Progress in Earth and Planetary Science最新文献

Recent experimental investigations of grain size evolution in bridgmanite-ferropericlase assemblages have suggested very slow growth for these bimodal phases. Despite numerous speculations on grain size-dependent viscosity, a comprehensive test with realistic grain size evolution parameters compatible with the lower mantle has been lacking. In this study, we develop self-consistent 2-D spherical half-annulus geodynamic models of Earth's evolution using the finite volume code StagYY to assess the role of grain size on lower mantle viscosity. We explore several models with and without grain size evolution to compare their effects on mantle viscosity. In models with grain size evolution, we consider three scenarios: (1) uniform grain growth throughout the entire mantle with a composite rheology, (2) different grain growth in the upper and lower mantle with a composite rheology, and (3) different grain growth in the upper and lower mantle with purely diffusion creep rheology. In the case of different grain size evolution, the upper mantle's grain size evolution law is controlled by forsterite-enstatite grain growth, while the lower mantle's grain size evolution law is controlled by bridgmanite-ferropericlase grain growth. Our results suggest that mantle viscosity is primarily controlled by temperature, whereas grain size has a minor effect compared to the effect of temperature. We attribute two primary reasons for this: First, the bridgmanite-ferropericlase growth is very slow in the lower mantle and the grain size variation is too small to significantly alter the mantle viscosity. Secondly, if grains grow too fast, thus the mantle deforms in the dislocation creep regime, making viscosity grain size-independent. To establish the robustness of this finding we vary several other model parameters, such as surface yield strength, phase transition grain size reset, different transitional stresses for creep mechanisms, pressure dependence on grain growth, and different grain damage parameters. For all our models, we consistently find that grain size has a very limited effect on controlling lower mantle viscosity in the present-day Earth. However, large grain size may have affected the lower mantle viscosity in the early Earth as larger grains of single phase bridgmanite could increase the viscosity of the early mantle delaying the onset of global convection.

Aspects of the Quaternary sedimentary geology of South-East Asia have proven problematic in terms of interpretation as to the origins and relationships of the surface sediment layers. The MIS 20 large meteorite impact (c., 788 to 785 ka) occurred within mainland South-East Asia, evident from the well-researched 'Australasian Tektite Strewn Field' which extends over at least one tenth of the surface of the Earth. Key questions include: 1) whether the sedimentary impact signature is preserved in the Quaternary sediment cover of the region and 2) whether stratigraphic indicators and dating methods can discriminate meteorite impact-related associations of sedimentary strata, despite subsequent reworking and diagenesis. The importance of the questions raised relate to the search for the impact site, which has not been located conclusively. Moreover, the sedimentary signatures of meteorite impacts are not well known and the descriptions in this study should aid the recognition of impact signatures elsewhere in the world. An hypothesis was developed: Surface Quaternary sediments across a wide area of mainland South-East Asia represent the effects of a regionally significant meteorite impact. Over one hundred sedimentary sections were logged across five countries in mainland South-East Asia. Methods used, defining the stratigraphy and sedimentology, include computed tomography and X-ray scanning, geochemistry, magnetic susceptibility, and environmental luminescence as well as conventional grain size analyses. Luminescence analyses were applied to samples from key strata to provide age constraints and indications of reworking through dose distributional analysis of quartz fractions. The results of the investigation explain the nature of the stratigraphy and relate it specifically to the meteorite impact. In this manner, the strata and sedimentary signatures of the ejecta from a large cosmic impact are defined across a broad region, rather than being described at singular and isolated sections. The novelty is the spatial scale of the investigation which nevertheless remains detailed. A summary model of impact stratigraphy is presented that applies to the regional ejecta blanket covering at least 300,000 km². Tektites were co-deposited with the ejecta and not introduced by surface processes reworking the deposits. Similar models may be applicable outside of mainland South-East Asia, wherever other large impacts are suspected to have occurred.

Supplementary information: The online version contains supplementary material available at 10.1186/s40645-024-00660-9.

Polymodal particle size distributions are generally analyzed by separating them into lognormal distributions, but estimating the precise number of lognormal components required remains a considerable problem. In the present study, appropriate evaluation criteria for the estimation of the number of components were examined by using artificial data for which the true number of components was known. The characteristics of estimations of the number of components by four evaluation criteria, the mean square error (MSE), Akaike information criterion (AIC), Bayesian information criterion (BIC), and adjusted R-squared (ARS), were investigated. The results showed that the MSE and ARS were less sensitive to the true number of components and tended to overestimate the number of components. By contrast, the AIC and BIC tended to underestimate the number of components, and their correct answer rates decreased as the true number of components increased. The BIC tended to include the true number of components among its higher ranked models. The present evaluation results suggest that the MSE, although frequently used, is not necessarily the most appropriate evaluation criterion, and that the AIC and ARS may be more appropriate criteria. Furthermore, checking whether the number of components estimated by the AIC or ARS is included among higher ranked BIC models might prevent overestimation and thereby allow for more valid estimation of the number of components. When the criteria were applied to grain-size distributions of lacustrine sediments, it was possible to estimate the number of components that reflected differences in grain-size distribution characteristics.

