Distribution Of Physical Properties Research Articles

Effect of Farming Systems on the Spatial Variability of Soil Physical Properties and Soybean Yield

In no-tillage (NT) and minimum tillage (MT) areas, spatial variability of soil physical properties may affect crop yield. The aim of this study was to assess the spatial distribution of soil physical properties, as well as the yield components and grain yield of soybean (GY), based on the mapping of areas under soil conservation farming systems. We assessed yield components, GY and the physical properties of an Oxisol, under NT and MT using the t-student test, and geostatistics to assess spatial variability. The largest population of NT plants showed no spatial dependence and did not influence GY, but the components related to plant height and soil properties differed between systems. From a spatial standpoint, the kriging maps demonstrated that mass of one thousand grains (MOG), total porosity (TP) and soil bulk density (BD) influenced GY under NT, whereas TP1 exerted the most influence under high soil moisture conditions and MT. The maps make it possible to assess the spatial distribution of soil physical properties and the influence on GY, making them an important tool for more accurate production planning in soil conservation systems.

The VANDELS survey: the star-formation histories of massive quiescent galaxies at 1.0 < z < 1.3

ABSTRACT We present a Bayesian full-spectral-fitting analysis of 75 massive ($M_* \gt 10^{10.3} \, \mathrm{M_\odot }$) UVJ-selected galaxies at redshifts of 1.0 < z < 1.3, combining extremely deep rest-frame ultraviolet spectroscopy from VANDELS with multiwavelength photometry. By the use of a sophisticated physical plus systematic uncertainties model, constructed within the bagpipes code, we place strong constraints on the star-formation histories (SFHs) of individual objects. We first constrain the stellar mass versus stellar age relationship, finding a steep trend towards earlier average formation time with increasing stellar mass (downsizing) of $1.48^{+0.34}_{-0.39}$ Gyr per decade in mass, although this shows signs of flattening at $M_* \gt 10^{11} \, \mathrm{M_\odot }$. We show that this is consistent with other spectroscopic studies from 0 < z < 2. This relationship places strong constraints on the AGN-feedback models used in cosmological simulations. We demonstrate that, although the relationships predicted by simba and illustristng agree well with observations at z = 0.1, they are too shallow at z = 1, predicting an evolution of ≲0.5 Gyr per decade in mass. Secondly, we consider the connections between green-valley, post-starburst, and quiescent galaxies, using our inferred SFH shapes and the distributions of galaxy physical properties on the UVJ diagram. The majority of our lowest-mass galaxies ($M_* \sim 10^{10.5} \, \mathrm{M_\odot }$) are consistent with formation in recent (z < 2), intense starburst events, with time-scales of ≲500 Myr. A second class of objects experience extended star-formation epochs before rapidly quenching, passing through both green-valley and post-starburst phases. The most massive galaxies in our sample are extreme systems: already old by z = 1, they formed at z ∼ 5 and quenched by z = 3. However, we find evidence for their continued evolution through both AGN and rejuvenated star-formation activity.

A Preliminary Assessment of Physical Properties of Sediment at Sembrong Reservoir

Reservoir is one of the main sources of water supply and provides functions to hydroelectric power, domestic use, agriculture, flood protection and recreation. Due to the rapid farming, agricultural activities and oil palm trees plantation development that contribute to the increment of sediment, the Sembrong reservoir is slowly endangered due to the shrinkage of reservoir storage. Therefore, this research is initiated to characterize the sediment which could be used as control in future research in the treatment of contaminated sediment. The objective of this study was to determine distribution of physical properties of sediment based on Sembrong Reservoir sediment. In this study, sediment samples were collected using gravity corer at different points in the reservoir area. The physical properties of sediment was investigated by conducted experiments include particle size distribution, bulk density test, moisture content, loss of ignition and scanning electron microscope (SEM). From the data obtained, the bulk density at horizontal distribution was almost the same and the average values obtained was between 10-17 kN/m3. Moisture content of sediment at horizontal distribution at location 3 was the highest with 185.04%. From the particle size distribution, it can be concluded that location 2 had the highest percentage of finer sand where the value was 81% while 19% were silt and clay. For horizontal distribution of organic content, location 5 had the highest result with the percentage of 12.768%. For particle shape of sediment, it was in bulky and flaky categories while the void ratio was getting smaller by the depth.

