Gradistat Version 8 Download
Corene Ollig
Base station data were post-processed through the National Geodetic Survey (NGS) On-Line Positioning User Service (OPUS). The time-weighted position calculated from all base station occupations did not differ significantly from the NPS REST control coordinates; therefore, the control coordinates were used for post-processing. The base-station coordinates were imported into GrafNav, version 8.5 (NovAtel Waypoint Product Group), and the data from the rover GPS were post-processed to the concurrent base-station session data. During GPS data acquisition at core location P25-C07, a recording error occurred, and no concurrent base-station data were recorded at REST. Rover data from this location were instead post-processed to the concurrent data recorded at the NGS Continuously Operating Reference Station (CORS) ZNY1 on Long Island. The final core locations, including elevation, are the post-processed DGPS coordinates; baseline distances to all core locations were 21 km or less. These data are included in the site locations files accessible from Bernier and others (2018).
Each vibracore was split lengthwise, photographed with a Canon Powershot SX20 IS digital camera, described macroscopically using standard sediment-logging methods, and subsampled for grain-size analysis. Grain-size samples consisted of 2-cm sections sampled at varying intervals down-core depending on the number and thickness of the observed sedimentologic units. The core logs and photographs can be downloaded from Bernier and others (2018). Textural descriptions for the core logs are based on macroscopic observations; the quantitative grain-size data are represented by down-core plots on the core logs (fig. 5).
Figure 5. Example grain-size plots and log from vibracore P22-C14. Sediment color is based on the Munsell soil color system. Colored lines with dates indicate depths where the core intersected previously surveyed beach surfaces (beach elevation profiles) (value plotted is Dcomp (m), in 2016-322-FA_ProfElev_to_CoreDepth.pdf, also see figs. 6, 7, and 8). [cm, centimeter; mm, millimeter][Click figure to enlarge]
Grain-size analyses were performed using a Coulter LS 13 320 particle-size analyzer, which uses laser diffraction to measure the size distribution of sediments ranging in size from 0.4 micron (μm) to 2 millimeters (mm) (clay to very coarse-grained sand). A total of 315 samples were analyzed from 14 vibracores. Prior to analysis, each sample was dried at 40 degrees Celsius (C) for 24 hours.
Two subsamples (sets) from each sample were processed (four runs per set) through the LS 13 320 particle-size analyzer, which measures the particle-size distribution of each sample by passing sediment suspended in solution between two narrow panes of glass in front of a laser. Light is scattered by the particles into characteristic refraction patterns measured by an array of photodetectors as intensity per unit area and recorded as relative volume for 92 size-related channels (bins). To prevent shell fragments and coarse material from damaging the LS 13 320, particles greater than 2 mm in diameter were separated from each subsample prior to analysis using a number 10 (2,000 μm [2 mm]) U.S. standard sieve, which meets the American Society for Testing and Materials (ASTM) E11 standard specifications for determining particle size using woven-wire test sieves. The fraction of sediment greater than 2 mm was recorded as a percentage of the subsample dry weight.
The raw grain-size data were run through GRADISTAT, version 8 (Blott and Pye, 2001), which calculates the geometric (in metric units) and logarithmic (in phi units, φ; Krumbein, 1934) mean, sorting, skewness, and kurtosis of each sample, using the Folk and Ward (1957) method as well as the cumulative particle-size distribution. GRADISTAT also calculates the fraction of sediment from each sample by size category (for example, clay, coarse silt, fine sand) based on a modified Wentworth (1922) size scale. A macro developed by the USGS was applied to calculate the average and standard deviation of each sample (four runs per set, eight runs per sample) and highlight runs that varied from the set or sample average by more than plus or minus () 1.5 standard deviations. Excessive deviations from the mean are likely the result of equipment error or extraneous material in the sample and, therefore, are not considered representative of the sample. Those runs were removed from the results, and the sample average was recalculated using the remaining runs. The grain-size data are included as down-core plots with the core logs (fig. 5); the individual run statistics as well as the averaged sample statistics (summary statistics) and graphical representations of the data are also available from Bernier and others (2018).
