Sediments and the Sea Floor of the Continental Shelves and 

Coastal Waters of the United States—About the usSEABED 

Integrated Sea-Floor-Characterization Database, Built With 

the dbSEABED Processing System

Prepared in cooperation with the Institute of Arctic and Alpine Research at the University of 

Colorado Boulder

Sediments and the Sea Floor of the Continental Shelves and 

Coastal Waters of the United States—About the usSEABED 

Integrated Sea-Floor-Characterization Database, Built With 

the dbSEABED Processing System

Open-File Report 2020–1046

U.S. Department of the Interior

U.S. Geological Survey
Cover. Bottom photographs collected on U.S. Geological Survey Woods Hole Coastal and Marine 

Science Center Field Activity 2007–003–FA. Map image showing the distribution of usSEABED data 

output files US9_EXT (blue triangles) and US9_PRS (pink circles) around the continental United States, 

Hawai’i, and Puerto Rico.
Sediments and the Sea Floor of the 

Continental Shelves and Coastal Waters of 

the United States—About the usSEABED 

Integrated Sea-Floor-Characterization 

Database, Built With the dbSEABED 

Processing System

By Brian J. Buczkowski, Jane A. Reid, and Chris J. Jenkins

Prepared in cooperation with the Institute of Arctic and Alpine Research at the 

University of Colorado Boulder

Open-File Report 2020–1046

U.S. Department of the Interior

U.S. Geological Survey
U.S. Department of the Interior

DAVID BERNHARDT, Secretary

U.S. Geological Survey

James F. Reilly II, Director

U.S. Geological Survey, Reston, Virginia: 2020

For more information on the USGS—the Federal source for science about the Earth, its natural and living resources, 

natural hazards, and the environment—visit https://www.usgs.gov or call 1–888–ASK–USGS.

For an overview of USGS information products, including maps, imagery, and publications, visit 

https://store.usgs.gov/.

Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the 

U.S. Government.

Although this information product, for the most part, is in the public domain, it also may contain copyrighted materials 

as noted in the text. Permission to reproduce copyrighted items must be secured from the copyright owner.

Suggested citation:

Buczkowski, B.J., Reid, J.A., and Jenkins, C.J., 2020, Sediments and the sea floor of the continental shelves and 

coastal waters of the United States—About the usSEABED integrated sea-floor-characterization database, built 

with the dbSEABED processing system: U.S. Geological Survey Open-File Report 2020–1046, 14 p., https://doi.org/ 

10.3133/ ofr20201046.

Associated data for this publication:

Buczkowski, B.J., Reid, J.A., Schweitzer, P.N., Cross, V.A., and Jenkins, C.J., 2020, usSEABED—Offshore surficialsediment database for samples collected within the United States Exclusive Economic Zone: U.S. Geological Survey 

data release, https://doi.org/10.5066/P9H3LGWM.

ISSN 2331-1258 (online)
iii

Acknowledgments

The usSEABED project has benefitted from the efforts of many individuals and institutions 

contributing data to the usSEABED database; careful interns entering, coding, and testing data; 

and reviewers’ quality-control testing the database in its various incarnations. We thank the 

following U.S. Geological Survey interns for their assistance in entry, coding, and testing of data 

and assistance with metadata: Carolynn Box, Emily Denham, Amalia Slovacek Hansen, Monica 

Iglecia, Adam Jackson, K. Halimeda Kilbourne, Tara Kneeshaw, Jennifer Mendonça, Emma 

Mitchell, Ariadne Prior-Grosch, Shea Quinn, April Villagomez, Viness Ubert, Hannah Waiters, 

and Paul Waiters. We thank Mark Zimmermann of the Alaska Science Center, of the National 

Oceanic and Atmospheric Administration (NOAA) National Marine Fisheries, for his early and 

continuing support of usSEABED along the Pacific coast and in the Gulf of Alaska.

The collaborators of usSEABED thank the following for their contributions of data: Humboldt 

State University; Louisiana Department of Natural Resources; Maryland Geological Survey; 

Moss Landing (California) Port Authority; New Jersey Geological Survey; NOAA National 

Centers for Environmental Information; NOAA National Marine Fisheries Service; NOAA 

National Marine Sanctuaries Program; NOAA National Ocean Survey; NOAA National Status 

and Trends Program; North Carolina Department of Transportation; Oregon State University; 

San Jose State University; Santa Cruz Harbor (California) Authority; Scripps Institute of 

Oceanography; Skidaway Institute of Oceanography; Smithsonian Institution; Southern California 

Coastal Water Research Project; State University of New York at Stony Brook; U.S. Army 

Corps of Engineers; U.S. Coastal and Geodetic Surveys; U.S. Environmental Protection Agency; 

U.S. Naval Oceanographic Office; U.S. Naval Postgraduate School; U.S. Naval Research Lab; 

U.S. Office of Naval Research; University of California, Berkeley; University of Colorado Boulder; 

University of New Orleans; University of Southern California; University of Southern Florida; 

University of Texas; University of Washington (Seattle); Virginia Institute of Marine Sciences 

(College of William and Mary); Woods Hole Oceanographic Institution; and the contributions 

from many nonauthor USGS sources.

The processing software at the core of usSEABED, dbSEABED, has benefited from the contributions of many people and institutions. It is a community structure, currently managed from the 

University of Colorado. Funding is from the Australian Department of Defence, Commonwealth 

Scientific and Industrial Research Organisation Australia, Geosciences Australia, U.S. Geological 

Survey, Institute of Arctic and Alpine Research (INSTAAR)/University of Colorado, Institute 

für Ostseewissenschaften-Warnemünde (IOW, Germany), Lamont Doherty Earth Observatory, 

NOAA National Geophysical Data Center (Boulder), U.S. Office of Naval Research, and Victoria 

Department of Natural Resources and the Environment (Australia).

Ideas for development of the dbSEABED software have been contributed in discussions by 

L. Hamilton and P. Mulhearn (Defence Science and Technology Organization); G. Rawson 

and A. Short (University of Sydney); P. Sliogeris (Royal Australian Navy Meteorology and 

Oceanography Services [Australia]); T. Wever (Forschungsanstalt der Bundeswehr für 

Wasserschall und Geophysik, Germany); J. Harff, B. Bobertz, and B. Bohling (IOW); P. Morin 

(University of Minnesota); M. Kulp and S. Briuglio (University of New Orleans); J. Goff 

(University of Texas); G. Sharman and C. Moore (NOAA National Geophysical Data Center); 

J. Flocks, J. Kindinger, C. Holmes, and C. Polloni (USGS); the URS Corporation; and the 
iv

Smithsonian Institution. M. Field and J. Gardner of the USGS first arranged to apply dbSEABED 

software to the U.S. Exclusive Economic Zone in 1999. Finally, the authors would like to express 

a special, last-but-certainly-not-least thank you to Jeff Williams, former Marine Aggregates 

Resources and Processes project chief at the USGS Woods Hole Coastal and Marine Science 

Center and continued advocate for the usSEABED project. Thank you, Jeff, for your years of 

dedication to the usSEABED project, for your encouragement, and your advice.
v

Contents

Acknowledgments........................................................................................................................................iii

Abstract...........................................................................................................................................................1

Introduction.....................................................................................................................................................1

Applications...........................................................................................................................................1

The Data in usSEABED..................................................................................................................................2

How usSEABED is Built........................................................................................................................2

Sources of Data.....................................................................................................................................2

Output Files ............................................................................................................................................4

Relational Keys .............................................................................................................................4