Efficient lattice Boltzmann method for electrohydrodynamic solid-liquid phase change.

Melting in the presence of electrohydrodynamic (EHD) flow driven by the Coulomb force in dielectric phase change material is numerically studied. A model is developed for the EHD flow in the solid-liquid phase change process. The fully coupled equations including mechanical equations, electrical equations, energy equations, and the continuity equations in the solid-liquid interface are solved using a unified lattice Boltzmann model (LBM). Firstly, the numerical model is validated by several cases in the hydrostatic state, and all LBM results are found to be highly consistent with analytical solutions. Besides, our LBM code is able to reproduce the step changes in the distribution of charge density and electric field due to the discontinuous distribution of physical properties at the interface. Then, a systematical investigation is conducted on various nondimensional parameters, including electric Rayleigh number T, Prandtl number Pr, and Stefan number St. Results are presented for the transient evolutions of temperature, fluid flow, charge density fields, and liquid fraction. Four flow stages in the melting process together with three kinds of flow instabilities are observed. It is found that the electric field has significant influence on the melting, especially at high T and Pr and low St. Over the tested cases, a maximum melting time saving of around 50% is found.

Study on geotechnical characteristics of marine soil at Hong Kong–Zhuhai–Macao tunnel

The Hong Kong–Zhuhai–Macao Bridge (HZMB) immersed tunnel is part of the HZMB, connecting three vibrant cities and crossing the Lingding Sea in South China. It is at the west channel of the Pearl River Estuary and the Tonggu West Channel. The stratum in this area includes deep soft soil with large differences in soil distribution. Based on geotechnical investigation data on marine soil at the HZMB tunnel, the spatial distribution of physical and mechanical properties of deposit soils was statistically analyzed. A field investigation revealed that deposit soils could be subdivided into four strata: Holocene marine deposit stratum, late Upper Pleistocene continental, mid Upper Pleistocene marine alluvial deposit, and early Upper Pleistocene alluvial deposit stratum. Field and laboratory test results indicate that Holocene marine deposit soils had a high water content (42.5% < < 81.2%), large void ratio (1.21 < < 2.43), flow-plastic state ( > 1), high compressibility ( < 5 MPa), low shear strength (2.5 kPa < < 28.5 kPa), and low bearing capacity (58.9 < < 120 kPa); therefore, they could not be used as a natural foundation. The late, mid, and early Upper Pleistocene continental deposit strata had low water content (1.5% < < 40.5%), high bearing capacity ( > 120kPa), low void ratio (0.41 < < 1.24), and low flow-plastic to liquid state ( < 0.95), Additionally, the results of regression analysis showed that the compression ratio was positively correlated with natural water content. Finding from field and laboratory tests provide a basis for immersed tunnel design and construction.

Studi Pemodelan Distribusi Konduktivitas Bawah Permukaan 3-D Berbasis Data Resistivitas Menggunakan Program Aplikasi DCIP3D Versi 2.1

Human needs will increase in various aspects, including the need for content in the earth. In describing the distribution of physical properties beneath the earth's surface, 3-D modeling is the best way to find out the details of the subsurface content of the earth. To facilitate the making of 3-D models from the earth, the DCIP3D application program is used. DCIP3D develops a series of inversion algorithms to restore 3-D conductivity models. The research was conducted to optimize the use of DCIP3D version 2.1. The data used are secondary and synthetic data. Secondary data is resistivity geoelectric data consisting of four line of the results of the PTBGN-BATAN survey in the Ahu Mamuju, West Sulawesi, 2017. Data processing is done by making mesh cell size which is a discritization of the earth model. The variation of the mesh core size is done (5 × 5 × 5) m, (10 × 10 × 10) m and (15 × 15 × 15) m. By completing various input files for DCIP3D, the 3-D subsurface conductivity model was successfully restored. Adding topographic values to the model causes the model to look even more real. The best model that describes the conductivity of the secondary data survey area is a model with a core cell (10 × 10 × 10) m because it corresponds to the distance between the electrodes used during data acquisition.

