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Computers & Geosciences 33 (2007) 773–788

www.elsevier.com/locate/cageo

GIS application in mineral resource analysis—A case study ofoffshore marine placer gold at Nome, Alaska

Wei Zhou�, Gang Chen, Hui Li, Huayang Luo, Scott L. Huang

Department of Mining and Geological Engineering, University of Alaska Fairbanks, P.O. Box 755800, Fairbanks, AK 99775-5800, USA

Received 19 July 2005; received in revised form 12 November 2006; accepted 15 November 2006

Abstract

Geographic information system (GIS) technology has been applied to analyze the offshore marine placer gold deposits

at Nome, Alaska. Two geodatabases, namely Integrated Geodatabase (IG) and Regularized 2.5D Geodatabase (R2.5DG),

were created to store and integrate digital data sets in heterogeneous formats. The IG served as a data warehouse and used

to manage various geological data, such as borehole, bedrock geology, surficial geology, and geochemical data. The

R2.5DG was generated based on the IG and could be used for gold resource estimate at any given spatial domain.

Information on placer gold deposits can be updated, queried, visualized, and analyzed by making use of these

geodatabases. Ore body boundaries, gold distribution, and the resource estimation at various cutoff grades can be

calculated in a timely manner. Based on the enhanced GIS architecture, a web-based GIS (http://uaf-db.uaf.edu/website/)

was developed to facilitate remote users to access the offshore marine placer gold data. Users can integrate local data

sources with remote data sources for query, visualization and analysis via a web browser. The GIS architecture developed

in this project can be readily adapted to mineral resource management in other areas of the state.

r 2007 Elsevier Ltd. All rights reserved.

Keywords: Offshore mineral resources; Placer gold mining; Geodatabase; Resource estimate; Cutoff grades

1. Introduction

Miners, geologists, and engineers have beendealing with problems related to the analysis andmanipulation of geo-referenced information (i.e.spatial data) for many decades. Conventionally,analog maps are used to store and display suchinformation. A major component of many geologi-cal surveys consists of mapping the composition andstructure of bedrock and surficial geology using

e front matter r 2007 Elsevier Ltd. All rights reserved

geo.2006.11.001

ing author. Tel.: +1907 474 5928;

6635.

ess: ffwz@uaf.edu (W. Zhou).

traditional field methods as well as geochemical,geophysical and remote-sensing techniques. Integra-tion of field survey data, maps, and other pertinentinformation for the purpose of mineral resourceexploration and resource estimation is a very time-consuming task. However, geographic informationsystems (GIS) can accomplish such a task in a time-efficient and cost-effective manner. A GIS is acomputer system capable of assembling, storing,manipulating, and displaying geographically refer-enced information, i.e. data identified according totheir locations (USGS web site). Practitioners alsoregard the total GIS as including operating person-nel and the data that go into the system. With the

.

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increasing popularity and functional developmentof GIS in recent years, many mining companiesstarted using GIS as the preferred tool for mineplanning, analysis, and management. Moreover, thestate and federal agencies involved in the minepermitting process are adopting the GIS format asthe standard for communicating spatial data.

Abundant beach and offshore resources of heavyrefractory minerals occur along the coast of Alaska.Nome, for example, is one of the most recentlyactive areas of marine and beach placer mining inthe State of Alaska (Koschmann and Bergendahl,1968; Garnett, 2000). From as early as 1897–1962,the Nome area produced about 5 million ounces ofgold (Koschmann and Bergendahl, 1968). Becauseof the huge amount of data in variety of formsaccumulated from placer gold exploration andproduction over the past century, the analysis andmanagement of these data for future developmentof the Nome offshore gold resource is an enormoustask. GIS technology is a tool well suited to meetthis challenge. First, GIS technology facilitatesbuilding and maintaining geodatabases for theproject. A geodatabase is an object-oriented datamodel that represents geographic features andattributes as objects and the relationships betweenobjects but is hosted inside a relational databasemanagement system. A geodatabase can storeobjects, such as feature classes, feature data sets,non-spatial tables, and relationship classes (ESRI,2004a). Data queries, analysis, and visualization canbe then carried out based on these geodatabases(Bonham-Carter, 1994). Such an approach to datamanagement provides an in-depth understanding ofthe Nome offshore gold deposit, which, in turn, willgreatly assist the development of the offshore goldresource.

In the Nome Offshore Marine Mineral ResourcesProject, two relational geodatabases for storingvarious maps integrated with digital data sets havebeen created. A relational geodatabase is a methodof structuring data as collections of tables that arelogically associated with each other by sharedattributes. Any data element can be found in arelation by knowing the name of the table, theattribute (column) name, and the value of theprimary key (ESRI, 2004a). One geodatabase isknown as the Integrated Geodatabase (IG). It servesas a data warehouse and stores all the relevant datathat could possibly be collected in the Nome area,such as borehole data, bedrock geology, surficialgeology, and geochemical data. Another geodata-

base, known as the Regularized 2.5D Geodatabase(R2.5DG), has been generated based on the IG. TheR2.5DG is used to define orebody boundaries,perform grade interpolation, and estimate theresource at any given spatial domain. In addition,specialized GIS models can be developed from thesegeodatabases to meet the various needs of a project.