Source Data (US9_SRC) .............................................................................................................4

Textural and Other Basic Information (US9_EXT, US9_PRS, US9_CLC, and 

US9_ONE) ........................................................................................................................4

Extracted Data .....................................................................................................................5

Parsed Data .........................................................................................................................5

Calculated Data ...................................................................................................................5

Combined Data ....................................................................................................................5

Component/Feature and Facies Data (US9_CMP and US9_FAC) ........................................6

Relationship Between Parsed and Component Data ............................................................6

Quality Control.....................................................................................................................................13

Uncertainties in the Data ...................................................................................................................13

Accessing the usSEABED Database ........................................................................................................13

References Cited..........................................................................................................................................13

Tables

1. Key to data themes in usSEABED output files and examples of the types of data 

that may be included in the themes ...........................................................................................3

2. usSEABED output files .................................................................................................................4

3. Field parameters, format, units, range, meaning, and comments for extracted, 

parsed, and calculated data in the US9_ONE (extending to US9_EXT, US9_PRS, 

and US9_CLC) file .........................................................................................................................7

4. Components and features processed for the usSEABED database ..................................10

5. Field parameters for the US9_FAC file, featuring facies data and their 

component makeup ....................................................................................................................12

6. Biological components that may have textural implications ..............................................12
vi

Abbreviations

CLC calculated [data]

CMP component [data]

EXT extracted [data]

FAC facies [data]

GIS geographic information system

IOW Institute für Ostseewissenschaften-Warnemünde

NOAA National Oceanic and Atmospheric Administration

PRS parsed [data]

SRC source [data]

USGS U.S. Geological Survey
Sediments and the Sea Floor of the Continental Shelves 

and Coastal Waters of the United States—About the 

usSEABED Integrated Sea-Floor-Characterization 

Database, Built With the dbSEABED Processing System

By Brian J. Buczkowski,1 Jane A. Reid,1 and Chris J. Jenkins2

Abstract

Since the second half of the 20th century, there has been 

an increase in scientific interest, research effort, and information gathered on the geologic sedimentary character of the 

continental margins of the United States. Data and information from thousands of sources have increased our scientific 

understanding of the character of the margin surface, but 

rarely have those data been combined and integrated. Initially, 

the U.S. Geological Survey (USGS), in cooperation with the 

Institute of Arctic and Alpine Research at the University of 

Colorado Boulder, created the usSEABED database to provide 

surficial sea-floor-characterization data for USGS assessments 

of marine-based aggregates and for studies of sea-floor habitat. 

Since then, the USGS has continued to build up the database 

as a nationwide resource for many uses and applications.

Previously published data derived from the usSEABED 

database have been released as three USGS data series publications containing data covering the U.S. Atlantic margin, the 

Gulf of Mexico and Caribbean regions, and the Pacific coast. 

An updated USGS data release unifies the three publications, 

incorporates additional data and sources including data from 

Alaska, Hawaii, and U.S. overseas territories, and provides 

revised output files that fix known errors and add known or 

inferred sampling dates. This report accompanies the data 

release and contains information on the methodology and 

products of the usSEABED database.

Introduction

The usSEABED integrated database created by the 

U.S. Geological Survey (USGS), in cooperation with the 

Institute of Arctic and Alpine Research at the University 

of Colorado Boulder, offers data on surficial sediment 

1U.S. Geological Survey.

2University of Colorado Boulder.

and other characteristics of the U.S. continental shelves, 

coastal and large inland waterways, and other areas of the 

U.S. Exclusive Economic Zone. The associated USGS data 

release (Buczkowski and others, 2020) provides online access 

to usSEABED data and offers an expanded dataset that both 

includes updated data originally in prior publications (Reid 

and others, 2005; Buczkowski and others, 2006; Reid and 

others, 2006) and additional data from dozens of new sources.

usSEABED is a digital, numerical, and georeferenced 

database that integrates measurements and observations of the 

surficial sea floor, including textural, statistical, geochemical, 

geophysical, and compositional information of the sediment 

cover, across multiple original sea-floor datasets. The database 

is generated by using the most recently available version of 

the dbSEABED data mining and processing software, which 

extends the coverage of information in areas where data 

coverage is more descriptive than quantitative (Jenkins, 1997, 

2002). This accompanying report describes the structure of 

the usSEABED integrated database and explains how it is 

built, how the data should be interpreted, and how they are 

best used.

Applications

The usSEABED database is a large compilation (more 

than 1 million points, nationwide) and contains complex 

assortments of data and geologic information on the surficial 

character of the sea floor. Although the database was initially 

developed for use in studies of offshore sedimentary character 

for assessing marine aggregates and characterizing benthic 

habitats, it has potential for broader application by the marine 

science community and other users. Users are encouraged to 

generate their own queries and extract information to meet 

specific needs. Some possible applications where data and 

maps from the usSEABED database are useful include the 

following:

• Observation and monitoring research

• Management and planning of coastal zones
2 About the usSEABED Integrated Sea-Floor-Characterization Database, Built With the dbSEABED Processing System

• Homeland security and military applications

• Sea-floor engineering, planning, and design

• Placement and monitoring of ocean disposal sites

• Cultural resource protection

• Fisheries management and marine protected areas

• Scientific studies and seabed mapping requiring data on 

seabed roughness, bedform distribution, critical shear 

stress, and sediment transport flux

• Educational activities requiring illustrations or datasets

• Coastal change hazard predictive models requiring 

determination of sea-floor bottom-friction values for 

calibration

The Data in usSEABED

The usSEABED database is based on published and 

unpublished laboratory measurements and descriptive observations that have been processed and extended by using the 

dbSEABED processing software (for detailed information on 

dbSEABED software, please refer to the information provided 

in Reid and others, 2005; Buczkowski and others, 2006; and 

Reid and others, 2006). The usSEABED database includes not 

only standard forms of numerical data but also a vast store of 

data about the sea floor derived from word-based descriptions 

that can be rich in information but, in their original form, are 

difficult to quantify, map, plot, or use in comparative analyses 

or models. The database provides numerical values for typical 

seabed characteristics that are based on these descriptive data. 

The usSEABED database differs from other marine databases 

in that it not only incorporates a variety of information about 

sea-floor sediment texture but also includes information on 

composition, color, biota, and rocks, as well as sea-floor characteristics such as hardness, acoustic properties, and geochemical and geotechnical analyses where available.

Although the dbSEABED software makes data coverage 

more comprehensive for mapping and analysis, the inclusion 

of sites geospatially located by now outmoded navigational 

techniques and sampled with a variety of sampler types adds 

locational uncertainty for some sites. Furthermore, the process 

of unifying the data into common units, combining data with 

different lab protocols, and creating numerical data from 

imprecise word-based descriptions means that absolute data 

precision is unavailable. Users of usSEABED data are encouraged to use the data on small scales (that is, over large areas) 

and make their own assessments of data reliability.

These reliability assessments can be made by reading 

this entire document, as well as detailed information on 

“dbSEABED (processing)” included in each of Reid and 

others (2005), Buczkowski and others (2006), and Reid and 

others (2006), before downloading the data files from the 

data release (Buczkowski and others, 2020). Source citation 

information is available on the usSEABED project website 

and includes information about individual sources’ methods 

and procedures, estimates of accuracy, dates of collection, and 

other pertinent information.

The database can be queried online and the downloaded 

output files are provided as comma-delimited text files for ease 

of use. These files are ready for inclusion into many different 

geographic information system (GIS), relational database, and 

other software applications.

