical analysis shows that the depletion of fluid‐mobile elements and enrichment of fluid‐immobile elements in scaly fabrics increases with temperature. Assuming the conservation of Ti, we apply mass balance constraints to calculate the volumetric strain in scaly fabrics. Results indicate average volumetric strain of 28% and 95% in the individual scaly fabrics of the Lower Mugi and Makimine mélanges in Japan, which record temperatures near the updip and downdip isotherms bounding the seismogenic zone, respectively. To determine the total volume strain within an area of interest, we integrate the amount of volume loss along individual microstructures across the network using image analyses, which ranges from 3% to 14% for the mélanges. Our approach demonstrates the potential to fully describe the deformation related to mass transfer by connecting characterization in different scales with geochemical analyses.

 

Abstract We demonstrated under six minutes automatic provisioning of optical paths over fielddeployed alien access links and WDM carrier links using commercial-grade ROADMs, whitebox muxponders, and multi-vendor transceivers. With channel probing, transfer learning, and Gaussian noise model, we achieved an estimation error (Q-factor) below 0.7 dB. ©2023 The Author(s) 

Low-lying coastal cities across the world are vulnerable to the combined impact of rainfall and storm tide. However, existing approaches lack the ability to model the combined effect of these flood mechanisms, especially under climate change and sea level rise (SLR). Thus, to increase flood resilience of coastal cities, modeling techniques to improve the understanding and prediction of the combined effect of these flood hazards are critical. To address this need, this study presents a modeling system for assessing the combined flood impact on coastal cities under selected future climate scenarios that leverages ocean modeling with land surface modeling capable of resolving urban drainage infrastructure within the city. The modeling approach is demonstrated in quantifying the impact of possible future climate scenarios on transportation infrastructure within Norfolk, Virginia, USA. A series of combined storm events are modeled for current (2020) and projected future (2070) climate scenarios. The results show that pluvial flooding causes a larger interruption to the transportation network compared to tidal flooding under current climate conditions. By 2070, however, tidal flooding will be the dominant flooding mechanism with even nuisance flooding expected to happen daily due to SLR. In 2070, nuisance flooding is expected to cause a 4.6% total link close time (TLC), which is more than two times that of a 50-year storm surge (1.8% TLC) in 2020. The coupled flood model was compared with a widely used but physically simplistic bathtub method to assess the difference resulting from the more complex modeling presented in this study. The results show that the bathtub method overestimated the flooded area near the shoreline by 9.5% and 3.1% for a 10-year storm surge event in 2020 and 2070, respectively, but underestimated the flooded area in the inland region by 9.0% and 4.0% for the same events. The findings demonstrate the benefit of sophisticated modeling methods compared to more simplistic bathtub approaches, in climate adaptive planning and policy in coastal communities. 

Context. SN 2020qlb (ZTF20abobpcb) is a hydrogen-poor superluminous supernova (SLSN-I) that is among the most luminous (maximum M g  = −22.25 mag) and that has one of the longest rise times (77 days from explosion to maximum). We estimate the total radiated energy to be > 2.1 × 10 51 erg. SN 2020qlb has a well-sampled light curve that exhibits clear near and post peak undulations, a phenomenon seen in other SLSNe, whose physical origin is still unknown. Aims. We discuss the potential power source of this immense explosion as well as the mechanisms behind its observed light curve undulations. Methods. We analyze photospheric spectra and compare them to other SLSNe-I. We constructed the bolometric light curve using photometry from a large data set of observations from the Zwicky Transient Facility (ZTF), Liverpool Telescope (LT), and Neil Gehrels Swift Observatory and compare it with radioactive, circumstellar interaction and magnetar models. Model residuals and light curve polynomial fit residuals are analyzed to estimate the undulation timescale and amplitude. We also determine host galaxy properties based on imaging and spectroscopy data, including a detection of the [O III] λ 4363, auroral line, allowing for a direct metallicity measurement. Results. We rule out the Arnett 56 Ni decay model for SN 2020qlb’s light curve due to unphysical parameter results. Our most favored power source is the magnetic dipole spin-down energy deposition of a magnetar. Two to three near peak oscillations, intriguingly similar to those of SN 2015bn, were found in the magnetar model residuals with a timescale of 32 ± 6 days and an amplitude of 6% of peak luminosity. We rule out centrally located undulation sources due to timescale considerations; and we favor the result of ejecta interactions with circumstellar material (CSM) density fluctuations as the source of the undulations.