The GIS procedure developed in this project canbe readily adapted for the analysis of offshoremarine mineral data in other areas. This procedurealso enables web-based GIS, which can be used byauthorized users from remote computers.

2. Data collection

Data collected in this study has been gatheredfrom private records held by mining and explora-tion companies, published professional literature,reports from engineering firms, maps and open filereports from government agencies, documents fromrecording offices, and information through theInternet. One large set of data, including databasesand documents, is from government agencies, suchas USBM (United States Bureau of Mines), ADNR(Alaska Department of Natural Resources), USGS(United States Geological Survey), and NOAA(National Oceanic and Atmosphere Administra-tion). Another large set of data is from WestGoldExploration Mining Company, Limited Partnership(WestGold). WestGold operated the bucketlinedredge from 1985 to 1990 (Howkins, 1992). Duringthe summers of 1986 and 1987, WestGold carriedout 3400 line km of high-resolution seismic surveysof the lease area. Seismic data were interpreted toprovide facies interfaces and thicknesses, allowingfaulting to be identified and profiles to be drawn.Simultaneous side scan sonar surveys, with a 3-mmpenetration, were used to map sediment type on theseafloor. From 1987 to 1989, WestGold completed2530 holes and collected 57 bulk samples. Each holewas drilled in 1-m increments. The sediment fromeach 1-m interval was collected and stored, a briefsediment description was recorded and the goldcontent was assayed (Bronston, 1989).

Data files from 3468 drill holes in the offshorearea at Nome were reformatted and complied.These file types are the principal sources ofinformation for this project (Huang et al., 2001).Most drill hole logs record lithology, gold concen-tration value, and penetration blow count. Blowcount data, the number of blows needed to driveeach barrel through a sample length of 30 cm,

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provides sediment hardness information. A geologickey describes lithologic types intercepted. Goldconcentration values are tabulated in oz/m3 andoz/yd3, along with ‘‘normalized’’ and ‘‘intensity’’values. The normalized value is the relative goldconcentration as compared with a reference goldconcentration. The intensity value shows normal-ized gold values (from 0 to 45) as 9 even intervalswith intensity 9 signifying highest gold concentra-tion.

The authors then re-compiled and integrated theinformation to better understand the geologiccharacteristics, geochemical and geophysical signa-tures, borehole data, economic considerations,oceanographic factors, submarine topography, andpotential environmental impacts.

2.1. Geology of Nome area

Because of the extent and richness of the Nomegold resources, the area was studied extensively, andgeological, geophysical, and geochemical character-istics of offshore gold deposits were well documen-ted in the published literature. Various aspects ofgeology of the Nome placer deposits onshore andoffshore have been described by Nelson andHopkins (1972), and Bronston (1990).

There are 22 metasedimentary, metavolcanic, andmetaplutonic bedrock units in the area. The NomeGroup is a series of four lithostratigraphic units thatare locally deformed by low-angle thrust faults. TheNome Group consists of the following four sub-units (Bundtzen et al., 1994):

(1)

a basal, complexly deformed quartz-rich peliticschist,

(2)

a unit of mafic and pelitic schists and marble, (3) a mafic-dominated schist assemblage, and (4) a dirty marble (Bundtzen et al., 1994).

The bedrock topography (Pleistocene–Pliocenecontact) was interpreted from high-resolution seis-mic survey (Bronston, 1990).

Extensive faulting in the Precambrian basementrock displaced the Pliocene sediments, producing anasymmetrical, concave trough synform, whichplunges to the south. The east–west trending fault,which is named the ‘‘basin boundary fault’’(Bronston, 1990), delineates the northern extent ofthe synform. Numerous smaller faults strike north-east and northwest at various angles to the basinboundary fault.

2.2. Offshore geology

Nome, Alaska is located on the southern coast-line of the Seward Peninsula, on the northern coastof Norton Sound, which is a part of the north-eastern Bering Sea (Fig. 1). Gold is abundantoffshore (Koschmann and Bergendahl, 1968; Gar-nett, 2000) at Nome because fluvial and glacialprocesses transported gold from gold-enriched bed-rock in the uplands into the marine environmentwhere it was further concentrated by wave andcurrent action. The USGS and USBM havesummarized much of the geology of the area(Nelson and Hopkins, 1972; Tagg and Greene1973).

2.3. Offshore sediment lithology

The lithologies of offshore sediments in the Nomearea include arenites and gravels composed of redgranite and quartz monzonite (Howkins, 1992).Fine-grained marine sediments deeply bury offshorebedrock east of Nome. Offshore bedrock is justbelow the sea bottom to the west. Fig. 2 shows alithologic map of the offshore sediments.