Comparison Between Hydraulic Conductivity Anisotropy and Electrical Resistivity Anisotropy From Tomography Inverse Modeling

Hydrogeophysics is increasingly used to understand groundwater flow and contaminant transport, essential basis for groundwater resources forecast, management and remediation. It has proven its ability to improve the characterization of the hydraulic conductivity (\textit{K}) when used along with hydrogeological knowledge. Geophysical tools and methods provide high density information of the spatial distribution of physical properties in the ground at relatively low costs and in a non-destructive manner. Amongst them, the Electrical Resistivity Tomography (ERT) has been widely used for its high spatial coverage and for the strong theoretical links between electrical resistivity ($\rho$) and key hydrogeological parameters such as \textit{K}. Historically, ERT data processing was based on isotropic hypothesis. However, the unconsolidated aquifers in Canada reveal in most cases a strong anisotropic behaviour for \textit{K} both with \textit{in situ} or laboratory measurements. Recently, electrical anisotropy has been considered model-wise, but it is seldom considered as an interpretation tool or in the characterization process of the anisotropy of \textit{K}. In order to evaluate the potential of ERT to assess the anisotropy of electrical resistivity, we developed a forward and inverse modeling code. These codes have been validated and tested on a realistic synthetic case reproducing the behaviour of a real aquifer extensively characterized, the site of Saint-Lambert-de-Lauzon in Quebec (Canada). On this site, innovative \textit{in situ} hydraulic tomography has revealed a strong anisotropy, with up to three orders of magnitude between horizontal and vertical \textit{K} components. In order to confirm the link between \textit{in situ} \textit{K}- and $\rho$-anisotropies, an ERT survey has been performed, using the same wells as for the hydraulic tomography. The inversion confirms a strong link between \textit{K}- and $\rho$-anisotropies. It demonstrates the suitability of the anisotropic ERT approach coupled with well measurements to provide better estimates of \textit{K} and its anisotropy at the scale of a site.

Deep learning electromagnetic inversion with convolutional neural networks

Geophysical inversion attempts to estimate the distribution of physical properties in the Earth's interior from observations collected at or above the surface. Inverse problems are commonly posed as least-squares optimization problems in high-dimensional parameter spaces. Existing approaches are largely based on deterministic gradient-based methods, which are limited by nonlinearity and nonuniqueness of the inverse problem. Probabilistic inversion methods, despite their great potential in uncertainty quantification, still remain a formidable computational task. In this paper, I explore the potential of deep learning methods for electromagnetic inversion. This approach does not require calculation of the gradient and provides results instantaneously. Deep neural networks based on fully convolutional architecture are trained on large synthetic datasets obtained by full 3-D simulations. The performance of the method is demonstrated on models of strong practical relevance representing an onshore controlled source electromagnetic CO2 monitoring scenario. The pre-trained networks can reliably estimate the position and lateral dimensions of the anomalies, as well as their resistivity properties. Several fully convolutional network architectures are compared in terms of their accuracy, generalization, and cost of training. Examples with different survey geometry and noise levels confirm the feasibility of the deep learning inversion, opening the possibility to estimate the subsurface resistivity distribution in real time.

Delineating Facies Spatial Distribution by Integrating Ensemble Data Assimilationand Indicator Geostatistics With Level‐Set Transformation

AbstractThe facies‐based approach has been widely adopted to delineate an aquifer into distinct geological units with unique distributions of hydraulic, physical, and/or chemical properties. The recent development in ensemble‐based data assimilation methods allows both the direct and indirect data to be used to improve facies delineation. A major difficulty in those applications is to honor the spatial continuity and avoid overfitting after data assimilation. We introduce a new facies delineation framework to integrate ensemble data assimilation with traditional transition probability‐based geostatistics. A level‐set concept is used to parametrize discrete facies indicators and for updating facies shape. During the iterative data assimilation process, we impose spatial continuity by conditioning facies field generation on points selected adaptively based on their sensitivity to observation data. This reconditioning step is a key step to maintain spatial continuity and overcome overfitting problems in inversion. We selected two examples to evaluate the performance of the new framework in estimating facies‐based permeability field. The first example is a two‐dimensional synthetic system with transient head data induced by pumping tests used for delineating two facies. The second example is a three‐dimensional case with three facies, conceptualized from a field tracer experiment within the Columbia River corridor in Washington State, USA. Both examples demonstrate that the new method can adequately capture the spatial pattern of hydrofacies with reconditioning, which leads to the improved prediction of system behaviors.

