Predict Bulk Density Research Articles

Ex Vivo Assessment of Cortical Bone Properties Using Low-Frequency Ultrasonic Guided Waves.

The early diagnosis of osteoporosis through bone quality assessment is a major public health challenge. Research in axial transmission using ultrasonic guided waves has shown the method to be sensitive to the geometrical and mechanical properties of the cortical layer in long bones. However, because of the asymmetric nature of cortical bone, the introduction of a more elaborate numerical model than the analytical plate and cylinder models, as well as its inversion, continues to be challenging. The aim of this article is, therefore, to implement a bone-like geometry using semianalytical finite-element (SAFE) modeling to perform the inverse characterization of ex vivo radii at low frequencies (< 60 kHz). Five cadaveric radiuses were taken from donors aged between 53 and 88 and tested using a typical axial transmission configuration at the middle of the diaphysis. The dispersion curves of the propagating modes were measured experimentally and then systematically compared with the solutions obtained with the SAFE method. For each sample, four parameters were estimated using a parameter identification procedure: 1) the bulk density; 2) the thickness; 3) the outer diameter; and 4) a shape factor (SF). The results showed a moderate agreement between the predicted bulk density and the average voxel value calculated from X-ray computed tomography images. Furthermore, a good agreement was observed between the geometrical parameters (thickness, outer diameter, and SF) and the reference images.

The formation bulk density prediction for intact and fractured siliciclastic rocks

The formation bulk density is one of the most important rock properties required for reservoir evaluation and geomechanical analysis. In intervals where the formation bulk density logs are not acquired, the industry practice is to estimate the formation bulk density from the compressional-wave velocity using empirical relationships. The major problems with the existing empirical relationships are: (1) they were developed primarily for specific lithologies (in most cases clean formations) and have failed to produce reasonable estimates when applied over a lithological column that consists of several stratigraphic units; (2) they are not applicable to rocks that contain microcracks/fractures. In this paper, a new formation bulk density prediction method that can be applied to a wide range of intact and fractured siliciclastic rocks is being proposed based on experimental data. The model is then validated using wireline log data acquired from an onshore well in the tertiary deltaic system of the Niger Delta basin. In the new model, the formation bulk density is expressed as a function of sonic velocity difference and shale volume factor. In general, an excellent agreement exists between the predicted and measured formation bulk density using the new technique. The statistical analysis shows that the new formation bulk density prediction model outperforms the most widely used empirical relationships with the least-root-mean square errors and least residual values.

The new formation bulk density predictions for siliciclastic rocks

Accurate determination of the overburden pressure obtained by integrating the formation bulk density from surface to the depth of interest is very critical to pore pressure prediction. When information about the formation bulk density is not available, the current practice is to estimate formation bulk density from compressional-wave velocity using empirical relationships. There is no single formation bulk density prediction model that considers lithologic variation in siliciclastic settings. This imposes severe limitations on the application of the existing empirical relationships to any lithological column that consists of several stratigraphic units. In this paper, attempt is made to develop the new formation bulk density prediction models that can be applied to a wide range of lithologies in siliciclastic environments. The new models are validated using wireline log data acquired from two wells in the tertiary deltaic system of the Niger Delta basin. In the new models, formation bulk density is expressed as a function of compressional-wave velocity and shale volume factor. The accuracy of the new models is quantified using statistical analysis. In general, a good agreement exists between the predicted and measured formation bulk density using the newly proposed models. When compared to the existing models, the new models clearly outperform the most widely used empirical relationships. The new models produce the least root-mean-square errors, normal error distributions and least residual values. Unlike any of the existing empirical relationships, the newly developed models can be applied to clean sands, shaly sands and shales with good accuracy.

Quantification of organic carbon concentrations and stocks of tidal marsh sediments via mid-infrared spectroscopy

