Input:

The crystal data file must contain all the information that would be required for magnetic structure factor calculations viz:

Symmetry (S) cards
Cell (C) card
Atom (A) cards
Form factor (F) cards
Magnetic structure (Q) cards

in addition special "X" cards are required to describe the graphical output these are:

X ARRO <Data> to define the proportions of arrows representing

the magnetic moments.
The <Data> are 5 real numbers: the head length, the head radius, the tail radius and the linewidth, all given as fractions of the total length of the arrow. The final number is the scale relating the length of an arrow in Angstroms to its moment in Bohr magnetons.

X CM/A <Scale>

<Scale> gives the scale of the picture in cms/Angstrom unit.

X PERS <Data> to define the perspective

The <Data> are 3 real numbers: the cosine and sine of the angle that the direction perpendicular to the plane of the projection makes, in projection, with the negative x axis of the picture; and a factor giving the contraction in projection of vector components perpendicular to the plane of projection.

X <Atom Name> SYMB <Data> indicating how to draw each type of

atom.
There should be one SYM B card with a matching <Atom Name> for each A card .
The <Data> are 2 real numbers: the radius of the atom in Angstrom units and the grey level to be used to paint it. Grey levels are in the range 0 to 1 with 0 being jet black and 1 pure white.

X TRAN <Data> The transformation matrix

The <Data> are 9 real numbers describing the transformation from orthogonal crystallographic axes to the projection axes for which x is perpendicular to the projection plane, z is vertical and y horizontal in the projection plane. The data are the direction cosines of the projection axes x,y,z on the crystal axes.

Example X cards for Fe2Ti
X PERS .5 .7660 .6428
X ARRO .333 0.1667 .0833 .02 1.2
X TRAN 1 0 0 0 1 0 0 0 1
X SYMB Fe1 .25 0.25
X SYMB Fe2 .25 0.25
X SYMB Ti .4 .8
X CM/A 1

Output:

The usual listing file MAG3D.LIS reporting what was read from the crystal data file and how it was interpreted.
An optional screen dump of the graphic terminal can be obtained after each picture is drawn if the plotter driver supports "hard copy".
At the end of the program a "postscript" output file can be written which gives a high quality rendering of the final picture. This file will have the same name as the crystal data file and the extension .PS.

Notes:

The program can be run in conjunction with any graphical output device for which a CCSL graphical driver(PIGLET) is available. It is most conveniently run from an X-terminal or one which emulates a
Tektronix 4010.

Running the program:

Once running, the program the user will be asked in the usual way for the name of the crystal data file. Next the numbers of unit cells to be drawn in the directions of the a,b and c crystallographic axes, are required. They should be given in that order.
The screen will then switch to graphic mode and the magnetic structure will be drawn. When the drawing is complete the user is asked whether a hard copy of the graphic screen is required. There is then an option to rotate the picture about any of the the projection axes; if this is selected the rotated structure is drawn. When no further rotation is requested the user may, if he wishes, record a new crystal data file containing the latest rotation matrix. Finally the user is given the option of making a "PostScript" output file. If this is chosen a file with a name of the form ‘CDF’.ps (where ‘CDF’ is name of the crystal data file) is written before the program ends.

Calls:

AROW3D ARTILT ASK ATLABS CIRCLE CLOFIL DEGREE EQPOS ERRCHK ERRMES FILNOM FINDCD FRAME GMADD GMEQ GMPRD GMSCA GMSUB GMUNI GMZER INDFIX INDFLO INPUTN JGMEQ JGMZER KANGA1 KANGA2 LABAXE LATGEN MAG3DX MAGCNC MESS NEWCRY NFIND ORTHO PERSPC PIGLET PLTRIN POSOUT PREFIN RADIAN RDNUMS ROTSYM SAYS SCALPR SETFCM SPCSET SPHPOL UNIVEC

Common blocks used:

/ARRAYS/ to use all members

/ATMLAB/ to use all members

/ATNAM/ to use ATNAME

/CONSTA/ to use TWOPI

/IOUNIT/ to use LPT ITO IPLO

/MAGDAT/ to use NMAG MAGAT ANGM SMOD PHIH LPHI SPIND

/NTITL/ to use NTITLE

/PICDAT/ to use all members

/PICDEF/ to use CMPERA APERMB PWIDTH PHGHT X0 Y0

/PLODAT/ to use ASPECT CHUNIT

/POSTSC/ to use all members

/POSNS/ to use NATOM

/NSYM/ to use NCENT NOPC NLAT

/SATELL/ to use PROP KSTAB NKC

/SYMDA/ to use TRANS ALAT

/SYMMAG/ to use OTRSYM MTYP MODUL

/TITLE/ to use all members

*** MAG3D Postscript arrows included C110 ***

When entering data in Drillhole, you must start with collar data. The system creates a unique collar database that you can use to view all drillholes in the project at a glance. You can also plot an initial plan map showing all holes for review purposes. A collar file must exist before survey or assay data can be imported, and before plans and sections can be created.