How usSEABED is Built

The usSEABED database is generated by using the 

dbSEABED processing software created at the University 

of Sydney (Australia) and the University of Colorado. It has 

companion databases built along similar lines, including the 

Australian auSEABED, Baltic Sea balticSEABED, and a 

global database, goSEABED. Each of these databases relies on 

preexisting data from a variety of sources to mine and extrapolate useful information about the seabed.

The dbSEABED software allows these source datasets to 

be compiled in a standardized format and integrates information across a series of data themes (table 1). Each data theme 

holds multiple fields of numerical and (or) word-based information. Original data are from samples collected with physical 

equipment (grabs, cores, or probes), are from observations that 

are remotely sensed (such as descriptions from photographs 

and videos), or are gathered through geophysical methods. 

Source data may be numerical lab- or instrument-based 

textural, acoustic, geochemical, or geophysical data; verbal 

(linguistic) descriptions of grabs, cores, or photographs; or a 

combination of any of these.

In building the usSEABED database, many data sources 

are processed by dbSEABED software to determine associated 

data beyond what is provided in the source document, such 

as statistical parameters from a series of grain-size analyses. 

The additional information increases the spatial and thematic 

completeness of the database. Even with the extra fields produced through the dbSEABED processing, few source reports 

contain all data types reportable in the usSEABED database; 

null values are given in those fields without data.

Sources of Data

usSEABED relies on previously collected data, both 

published and unpublished, from Federal, State, regional, 

and local agencies and consortiums, as well as research 

institutions. For the offshore areas within the U.S. Exclusive 

Economic Zone, many of the data are from the USGS, 

including published and unpublished data from the 1960s to 

the present.
The Data in usSEABED 3

Table 1. Key to data themes in usSEABED output files and examples of the types of data that may be included in the themes.

Acronym Meaning and examples

ACU Acoustic properties (measured P- and S-wave velocities; acoustically derived density, porosity, and void ratio data)

AGE Sample ages (carbon-14 age dates, upper and lower age confidence limits, sedimentation rates)

BIO Biota descriptions (size, type, abundance, and form of biota, including infauna and epifauna)

CLS Landform classifications (landform and reef structures; proportions of rocky, sandy, and muddy coasts)

CMP Sediment composition analyses (weight percent of carbonate and nitrogen, as well as iron, titanium, and aluminum oxides)

COL Sediment colors (color descriptions, Munsell color codes)

DIV Diver reports (current, turbidity, wave period and height, seabed description)

DYN Experimental hydrodynamic analyses (settling velocities; experimental bedload grain sizes; statistical data, including mean and 

standard deviation grain sizes, and graphical skewness and kurtosis)

ENV Environmental observations (pH, reduction potential, carbon to nitrogen ratios)

GCM Geochemistry analyses (constituent chemical components and their abundances)

GRZ Grain-size analyses (coarse and fine grain-size limits, abundances of coarse and fine fractions)

GTC Geotechnical properties (penetrometer strength, thermal conductivity, plasticity, shear strength)

IMG Imagery interpretations (type of image, number of photos, height above sea floor)

ISO Isotopic analyses (18O, 13C, 15C, and 210Pb ratios)

LTH Lithologic descriptions (lithologic descriptions, including compaction, texture, layering, structure, sorting, and alteration); may 

include Folk codes (Folk, 1954)

MSL Analyses from multisensor core loggers (P-wave amplitudes and velocities, gamma ray densities, acoustic impedance, and fractional porosities)

OCE Oceanographic data (temperatures, salinities, pHs, currents, dissolved oxygen levels)

PAL Paleontological observations (descriptions of fossil components, preservation, and mode of occurrence)

PET Grain petrographic analyses (grain types, shapes, sorting, character)

PRB Field data from electronic probes (penetrometer bearing strengths and ranges)

SDT Sediment thickness data (unit thicknesses, top and basal horizon descriptions)

SFT Sea-floor type descriptions (descriptions of the sea floor, including wavelength, height, and slope of the seabed)

TRB Turbidity observations (Secchi disk observations, suspended sediment concentrations, transmissivity)

TXG Graphical texture statistics (grain-size percentiles, Inman (1952), Folk and Ward (1957) grain-size statistics, including mean and 

median grain sizes, skewness, and standard deviation)

TXN Taxonomic observations (taxonomic names)

TXR Texture statistics (grain-size data, including gravel, sand, silt, clay, and mud weight percent; grain-size statistical data, including 

mean and median grain sizes, skewness, and kurtosis)

XRD X-ray diffraction analyses (mineral names and calculated abundances, peak counts and spacings)

Data gathered by the National Oceanic and Atmospheric 

Administration (NOAA) National Ocean Service during its 

many sounding surveys in the 1800s to 2000s are included, as 

archived by the Smithsonian Institution and as provided by the 

National Geophysical Data Center. Theses and dissertations 

from many universities, U.S. Army Corps of Engineers 

reports, local and regional coastal management agencies, state 

geological surveys, and U.S. Navy reports are also included. 

Large data compilations also contribute to the database, 

including the joint USGS–NOAA Gulf of Alaska National 

Ocean Service digitization project (Golden and others, 

2016) and the U.S. Geological Survey East-Coast Sediment 

Texture Database (Poppe and others, 2014). A citations list for 

usSEABED sources contributing to the data release is in the 

usSEABED source file, US9_SRC.

Efforts have been made to reduce data duplications within 

usSEABED that may result if data from the same field activity 

or site are published in more than one source report or data 

compilation. For example, the National Geophysical Data 

Center's Seafloor Surficial Sediments (Deck 41) compilation 

contains information from several sources. If data from original and more complete sources are included in the usSEABED 

database, data for those same sites are not imported into the 

usSEABED database from the National Geophysical Data 

Center’s Deck 41 dataset. (See the section “dbSEABED 

(processing)” included in Reid and others, 2005; Buczkowski 
4 About the usSEABED Integrated Sea-Floor-Characterization Database, Built With the dbSEABED Processing System

and others, 2006; and Reid and others, 2006, for information 

on how data are incorporated by the dbSEABED software.) In 

other instances, data from multiple sources for a given site are 

included in the usSEABED database if additional data fields 

are included as a result. For example, one source may report 

only grain size for a particular site, but another source may 

include geophysical properties for the same site.

Output Files

The USGS data release for the usSEABED database 

(Buczkowski and others, 2020) enables search and download 

of six interlinked files of output data and a seventh file that 

provides linked information about the original data sources 

(table 2).

These files can be downloaded in their entirety and are 

also searchable through an online interface that allows for 

search and selection either through a GIS display or through a 

web form. Both options query and export selected portions of 

the usSEABED database.

Relational Keys

Values in the usSEABED data file types are linked 

relationally by three relational keys: DataSetKey (for source 

datasets), LocnKey (for individual sites), and the ObsvnKey 

(for individual analyses). The DataSetKey field gives the relationship of the data to the original source using the information provided in the US9_SRC file. When files are loaded into 

a relational database or GIS, the keys-in-common can be used 

to construct relationships between the tables, thereby joining 

the data across files.

Source Data (US9_SRC)

Information about the original data is in the source 

(US9_SRC) file. Each value in the output data files discussed 

below is linked to the corresponding item in the US9_SRC 

file by the DataSetKey field. Information on data sources is 

also provided with the online data portal in a more traditional 

bibliographic format.