Tidal marshes are coastal ecosystems that store large amounts of sedimentary organic carbon (OC). Reducing the current uncertainty on the amount of OC stored these sediments requires the analysis of a large number of sediment samples. Soil sensing techniques, using mid infrared (MIR) spectroscopy combined with partial least squares regression (PLSR), have been proven to be a valid alternative for standard OC measurements in a wide range of terrestrial ecosystems. However, the application of these techniques to tidal marsh sediments has been very limited and the error associated with calculated sedimentary OC stocks using MIR spectroscopy/PLSR data has up till now not been evaluated. Therefore, we assessed the potential of MIR spectroscopy/PLSR to predict the OC concentration and OC stock of tidal marsh sediments in a temperate estuary (Scheldt estuary, Belgium and The Netherlands). Based on a cross-validation procedure, the results show that MIR spectroscopy/PLSR predicts the OC concentration of tidal marsh sediments along the entire estuary with a high accuracy (R2 = 0.94, RMSE = 0.56% OC). Similar accuracies were obtained for the prediction of sedimentary OC concentrations from independent tidal marshes. Organic carbon concentrations from tidal marshes in the salt portion of the estuary were, however, predicted more accurately using a PLSR model trained exclusively with sediment samples from this salinity zone. Combining depth profiles of predicted OC concentrations with measured bulk densities resulted in predictions of total OC stocks with a relative error < 4% for freshwater and brackish marshes, while errors for salt marshes were up to 30%. As MIR spectroscopy/PLSR is not able to accurately predict bulk density or OC density, standard field measurements of bulk density remain necessary to reliably predict sedimentary OC stocks in tidal marshes.

Modelling and Comparison of Wheat Flour Extrusion Cooking Behaviours in Two Different Twin-Screw Extrusion Systems

The aim of this study was to investigate the extrusion cooking behaviours of two wheat flour samples (W1DK, W2CH) in two different twin-screw extrusion systems, where the wheat flour samples were characterised by rapid visco analyser (RVA) measurements. A new method is proposed to model and compare the wheat flour extrusion behaviours in the two systems through the RVA measurements, an extrudate bulk density model and a melt viscosity calculation. A new method is suggested to calculate the melt viscosity in the extrusion process. The average viscosity value of the W1DK sample was 64% higher than that of the W2CH sample in the RVA measurements. The comparison results show that the high viscosity for W1DK obtained from the RVA corresponds to a high reduced melt viscosity (η r , (1/s)γ) for W1DK, i.e., η r = 0.25–2.61 (1/s)γ (η r = 0.042–0.082 (1/s)γ for W2CH), a high specific mechanical energy (SME) for W1DK, i.e., SME = 8.16–11.28 kJ/kg (SME = 5.57–9.99 kJ/kg for W2CH) and a low extrudate bulk density (ρ B ) for W1DK, i.e., ρ B = 185–488 g/L (1 g/L = 1 kg/m3) (ρ B = 406–510 g/L for W2CH) in the two extrusion systems. The RVA viscosity and the calculated melt viscosity can be used to identify a recipe characteristic in terms of torque and extrudate bulk density in the two extrusion systems. A comparison has also been made for the bulk density model and response surface methodology in the extrudate bulk density prediction.

Predicting bulk density using pedotransfer functions for soils in the Upper Anthemountas basin, Greece

Special equations called Pedotransfer Functions (PTFs) have been broadly implemented as state-of-the-art indirect cost-effective and time-saving methods in predicting soil bulk density (BD). The objectives of the present study are: a) to perform a comprehensive literature review and record published PTFs that estimate BD, b) to evaluate the performance of the recorded PTFs by applying on a soil dataset, built from soil samples collected from a Mediterranean river basin in Northern Greece and c) to further assess this soil dataset in order to develop new functions via multiple forms of regression analysis. In total, 56 PTFs derived from literature were cataloged and evaluated and results display that the function best predicting BD is the one proposed by Rawls (2004). In addition, 13 new PTFs were developed and results reveal that better estimates of BD were obtained when soil texture parameters, contents (%) of Sand (Sa), Silt (Si) and Clay (Cl), were added as predictors. Ultimately, depending on the availability of soil data, two straightforward models: BD = 2.268 − 0.179 ∗ ln(Sa) – 0.345 ∗ ln(OC) or BD = 2.039 − 0.563 ∗ OC + 0.103 ∗ OC2 are proposed for predicting soil BD in the study area.

Building a pedotransfer function for soil bulk density on regional dataset and testing its validity over a larger area

Though soil bulk density (BD) is important for crop growth and land management, information about BD is often missing in soil database due to the fact that the determination of BD is time consuming and labor intensive. In order to fill this gap, pedotransfer functions (PTFs) have been developed for predicting bulk density during last decades. To avoid non-valid extrapolation, the validity domain of PTFs should be investigated. In this study, a PTFs for bulk density was built using GBM model on data from Region Centre and we tested its validity on data from mainland France except Region Centre. Standardized Euclidean distance was applied to distinguish dissimilar soil samples from Region Centre data and other regions data. The established PTFs were not suitable for predicting these dissimilar samples. In order to make a balance between prediction accuracy and the number of samples can be predicted with our PTFs, different cutoff limits of Standardized Euclidean distance was compared. When optimized distance cutoff limit was at 97%, compared to result without excluding dissimilar samples, RMSPE value on test data decreased from 0.179 to 0.163gcm−3. An additional sampling strategy based on these dissimilar samples can improve the predictive ability of PTFs. We also suggest that the purposive sampling strategy or the systematic and dense sampling strategy will do large contribution in robust modelling of PTFs.