Vibracores were collected repeatedly along previously occupied beach-profile elevation transects (profiles 10, 11, 22, 25, 26, and 29; fig. 3; beach elevation profile data, see U.S. Geological Survey, undated). The depth at which each core penetrated older beach surfaces (fig. 6), if preserved, is labeled on the core logs (fig. 5 and Core Viewer). Not all previously surveyed beach surfaces are preserved in the cores because beach width and elevation vary seasonally and in response to storms (Hapke and others, 2013; Brenner and others, 2018). Erosion of beach sediments can be identified by comparing the beach elevation profiles at a given location: if the elevation of a younger (more recent) survey is less than that of an older survey, the beach surface represented by the older survey will not be preserved in the sedimentary record. Conversely, if beach elevations increase between survey dates, the sediments that accumulated between those surveys will be preserved, and chronostratigraphic depositional packages can be identified.
the depth (Duncomp) of each preserved beach surface was converted to the compacted core-depth framework (equivalent to depth in the core barrel, Dcomp) by multiplying by the percentage core compaction at that core location.
Figure 6. Example of plot showing vibracore intersecting previously surveyed beach elevation profiles. [m, meter; NAVD88, North American Vertical Datum of 1988] [Click figure to enlarge]
Figure 7. Illustration showing relations of core length, penetration, and compaction with calculations to translate beach elevation profiles to compacted depths of cores. [surf, surface; Dcomp, depth compacted; Duncomp, depth uncompacted; Z, elevation; L, core length; P, core penetration; %, percentage] [Click figure to enlarge]
You may be familiar with the classic GRADISTAT for calculating particle size statistics. It is a set of macros written into a Microsoft Excel spreadsheet by Kenneth Pye and Simon Blott. At the time of writing it was last updated for use with Microsoft Excel 2007, and is becoming increasingly difficult to use with newer versions of Excel. After recently troubleshooting some odd GRADISTAT outputs for one of our lab users, I decided to see if there were alternatives available.
This study uses a thorough grain size analysis approach to the depositional environment in the Thamirabarani River basin, specifically in the Srivaikundam district of Tamil Nadu, India. There is a significant lack of research on the depositional settings and sediment features unique to the Thamirabarani River basin, even though there have been numerous sediments studies conducted in other river basins. Consequently, the purpose of this study is to investigate the depositional habitats and sediment characteristics of this area. The majority of the 18 sediment samples were determined to be medium- to fine-grained and poorly sort-able after extensive analysis using granulometric research and sieve methods. The low-energy formation process took place in a largely fine-grained or very fine-grained sandy soil, as indicated by the sediments platykurtic and mesokurtic morphologies. The depositional habitats within the region can be better understood by classifying the soil based on ternary plots of clay, silt, and sand, such as particle distribution curve. Furthermore, comparison with a unified soil classification chart aids in further categorizing the soil types. Soils can be classified and the depositional settings of different landscapes, such as rivers, estuaries, beaches, and aeolian terrains, can be better understood with the use of this comprehensive grain size study. Another interesting aspect of the Thamirabarani River basin sediment movement and deposition is the slow breakdown of rocks, especially feldspar and quartz, as they move through rivers and streams.
This technique evaluates consolidated sediments by passing them through a series of nested wire mesh stages. Larger particles remain in place, whereas tiny particles pass through. At April 2021, 18 samples were collected from the Thamirabarani River at Srivaikundam (Table 1). This study aimed to explain the depositional distribution of sediments in river environments (Fig. 3). The sieved materials were collected and weighed individually. A granulometric research was conducted to determine the weights of the various fractions. GRADISTAT version 4.0 by Blott and Pye [10] was used in this investigation (Figs. 4 and 5). It comes in Microsoft Excel format, which allows for both spreadsheet and graphical output. The software is best suited for interpreting data from sieve or laser granulometer analyses (see Table 2). Granulometric analysis was carried out utilizing visual [14] and moment approaches to better understand sediment transport and depositional environments [15]. The sediment samples were thoroughly examined in the mechanical laboratory of the Department of Geology at V. O. Chidambaram College in Thoothukudi. GRADISTAT tools, such as grain size analysis and statistical methodologies, offer a methodical approach to comprehending these differences. Researchers can learn about sedimentary processes, environmental changes, and depositional histories by evaluating sediment features at various spatial and temporal scales. This contributes in paleo-environmental reconstruction, sedimentary basin research, and resource exploration. The soil was classed based on the results of both analyses. The soil was classified using the water limit value, the particle distribution curve, and the clay, silt, and sand ternary plot [12]. The data collected was compared to the soil classification chart from the Unified Soil Classification. The percentages of sand, silt, and clay should be computed and compared to the highlighted areas in the ternary or triangular picture above [19]. , The collected percentages of sand, silt, and clay were shown in a ternary diagram for soil classification.
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