Textural and Other Basic Information (US9_EXT, 

US9_PRS, US9_CLC, and US9_ONE)

Textural, statistical, geochemical, geophysical, dominant 

component, and color data are available in three data files, 

which represent the three ways the data were obtained. The 

US9_EXT data file contains data categorized as extracted from 

lab-based analytical data and generally represents statistical 

results as given by the original source. The US9_PRS data file 

contains so-called parsed data; that is, numerical information 

determined within the dbSEABED software through processes 

based on fuzzy-set theory, applied to written descriptions. The 

US9_CLC data file contains calculated data created through 

the application of known relationships between analytical values or through the application of empirical relationships (see 

“Parsing Description: A simplified description of dbSEABED 

processing” in Reid and others, 2005; Buczkowski and others, 

2006; and Reid and others, 2006). In some samples where 

subsample depths are not provided by the original data source, 

assumptions are made about sediment-layer thickness within 

the dbSEABED software, based on the sampler type. Many 

original datasets include information that fits into more than 

Table 2. usSEABED output files.

Data file Contents

US9_ONE Combined information including extracted, parsed, and calculated information about grain size, sediment texture, color, age, 

and other information about the sea floor derived from quantitative observations and analyses of samples (extracted); information, from written descriptions from cores, grabs, photographs, and videos (parsed); and information from calculations 

based on empirical relations or known functions performed by dbSEABED software (calculated)

US9_EXT Extracted information about grain size, sediment texture, color, age, and other information about the sea floor derived from 

quantitative observations and analyses of samples

US9_PRS Parsed information about grain size, sediment texture, color, age, and other information about the sea floor derived from written 

descriptions from cores, grabs, photographs, and videos

US9_CLC Calculated information about grain size, sediment texture, color, age, and other information about the sea floor derived from 

calculations based on empirical relations or known functions performed by dbSEABED software

US9_FAC Concatenated information about components (minerals and rock type), genesis (igneous, metamorphic, carbonate, terrigenous), 

and other appropriate groupings of information about the sea floor parsed from written descriptions of cores, grabs, photographs, and videos

US9_CMP Numerical data about selected components (for example, minerals, rock type, microfossils, and benthic biota) and sea-floor 

features (for example, bioturbation, structure, and ripples) at a given site; values in the attribute fields are measures of membership in that attribute’s fuzzy set

US9_SRC Information about the original source data incorporated into the usSEABED database
The Data in usSEABED 5

one of the extracted, parsed, and calculated categories, giving 

overlapping results for most samples. US9_ONE combines the 

information from the three data textural files.

Extracted Data

Extracted data are those data from lab-based, numerical 

analyses. Most data in this file are listed as reported by the 

source data report; generally, only minor unit changes have 

been performed. Typical data themes include textural distributions and statistics (TXR: gravel, sand, silt, clay, mud, and 

various statistics), phi-based grain-size classes (GRZ), chemical compositions (CMP), acoustic measurements (ACU), color 

(COL), and geotechnical parameters (GTC). Extracted data are 

based on lab-determined values and are the most reliable data. 

Limitations, however, exist when there is uncertainty about 

how much of the sample has been analyzed. For example, the 

analysis may have been performed only on the matrix, ignoring larger particles such as shells or pebbles.

Parsed Data

Numerical data derived from verbal logs, core or grab 

sample descriptions, shipboard notes, and photograph descriptions are classified as parsed data. Verbal input data are 

maintained in the terms employed by the original researchers 

but are coded by using phonetically derived abbreviations for 

easier processing by the dbSEABED software. Data in longer 

descriptions are sometimes divided by theme (table 1). The 

descriptions often include information on associated biota, 

sea-floor features, and structure. Typical data themes for 

the parsed dataset are lithologic descriptions (LTH), biology (BIO), color (COL), and sea-floor type (SFT, descriptions from photos and videos). Data values are calculated by 

using the dbSEABED software parser, which uses fuzzy-set 

theory to assign field values based on the form and content 

of a description. The application of fuzzy-set theory to verbal 

descriptions allows a sample or observation to belong partially to an attribute in the database, referred to as a “set.” 

How much an entry in the database is represented by a set is 

referred to as degree of membership. Fuzzy-set theory suits 

words because they are often partial carriers of categorical meaning. For example, a sample described as “gravelly 

mud” is partially gravel and partially mud, with mud being 

the predominant component (for more details, see “Parsing 

Description: A simplified description of dbSEABED processing” in Reid and others, 2005; Buczkowski and others, 2006; 

Reid and others, 2006; and Jenkins, 1997, 2002, and 2003). 

Similarly parsed verbal information is also used to produce 

the US9_CMP and US9_FAC data, described in the report 

section “Component/Feature and Facies Data (US9_CMP and 

US9_FAC).”

The parsing process was tested and calibrated by comparing the outputs against analytical results for the same samples 

(see the section “Calibrations” included in each of Reid and 

others, 2005; Buczkowski and others, 2006; and Reid and 

others, 2006). The fuzzy-set theory used in the parser produces 

output data that are degrees of membership in fuzzy sets (Mott 

and others, 1986). In the example given above, “gravelly mud” 

applied to fuzzy-set theory would result in degrees of membership assigned for both “Gravel” and “Mud” attributes, where 

the degree of membership for “Mud” is greater than the degree 

of membership for “Gravel.” Output data are expected to be 

somewhat close to, but not fully equivalent to, the percent 

abundance of a characteristic in the sample or seabed being 

observed. Calibrations within the dbSEABED software provide assurance that the output degrees of membership reflect 

absolute abundances to some degree of accuracy.

For a laboratory sample of well-sorted fine sediments, 

the descriptive results in the parsed file will be less accurate 

than measured values in the extracted file. For complex, living 

seabeds, the data in the parsed file can be more representative of the sample and seabed as a whole, as they may include 

description of objects such as shells, stones, algae, and other 

objects that are a textural component of the seabed and are 

often left out of laboratory samples that are analyzed, particularly when a machine analysis is employed.

Calculated Data

For the extracted and parsed data sources, the usSEABED 

database provides some values that are not reported by the 

original source but can be calculated directly or estimated 

by standard derivative equations by using assumptions about 

the conditions or variables (for detailed information, see the 

section “Summary of the onCALCULATION Methods used 

in dbSEABED” included in each of Reid and others, 2005; 

Buczkowski and others, 2006; and Reid and others, 2006). 

In the usSEABED database, these values are reported as 

calculated data. Calculated data are the least reliable of the 

three data types and should be used with caution.

Combined Data

The US9_ONE file provides a single, concise coverage 

of the sea floor that combines extracted (EXT), parsed (PRS), 

and calculated (CLC) data. Analyzing information telescoped 

in the US9_ONE file can be advantageous over comparing 

extracted, parsed, and calculated files to each other by 

enabling the user to see data from all three data files together 

with their data merged into a single output. However, it is 

important for users to understand the inherent limitations 

of the original data files (US9_EXT, US9_PRS, US9_CLC) 

to choose the value, or combination of values, from this 

combined data file that are most appropriate for a particular 

use. Each entry in the US9_ONE file contains a “DataTypes” 

code (produced by the dbSEABED software) to identify 

which output file (extracted, parsed, or calculated) provided 

the data listed in each attribute for the sample or observation. 

This code consists of 20 characters, which represent the 

following data attributes, in order: Gravel, Sand, Mud, Clay, 

Grainsze, Sorting, Facies, FacMshp, FolkCde, RckMshp, 

VegMshp, Carbonate, MunslColr, OrgCarbn, lShearStr, 

Porosity, PWaveVel, Roughness, lCritShStr, and GeolAge 
6 About the usSEABED Integrated Sea-Floor-Characterization Database, Built With the dbSEABED Processing System

(table 3). Attributes identified with “E” have data derived from 

extracted data, attributes identified with “P” have data derived 

from parsed data, and attributes identified with “C” have data 

derived from calculated data. Attributes identified with an “x” 

have no recorded extracted, parsed, or calculated data.