2.4. Glaciation Events

Moraine deposits from glacier activity coversmost of the area (Fig. 3), the most extensive ofwhich is a surface drift sheet deposited during theNome River glaciation period of the middlePleistocene (Bundtzen et al., 1994). Subsequentperiods (Stewart River, Salmon Lake, and MountOsborn glaciations) were less extensive and re-stricted to higher elevations and mountain valleys(Howkins, 1992).

According to Nelson and Hopkins (1972) glaciershave advanced past the present Nome coast at leasttwice (the early Pleistocene, and Illinoisan glacia-tions). These glacial activities resulted in theemergent beach deposits in the Nome area being amajor source of placer gold by eroding andconcentrating gold from bedrock.

2.4.1. Economic geology

Gold in the Nome area was sourced originallyfrom gold–polymetallic–quartz–carbonate veins,stratiform, massive sulfide–barite deposits asso-ciated with felsic metavolcanic schist and metafelsitecenters, massive sulfide–iron deposits hosted incarbonate-dominated terranes of uncertain origin,

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Fig. 1. Location of Nome, Alaska (modified from http://maps.yahoo.com/).

Fig. 2. Lithologic map of the ocean floor off the coast of Nome (surficial geology digitized from Howkins, 1992).

W. Zhou et al. / Computers & Geosciences 33 (2007) 773–788776

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Fig. 3. Distribution of glacial deposit in Nome (Bundtzen et al., 1994).

W. Zhou et al. / Computers & Geosciences 33 (2007) 773–788 777

and heavy mineral placer deposits (Boyle, 1979).These materials were transported via streams andglaciers to a coastal environment, and subsequentlyconcentrated in the marine and near-marine envir-onments.

The formation of offshore gold deposits at Nomemight be divided in three stages. During the firststages, lean gold gravels were deposited along themargins of the Bering Sea as stream deltas and fans.The next stage involved the coastal uplift. Afteruplift, the new beach was subjected to erosion byocean wave, and gold-rich gravels became moreconcentrated. The final stage deals with the conceptthat glaciation could lead to concentration of gold,

as is seen in Nome. As glaciers moved, goldsegregated from the sediments carried by glaciersdue to its high density. According to the concentra-tion processes in these three stages, other placers inareas of heavy glaciation, especially valleys coveredin glacial sediment may prove to be worthwhileexploration targets (Boyle, 1979).

3. Development of geodatabases

A considerable amount of data in map, image andtabular formats were collected, complied, andintegrated. These data were further processed andstructured into an IG. Database is strictly defined as

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Table 1

List of feature classes in integrated geodatabase

Feature class Feature type Description

DH_location Table Drill hole location info

DH_segmentinfo Table Borehole segment

attributes

DH_sliceinfo Table Layered orebody

information

GeoDH_sliceinfo Point Layered orebody

information

Lith_slice_Poly Polygon Layered lithology

OnshoreGeo Polygon Onshore geology

OffshoreGeo Polygon Ocean floor geology

Structure Polygon Offshore sediment

structure elements

Permitblk Polygon Exploration permit

blocks

Rivers Polygon Rivers, town and roads

of Nome

Study area Polygon Study area boundary

W. Zhou et al. / Computers & Geosciences 33 (2007) 773–788778

one or more structured sets of persistent data,managed and stored as a unit and generallyassociated with software to update and query thedata (Litton, 1987; Navathe and Elmasri, 2002;Date, 2003). A geodatabase in GIS includes dataabout the spatial locations and shapes of geographicfeatures recorded as points, lines, areas, pixels, gridcells, or TINs, as well as their attributes. Ageodatabase is a collection of geographic data setsfor use by ArcGIS (ESRI, 2004a).

The IG in this project stores all data that arerelated to the placer gold deposits that possiblycould be collected in the Nome area, such asborehole, bedrock geology, surficial geology, andgeochemical data. The IG is good for data manage-ment and information query. In order to facilitateresource estimation of the placer gold in the Nomearea, another geodatabase known as R2.5DG wascreated. The two geodatabases are relational data-bases in which each item and its attributes arelinked and related (cross-referenced) to every otheritem and its attributes. The flowchart in Fig. 4shows the conceptual GIS model of this project.Detailed discussions are given in the followingsections. Building database is the primary task ofthe project.

3.1. Building the integrated geodatabase

Geodatabase is a container for storing spatial andattribute data as well as the relationships amongthem (ESRI, 2004a). It manages the same types ofgeographic information in a relational databasemanagement system (RDBMS) using programs,such as DB2, Informix, Oracle, SQL Server, orMS Access database. MS Access was used to createthe geodatabases in this project. However, MSAccess only supports a simplified geodatabase that

Original Drillhole

Data

Integrated Geodatabase

Regularized 2Geodatabas

Information Query Statistics Analysis

Internet Distrib

ArcMap ArcCatalog ArcScene

Fig. 4. Conceptual GIS arch

cannot store raster data. Therefore, most of thespatial data layers and non-spatial tables collectedare stored in the geodatabase known as IG. Table 1lists the feature classes stored in the IG. Ageodatabase feature class stores text or graphicsthat provide additional information about featuresor general areas of a map (ESRI, 2004b).