Bulk Density Prediction for Histosols and Soil Horizons with High Organic Matter Content

Bulk density (Bd) can easily be predicted from other data using pedotransfer functions (PTF). The present study developed two PTFs (PTF1 and PTF2) for Bd prediction in Brazilian organic soils and horizons and compared their performance with nine previously published equations. Samples of 280 organic soil horizons used to develop PTFs and containing at least 80 g kg-1 total carbon content (TOC) were obtained from different regions of Brazil. The multiple linear stepwise regression technique was applied to validate all the equations using an independent data set. Data were transformed using Box-Cox to meet the assumptions of the regression models. For validation of PTF1 and PTF2, the coefficient of determination (R2) was 0.47 and 0.37, mean error -0.04 and 0.10, and root mean square error 0.22 and 0.26, respectively. The best performance was obtained for the PTF1, PTF2, Hollis, and Honeysett equations. The PTF1 equation is recommended when clay content data are available, but considering that they are scarce for organic soils, the PTF2, Hollis, and Honeysett equations are the most suitable because they use TOC as a predictor variable. Considering the particular characteristics of organic soils and the environmental context in which they are formed, the equations developed showed good accuracy in predicting Bd compared with already existing equations.

Experimental investigation of fire propagation in single live shrubs

This work focuses broadly on individual, live shrubs and, more specifically, it examines bulk density in chaparral and its combined effects with wind and ignition location on the resulting fire behaviour. Empirical functions to predict bulk density as a function of height for 4-year-old chaparral were developed for two typical species of shrub fuels in southern California, USA, namely chamise (Adenostoma fasciculatum Hook &amp; Arn.) and manzanita (Arctostaphylos spp. Adans.). Fuel beds of chamise foliage and small-diameter branches were burned in an open-topped wind tunnel. Three levels of bulk density, two ignition locations and two wind speeds were examined, focusing on overall fire behaviour. Mean maximum mass loss rate, elapsed time at which maximum mass loss rate occurred, flame height, flame angle, peak gas temperature and its peak change rate were measured. The mean maximum mass loss rate was not significantly affected by wind speed, ignition location, bulk density or moisture content. Both wind speed and ignition location significantly affected the time that maximum mass loss rate occurred. Only wind speed affected flame height and flame angle. The peak gas temperature within the shrub burning area was found to be mostly affected by the bulk density.

Estimating soil bulk density with information metrics of soil texture

Using the Shannon Information Entropy (IE) as a soil structure metric is proposed to analyze the effect of the particle size distribution (PSD) heterogeneity on soil bulk density values. A data base including 6239 soil samples from Florida is used. For each soil the IE is computed using mass proportions of the seven texture fractions that the data base provides. The range of IE values is divided into subintervals of equal length to study how differences in the soil texture metric are reflected in differences in soil bulk density values. The total range of IE values divided into equal subintervals, each subinterval corresponds to the group of soils with IE in this subinterval, average bulk density for soils in each subinterval is found, and the average information entropy value in any of the subintervals is plotted versus the average soil bulk density values. Coefficients of determination of the linear regressions ‘average IE vs. average bulk density’ were 0.99 and 0.98 for 10 and 15 subintervals respectively. Predictions based in that linear relationship give the mean predicted error (MPE) equal to 0.0015g/cm3 over the total number of soils, and the normal distribution of prediction errors with the standard deviation of to 0.16g/cm3. These results strongly support the hypothesis that Information Entropy can serve as an indicator of the typical bulk density for a soil with a given PSD. Values of IE were also computed for all samples in the database using only three texture fractions: clay, silt and sand content. Simple linear regression using the IE value as the input variable was implemented to predict bulk density value. Additionally, several published bulk density pedotransfer functions (PTFs), including organic carbon (OC) content and texture inputs, were applied to the same data base. Results show the root-mean-squared error of predictions close to 0.16g/cm3 when the IE is used as the sole input. Estimation of bulk density using Information Entropy as predictor became worse for soils in horizon A than in the horizon E, respectively, possibly due to the influence of the different organic carbon content in those horizons.Overall, the Information Entropy metric of soil texture provides a useful input for estimating bulk density, which also might be used together with other inputs as depth or OC content.