Retrieval of the water ice column and physical properties of water-ice clouds in the martian atmosphere using the OMEGA imaging spectrometer

Using spectral images recorded by the OMEGA instrument on Mars Express (Observatoire pour la Minéralogie, l'Eau, les Glaces et l'Activité), we are able to derive physical properties of aerosols in water-ice clouds on Mars for a distribution of pixels over an observed cloud formation. These properties, mean effective radius, reff, and optical depth (at 0.67 μm), τi, were used to estimate the water ice-column (WIC), and we found an empirical relationship between the WIC and an ice cloud index (ICI). The overall mean of retrieved reff is ∼2.2 μm, with a standard deviation of 0.8 μm, and cloud formations with reff between 4.4 and 5.4 μm are observed. The optical depth varies between 0.2 and 2.0. The OMEGA spectra are primarily sensitive to water ice mass due to absorption, and we find that the ICI, very easy to compute, is a good proxy for the mass of the water-ice column (WIC) along the optical line of sight. Our retrieval of physical properties is limited in time (to before 2010) by the exhaustion of coolant for one of the OMEGA channels, and in space (to equatorial observations between 140∘W and 90∘E) by the availability of surface albedo measurements. However, we used the ICI to compute WIC values for the entire OMEGA data set, which has near-global coverage for Mars years 26–32, and we present a climatology of the WIC derived from the OMEGA data, which features enhancements on the order of 1.2–1.6 pr. μm over the aphelion cloud belt, and 1.5–2.5 pr. μm over the polar hoods. The data set analyzed is for observations between 140°W and 90°E, and between 35∘S and 35∘N. No restriction is placed on season, but the majority of cloudy observations were during the aphelion period from Ls 35∘ to 135∘. This work was motivated by the ability of the OMEGA instrument to observe the distribution of water-ice cloud physical properties, and by the availability of new a priori data sets, especially multi-spectral, aerosol-free surface albedo retrieved from a subset of the OMEGA data featuring a cloud-free sky. The main limitations of the retrieval algorithm are linked to the uncertainties on surface albedo, the dust opacity, and the quantity of water-ice suspended in the atmosphere, which can lead to spectral fits with lower accuracy or unrealistic results. We present distributions of each retrieved parameter, goodness of fit, ICI, and cloud mass, and our investigation of relationships between each parameter. Our approach was to maximize the amount of data analyzed, apply stringent data quality cuts and take a statistical approach to interpretation.

Forging new worlds: high-resolution synthetic galaxies with chained generative adversarial networks

Astronomy of the 21st century increasingly finds itself with extreme quantities of data. This growth in data is ripe for modern technologies such as deep image processing, which has the potential to allow astronomers to automatically identify, classify, segment and deblend various astronomical objects. In this paper, we explore the use of chained generative adversarial networks (GANs), a class of generative models that learn mappings from latent spaces to data distributions by modelling the joint distribution of the data, to produce physically realistic galaxy images as one use case of such models. In cosmology, such datasets can aid in the calibration of shape measurements for weak lensing by augmenting data with synthetic images. By measuring the distributions of multiple physical properties, we show that images generated with our approach closely follow the distributions of real galaxies, further establishing state-of-the-art GAN architectures as a valuable tool for modern-day astronomy.

A short user guide for UBC-GIF gravity and magnetic inversions

Obtaining reliable predictions of the subsurface will provide a critical advantage for explorers seeking mineral deposits at depth and beneath cover. A common approach in achieving this goal is to use deterministic property-based inversion of potential field data to predict a 3D subsurface distribution of physical properties that explain measured gravity or magnetic data. The non-uniqueness of inversions of potential field data mandates careful and consistent parameterization of the problem to ensure realistic solutions. Including all prior geological knowledge as constraints on the inversion also helps ensure that the recovered predictions are consistent with both the geophysical data and the geological knowledge.