In order to allow users to use their preferred type of data, 

the full and independent files of EXT, PRS, and CLC data are 

also provided in the data release publication. Data may exist 

in each of the three files for a given subsample, consistent 

with the original data. The same fields are reported in each file 

(table 3) and can be linked by the relational keys (DataSetKey, 

LocnKey, and ObsvnKey).

Component/Feature and Facies Data (US9_CMP 

and US9_FAC)

Two usSEABED data files (US9_CMP and US9_FAC) 

contain information about compositional content, biota, and 

sediment structure present on the sea floor. These data can 

be relationally linked to data in the four files that contain 

textural and other typical sea-floor data using the ObsvnKey 

field. The data reported in US9_CMP and US9_FAC files 

are class names defined by the thesaurus in the dbSEABED 

parsing software, which clusters comparable identifying terms 

together. For example, the “quartz sediment” component 

can be indicated by the terms “quartz feldspar”, “quartzite”, 

“quartzose”, “silica sediment”, etc. The “granite” facies 

represents significant use of the words “granite,” “aplite,” 

“granodiorite,” and “pegmatite”; the “laminated” structure 

represents “laminated,” “laminations,” or “lamina.” Individual 

components and features (terms like “feldspar,” “phosphorite,” 

“bivalves,” “seagrass,” and “wood”) are held in the US9_CMP 

data file (table 4). Here, a significant use is defined as a membership of greater than 33 to the component or facies fuzzy set. 

Combinations of components with significant occurrences in a 

subsample are held in the facies (US9_FAC) data file (table 5). 

As with the parsed data in the US9_ONE and US9_PRS file, 

the values held within the US9_CMP and US9_FAC files are 

the results of applying functions of fuzzy-set membership to 

verbal data and represent a measure of veracity about the attribute, not percentages or defined values. (These files indicate 

only presence, not absence, of material; it is rare that a report 

might state, “no bivalves” or “no phosphorite.”)

The US9_CMP file contains information about compositional content (individual minerals, rocks), genesis (terrigenous, carbonate), and certain biota. These components 

are evaluated by the dbSEABED software, and the value for 

each attribute is based solely on the relationships of attributes 

within the original description. The flora and fauna included 

in the compositional components are those that may affect 

textural determinations in the parsed data, such as halimeda, 

bivalves, or foraminifera (table 6). The values within these 

attribute fields range between 0 (no membership, possibly due 

to no information) to 100 (complete membership; for example, 

shell hash is 100 membership in the shell debris set).

The US9_CMP file also includes information on sea-floor 

features that appear in more than 0.01 percent of samples, such 

as odors, including hydrogen sulfide (H2S). Unlike the compositional content information, which is construed as an abundance within the sample, these attributes are not included in 

the textural determination found in parsed data. Values within 

the attribute fields range from 0 (no membership, possibly due 

to no information) up to 100 (maximum feature development). 

In contrast to the component abundances, the sum of feature 

memberships in a sample is allowed to exceed 100.

The 44 components and the 3 features (codes tagged 

with “_F”) that appear in more than 0.01 percent of parsed 

samples within the U.S. Exclusive Economic Zone are given 

in the US9_CMP file. Table 4 lists the components and gives 

basic forms of descriptive terms that contribute to membership 

for each.

The facies file (US9_FAC) summarizes the presence of 

related groups of components, denoted as facies, such as igneous, metamorphic, ooze, and foraminifera. The dbSEABED 

facies processing is limited to a maximum of six components 

per facies; however, not all facies have six defining components. Table 5 lists the most common facies types and the 

basic forms of components that comprise each facies group.

Again, the US9_CMP and US9_FAC files only indicate 

presence, not absence, of material; it is rare that a source 

report states, “no bivalves” or “no phosphorite,” for example. 

The values within this attribute field range between 0 (no 

membership in the fuzzy set, possibly due to no information) 

to 100 (complete membership, for example, a schist membership of 100 produces a membership of 100 in the metamorphic set).

Relationship Between Parsed and Component 

Data

The dbSEABED software recognizes that many skeletonized biota, such as halimeda, rhodoliths, and shells (broken 

and unbroken), often constitute all or part of a sediment 

sample. Such biological terms are included in the parsing of 

the textural values. The selected biota with textural implications are listed in table 6. When using the parsed data, it may 

be important to cross-check with the component file by using 

the relational keys (LocnKey, ObsvnKey) to determine if biota 

are included in the textural outputs.

Within the parsed data textural file (US9_PRS), the 

“Facies” and “FacMshp” fields indicate the dominant compositional facies and the fuzzy-set membership of a sample 

in that facies. Other components and observations may also 

be listed for that sample in the US9_CMP file, linked by the 

relational keys.
The Data in usSEABED 7

Table 3. Field parameters, format, units, range, meaning, and comments for extracted, parsed, and calculated data in the US9_ONE 

(extending to US9_EXT, US9_PRS, and US9_CLC) file.

[%, percent; kPa, kilopascal; m/s, meter per second; P/T, high pressure and temperature; V:H, ratio of vertical height to horizontal length]

Attribute Parameter Data format Units, range, meaning Comment

Latitude Latitude Decimal 00.00000 Decimal degrees, 90° to 

−90° range

Represents a mix of datums; users of 

the data should note the available 

information found in the original 

sources and use their own criteria 

for assessing the accuracy of the 

locations.

Longitude Longitude Decimal 000.00000 Decimal degrees, −180° to 

180° range

Represents a mix of datums; users of 

the data should note the available 

information found in the original 

sources and use their own criteria 

for assessing the accuracy of the 

locations.

WaterDepth Water depth Integer 00000 Meters Corrections for tides should not be 

assumed.

ObsvnTop Observation top Decimal 000.00 Meters below seabed surface Observation top as noted in source 

report.

ObsvnBot Observation bottom Decimal 000.00 Meters below seabed surface Observation bottom as noted in source 

report.

LocName Location name Character Xxxx.... Survey or laboratory code 

for the sampling site

Not unique; site name as given in 

report; sometimes linked to cruise 

name or other information to decrease site name overlap.

DataSetKey Dataset number key Integer 000 Generated by dbSEABED 

processing software

Relational key linking to data source 

(US9_SRC) file.

LocnKey Location key Integer 0000000 Generated by dbSEABED 

processing software

Relational key linking to data from 

the same location in other data files. 

Each location counted sequentially 

as total output; core data may have 

more than one sample per site.

ObsvnKey Observation key Integer 0000000 Generated by dbSEABED 

processing software

Relational key linking to data from 

same observation or sample in other 

data files. Each sample or observation site counted sequentially as total 

output; multiple samples may be at 

each site (that is, within core).

Device Device type Character Xxxx.... Type of sampling or observation deviceAs given in source report; recovery 

(“rcvy”) or penetration (“pen”) 

length appended if given in source 

report.

DataTypes Data types Character 

xxxxxxxxxxxxxxxxxxxx

Code produced by the 

dbSEABED processing 

software to identify which 

output file (extracted, 

parsed, or calculated) 

was used, or data themes 

recorded during data entry

Code used in two ways. In the 

US9_ONE file that identifies which 

type of data (extracted, parsed, or 

calculated) is provided in the attribute fields for the observation. In 

the outputs of US9_EXT, US9_PRS, 

and US9_CLC, this field provides 

the dbSEABED software’s data 

themes for the observation.