The thermal structure of subduction zones is fundamental to our understanding of the physical and chemical processes that occur at active convergent plate margins. These include magma generation and related arc volcanism, shallow and deep seismicity, and metamorphic reactions that can release fluids. Computational models can predict the thermal structure to great numerical precision when models are fully described but this does not guarantee accuracy or applicability. In a trio of companion papers, the construction of thermal subduction zone models, their use in subduction zone studies, and their link to geophysical and geochemical observations are explored. In this part II, the finite element techniques that can be used to predict thermal structure are discussed in an introductory fashion along with their verification and validation.

Steady-state thermal structure for the updated subduction zone benchmark. a) Temperature predicted by TF for case 1; b) temperature difference between TF and Sepran using the penalty function (PF) method for case 1 at f_m=1 where f_m represents the smallest element sizes in the finite element grids near the coupling point; c) slab top temperature comparison for case 1; and d)–f) as a)–c) but now for case 2. The star indicates the position or temperature conditions at the coupling point.

In this work, snowdrift experiments which are equivalent to one drifting snow event are performed by the snowdrift model. The model consisted of the computational fluid dynamics part of the large-eddy simulation with the lattice Boltzmann method and the drifting snow part of the conventional advection algorithm for representative Lagrangian particles. The observed vertical wind profile of a 4 h drifting snow event in Teshikaga Town was used as the inflow boundary conditions in the model to compare the results of the snowdrift estimated by the model and the observed snowdrift distribution. Parallelization enabled us to simulate the snowdrift distribution in a realistic domain and on the time scale of a single drifting snow event. We demonstrated that the upgraded model could quantitatively reproduce the height and position of the observed snowdrift along the center of a three-dimensional fence.

InSAR time series analysis has become a major tool for nationwide land deformation monitoring. Sentinel-1 SAR data have enabled us to measure and monitor ground deformation globally with high accuracy and resolution through InSAR time series analysis, due to its constant and frequent global coverage and open data policy since 2014. Although several datasets from previous SAR satellites were available before Sentinel-1, such comprehensive deformation monitoring was not performed due to several limitations such as data quality, analysis technique, data policy, and processing capacity at that time. However, since a large amount of ALOS InSAR products and an open-source InSAR time series analysis tool LiCSBAS have become openly and freely available, we can easily derive the deformation from 2006 to 2011 by using them. In this study, we detected the deformation time series and velocity in all major urban areas in Japan from 2006 to 2011 and compared the results with the deformation from 2014 to 2020 detected by Sentinel-1 data. The two deformation datasets with different time periods revealed various 15-year deformation histories, such as long-term constant subsidence in Tomakomai and Niigata, changes in deformation areas and/or velocities in Hirosaki, Kujyukuri, Kanazawa, and Matsushiro, and appearance or disappearance of deformation in Joso, Yoyogi, and Kyoto. Future abundant and continuous SAR data acquisitions will reveal more long-term deformation transitions and help to understand the details of the mechanisms.

A technique is developed to quantify the ultra-trace ²³¹Pa (35–3904 ag) concentration in seawater using multi-collector inductively coupled plasma mass spectrometry (MC-ICP-MS). The method is a modification of the process developed by Shen et al. (Anal Chem 75(5):1075–1079, 2003. https://doi.org/10.1021/ac026247r) and extends it to the application of very low levels of actinides, and the 35 ag ²³¹Pa can be measured with a precision of 15%. The total process blank for the water column was 0.02 ag/g, while the values of the large and small particles were ~ 30 ag/g. The ionization efficiency (ions generated/atom loaded) varies from 0.7 to 2.4%. The measurement time is 2–5 min. The amount of ²³¹Pa needed to produce ²³¹Pa data with an uncertainty of ± 0.8–15% is 35–3904 ag (~ 0.9 × 10⁵ to 10 × 10⁶ atoms). Replicate measurements of known standards and seawater samples demonstrate that the analytical precision approximates that expected from counting statistics, and that based on detection limits of 52 ag, 55 ag, and 28 ag, protactinium can be detected in a minimum seawater sample size of ~ 2.6 L for small suspended particulate matter (> 0.8 μm and < 51 μm), ~ 3.0 L for large suspended particulate matter (> 51 μm), and ~ 56 mL for filtered (< 0.45 μm) seawater. The concentration of ²³¹Pa (several attograms per liter) can be determined with an uncertainty of ± 2–8% (2σ) for suspended particulate matter filtered from ~ 60 L of seawater. For the dissolved fraction, ~ 1 L of seawater yields ²³¹Pa measurements with a precision of 0.8–10%. The sample size requirements are several orders of magnitude less than traditional decay-counting techniques, and the precision is better than that previously reported for ICP-MS techniques. Our technique can also be applied to other environmental samples, including river, lake, and cave water samples.