A structure-tensor-constrained cooperative inversion using an extracted physical property distribution

This work was supported by the Human Resources Development Program of the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grants funded by the Korea government Ministry of Trade, Industry and Energy (No. 20164010201120). This research was also supported by the basic research project of the Korea Institute of Geoscience and Mineral Resources, Daejeon, South Korea, funded by the Ministry of Science and ICT of Korea (GP2017-022).

Optimizing site-specific geostatistics to improve geotechnical spatial information in Seoul, South Korea

Subsurface soil and rock profiles are commonly interpreted from borehole log datasets. These datasets include three-dimensional spatial coordinate information, layer information, and standard penetration test results. More reliable spatial distribution of target physical properties can be obtained from additional testing at locations characterized by outlier observations and geotechnical uncertainties. At a given site, irregular measurements typically differ significantly from bulk measurements or proximal observations. In this study, a process for optimizing site-specific geostatistics, which uses geotechnical spatial information and applies optimum outlier thresholds with a multi-clustering method, is proposed to incorporate site-specific geo-layer uncertainties and identify their geotechnical value. Optimized geostatistical characteristic information for geological strata boundaries was derived and verified based on a sequential procedure applied to representative test areas in Seoul, South Korea.

Profile Distribution of Physical and Chemical Soil Properties in Izombe, Rainforest Zone of Nigeria

Profile Distribution of Physical and Chemical Soil Properties in Izombe, Rainforest Zone of Nigeria

Assessment of land degradation using comprehensive geostatistical approach and remote sensing data in GIS-model builder

This study was conducted to assess Land Degradation (LD) status under different land use. Geostatistical technique used to interpolate spatially distribution of soil physical, chemical and biological properties. Salinity indices were applied on Hyperspectral and Multispectral Data to predict the salt affected areas. Arc GIS model-builder implemented to integrate the available LD methodologies and produce the overall degradation map of the study area in Chamrajanagar district (CDK), Karnataka, India. Remote sensing data was found to be useful tools to map land resources, especially in the areas where accessibility is limited like mountains. This study determined spatial distribution by calculating different soil properties for soils profiles. For LD calculations, eighteen soil profiles were dug and 79 samples were analyzed. This along with the parameters taken into consideration i.e., soil, slope, rainfall, DEM, land use, and land characteristics maps. It was found by adopting the logical criteria that LD of CDK categorized as very high, high, moderate, low and very low. The result of this research work could be potentially used as a useful tool to guide policy decision makers for sustainable land resource management in CKD. Based on the imagery interpretation and soil map unit description, hotspots were identified for representing different types of degraded soils. From the physical and chemical characteristics of pedons studied, it has been found that soils of CDK are exposed to degradation in the surface and sub surface horizons.

A STUDY ON INTENSITY DISTRIBUTION OF CONCRETE IN STRUCTURE WITH DIFFERENT BUILDING AGES

Recently, seismic diagnosis and earthquake resistant reinforcement works of buildings built before the new earthquake resistant standards are conducted in various places. However, there is no uniformity in sampling methods, and it seems that there are occasional extreme variations in the determination of representative intensity of the building. In addition, the physical properties of structure concrete which factor of the above-mentioned variations are considered to reflect the horizontal and vertical distribution at the time of concrete placing. Therefore, this study, the core of the existing buildings to be disassembled was collected, and the distribution of physical properties of structure concrete was examined. All target buildings were reinforced concrete constructions, and cores were collected from pillars and waist walls. In addition, there are parts that were built at the time without the ready-mixed concrete plant (from 1960) and pump truck (from 1964). Experimental results of horizontal and vertical distributions of compressive intensity at the pillars and waist walls revealed that there was no greatest decrease in strength and there was not much difference between the maximum strength of the pillar and 300 mm from the vicinity of the pillar and about 300 mm from F. L. Therefore, from this study, it seems that the position of 300 mm from the pillar and F. L. is suitable for the measurement position which develops the representative intensity of the building.

