Acta Geotechnica - Liquid bridges in unsaturated soils attach to grain contacts and contribute to strengthening microscopic bonding forces, which leads to macroscopic high strength and stiffness... 相似文献
I present the derivation of the Preconditioned Optimizing Utility for Large-dimensional analyses (POpULar), which is developed for adopting a non-diagonal background error covariance matrix in nonlinear variational analyses (i.e., analyses employing a non-quadratic cost function). POpULar is based on the idea of a linear preconditioned conjugate gradient method widely adopted in ocean data assimilation systems. POpULar uses the background error covariance matrix as a preconditioner without any decomposition of the matrix. This preconditioning accelerates the convergence. Moreover, the inverse of the matrix is not required. POpULar therefore allows us easily to handle the correlations among deviations of control variables (i.e., the variables which will be analyzed) from their background in nonlinear problems. In order to demonstrate the usefulness of POpULar, we illustrate two effects which are often neglected in studies of ocean data assimilation before. One is the effect of correlations among the deviations of control variables in an adjoint analysis. The other is the nonlinear effect of sea surface dynamic height calculation required when sea surface height observation is employed in a three-dimensional ocean analysis. As the results, these effects are not so small to neglect. 相似文献
It is important to estimate hard-to-observe parameters in the ocean interior from easy-to-observe parameters. This study therefore
demostrates a reconstruction of observed temperature and salinity profiles of the sea east of Japan (30°≈40°N, 140°≈150°E).
The reconstruction was done by estimating suboptimal state from several values of the observed profiles and/or sea surface
dynamic height (SDH) calculated from the profiles. The estimation used a variational method with vertical coupled temperature-salinity
empirical orthogonal function (EOF) modes. Profiles of temperature and salinity in the subtropical region are effectively
reconstructed from in situ temperature profile data, or sea surface temperature (SST) and SDH. For example, the analyzed temperature field from SST
and SDH has an accuracy to within 1°C in the subtropical region. Salinity in the sea north of Kuroshio, however, is difficult
to estimate because of its complex variability which is less correlated with temperature than in the subtropical region. Sea
surface salinity is useful to estimate the subsurface structure. We also show the possibility that the estimation is improved
by considering nonlinearity in the equation calculating SDH from temperature and salinity analysis values in order to examine
the misfit between analysis and observation. Analysis using TOPEX/POSEIDON altimetry data instead of SDH was also performed.
This revised version was published online in July 2006 with corrections to the Cover Date. 相似文献
We assess validity of a Gaussian error assumption, the basic assumption in data assimilation theory, and propose two kinds
of constraints regarding non-Gaussian statistics. In the mixed water region (MWR) off the east coast of Japan exhibiting complicated
frontal structures, a probability density function (PDF) of subsurface temperature shows double peaks corresponding to the
Kuroshio and Oyashio waters. The complicated frontal structures characterized by the temperature PDF sometimes cause large
innovations, bringing about a non-Gaussianity of errors. It is also revealed that assimilated results with a standard three-dimensional
variational (3DVAR) scheme have some issues in MWR, arising from the non-Gaussianity of errors. The Oyashio water sometimes
becomes unrealistically cold. The double peaks seen in the observed temperature PDF are too smoothed. To improve the assimilated
field in MWR, we introduce two kinds of constraints, Jc1 and Jc2, which model the observed temperature PDF. The constraint Jc1 prevents the unrealistically cold Oyashio water, and Jc2 intends to reproduce the double peaks. The assimilated fields are significantly improved by using these constraints. The
constraint Jc1 effectively reduces the unrealistically cold Oyashio water. The double peaks in the observed temperature PDF are successfully
reproduced by Jc2. In addition, not only subsurface temperature but also whole level temperature and salinity (T–S) fields are improved by
adopting Jc1 and Jc2 to a multivariate 3DVAR scheme with vertical coupled T–S empirical orthogonal function modes. 相似文献
