Sediment sorting is a fundamental concept in geology, sedimentology, and environmental science that describes the uniformity of grain sizes within a sediment sample. When sediments are deposited by natural processes like water currents, wind, or ice, they don’t all end up being the same size. Some deposits contain a wide mix of tiny clay particles, sand grains, and large pebbles, while others consist of remarkably uniform particles.

The degree of sorting tells geologists a powerful story about the environment where the sediment was deposited. A well-sorted beach sand, with its almost uniform grain size, reveals constant wave action that gradually separated particles by size over thousands of years. In contrast, a poorly sorted glacial till, containing everything from microscopic minerals to massive boulders, speaks of the raw, unselective power of ice that simply drops everything it was carrying when it melts.

Understanding sorting helps scientists reconstruct ancient environments, predict groundwater flow through aquifers, assess the stability of building foundations, and even locate oil and gas reservoirs. For engineers, sorting influences how erosion control structures should be designed. For environmental scientists, it indicates habitat quality for bottom-dwelling organisms.

Our calculator uses the internationally recognized Folk & Ward graphical method, developed by geologists Robert Folk and Bill Ward in 1957. This approach calculates statistical parameters from percentile values on the cumulative frequency curve of grain size distribution.

The method provides four key parameters:

Mean Grain Size represents the average particle size and indicates the overall energy level of the depositional environment. Coarser sediments suggest high-energy environments like fast rivers or wave-dominated beaches, while finer sediments indicate low-energy settings like deep lakes or quiet lagoons.

Sorting Coefficient measures the standard deviation of grain sizes. This is the most crucial parameter for understanding sediment maturity. Lower values mean better sorting and longer transport history.

Skewness reveals whether the distribution is symmetrical or skewed toward finer or coarser particles. Positive skewness means an excess of fine particles, often indicating a waning energy environment. Negative skewness suggests an excess of coarse particles, typical of immature sediments.

Kurtosis describes the “peakedness” of the distribution. High kurtosis values indicate a very selective transport process, while low values suggest mixed populations from different sources.

Before using the calculator, gather your grain size measurements. You can collect samples through sieve analysis, where sediment is passed through a series of increasingly fine mesh screens, or through direct measurement under a microscope for larger particles. Ensure your measurements are accurate and representative of the entire sample.

The calculator offers two flexible input options:

Raw Data Input is perfect when you have individual measurements of particle diameters. Simply enter your values separated by commas. For example, if you measured sand grains of 0.25 mm, 0.5 mm, 0.75 mm, and 1.0 mm, you would type: 0.25, 0.5, 0.75, 1.0

This method works best for samples where you’ve measured each grain individually, typically for smaller sample sizes or when using image analysis software.

Frequency Distribution Input is ideal for sieve analysis results. Here you enter size classes with their corresponding percentages. For example, from a sieve analysis you might enter: -2=5, -1=15, 0=25, 1=30, 2=20, 3=5

This represents that 5% of your sample is -2φ (4 mm), 15% is -1φ (2 mm), and so on. The percentages must add up to 100%. This method is perfect for standard sieve analysis data and larger sample sets.

Select whether your input data is in millimeters (mm) or phi (φ) units. Most laboratory equipment reports in millimeters, but many scientific publications use phi units. The phi scale is logarithmic and more convenient for geological analysis because each integer step represents a halving or doubling of grain size.

Type or paste your data into the appropriate text area. The calculator validates your input in real-time, highlighting errors immediately. If you’re using raw data, ensure you have at least four values for meaningful statistical analysis. For frequency data, double-check that your percentages sum to exactly 100.

Click the “Calculate Sorting” button. The calculator performs all Folk & Ward calculations instantly and displays a comprehensive results panel. The smooth animations guide your attention through each result sequentially.

The sorting coefficient appears first because it’s the most environmentally diagnostic parameter. Values below 0.35 indicate very well-sorted sediments, typical of mature beach sands or desert dunes. Values between 0.35 and 0.50 represent well-sorted materials, often found in river point bars or nearshore environments.

Moderately sorted sediments (0.50-0.71) suggest moderate energy with some mixing, common in tidal flats or protected beaches. Poorly sorted sediments (0.71-1.00) indicate high-energy, rapid deposition, like storm deposits or alluvial fans. Very poorly sorted sediments (1.00-2.00) and extremely poorly sorted materials (>2.00) reveal minimal transport and sorting, characteristic of glacial deposits, debris flows, or landslides.