The IG was created using the functions inMicrosoft (MS) Access, ArcMap, and ArcCatolog.The following four tasks were performed using MSAccess:

1.

.5D e

utio

AA

itec

The borehole location data was imported intoMS Access and integrated to form a boreholelocation information table.

2.

The borehole layered lithologies and goldvalue logs were imported and integrated to form

Orebody Boundary Grades Prediction

Advanced Query Advanced Statistics Advanced Analysis

n

rcIMS rcObject VBA

VBA, VC MapObject ArcObject

ture of the project.

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Table 2

Partial listing of field attributes of DH_sliceinfo

Field name Data type Description

W. Zhou et al. / Computers & Geosciences 33 (2007) 773–788 779

a borehole segment attributes table. Bothmetric and imperial systems were used for goldcontent for the convenience of the databaseusers.

DHID Number Serial number of drillhole

3. Seaflr_depth Number Depth of sea floor from sea

level

Ore_begin Number Starting depth of orebody

(g41mg/m3)

Ore_end Number Ending depth of orebody

(g41mg/m3)

Ore_Thickn Number (Ore_begin)–(Ore_end)

Ave_MGCM Number S(content� thickness)/

(Ore_Thickn);(mg/m3)

Ave_OZCM Number (oz/m3)

Ore_begin18 Number Orebody parameter above

�18m sea level

Ore_end18 Number

Ore_thickn18 Number

GH18 Number S(content� thickness) At

above �18m sea level

Ore_aveG18 Number (GH18)/(Ore_Thickn18)

Ore_begin20 Number Orebody above �20m of

sea level

Ore_end20 Number

Ore_thickn20 Number

GH20 Number

Ore_aveG20 Number

Underseafloor2m_g Number Gold concentration on the

plane 2m under sea floor

Underseafloor4m_g Number Gold concentration on the

plane 4m under sea floor

Lith8m Text Lithology on the plane 8m

The calculation of average gold grades for asingle borehole based on various criteria is acrucial task in this study. Since orebody bound-aries are different at various specified planes andcutoff grades, their associated thickness andaverage grade also are different. The specifiedplanes in this study are either parallel to the sealevel (�18, �20, �22, �24, �26, �28, �30,�42m) or to the sea floor (�1, �2, �4, �6, �8,�10, �12, �14, �27m). The selection of theabove intervals was based on the consideration ofaverage sea floor depth and average boreholedepth in the study area, as well as the balancebetween precision and computer capacity. Thecalculations are performed repetitively in MSAccess using SQL language, based on segmentrecords stored in the borehole segment informa-tion table (DH_segmentinfo) in the IG. Thecalculated average grades and thickness atvarious layers form the layered orebody informa-tion table (DH_sliceinfo). Detailed informationon the procedure of average grade calculationscan be found in Table 2.

under sea level

4. Lith12m Text Lithology of on the plane

12m under sea level

Field counts ¼ 84; Records counts ¼ 3468

The above two tables are linked by a related field,i.e. borehole ID in this case, to create therelationships in the IG.

The two tables created in the steps 2 and 3contain no spatial data sets, i.e. there are nospatial attributes, such X, Y coordinates or long-itude and latitude. A new spatial feature layercould be created in a shape file format basedon the coordinate information stored in thedrill hole location table using ArcMap, andthen the new spatial data set could be convertedinto a geodatabase file. A shape file in ArcGISis a vector data storage format for storingthe location, shape, and attributes of geogra-phic features (ESRI, 2004b). To achieve this, thefollowing three tasks were performed using Arc-Map:

(1)

A borehole distribution map (i.e. a drill holelayer in ArcMap) was created based on theborehole location information table.

(2)

The layered orebody information table wasrelated to the drill hole layer by related field ofborehole ID.

(3)

The above layer was exported to a geodatabase(Nome_ori_GDB.mdb) in the form of featureclass named GeoDH_sliceinfo.

This GeoDH_sliceinfo geodatabase feature classfile is compatible with both ArcGIS and MS Access.The attributes of GeoDH_slicesinfo table include allof those attributes in both tables—the boreholelocation information table and the layered orebodyinformation table.

In addition, existing ArcInfo shape files andcoverage files (onshore geology, and permit block)were converted into the IG (Nome_ori_GDB.mdb)using the Import tool in ArcCatalog. Coverages aredata models for storing geographic features (ESRI,2004b). Several new feature classes (offshore geol-ogy, structure element, rivers and study area) werealso created using ArcCatalog and edited usingArcMap. ArcScan, another application program of

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ArcGIS, was used to vectorize the scanned image ofpaper maps.

3.2. Building a Regularized 2.5D Geodatabase

The IG is capable of performing GIS analysesusing a number of ArcGIS extensions, such as 3DAnalyst, Geostatistics Analyst, and Spatial Analyst.3D Analyst is a 3D visualization and analysisextension of ArcGIS (ESRI, 2004c). 3D Analystallows one to perform surface creation and analysistasks. It, however, does not have the capability forsolid 3D analyses and cannot yield information onvolume calculation and resource estimation. Inorder to be able to perform resource estimation, aquasi-3D geodatabase, namely a Regularized 2.5DGeodatabase (R2.5DG), was developed by subdi-viding the study area into cells. The R2.5G is able todefine orebody boundaries, perform grade extra-polation, and estimate the resource at any givenspatial domain.