To Import Collar Data:

1. On the DH-Data menu click Import, and then click the Text file. The Drill Hole – Ascii Import Wizard dialog is displayed.

2. Using the [Browse] button, select the collar file from your working directory and click the [Open] button. The system returns you to the Drill Hole – Ascii Import Wizard dialog box and displays the file name you wish to import.

3. Click the [OK] button. The system scans the file and displays the first of four dialog boxes from the Drill Hole Import Wizard. Note that the system determines the Data Input format and has intuitively predicted that the Types of Data to import is Hole Collar Data.

• The Drillhole Drill Hole Import Wizard enables you to easily import data from any ASCII spreadsheet or text file. The Import Wizard supports both Delimited and Fixed Field ASCII files. The Import Wizard also imports Microsoft Excel Comma Seperated Value (CSV), Comma Delimited, White Space Delimited and Tab Delimited data files. The window at the bottom of the dialog box shows the file that is being imported. For more information about the Drill Hole Import Wizard settings, read the ASCII Import Wizard help topic or click the [Help] button on the dialog box.

1. Use the horizontal scroll bar to see all of the fields in the file or simply click the [Next>] button. The system displays the second dialog box in the Drill Hole Import Wizard.

2. The system determines the File Type and in the four fields in the middle of the dialog box specifies which line in the file contains the data headings (i.e. channel names), data units ("m" or "ft" etc.), which line to begin importing data and the number of lines to display in the preview window.

3. Click the [Next>] button. The system displays the third dialog box in the Drill Hole Import Wizard.

4. Specify the Column delimiters for the type of character used to separate the column text. The system displays the data in columns by drawing lines in the preview window indicating the way in which it is preparing to import your data.

5. Click the [Next>] button to continue. The system displays the fourth and final dialog box in the Drill Hole Import Wizard.

6. The Import Wizard has scanned your data and determined the type of data with which you are working (i.e. Channel Type). It is always good practice to review your data to ensure that the wizard has selected the correct columns. The Parameters area in the dialog box shows the name and type of data of the column highlighted in the preview window.

• The Data Type of Database Fields that contain alphanumeric data (for example, sample numbers, rockcodes etc.) must be classified as String.

1. Click the [Finish] button. The system imports the collar data and displays it in the spreadsheet window.

2. At this point, we recommend that you examine the database table carefully. Start by confirming that all columns of data in the original CSV file are present in the database.

The directions below describe how to open a Geosoft GDB Database using the Viewer (free version).

1. On the Data menu, click Open then click, Open database.

2. In the File name box, specify the name of database you want to open.

3. Click [OK] the database is displayed in the spreadsheet window.

Overview

This capability was added to Surfer as an integrated data analysis tool. The primary purpose of the variogram modeling subsystem is to assist you in selecting an appropriate variogram model when gridding with the kriging algorithm. Variogram modeling may also be used to quantitatively assess the spatial continuity of data even when the kriging algorithm is not applied.

Variogram modeling is not an easy or straightforward task. The development of an appropriate variogram model for a data set requires the understanding and application of advanced statistical concepts and tools: this is the science of variogram modeling. In addition, the development of an appropriate variogram model for a data set requires knowledge of the tricks, traps, pitfalls, and approximations inherent in fitting a theoretical model to real world data: this is the art of variogram modeling. Skill with the science and the art are both necessary for success.

The development of an appropriate variogram model requires numerous correct decisions. These decisions can only be properly addressed with an intimate knowledge of the data at hand, and a competent understanding of the data genesis (i.e. the underlying processes from which the data are drawn). The cardinal rule when modeling variograms is know your data. The variogram is a measure of how quickly things change on the average. The underlying principle is that, on the average, two observations closer together are more similar than two observations farther apart. Because the underlying processes of the data often have preferred orientations, values may change more quickly in one direction than another. As such, the variogram is a function of direction.

The variogram is a three dimensional function. There are two independent variables (the direction q, the separation distance h) and one dependent variable (the variogram value g(q,h)). When the variogram is specified for kriging we give the sill, range, and nugget, but we also specify the anisotropy information. The variogram grid is the way this information is organized inside the program. The variogram (XY plot) is a radial slice (like a piece of pie) from the variogram grid, which can be thought of as a “funnel shaped” surface. This is necessary because it is difficult to draw the three-dimensional surface, let alone try to fit a three dimensional function (model) to it. By taking slices, it is possible to draw and work with the directional experimental variogram in a familiar form – an XY plot. Remember that a particular directional experimental variogram is associated with a direction. The ultimate variogram model must be applicable to all directions. When fitting the model, the user starts with numerous slices, but must ultimately mentally integrate the slices into a final 3D model.