The mean value appears in phi units, which you can interpret using the built-in size classification. A mean of 0φ corresponds to 1 mm sand (coarse sand). A mean of 2φ equals 0.25 mm (medium sand). Negative phi values indicate larger particles: -1φ is 2 mm (granules), -2φ is 4 mm (pebbles).

The calculator automatically translates your mean value into plain language, telling you whether your sediment classifies as clay, silt, sand, granules, pebbles, cobbles, or boulders.

Skewness values near zero indicate symmetrical distributions, typical of stable environments. Positive skewness (0.1 to 0.3) reveals an excess of fine particles, often signaling a change from high to low energy, like a river entering a lake. Negative skewness (-0.1 to -0.3) shows excess coarse particles, common in environments receiving coarse material from a nearby source, like a beach below eroding cliffs.

Kurtosis values around 1.0 indicate normal, mesokurtic distributions. Values above 1.11 (leptokurtic) suggest highly selective processes, like wind sorting in desert dunes. Values below 0.90 (platykurtic) indicate mixed populations, perhaps from multiple source areas or depositional events.

The calculator generates a histogram showing how grain sizes are distributed across size classes. This visual representation helps identify bimodal distributions, which might indicate mixing from two different sources, or the degree of tail development in your distribution.

Students and researchers use sorting analysis to characterize modern and ancient sediments. Whether studying Pleistocene lake deposits, Jurassic sandstones, or modern river systems, sorting parameters provide quantitative data for environmental reconstruction and correlation between different locations.

Civil engineers assess foundation stability by analyzing the sorting of subsurface materials. Well-sorted sands provide predictable load-bearing capacity, while poorly sorted sediments require careful engineering to prevent differential settlement. Sorting also influences permeability, affecting drainage design and contamination migration studies.

Ecologists evaluate habitat quality for benthic organisms based on sediment sorting. Many species have specific preferences for particular grain sizes. Well-sorted sediments often support more diverse communities because they provide consistent interstitial spaces for organisms.

Petroleum geologists use sorting analysis to identify potential reservoir rocks. Well-sorted sandstones typically have higher porosity and permeability, making them excellent hydrocarbon reservoirs. Poorly sorted rocks often have reduced storage and flow capacity.

Water resource professionals predict groundwater movement through aquifers by studying sediment sorting. Well-sorted formations transmit water more uniformly, while poorly sorted materials create complex flow patterns that affect well productivity and contaminant transport.

Collect representative samples by removing surface vegetation and scooping material from a defined volume. For heterogeneous deposits, combine multiple subsamples to create a composite that’s truly representative. Always record the exact location with GPS coordinates and photograph the site.

Dry samples completely in an oven at 105°C to remove moisture. Disaggregate clumps gently using a rubber-tipped pestle, being careful not to crush individual grains. For organic-rich samples, remove organics with hydrogen peroxide treatment.

For sieve analysis, shake for at least 10 minutes to ensure complete separation. Check that no grains are stuck in mesh openings. For direct measurement, use calibrated microscopes or calipers and measure multiple axes of each particle, averaging the results.

Aim for at least 300 grain measurements for reliable statistical analysis. Smaller samples can produce misleading results. The calculator works with as few as four values for teaching demonstrations, but real-world applications require larger datasets.

If you receive validation errors, check that you’ve used commas to separate values and periods (not commas) for decimal points. Ensure frequency distribution percentages sum to exactly 100%. Look for accidental double commas or missing values.

Extremely high sorting coefficients (>3.0) often indicate mixed populations that shouldn’t be analyzed as a single sample. Consider separating your material into distinct subpopulations based on visual characteristics and analyzing them separately.

Negative skewness values might seem counterintuitive but are scientifically valid, indicating coarse-skewed distributions. Similarly, kurtosis values below 0.67 or above 1.50 are possible and meaningful, representing unusual depositional processes.

Remember that the phi scale is inverse: larger phi values mean smaller particles. If your results seem backwards, double-check whether you selected the correct unit type. When in doubt, convert phi to millimeters mentally: 0φ = 1 mm, 1φ = 0.5 mm, 2φ = 0.25 mm, etc.