Within approximately 40.43 km2 of the entirestudy area, a total of 3468 drill holes spaced from 50to 120m apart, are distributed irregularly. Todevelop the regularized geodatabase, the area wasdivided into 10� 10m grids, forming 404 319 spatialrecords with one record for each cell. Obviously, thefiner the grid is and the more precise the result is. Onthe other hand, however, it would need morestorage on the computer and take longer time toperform ore resource estimation. For the purpose ofthis project, a 10� 10m grid is sufficient for theprecision and yet time efficient to perform oreresource estimation.

Fig. 5. 3D drill holes and

Orebody attribute data in the GeoDH_slicesinfofeature class was utilized to build surface models.For each cross section, three surfaces are generated:orebody upper boundary, orebody lower boundary,and a hypothetical gold concentration surfaces(Fig. 5). The orebody boundary surfaces aregenerated by interpolation using Natural Neighbormethod, and the gold concentration surfaces aregenerated by the Inverse Distance Weighted (IDW)interpolation method. The choice of interpolationmethod was based on the comparative analysis ofinterpolation methods, which will be discussed inthe Resource Estimation section.

These surfaces were converted into 10m� 10mcell size grid files, and then saved as personal geo-database layers in the R2.5DG. A personal geo-database is a geodatabase that stores data in asingle-user relational database management sys-tem. A personal geodatabase can be read simulta-neously by several users, but only one user at a timecan write data into it (ESRI, 2004a). The R2.5DGwas formed by integrating all personal geodata-base layers together based on spatial locations inArcMap.

4. Case study—Nome offshore gold resource analysis

There is much potential in GIS technologyfor marine mineral resource analysis. Someuseful and powerful functions in the ArcGIS havebeen utilized to query, analyze, and visualize thedata, and to provide useful information for miningindustry.

ore body boundaries.

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4.1. Relational database query

The geodatabases constructed supports the sto-rage and analysis of geo-referenced data. Therelational database management system (RDBMS)allows flexible data extraction, called a ‘‘query’’,with a single criterion or multiple criteria, based onStructured Query Language (SQL). The geodata-base, however, is more than RDBMS. It can storeand manage the geographic data, as well asimplement logic operations, for example, buildingrelationships between data types, such as topologiesand geometric networks, validating data, andcontrolling access.

The two geodatabases built around this project arerelational. Since a relationship was constructed sothat each item and its attributes are linked andrelated (cross-referenced) to every other item and itsattributes (Davis, 1996), significant advantages existin a simple data table. Simple and complex queriescan be linked to spatial objects. Various queries, suchas drill hole information query and multi-variablequery of gold resource, can be performed throughthese geodatabases. With a single click on the drillhole distribution map, a pop-up window will list allthe information about that borehole in tabularformat. Multi-variable query can be conducted bydrawing a polygon with a mouse. A pop-up windowof the polygon area will show information onselected drill holes, resource estimation, as well asstatistical characteristics of the selected area (Fig. 6).

4.2. Data visualization

Visualization is the presentation of data ingraphic format. Graphics provide the most intuitive

Fig. 6. Procedure of information query on gold

representation of the data, while a detailed numer-ical table is useful for in-depth analysis.

The 3D drill hole plots with orebody boundaries(Fig. 5) can be created using ArcScene, an applica-tion program of ArcGIS. Vertical cross-sectionalorebody profiles can be created by using 3D Analyst(Fig. 7) along any defined polyline for each gridlayer, based on parameters, such as gold grade, golddeposit thickness, sea floor elevation and others.Such profiles provide useful information for miningoperation design. For example, it is often necessaryto compare the distribution of an orebody with thedistribution and characteristics of bedrock andsediments along vertical cross-sectional profiles.

4.3. Sediment data analysis

The sediment type of each sample segment of drillhole was examined in the laboratory and recordedin original drill hole log. Nine sediment types wereclassified. Table 3 lists the criteria for sedimentclassification and code assignments.

Based on the depositional environment, Howkins(1992) divided the sediment facies of Nome offshoreinto five major facies groups—diamict, gravel, sand,mud and peculiar facies. To determine the horizon-tal and vertical distribution of sediments, layeredlithology Thiessen polygon feature classes (Lith_sli-ce_Poly) were created from layered lithology pointfeature classes (DH_sliceinfo) using ArcInfo andstored into the IG. A series of layered sedimentdistribution maps were then created using ArcGISat various levels parallel to sea surface level (�8,�12, �16, �20, �24, �28m) and parallel to seafloor level (�1, �4, �8, �12m) to illustrate thechanges in the sediment with depth. The distribution

resource within a selected polygon area.

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Fig. 7. Vertical cross-sectional profiles of ore body boundaries.