The distinctive bathymetric feature exists in the Suruga Bay, Japan. It has been called as Senoumi (Stone flower sea) from old times. Senoumi is a 30?km wide and 20?km long concave feature. Its origin has not been explained yet; however, the feature might be a combined consequence of intensive tectonic activity in the plate border, landslides, and a submarine flow coming from the Oi River. If the Senoumi was caused by a landslide, the latter would be larger than any on-land landslide in Japan. The downshelf “exit” from this feature is much narrower than its central part. This is not usual shape of landslides, but it is similar to the liquefied landslides such as those in quick clays which mobilize great strength reduction after failure. To study Senoumi as a landslide, the shear behaviors of the following three soil samples were investigated by the cyclic and seismic undrained stress control ring shear tests. One sample is volcanic ash taken from the base of landslide deposits (mass transport deposits), from 130 to 190?m deep layer below the submarine floor which was drilled and cored by the Integrated Ocean Drilling Program Expedition 333. Another two samples are the Neogene silty–sand and silt taken from the Omaezaki hill adjacent to the Senoumi, because the shear zone might have been formed in Neogene layers extending from on-land to the continental shelf. The largest strength reduction from peak to steady-state shear resistance in the undrained cyclic loading test was found in volcanic ash. The strength reduction in Neogene silty–sand was smaller than volcanic ash, while the Neogene silt mobilized the least post-failure strength reduction. An integrated model simulating the initiation and motion of earthquake-induced rapid landslides (landslide simulation (LS)-RAPID, Sassa et al. Landslides 7–3:219–236, 2010) was applied to this study. The steady-state shear resistance and other geotechnical parameters measured by the undrained ring shear tests and the greatest strong motion record in the 2011 off-the-Pacific Coast of Tohoku earthquake (Mw 9.0), also known as “2011 Tohoku Earthquake” at the observation point MYG004 (2,933?gal) were input to this model. As the result, it was found that landslides would be triggered by 0.30–1.0 times of MYG004 in volcanic ash, 0.4–1.0 times of MYG004 in Neogene silty–sand and Neogene silt, though the depth and area of triggered landslides were different in soils and intensity of shaking. Feature, created by LS-RAPID using the parameters of volcanic ash, was most similar to the Senoumi in depth and extent. The result obtained from this study includes a hypothesis to be proved, but presents the strong need to investigate the risk of the large-scale submarine landslides which could enhance tsunami wave and possibly enlarge the submarine landslide retrogressively into the adjacent coastal plain by the upcoming mega earthquake in the Nankai Trough. 相似文献
The temperature distribution at depth is a key variable when assessing the potential of a supercritical geothermal resource as well as a conventional geothermal resource. Data-driven estimation by a machine-learning approach is a promising way to estimate temperature distributions at depth in geothermal fields. In this study, we developed two methodologies—one based on Bayesian estimation and the other on neural networks—to estimate temperature distributions in geothermal fields. These methodologies can be used to supplement existing temperature logs, by estimating temperature distributions in unexplored regions of the subsurface, based on electrical resistivity data, observed geological/mineralogical boundaries, and microseismic observations. We evaluated the accuracy and characteristics of these methodologies using a numerical model of the Kakkonda geothermal field, Japan, where a temperature above 500 °C was observed below a depth of about 3.7 km. When using geological and geophysical knowledge as prior information for the machine learning methods, the results demonstrate that the approaches can provide subsurface temperature estimates that are consistent with the temperature distribution given by the numerical model. Using a numerical model as a benchmark helps to understand the characteristics of the machine learning approaches and may help to identify ways of improving these methods.
This paper describes a rockfall event in the Daisekkei Valley of Mount Shirouma-dake (2,932 m), the northern Japanese Alps.
The rockfall occurred on a steep cliff comprising well-jointed felsites and produced debris of ≥8,000 m3. Most debris was deposited on an elongated snowpatch located immediately beneath the cliff, and it caused casualties among
people who were trekking along a trail on the snowpatch. Additionally, a large rock block slipped 1 km on the snowpatch. The
rockfall could have been due to the differential retreat of the rockwall, which contains areas of high- and low-density joints.
Seasonal and diurnal freeze–thaw activities and snow avalanches and wash appear to be important factors responsible for the
retreat. Although some rock blocks that can collapse further remain on the rockwall, the position of the mountain trail in
the Daisekkei Valley is fixed. Fundamental reform of tourism systems for climbers, including education on natural hazards,
is required. 相似文献