Gravel Gravel Integer 000 Gravel grain-size fraction, % Textural class.

Sand Sand Integer 000 Sand grain-size fraction, % Textural class.
8 About the usSEABED Integrated Sea-Floor-Characterization Database, Built With the dbSEABED Processing System

Table 3. Field parameters, format, units, range, meaning, and comments for extracted, parsed, and calculated data in the US9_ONE 

(extending to US9_EXT, US9_PRS, and US9_CLC) file.—Continued

[%, percent; kPa, kilopascal; m/s, meter per second; P/T, high pressure and temperature; V:H, ratio of vertical height to horizontal length]

Attribute Parameter Data format Units, range, meaning Comment

Mud Mud Integer 000 Mud grain-size fraction, % Textural class.

Clay Clay Integer 000 Clay grain-size fraction, % Textural class; output for extracted 

data only, as clay value can be determined only by analysis.

Grainsize Grain size Decimal 00.00 Phi characteristic grain size Characteristic grainsize, based on 

mean and median grain size values.

Sorting Sorting Decimal 0.00 Phi grain-size dispersion Standard deviation.

Facies Sea-floor facies Character Xxxx.... Facies with the maximum 

fuzzy-set membership, if 

above 30%

Output for parsed data only.

FacMshp Facies membership Decimal 000 Fuzzy-set membership (%) 

of the class (or facies), 

noted above

Output for parsed data only.

FolkCde Folk classification Character xx.XX.... Grain-size classification 

based on the Folk Code 

(Folk and others, 1970)

RckMshp Rock index Integer 000 Fuzzy-set membership (%) Degree of membership of sample in 

the rock fuzzy set; reported only for 

parsed data.

VegMshp Vegetation index Integer 000 Fuzzy-set membership (%) Degree of membership of sample in 

the vegetation fuzzy set; reported 

only for parsed data.

Carbonate Carbonate Integer 000 % For parsed data, this value is a degree 

of fuzzy-set membership

MunslColr Munsell color code Character XXXX.... Standard alphanumeric coding of color partitioned 

into hue, value, and 

chroma

Example: “5YR 6/4” (Rock-Color 

Chart Committee., 1991).

OrgCarbn Organic carbon Integer 000 % For parsed data, minimum value from 

descriptions is 0.1%.

IShearStr Log shear strength Decimal 00.0 kPa, undrained, unconfined As reported in sources; see source 

documentation for instrumentation 

used.

Porosity Porosity Decimal 00.00 %

PWaveVel P-wave velocity Decimal 00.0 m/s Usually not corrected for P/T effects.

Roughness Roughness Decimal 0000.00 Coded to express the height 

and length of the bottom feature with greatest 

aspect ratio

In a coding that expresses the height 

and length of the bottom feature 

with greatest aspect ratio; a coded 

output representing the V:H of the 

roughness element with greatest 

aspect ratio, values expressed as 

(rounded) integer log2.
The Data in usSEABED 9

Table 3. Field parameters, format, units, range, meaning, and comments for extracted, parsed, and calculated data in the US9_ONE 

(extending to US9_EXT, US9_PRS, and US9_CLC) file.—Continued

[%, percent; kPa, kilopascal; m/s, meter per second; P/T, high pressure and temperature; V:H, ratio of vertical height to horizontal length]

Attribute Parameter Data format Units, range, meaning Comment

ICritShStr Log critical shear stress Decimal 0000.00 Log10 of tau, in kPa Log 10 of tau, in kPa, being the shear 

stress required to initiate easily 

observable erosion and transport, 

whether by traction or suspension; taken from a compilation of 

published relationships ranging from 

large boulder to muds, through a 

range of grain shapes (for example, 

shell) (see the section “Summary of 

the onCALCULATION Methods 

Used in dbSEABED” included 

in each of Reid and others, 2005; 

Buczkowski and others, 2006; and 

Reid and others, 2006).

GeolAge Geologic age Character Xxxx.... Age of the sample as defined 

by the geologic time scale

Geologic age of the sample or observed materials.

ObsvnDetai Observation details Character Xxxx.... Additional information about 

the observation

Detailed observations recorded for a 

sample or observation.

Key Primary key Character Xxxx.... Assigned during formatting of the usSEABED 

database

Unique identifier of the sample entry 

and primary key for the database 

table.

ObsvnDate Observation date Character Xxxx.... Date provided by the data 

source or by the persons 

entering the data into usSEABED

Date of collection, or the start of a 

range of collection dates for the 

sample or observation.

DateSrc Source of date Character Xxxx.... Date provided by the data 

source or by the persons 

entering the data into usSEABED

Source of the date provided for the 

sample or observation.
10 About the usSEABED Integrated Sea-Floor-Characterization Database, Built With the dbSEABED Processing System

Table 4. Components and features processed for the usSEABED database.

[The 48 components with the highest cited occurrences (greater than 0.01 percent) for U.S. waters are given in the US9_CMP file. As usSEABED uses the same 

thesaurus as its sister data compilations (auSEABED, goSEABED, and so on), some triggering words listed below may not occur for U.S. waters. Only one of 

the possible word variations is listed below; for example, the word “mollusc" incorporates “mollusk” and “Mollusca,” and “chlorite” includes “chloritic”. Nontextural or compositional features are indicated by “_F” at the end of the senior synonym]

Senior synonym Triggering words (word variations not included)

Terms related to shells

shl Shell, shell (-bed, -bank, -carpet, -fraction, -content, -material), shellfish, valves

shl_dbr Shell debris, shell hash, coquina, shell (-bit, -conglomerate, -fragments, -festoon, -grit, -lag, -mash, -material, -piece, 

-particle)

skl_dbr Skeletal debris, -sand, -fragment, -carbonate, -calcarenite, -clasts; bioclastic debris, -gravel; biogenic debris, faunal debris

mlsc Mollusc

biv

Bivalve, arctica, astarte, cardium, chama, chione, chlmys, clam (-shell, -flat material, -hash, -valves), cockle (anadara, 

-shell), donax, glycymeris, katalysia, lamellibranch, macoma, mercenaria, mulinia, mussel (-bed, -bank, -shell), mya, 

mytilus, nucula, pelecypod, quahog, rangia, seep mytilid, slipper shells, surf clam, tellina, tellinid, venerid, venus 

clams, vesicomyid, yoldia

musl Mussel, mytilus, Atrina

Terms related to rocks

rck Rock, outcrop, substrate, reef, pavement, banks, pinnacle, mound, boulder, platform, hard bottom

rck_frg Rock fragment, rock chips, rock particles

hrdgrond Hard ground

mudstn Mudstone, calcareous (-mudstone, -siltstone), clay (-rock, -shale, -stone), marlstone, mud (-rock, -stone), pelite, shale, 

siliceous shale, siltstone

sed_rck Terrigenous breccia, sand rock, cemented sand

sndstn Sandstone, gritstone, graywacke, labile sandstone, sandstone reef, wacke

bluschst Blue schist, crossite-albite schist, crossite-quartz schist, glaucophane, quartz crossite schist, quartz glaucophane schist

Terms related to sediments

sed Sediment, detrital, mud, clay, silt, sand, pebbles, cobbles, gravel, rubble, granule, fraction, moraine, lag deposit, clastic