Direct measurement and prediction of bulk density on alluvial soils of central Chile

The significance of soil bulk density ( rb) as a key indicator of soil quality was examined in this study. Bulk density values obtained by direct methods (clod, cylinder, and excavation) with three sample sizes (small, medium, and large) were compared with those obtained by 10 published pedotransfer functions (PTFs) for two alluvial soils (a massive fine-textured Fluventic Haploxeroll and an aggregated, coarse-textured Fluventic Haploxerept) of central Chile. With the exception of small cylinders in fine-textured soil, there were nonsignificant differences between the methods and sample sizes assessed. On the coarse-textured soil, there were nonsignificant differences between the excavation and clod methods, but mediumsized cylinders differed from other cylinder sizes. In general, the clod technique tended to give higher values than the other methods. Using basic information (texture and organic matter/C content) from the existing PTFs for both sites, a better fit for coarse-textured than fine-textured soils was obtained. This indicates that it is necessary to define a set of locally calibrated PTFs that address the complexity of the soil resource throughout Chile.

Compressibility and equivalent bulk modulus of shelled corn

An oedometer was used to measure changes in bulk density of shelled corn at different compression pressures and moisture contents. An equivalent confining pressure (ECP) was introduced to quantify the compression action in grain bins by considering non-uniformity and variability of compression pressures. Two models were developed for predicting bulk density and bulk modulus, respectively, as functions of ECP and moisture content. It was found that the uncompressed bulk density of shelled corn (before applying compression pressure) decreased with grain moisture and the relationship could be adequately described by a linear equation in the moisture range from 12.6 to 17.1% wb (wet basis). Shelled corn was more compressible at higher moisture content. However, bulk density approached to the same maximum value as compression pressure increased regardless of moisture content. In other words, moisture content had little effect on the maximum compressed bulk density. The equivalent bulk modulus of shelled corn increased with compression pressure and decreased with moisture content. The predicted bulk density and bulk modulus values were in good agreement with the experimental data.

PEDO-TRANSFER FUNCTIONS FOR ESTIMATING SOIL BULK DENSITY IN CENTRAL AMAZONIA

Under field conditions in the Amazon forest, soil bulk density is difficult to measure. Rigorous methodological criteria must be applied to obtain reliable inventories of C stocks and soil nutrients, making this process expensive and sometimes unfeasible. This study aimed to generate models to estimate soil bulk density based on parameters that can be easily and reliably measured in the field and that are available in many soil-related inventories. Stepwise regression models to predict bulk density were developed using data on soil C content, clay content and pH in water from 140 permanent plots in terra firme (upland) forests near Manaus, Amazonas State, Brazil. The model results were interpreted according to the coefficient of determination (R2) and Akaike information criterion (AIC) and were validated with a dataset consisting of 125 plots different from those used to generate the models. The model with best performance in estimating soil bulk density under the conditions of this study included clay content and pH in water as independent variables and had R2 = 0.73 and AIC = -250.29. The performance of this model for predicting soil density was compared with that of models from the literature. The results showed that the locally calibrated equation was the most accurate for estimating soil bulk density for upland forests in the Manaus region.

Hierarchical Pedotransfer Functions to Predict Bulk Density of Highly Weathered Soils in Central Africa

Bulk density (BD) is a key soil property for sustainable land management and C stock assessment. However, data on BD are often missing in various soil survey reports in Central Africa because its measurement is laborious, time consuming at large scales, and may pose expense and facilities challenges, particularly in resource-poor countries. Therefore, there was a need for developing pedotransfer functions (PTFs) to predict BD for soils of Central Africa as an alternative solution. To do so, two approaches, namely multiple linear regression (MLR) and a pattern-recognition approach (k-nearest neighbor [k-NN]) were tested to predict BD for 196 soils of the Lower Congo. Based on their needs and familiarity with one or both of the two approaches, potential users interested in predicting the BD of strongly weathered soils of Lower Congo in particular or of Central Africa in general can immediately use the proposed equations or later apply the k-NN algorithm. Using an independent data set of low-activity clay soils from different tropical countries, nine individual PTFs and three ensemble PTFs were tested for their predictive capability. The results showed that despite the use of various modeling approaches—MLR, k-NN, or ensembles—BD remains a soil property that is difficult to predict with a satisfactory level of reliability because it can have substantial short-term variability in response to natural as well as human-induced causes. We achieved a minimum root mean square difference value of 0.179 Mg m−3.