Characterization and Monitoring of Porous Media with Electrical Imaging: A Review

Electrical geophysical imaging is a widely used noninvasive technology for visualizing porous media at scales larger than individual pores. Originally developed for medical applications, the technology has been applied to investigate flow and transport properties/processes in a wide range of fields including earth sciences, civil and environmental engineering, agricultural sciences and biology. Conventional electrical imaging measures electrical conduction processes occurring through the fluids filling the interconnected pores and within the electrical double layer at the solid–fluid interface. Complex electrical imaging, an extension of the methodology, also measures electrical polarization processes within the electric double layer and in the presence of metals. Electrical imaging has been used to determine the spatial distribution of physical properties controlling flow/transport in porous media, image fluid dynamics, observe solute transport phenomena and capture matrix transformations. The method has been applied to investigate porous media processes in soils, rocks, concrete, engineered barriers, wastewater filters and living trees. Advances in the technology will result from (1) improving the understanding of the relationship of complex conductivity measurements to more processes occurring in porous media and (2) developing new instrumentation that will support longer-term autonomous electrical monitoring.

Functionalized Anatomical Models for Computational Life Sciences.

The advent of detailed computational anatomical models has opened new avenues for computational life sciences (CLS). To date, static models representing the anatomical environment have been used in many applications but are insufficient when the dynamics of the body prevents separation of anatomical geometrical variability from physics and physiology. Obvious examples include the assessment of thermal risks in magnetic resonance imaging and planning for radiofrequency and acoustic cancer treatment, where posture and physiology-related changes in shape (e.g., breathing) or tissue behavior (e.g., thermoregulation) affect the impact. Advanced functionalized anatomical models can overcome these limitations and dramatically broaden the applicability of CLS in basic research, the development of novel devices/therapies, and the assessment of their safety and efficacy. Various forms of functionalization are discussed in this paper: (i) shape parametrization (e.g., heartbeat, population variability), (ii) physical property distributions (e.g., image-based inhomogeneity), (iii) physiological dynamics (e.g., tissue and organ behavior), and (iv) integration of simulation/measurement data (e.g., exposure conditions, “validation evidence” supporting model tuning and validation). Although current model functionalization may only represent a small part of the physiology, it already facilitates the next level of realism by (i) driving consistency among anatomy and different functionalization layers and highlighting dependencies, (ii) enabling third-party use of validated functionalization layers as established simulation tools, and (iii) therefore facilitating their application as building blocks in network or multi-scale computational models. Integration in functionalized anatomical models thus leverages and potentiates the value of sub-models and simulation/measurement data toward ever-increasing simulation realism. In our o2S2PARC platform, we propose to expand the concept of functionalized anatomical models to establish an integration and sharing service for heterogeneous computational models, ranging from the molecular to the organ level. The objective of o2S2PARC is to integrate all models developed within the National Institutes of Health SPARC initiative in a unified anatomical and computational environment, to study the role of the peripheral nervous system in controlling organ physiology. The functionalization concept, as outlined for the o2S2PARC platform, could form the basis for many other application areas of CLS. The relationship to other ongoing initiatives, such as the Physiome Project, is also presented.

Distribution Inference for Physical and Orbital Properties of Jupiter’s Moons

According to the physical and orbital characteristics in Carme group, Ananke group, and Pasiphae group of Jupiter’s moons, the distributions of physical and orbital properties in these three groups are investigated by using one-sample Kolmogorov–Smirnov nonparametric test. Eight key characteristics of the moons are found to mainly obey the Birnbaum–Saunders distribution, logistic distribution, Weibull distribution, and t location-scale distribution. Furthermore, for the moons’ physical and orbital properties, the probability density curves of data distributions are generated; the differences of three groups are also demonstrated. Based on the inferred results, one can predict some physical or orbital features of moons with missing data or even new possible moons within a reasonable range. In order to better explain the feasibility of the theory, a specific example is illustrated. Therefore, it is helpful to predict some of the properties of Jupiter’s moons that have not yet been discovered with the obtained theoretical distribution inference.