For reliable results, aim for 300-400 measurements. The Folk & Ward method becomes statistically robust at this sample size. For classroom demonstrations or quick field estimates, 50-100 measurements can provide useful approximations. The calculator requires a minimum of four values but will flag results from very small samples as preliminary.

Yes, the calculator works for any particulate material ranging from clay-sized particles (about 0.002 mm) up to boulders. The mathematical methods are universal. However, ensure your sample preparation is appropriate for the material type. Clay-rich samples may require pretreatment to disperse aggregates, and very coarse materials may require different measurement techniques.

The phi scale provides a more statistically sound basis for calculating sediment statistics because grain size distributions in nature tend to be log-normal rather than normal. Using phi units makes the distributions symmetrical, allowing proper application of statistical methods. The conversion is automatic and doesn’t affect your ability to interpret results.

The calculator implements the exact Folk & Ward formulas used in professional geological software like GRADISTAT and SedStats. Results match within 0.001 φ for all parameters when identical input data is used. The mathematical methods are standardized and universally accepted.

Absolutely! Calculate each sample separately and record the results. For comparative studies, create a spreadsheet of your samples’ parameters. Well-sorted samples plot in different fields than poorly sorted ones on standard sediment classification diagrams, making visual comparison straightforward.

Bimodal samples produce intermediate sorting values that don’t accurately represent either population. The calculator’s histogram will reveal bimodality as two distinct peaks. In such cases, physically separate the populations if possible (e.g., by sieving at an appropriate cutoff) and analyze them independently. This provides more meaningful environmental information.

We recommend citing with: “Sediment sorting parameters were calculated using the Folk & Ward graphical method (Folk & Ward, 1957) implemented in the Sediment Sorting Calculator (Geological Tools, 2025).” Include the URL and access date in your references list.

Use the sharing feature to copy your results to the clipboard, then paste into spreadsheets or documents. For bulk analysis, institutional users can integrate the calculator’s open-source algorithms directly into their data processing workflows.

Well-sorted sediments generally have higher porosity because uniform grains pack more efficiently, leaving larger pore spaces. Permeability is dramatically higher in well-sorted materials because interconnected pores form continuous pathways. Poorly sorted sediments have reduced porosity as fine particles fill spaces between coarse grains, and permeability decreases sharply because flow paths become tortuous and disconnected.

Yes! The mathematical methods apply to any granular material, including manufactured aggregates, ceramic powders, metal powders for 3D printing, and pharmaceutical granules. Quality control engineers use the same statistical parameters to ensure product consistency.

Beach sands typically show sorting coefficients of 0.3-0.5. Desert dunes range from 0.4-0.6. River channel deposits are moderately sorted at 0.6-0.8. Glacial tills are very poorly sorted, usually exceeding 2.0. Deep sea clays are often well-sorted at 0.4-0.6 despite being fine-grained.

Weathering generally improves sorting over geological time. As rocks break down and particles are transported, abrasion rounds grains and size-sorting processes separate them. Mature sediments that have experienced multiple cycles of weathering and transport are typically better sorted than immature, first-cycle sediments derived directly from bedrock.

Be cautious about removing outliers. In geology, apparent outliers often represent real processes like isolated large clasts in a fine matrix (lag deposits) or occasional fines infiltrating coarse gravels. Only remove values if you have definitive evidence of measurement error. The Folk & Ward method is robust and accommodates natural variability.

In petroleum geology, sorting trends help identify key surfaces in sedimentary sequences. Upward-improving sorting often indicates rising sea level and increasing accommodation space, while upward-degrading sorting can signal falling sea level and basinward facies shifts.

By comparing sorting characteristics of sediments from different sources, geologists trace sediment transport pathways. Abrupt changes in sorting along a transport path reveal important boundary conditions or confluence points where different populations mix.

Ancient desert dunes and loess deposits preserve sorting signatures that reflect paleowind regimes. Well-sorted loess indicates consistent wind strength and direction, while changes in sorting through a loess section reveal climate transitions.

The Sediment Sorting Calculator makes these sophisticated analyses accessible to students, researchers, and professionals. Whether you’re conducting cutting-edge research, teaching the next generation of geologists, or solving practical engineering problems, this tool provides accurate, instant results with professional-grade precision. Bookmark this page for quick access whenever you need to transform raw grain size data into meaningful geological insights.