Table 3

Primary sediment type classification (Howkins, 1992)

Number

code

Text

code

Description

1 MUD Mud; silt and clay any mixture, may

contain a trace of sand but no more

than 5%

2 SAM Sandy mud; silt/clay coating sand

grains, may be cohesive

3 SAN Sand; clean sand may have a trace of

silt/clay, not enough to coat the grains,

no more than 5%

4 SAG Sand and gravel; mixture of sand and

gravel, may have a trace of silt/clay in

the water but not enough to coat the

grains, friable

5 SWD Slightly washed diamict; silt/clay, sand

and gravel. Silt/clay enough to coat the

grains. The mixture is moderately

cohesive but will crumble (forms a

crumbly ball)

6 DIA Diamict; silt/clay, sand and gravel.

Enough silt/clay to forma cohesive

matrix (for a firm ball)

7 CRD Clay-rich diamict; silt/clay with

10–20% sand and pebbles. Appears to

be a mud with rock fragments within its

matrix (sticky and cohesive)

8 WAD Washed diamict; sand, gravel, silt and

clay, silt/clay enough to coat the grains,

but does not give it cohesion (cannot

form a ball)

9 BER Bedrock; angular fresh rock chips of

one rock type, associated with high

blow counts and little penetration

W. Zhou et al. / Computers & Geosciences 33 (2007) 773–788782

of sediments is closely related to surfical geologywhen, comparing the ocean floor lithologic map(Fig. 2) with sediment distribution map at 1m belowthe sea floor (Fig. 8) generated from drill hole datain this study.

4.4. Gold distribution analysis

4.4.1. Gold distribution with sediment type

The proportions of gold mineralization of eachsediment type vary from 5% to 70%. Over 70% ofbedrock samples are mineralized. The high percen-tage mineralization of bedrock is in part due to thefact that the sample locations are at the interface ofbedrock and diamict, where the particulates of goldhave a higher chance to deposit immediately duringtransport. For other eight sediment types besidesbedrock, from finer sediments (MUD, SAN) tocoarser sediments (DIA, WAD), the percentages ofmineralization decreases from 37.5% to 5%. Thisindicates that marine transgression and the erosionof gold-bearing glacial debris are reasons for golddissemination.

The average gold contents of each sediment typeare calculated with non-mineralized samples in-cluded. The results can be divided to two groups:The coarser grain sediments, i.e. SAG, CRD, DIA,SWD, WAD, have higher gold contents rangingfrom 220 to 309mg/m3, and the finer grainsediments, i.e. MUD, SAM and SAN, have lowergold contents ranging from 119 to 154mg/m3. The

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Fig. 8. Sediment distribution map (1m below sea floor).

W. Zhou et al. / Computers & Geosciences 33 (2007) 773–788 783

higher average gold content in diamict facies andgravel facies indicates that glacial sediments areorigin of the gold.

Gravel facies, composed of SAG, which is anindicator of fluvial channel including outwashplains, beach, marine surface lag, and other high-energy sedimentary environments, logically have thehighest average gold content (309mg/m3), and alsohave a relatively higher proportion of mineraliza-tion (15.5%) among the coarse sediment group.Gravel facies sediments account for approximately44% of the total gold resource in the sediment.

4.4.2. Spatial gold distribution

Based on the gold grade attribute in layeredorebody feature class (GeoDH_sliceinfo) stored inIG, using the interpolation tools of GeostatisticsAnalyst or 3D Analyst extension in ArcGIS, a seriesof layered gold distribution maps (raster andcontour) are generated at various planes parallelto sea surface level (�18, �20, �22, �24, �26,�28,�30, and �42m) and parallel to sea floor (�2,�4, �6, �8, �10, �12, and �14m). InverseDistance Weighted (IDW) interpolation methodwas used for generating the gold concentrationsurfaces. An influence neighborhood radius of600m and an inverse power of 2 were used in theIDW method.

Fig. 9 shows the gold distribution contour map ofthe plane of 2m below sea floor. In this example, thegold distribution contour ranges from 200 to8400mg/m3.

Inverse distance weighted (IDW) is one of themany methods to perform interpolation of scatteredspatial data. A neighborhood about the point to beinterpolated is selected and a weighted average iscalculated of the observed values within thisneighborhood. The weighting factors of observedpoints are a function of the distance between theobserved points to the interpolated point, the closerthe distance the bigger the weights. The interpolat-ing function is constructed as a linear combinationof the observed values vi at point x multiplied withweight functions wi (Fisher et al., 1987):

f ðxÞ ¼Xn

i¼1

viwiðxÞ, (1)

where the weight factors wi(i ¼ 1, 2,y, n) areconstructed by normalizing each inverse distance:

wiðxÞ ¼ d�li

i ðxÞ

�Xn

j¼1

d�lj

j ðxÞ, (2)

where di(x) is the Euclidean distance from point x tonode xi, and li is the exponential power of weight.Each weight function wi is inversely proportional to

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164000

164000

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170000

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184000

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1166000 1166000

1168000 1168000

1170000 1170000

1172000 1172000

1174000 1174000

11760001176000

Under sea flooor 2m Gold distribution contour map

Cutoff grade: 200mg/m3

Legend

underseafloor2mcontour

CONTOUR

200

201 - 400

401 - 600

601 - 800

801 - 1200

1201 - 1600

1601 - 2400

2401 - 3400

3401 - 5400

5401 - 8400

Fig. 9. Gold distribution at 2m under sea floor.