Digital mapping of soil organic carbon contents and stocks in Denmark.

Estimation of carbon contents and stocks are important for carbon sequestration, greenhouse gas emissions and national carbon balance inventories. For Denmark, we modeled the vertical distribution of soil organic carbon (SOC) and bulk density, and mapped its spatial distribution at five standard soil depth intervals (0−5, 5−15, 15−30, 30−60 and 60−100 cm) using 18 environmental variables as predictors. SOC distribution was influenced by precipitation, land use, soil type, wetland, elevation, wetness index, and multi-resolution index of valley bottom flatness. The highest average SOC content of 20 g kg−1 was reported for 0−5 cm soil, whereas there was on average 2.2 g SOC kg−1 at 60−100 cm depth. For SOC and bulk density prediction precision decreased with soil depth, and a standard error of 2.8 g kg−1 was found at 60−100 cm soil depth. Average SOC stock for 0−30 cm was 72 t ha−1 and in the top 1 m there was 120 t SOC ha−1. In total, the soils stored approximately 570 Tg C within the top 1 m. The soils under agriculture had the highest amount of carbon (444 Tg) followed by forest and semi-natural vegetation that contributed 11% of the total SOC stock. More than 60% of the total SOC stock was present in Podzols and Luvisols. Compared to previous estimates, our approach is more reliable as we adopted a robust quantification technique and mapped the spatial distribution of SOC stock and prediction uncertainty. The estimation was validated using common statistical indices and the data and high-resolution maps could be used for future soil carbon assessment and inventories.

Density and phase equilibrium for ice and structure I hydrates using the Gibbs–Helmholtz constrained equation of state

A new, rigorous framework centered around the multi-scale GHC equation of state is presented for predicting bulk density and phase equilibrium for light gas–water mixtures at conditions where hexagonal ice and structure I hydrate phases can exist. The novel aspects of this new framework include (1) the use of internal energies of departure for ice and empty hydrate respectively to determine densities, (2) contributions to the standard state fugacity of water in ice and empty hydrate from lattice structure, (3) computation of these structural contributions to standard state fugacity from compressibility factors and EOS parameters alone, and (4) the direct calculation of gas occupancy from phase equilibrium. Numerical results for densities and equilibrium for systems involving ice and/or gas hydrates predicted by this GHC-based framework are compared to predictions of other equations of state, density correlations, and experimental data where available. Results show that this new GHC-based EOS framework accurately predicts the densities of hexagonal water ice and structure I gas hydrates as well as phase equilibrium for methane–water and CO2–water mixtures.

Soil Survey: Prediction of Bulk Density Using -Nearest Neighbor Approach

In soil survey, there is a need to estimate bulk density (at −33 kPa matric potential, Db,33) for the range of soils that occur in the United States. The objectives of this study were to develop a bulk density model using the k-nearest neighbor (k-NN) approach and validate this model against an independent dataset. The nearest-neighbor approach passively stores the development (or reference) dataset until the time of application, and then the dataset is searched for the 10 (k) most similar soils to that of the target soil, based on selected attributes (e.g., organic C, total sand, silt, and clay). Reference datasets were developed from the National Cooperative Soil Survey characterization database in Lincoln, NE. The model uses a decision tree to determine which reference dataset and attributes are to be used to predict Db,33. Validation results with an independent dataset showed a small, but significant bias or systematic error (ME = −0.0215 g cm−3). However, the overall prediction error (or RMSE) of 0.108 g cm−3 is smaller than most reported literature values. The k-NN model for estimating bulk density is suggested to provide an efficient and reasonably accurate tool for use in soil survey for a wide range of soils.