Terms related to terrigenous materials

qtz_sed Quartz sediment, quartz feldspar, quartzite, quartzose, silica sediment, clast, sand, gravel, grit, pebbles,

trrg Terrigenous, lithic, inorganic

volgls Volcanic glass, obsidian, hyaloclastite, pyroclastic, quenched, vitric, subvitreous

Terms related to carbonates

carb Allogenic grain, authigenic carbonate, biogenic, calcareous, calcilutite, calcarenite, calcirudite, calcareous biogenic, 

carbonate, limey, marl, skeletal micrite

dolmt Dolomite, dolostone, ankerite, molar magnesium carbonate

Terms related to clays

glauc Glauconite, greensand

claymin Clay mineral, bentonite, chlorite, collophane, illite, kaolinite

smect Smectite, bentonite, montmorillonite, nontronite

kaol Kaolinite

chlort Chlorite
The Data in usSEABED 11

Table 4. Components and features processed for the usSEABED database.—Continued

[The 48 components with the highest cited occurrences (greater than 0.01 percent) for U.S. waters are given in the US9_CMP file. As usSEABED uses the same 

thesaurus as its sister data compilations (auSEABED, goSEABED, and so on), some triggering words listed below may not occur for U.S. waters. Only one of 

the possible word variations is listed below; for example, the word “mollusc" incorporates “mollusk” and “Mollusca,” and “chlorite” includes “chloritic”. Nontextural or compositional features are indicated by “_F” at the end of the senior synonym]

Senior synonym Triggering words (word variations not included)

Terms related to corals

crl Coral, Acropora palmata, brain coral, Dendrophyllia, Madrepore, Manicina, Porite, sea twig

Terms related to microfossils

rad Radiolaria

diat Diatom, diatomite/diatomaceous

frm Calcareous foraminifera, foraminifera, globigerina bit, planktonic

plnk_frm Planktonic foraminifera, globerina, globorotalid, planktic foraminifera

bnth_frm Benthic foraminifera, archaias, bolivina, bulimina, coralline foraminifera, discorbis, eponides, homotrema, hyaline, lenticulina, loxostema, miliolid, nodosirid, nonien, notosirid, peneroplis, porcellanous, rotaiid, uvigerina

Terms related to minerals

min Mineral

mica Mica, biotite, chlorite, muscovite, sericite, talc

qtz

Quartz, arkosic sand, calcareous quartz sand, milky vein quartz, quartz (-content, -fragment, -grain, -granule, 

-groundmass, -mass, -rich, -vein, -veinlet, -crystal), quartzose, quartzite (-cobble, -gravel, -pebble), sandstone (-chunk, 

-fragment), silica

zeol Zeolite, clinoptolite

hvy_min

Heavy mineral, anatase, andalusite, apatite, black sand, brookite, cassiterite, clinozoisite, corundum, dumortierite, epidote, garnet, ilmenite, jadeite, kyanite, leucoxene, magnetite, monazite, ore mineral, piedmontite, rutile, sillimanite, 

sphene, spinel, staurolite, titanomagnetite, titanite, tourmaline, topaz, zircon, zoisite

maf Mafic, actinolite, aegirite, amphibole, augite, (brown- green- basaltic-) hornblende, bronzite, clinopyroxene, ferromagnesian, hypersthene, olivine, orthopyroxene, oxyhornblende, pyroxene, titanaugite, titaniferous, tremolite

Terms related to mineralized deposits

phspht Phosphate, phosphorite

Terms related to ooze

ooz Ooze

calc_ooz Calcareous ooze, nannofossil (-mud, -ooze), pteropod (-mud, -ooze), foraminiferal (-marl, -ooze, -mud), globigerina 

(-mud, -ooze)

Terms related to organic material

orgnc_dbr Organic debris, -content, -material, -parts, -matter

wood Wood, bark, twig

peat Peat, lignite

Terms related to clast size

cbl Cobble

bldr Boulder
12 About the usSEABED Integrated Sea-Floor-Characterization Database, Built With the dbSEABED Processing System

Terms related to odors

gas_F Gas

odr_F Odor, smell, stinky, fetid, sewage, urine-, rotten fish-, anoxich2s_F Hydrogen sulfide, H2S, sulfur

Table 5. Field parameters for the US9_FAC file, featuring facies data and their component makeup.

[Facies values are determined by a combination of components and their mined values from word-based descriptions. Numerical textural, geochemical, and geophysical information for word-based descriptions is found in the parsed (US9_PRS) data file. Values are degrees of membership in fuzzy sets, given as numbers 

between 0 and 100. A minimum membership value of 33 in component presence is required to be included in a given facies, and a component may be included 

more than one facies. Facies values notes presence only, not absence]

Field name Parameter Contributing components

Algae Algae Halimeda, rhodolith, coralline algae

Shells Shells Shells, shell debris, skeletal debris, mollusc, bivalves, mussels

Carb Carbonate Carbonate, marl, chalk, carbonate cementation, calcareous nodules, carbonate ooze

Clay Clay Clay minerals, glauconite, smectite, kaolinite, chlorite

Coral Coral Coral, coral reef

Fossil Fossil Radiolaria, diatom, nannofossil, fish debris, palynomorph

Forams Foraminifera Foraminifera (benthic, planktonic, arenitic)

AlSi Aluminosilicate Mica, quartz, zeolite, siliceous, opal

Hvy_min Heavy minerals Heavy mineral, mineral, mafic, ultramafic

Mineralized Mineralized Phosphorite, pyrite, sulfide, barite

Mn/Fe Ferromanganese Ferruginous, iron nodule, siderite, manganese nodule, ferromanganese oxide, ferromanganese crust

Organic Organic Wood, peat, coal, organic organic debris, organic carbon

Metam_rock Metamorphic rock Metamorphic, dolomite, schist, blueschist, gneiss, slate

Generic_rock Generic rock Rock, rock fragment, hard ground

Sed_rock Sedimentary rock Sedimentary rock, mudstone, sandstone, limestone, chert

Sediment Sediment Terrigenous, metamorphic debris, igneous debris, sediment, quartz sediment, volcanic sediment

Plant Plant Foliage, plant

Terrigenou Terrigenous Terrigenous, quartz, quartz sediment, feldspar, mafic

Seaweed Seaweed Seaweed, seagrass, weed

Table 6. Biological components that may have textural implications.

[Listed are only those components that are frequently noted in samples from U.S. waters. Biological components in bold appear in more than 0.01 percent of 

samples and are provided in the US9_CMP file]

Biological components

barnacles clypeasts diatoms nannofossils reefs

bivalves coralline algae echinoids pteropods scaphopods

brachiopods corals forams radiolaria serpulids

bryozoa crabs halimeda razor clams shells

calcareous algae crustaceans molluscs
References Cited 13

Quality Control

Quality control over the data is an iterative process 

beginning with visualization of each source file. First, graphical plots of site locations and parameter values are used to 

detect outliers, which are corrected if possible. Each dataset 

is viewed in a GIS to ensure that data locations are reasonable relative to survey extents; those sites with unresolvable 

location issues or known incomplete analyses are not included 

in the usSEABED database. (Note: the usSEABED database 

contains a small number of onshore samples.) Old sets may 

require more scrutiny than newer or well-exercised datasets.

Users of the output data are reminded to note the inherent limitations imposed by the source datasets as to navigational precision, sampler type, and analytical technique. To 

help inform users about the provenance of the data, samplecollection devices and observation methods, where provided, 

are recorded for each data entry in the usSEABED data files. 