W. Zhou et al. / Computers & Geosciences 33 (2007) 773–788784

the distance from the point xi where the value vi isprescribed. The sum of the proportional factorswi(i ¼ 1, 2 ,y, n) should equal to one.

The offshore area of anomalous gold mineraliza-tion is oriented more or less parallel to the coastover an east–west distance of about 25 km. Goldmineralization occurs at each block throughout thestudy area at the 200mg/m3 cutoff grade. Based onthe gold distribution, the study area is divided into27 sub-areas (Fig. 10) at the 200mg/m3 cutoff grade.Six out of the 27 orebodies, i.e. Red1, King1,Silver2, Humpy2, Tomc2 and Coho1, exceed 3 tonsof gold resource, comprising more than 70% of thetotal resource. With an increased cutoff grade, theareas of anomalous gold mineralization shrinksharply. At the 600mg/m3 cutoff grade, only 4specific zones are particularly anomalous, i.e. Red1,King1, Tomc2 and Coho1.

4.5. Resource estimation

Resource estimation is an important component ofthe mineral resource management. All gold resourcecalculation problems must deal with estimating twointer-related items: the grade and the associatedvolume. This grade could be the economic cutoffgrade or the average grade within a pre-specifiedvolume. The volume of ore can be estimated based

on orebody boundary. The total resource is thenestimated based on the volume. Table 4 lists the totalresource estimation at different cutoff grades withinthe entire study area.

The first step for resource calculation is todetermine orebody thickness and average gold gradefor every borehole. Calculation of average gold gradefor each borehole based on various cutoff grades is atime-consuming task. Since orebody boundaries aredifferent at various cutoff grades, their associatedthickness and average grade are also different. Thecalculations are performed repetitively in MS Accessusing SQL language, based on segment recordsstored in the borehole segment information table inthe IG. The calculated average grades and thick-nesses of various layers form the layered orebodyinformation table.

The second step is to create a point layer and gridcoverage and add them to the R2.5DG. The pointlayer is used to store the layered orebody grades andthicknesses. In this layer, the entire study area isdivided into 10m� 10m grids, and each pointfeature object in the center of a cell represents thiscell and all of the grades and thickness are stored inthis point object. The entire study area is approxi-mately 40.4319 km2. The R2.5DG contains 40 4319spatial point records, each point record representingone 10m� 10m-grid cell.

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Table 4

Resource estimations at different cutoff grades

Cutoff grade

(mg/m3)

Orebody area

(m2)

Average thickness

(m)

Average grade

(mg/m3)

Ore volume

(m3)

Resource

(kg)

Resource

(Oz)

0 40 429 800 7.63 0.233 308 325 290 71 840 2 309 664

200 18 060 600 4.86 0.554 87 737 850 48 607 1 562 718

500 2 438 900 5.28 1.047 12 886 388 13 492 433 772

800 740 400 5.33 1.441 3 948 949 5690 182 949

1000 404 600 4.53 1.929 1 834 422 3539 113 767

Fig. 10. Gold deposit distribution map (cutoff grade: 200mg/m3).

W. Zhou et al. / Computers & Geosciences 33 (2007) 773–788 785

The third step is to determine the thickness foreach cell created during the second step. Theattribute data in GeoDH_sliceinfo layer is utilizedto build orebody boundaries. To determine theorebody thickness of each regularized cell, twoorebody boundaries are generated based on thelevels of the orebody’s beginning depth and theending depth, respectively. The orebody boundariesare interpolated using the Natural Neighbor meth-od, which is an interpolation method that estimatesthe value of a cell using weighted values of the inputdata points that are their natural neighbors,determined by creating a triangulation of the inputpoints. The Natural Neighbor tool in ArcGIS canefficiently handle large numbers of input points.Other interpolators may have difficulty with largepoint data sets (ESRI, 2004d).

The next step is to interpolate the gold content foreach regularized cell. Five interpolation methods are

investigated and compared. IDW was selected forinterpolating the gold concentration contour maps.The following paragraph will briefly describe theselection procedure, for detailed discussion can befound in Li et al. (2005).