Prediction of Bulk Density of Soils in the Loess Plateau Region of China

Soil bulk density (BD) is a key soil physical property that may affect the transport of water and solutes and is essential to estimate soil carbon/nutrients reserves. However, BD data are often lacking in soil databases due to the challenge of directly measuring BD, which is considered to be labor intensive, time consuming, and expensive especially for the lower layers of deep soils such as those of the Chinese Loess Plateau region. We determined the factors that were closely correlated with BD at the regional scale and developed a robust pedotransfer function (PTF) for BD by measuring BD and potentially related soil and environmental factors at 748 selected sites across the Loess Plateau of China (620,000 km2) at which we collected undisturbed and disturbed soil samples from two soil layers (0–5 and 20–25 cm). Regional BD values were normally distributed and demonstrated weak spatial variation (CV = 12 %). Pearson’s correlation and stepwise multiple linear regression analyses identified silt content, slope gradient (SG), soil organic carbon content (SOC), clay content, slope aspect (SA), and altitude as the factors that were closely correlated with BD and that explained 25.8, 6.3, 5.8, 1.4, 0.3, and 0.3 % of the BD variation, respectively. Based on these closely correlated variables, a reasonably robust PTF was developed for BD using multiple linear regression, which performed equally with the artificial neural network method in the current study. The inclusion of topographic factors significantly improved the predictive capability of the BD PTF and in which SG was an important input variable that could be used in place of SA and altitude without compromising its capability for predicting BD. Thus, the developed PTF with only four input variables (clay, silt, SOC, SG), including their common transformations and interactive terms, predicted BD with reasonable accuracy and is thus useful for most applications on the Loess Plateau of China. More attention should be given to the role of topography when developing PTFs for BD prediction. Testing of the developed PTF for use in other loess regions in the world is required.

Calibration of an on-line sensor for measurement of topsoil bulk density in all soil textures

An on-line sensor for the prediction of bulk density (BD) has been previously developed, and calibrated for light textured soils. This study aims to expand the implementation of this sensor for all soil types, including clay soils. The on-line sensor consists of a single ended shear beam load cell to measure draught (D), a visible and near infrared (vis–NIR) sensor to measure moisture content (MC), and a wheel gauge equipped with a draw wire linear sensor to measure depth (d). This sensor was used to collect data from 14 fields across four European countries (Denmark, Czech Republic, the Netherlands and the UK) with different soil textures. The three measured parameters were substituted into a previously developed model to calculate BD. The calculated BD for 333 samples was compared with BD measured with a core sampling kit to validate the system and to calculate correction factors (CF) valid for each additional soil texture. A multiple linear regression (MLR), nonlinear multiple regression (NMR) and artificial neural network (ANN) analyses were carried out to establish models to predict CF, as a function of average field MC and soil texture fractions.For clay soils, the correlation between the measured and on-line predicted BD was satisfactory (R2=0.54–0.69), and suggested a large negative CF>−0.248Mgm−3. For other soil types with lighter soil textures, accuracy of measurements varied (R2=0.51–0.96), with lower CF values than those calculated for clay soils and ranged between negative and positive values (−0.007Mgm−3<CF<0.079Mgm−3). The ANN analysis resulted in the best model to predict CF as function of MC and soil texture fractions (R2=0.96), which allowed the utilisation of the on-line measurement system for any field having any texture and average MC. The on-line BD sensor was shown to be capable of predicting field dry BD rapidly for a large number of samples, as compared with traditional methods.

Near-surface Seismic Investigation of Barringer (Meteor) Crater, Arizona

We investigated the shallow subsurface of Barringer (Meteor) Crater, Arizona using high-resolution seismic methods. The seismic surveys were conducted in May, 2010 during a joint expedition by the University of Houston, the University of Texas at Austin, and the Lunar and Planetary Institute (LPI). We performed compressional (P)-wave refraction analysis on the seismic data and found P-wave velocities of 450–2,500 m/s for a 55-m deep model. Away from the crater rim (toward the south), the shallow P-wave low-velocity layers thin. We also estimated a near-surface, shear (S)-wave velocity structure using a surface-wave inversion method. S-wave velocities vary from 200–700 m/s for the top 16–20 m, increasing to 900–1,000 m/s at 38-m depth. We interpret a prominent change in S-wave velocity (at around 500–600 m/s) as the transition from the ejecta blanket (a sheet of debris thrown out of the crater during the impact) to the bed-rock Moenkopi sandstone. The ejecta is characterized as unconsolidated, low velocity, and low density. This S-wave transition takes place at a depth range of 12–20 m near the crater rim with a thinning away from the crater rim. This consistent P-wave and S-wave structure is interpreted as the ejecta blanket. Ultrasonic measurements on hand samples collected during the expedition give a range of P-wave velocities of 800–1,600 m/s for the Moenkopi. Predicted bulk densities from estimated S-wave velocities using modified Gardner's equation fall in the range of 1.8–2.5 gm/cm3, with low-density materials (ejecta) underlain by high-density materials (bedrock). These density results, along with available drilling information and residual gravity anomalies, also support the thinning of the ejecta blanket.