The US9_SRC source file includes information pertaining 

to the original data sources, such as the type of source material the data were taken from (published and unpublished 

reports, technical memoranda, and theses), as well as navigational methods, if known, and publication or other release 

dates. Citations for each data source are also available in the 

source file to direct users to additional information about 

data-collection methods and analytical techniques found in the 

original publication or dataset.

Next, built-in filters in the dbSEABED software detect 

implausible values for numerical fields, unknown verbal 

terms, incomplete analyses (for example, the total of percentages in grain-size classes is greater than 105 percent or less 

than 95 percent), and incorrect field types (string or number). The software also detects samples that seem to belong 

to a core though they are entered as independent samples. 

Through iterative testing, the detected issues are either fixed or 

excluded from usSEABED.

Parsing verbal descriptions within the dbSEABED 

software also includes a number of quality-control devices. 

The dbSEABED software contains a thesaurus where various terms used to describe the seabed are given lithologic, 

textural, and biological classes and weightings. If a term is 

not recognized in the dictionary, the process is aborted and 

null values are given to all appropriate fields. Likewise, if a 

description contains more than 32 terms, it is not parsed due to 

complexity.

Uncertainties in the Data

Users of the usSEABED database are reminded that 

many sea-floor regions are, by their nature, dynamic environments subject to a variety of physical processes, such as erosion, winnowing, reworking, and sedimentation or accretion, 

that vary on different spatial and temporal scales, and sea-floor 

samples represent a moment in time.

Because site locations are as given in the original sources, 

the usSEABED compilation may include uncertainties resulting from the navigational techniques and datums used. As 

many reports are decades old, users of usSEABED data should 

use their own criteria to determine the appropriateness of data 

from each source report for their particular purpose and scale 

of interest.

In addition, there are uncertainties in data quality associated with both the extracted data (from lab-based analytical 

analyses) and parsed data (from word-based descriptions). 

Some grain-size analyses are done solely on the sand fraction and exclude coarser material, such as shell fragments and 

gravel. Word descriptions of sediment samples may emphasize 

the proportion of one sediment fraction over another and may 

disregard other important textural or biological components. 

Incomplete data or that data that are known to produce erroneous results are not included in the usSEABED database.

Accessing the usSEABED Database

The usSEABED database is available online 

(Buczkowski and others, 2020) through an interface 

that allows download of all the data files that make up 

usSEABED, as well as search and selection of portions of 

the database through a GIS display or through a web form at 

https://cmgds.marine.usgs.gov/usseabed/. Search results are 

downloaded as comma-separated value files and come with 

customized metadata.

References Cited

Buczkowski, B.J., Reid, J.A., Jenkins, C.J., Reid, J.M., 

Williams, S.J., and Flocks, J.G., 2006, usSEABED—Gulf 

of Mexico and Caribbean (Puerto Rico and U.S. Virgin 

Islands) offshore surficial-sediment data release (ver 1.0): 

U.S. Geological Survey Data Series 146, 50 p. [Also available at https://doi.org/10.3133/ds146.]

Buczkowski, B.J., Reid, J.A., Schweitzer, P.N., Cross, V.A., 

and Jenkins, C.J., 2020, usSEABED—Offshore surficialsediment database for samples collected within the United 

States Exclusive Economic Zone: U.S. Geological Survey 

data release, https://doi.org/ 10.5066/ P9H3LGWM.

Folk, R.L., 1954, The distinction between grain size and 

mineral composition in sedimentary rock nomenclature: The 

Journal of Geology, v. 62, no. 4, p. 344–359. https://doi.org/

10.1086/626171.
14 About the usSEABED Integrated Sea-Floor-Characterization Database, Built With the dbSEABED Processing System

Folk, R.L., Andrews, P.B., and Lewis, D.W., 1970, Detrital 

sedimentary rock classification and nomenclature for 

use in New Zealand: New Zealand Journal of Geology 

and Geophysics, v. 13, no. 4, p. 937–968. https://doi.org/

10.1080/00288306.1970.10418211.

Folk, R.L., and Ward, W.C., 1957, Brazos River bar 

[Texas]—A study in the significance of grain-size parameters: Journal of Sedimentary Petrology, v. 27, no. 1, 

p. 3–26. https://doi.org/10.1306/74D70646-2B21-11D7-

8648000102C1865D.

Golden, N.E., Reid, J.A., Zimmermann, M., Lowe, E.N., and 

Hansen, A.S., 2016, Digitized seafloor characterization data 

from the Gulf of Alaska from historical National Ocean 

Service (NOS) smooth sheets: U.S. Geological Survey data 

release, accessed March 2020, at https://doi.org/10.5066/

F7CV4FT9.

Inman, D.L., 1952, Measures for describing the size distribution of sediments: Journal of Sedimentary Research, v. 22, 

no. 3, p. 125–145.

Jenkins, C.J., 1997, Building offshore soils databases: Sea 

Technology, v. 38, no. 12, p. 25–28.

Jenkins, C.J., 2002, Automated digital mapping of geological colour descriptions: Geo-Marine Letters, v. 22, no. 4, 

p. 181–187. [Also available at https://doi.org/10.1007/

s00367-002-0111-0.]

Jenkins, C.J., 2003, Data management of MARGINS geologic 

data, with emphasis on efficiency, quality control and data 

integration: MARGINS Newsletter, no. 10, p. 8–10. [Also 

available at https://www.nsf-margins.org/Publications/

Newsletters/Issue10.pdf.]

Mott, J.L., Kandel, A., and Baker, T.P., 1986, Discrete mathematics for computer scientists and mathematicians 2nd ed.: 

Englewood Cliffs, N.J., Reston Publishing Company, 751 p.

Poppe, L.J., McMullen, K.Y., Williams, S.J., and 

Paskevich, V.F., eds., 2014, USGS east-coast sediment 

analysis—Procedures, database, and GIS data (ver. 3.0, 

November 2014): U.S. Geological Survey Open-File Report 

2005–1001, accessed March 2020 at https://pubs.usgs.gov/

of/2005/1001/.

Reid, J.M., Reid, J.A., Jenkins, C.J., Hastings, M.E., Williams, 

S.J., and Poppe, L.J., 2005, usSEABED—Atlantic coast 

offshore surficial-sediment data release (ver. 1.0): U.S. 

Geological Survey Data Series 118, accessed March 2020 at 

https://pubs.usgs.gov/ds/2005/118.

Reid, J.A., Reid, J.M., Jenkins, C.J., Zimmermann, M., 

Williams, S.J., and Field, M.E., 2006, usSEABED—

Pacific coast (California, Oregon, Washington) offshore 

surficial-sediment data release (ver. 1.0): U.S. Geological 

Survey Data Series 182, 57 p., accessed March 2020 at 

https://pubs.usgs.gov/ds/2006/182/.

Rock-Color Chart Committee, 1991, The Geological Society 

of America rock color chart with genuine Munsell color 

chips: Boulder, Geological Society of America.
For more information about this report, contact:

Director, Woods Hole Coastal and Marine Science Center

U.S. Geological Survey

384 Woods Hole Road

Quissett Campus

Woods Hole, MA 02543–1598

WHSC_science_director@usgs.gov

(508) 548–8700 or (508) 457–2200

or visit our website at

https://www.usgs.gov/centers/whcmsc

Publishing support provided by the 

Pembroke and Rolla Publishing Service Centers
Buczkowski and others—About the usSEABED Integrated Sea-Floor-Characterization Database, Built With the dbSEABED Processing System—OFR 2020–1046

ISSN 2331-1258 (online)

https://doi.org/10.3133/ofr20201046