Based on statistical analysis, the gold concentra-tion data collected from the Nome offshore depositare of characteristics such that: (1) near lognormaldistribution of data, (2) high positive skeweddistribution, (3) a small number of extreme valuesdominating the resource estimation, and (4) apparentanisotropy of the variogram. Five geostatisticalmethods namely Inverse Distance Weighted (IDW),Ordinary Kriging (OK), Ordinary Kriging withlognormal transformation (OK-log), Simple Kriging(SK) and Indicator Kriging (IK) were selectedinitially for the development of an interpolationmodel (ESRI, 2004e). The results by the five methodswere evaluated and compared with each other. It was

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determined that the OK and SK algorithms were notsuitable for the Nome data set because of theirdifficulty of fitting the semivariogram model despitetheir lower root-mean-square error (RMSE) and thehigher root-mean-square standardized (RMSS). TheOK-log algorithm does not provide satisfactoryinterpolation for the Nome data set either, becauseit placed too much weight on the role of outliersresulting in overestimation and extremely highRMSE. Resource estimation was conducted usingeach of the interpolation methods and comparedwith the resource calculated using the conventionalpolygon method. The IWD and IK algorithmseemed to provide estimations more agreeable withthe conventional polygon method (Li et al., 2005).

The last step is to estimate the gold resourcewithin the study area. After the thickness (T) andaverage grade (G) for each cell are obtained, onenew column (G_T) can be created to store Gi�Ti.For any selected polygon area, S(Gi�Ti) and STi

can be obtained by using the ‘‘statistics’’ tool inArcMap

The gold resource within a selected polygon areais estimated by the following equation:

R ¼XðGi � TiÞ � 10� 10, (3)

where Gi is grade value of each cell above cutoffgrade and Ti is thickness of each cell.

The average grade (Gave) within a polygon area isgiven by

Gave ¼1PTi

XðGi � TiÞ. (4)

Programming All of Above Procedures

RegularizeGeodataba

Add MacVBA ProArcObje

Add Estimation Toll Button in Toolbox

Fig. 11. Procedure of building a custo

4.6. Customized resource estimation system

A customized resource estimation system wascreated around the project using Visual Basic forApplications (VBA). VBA is the customizationenvironment that comes with ArcGIS. Manydifferent programming languages can be used toexpand the ArcGIS functionality. Visual Basic andVisual C++ are the two programming languagesused in the Nome Project. Fig. 11 shows the stepsand techniques used in creating the customizedresource estimation system. With customization,resource estimation can be done by users withoutGIS knowledge. The procedure of performingresource estimation at any selected area is asfollows:

(1)

d 2.5se

ro ingramct

meriz

Press the Estimation button from tool bar.

(2) Select a polygon area interested. (3) Type in query parameters in the Estimation

dialog box (e.g. cutoff grade).

(4) Display the estimation table.

This customized tool allows users to query theresource information at any portion of the studyarea with a defined cutoff grade and definedreference plane, such as sea floor or sea level.Fig. 12 shows the resource estimation window inArcGIS.

4.7. Web GIS

In order to facilitate remote users to access thetwo geodatabases (IG and R2.5DG) built for the

VBA, VC Map Object ArcObject

D

Advanced Query Advance Statistics Advanced Analysis

ArcMap ming

ed resource estimation tool.

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Fig. 12. Resource estimation graphic user interface (GUI).

W. Zhou et al. / Computers & Geosciences 33 (2007) 773–788 787

Nome project, a web-based GIS (http://uaf-db.uaf.edu/website/) was developed for this project usingArcIMS, an application program of ArcGIS. Adetailed description about the web GIS of theproject can be found in Luo et al. (2005). The WebGIS structure developed in this project can bereadily adapted to mineral resource analysis in otherareas of the state.

5. Conclusions

GIS technology was applied for analyzing thedata of the offshore placer gold resources nearNome, Alaska. Two relational databases weredeveloped during this research, namely the Inte-grated Geodatabase (IG) and the Regularized 2.5DGeodatabase (R2.5DG). The IG contains most ofthe data collected, complied, and integrated. Itserves as a data warehouse, and was designed tostore, display, and disseminate offshore placer gold

resource information in Nome area. Three surfaces,namely the orebody upper boundary surface, theorebody lower boundary surface and a hypotheticalgold concentration surface were defined based ondata stored in the IG. There are various methodsavailable in ArcGIS for development of the inter-polation model. In this study, the Natural Neighbormethod was used for interpolation of the orebodyboundaries, and the IDW interpolation method wasused to generate gold concentration contours. TheR2.5DG facilitates data management, retrieval, andintegration. It can be used to perform moreadvanced tasks, such as advanced query, geostatis-tical and numerical modeling with a user-friendlygraphic user interface (GUI). Most importantly, theR2.5DG is capable of handling volume calculation,and resource estimation, which is a frequentlyperformed task for mineral resource management.A customized resource estimation tool was builtaround this project, which can be used to estimate

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gold resources at different cutoff grades and differentspatial domains in a time-efficient way. The totalplacer gold resource near Nome is estimated from113,767oz (with a cutoff grade of 1000mg/m3) to2,309,664 oz (with a cutoff grade of 0mg/m3). Theaverage grade ranges from 0.233 g/m3 (with a cutoffgrade of 0mg/m3) to 1.929 g/m3 (with a cutoff gradeof 1000mg/m3).

Acknowledgement

The authors would like to express their gratitudeto the MMS (Minerals Management Services, USDepartment of the Interior) for funding thisresearch and WestGold for providing valuable datato this research.

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