Kriging and Variograms

The kriging algorithm incorporates four essential details:

1. When computing the interpolation weights, the algorithm considers the spacing between the point to be interpolated and the data locations. The algorithm considers the inter-data spacings as well. This allows for declustering.

2. When computing the interpolation weights, the algorithm considers the inherent length scale of the data. For example, the topography in Kansas varies much more slowly in space than does the topography in central Colorado. Consider two observed elevations separated by five miles. In Kansas it would be reasonable to assume a linear variation between these two observations, while in the Colorado Rockies such an assumed linear variation would be unrealistic. The algorithm adjusts the interpolation weights accordingly.

3. When computing the interpolation weights, the algorithm considers the inherent trustworthiness of the data. If the data measurements are exceedingly precise and accurate, the interpolated surface goes through each and every observed value. If the data measurements are suspect, the interpolated surface may not go through an observed value, especially if a particular value is in stark disagreement with neighboring observed values. This is an issue of data repeatability.

4. Natural phenomena are created by physical processes. Often these physical processes have preferred orientations. For example, at the mouth of a river the coarse material settles out fastest, while the finer material takes longer to settle. Thus, the closer one is to the shoreline the coarser the sediments, while the further from the shoreline the finer the sediments. When computing the interpolation weights, the algorithm incorporates this natural anisotropy. When interpolating at a point, an observation 100 meters away but in a direction parallel to the shoreline is more likely to be similar to the value at the interpolation point than is an equidistant observation in a direction perpendicular to the shoreline.

Items two, three, and four all incorporate something about the underlying process from which the observations were taken. The length scale, data repeatability, and anisotropy are not a function of the data locations. These enter into the kriging algorithm via the variogram. The length scale is given by the variogram range (or slope), the data repeatability is specified by the nugget effect, and the anisotropy is given by the anisotropy.

The Variogram Grid

Users familiar with GeoEAS or VarioWinÒ should be familiar with pair comparison files [.PCF]. Surfer uses a variogram grid as a fundamental internal data representation, in lieu of a pair comparison file. The pair comparison file can be extremely large for moderately sized data sets. For example, 5000 observations create N(N-1)/2 pairs (12,497,500). Each pair requires 16 bytes of information for a pair comparison file, so a 5000-observation pair comparison file would take approximately 191 megabytes of memory to merely hold the pair comparison information. The time to read and search through this large file makes this approach impractical for many Surfer users.

Computational speed and storage are gained by using the variogram grid approach. Once the variogram grid is built, any experimental variogram can be computed instantaneously. This is independent of the number of observations. However, the ability to carry out on-the-fly editing of variograms on a pair-by-pair basis is lost by using the variogram grid approach in Surfer.

Unlike the grids used elsewhere in Surfer, which are rectangular grids, variogram grids are polar grids. Polar grids cannot be viewed in Surfer, and are only used within the context of variogram computation. The first coordinate in a variogram grid is associated with the polar angle, and the second coordinate is associated with the radial distance out from the origin.

There are eight angular divisions: {0°, 45°, 90°, 135°, 180°, 225°, 270°, 315°} and four radial divisions: {100, 200, 300, 400}. Thus, there are 32 individual cells in this variogram grid. Users familiar with VarioWin® will notice similarities between Surfer’s variogram grid and the “variogram surface” in VarioWin® 2.2. In Surfer, only the upper half of the grid is used. See the General Page for a more detailed explanation.

Consider the following three observation locations: {(50,50), (100, 200), and (500,100)}. There are three observations, so there are 3*(3-1)/2 = 3 pairs. The pairs are:

A (50,50), (100,200)

B (50,50), (500,100)

C (100,200), (500,100)

Each pair is placed in a particular cell of the variogram grid based upon the separation distance and separation angle between the two observation locations.

Using the above equations, the separation angle for the first pair of observations {(50,50), (100,200)} is 71.57 degrees and the separation distance is 158.11. This pair is placed in the cell bounded by the 100 circle on the inside, the 200 circle on the outside, the 45° line in the clockwise direction, and the 90° line in the counterclockwise direction. The location of this pair in the variogram grid is shown on the previous page as point A.

Pair

Separation Angle

Separation Distance

A

71.57

158.11

B

6.34

452.77

C

-14.04

412.31

The separation angle and separation distance for each pair

Since the separation distance of pairs B and C are greater than the radius of the largest circle (400), these pairs fall outside of the variogram grid. Pairs B and C are not included in the variogram grid and therefore, not included in the variogram. Using the above equations, every pair is placed into one of the variogram grid cells or it is discarded if the separation distance is too large.

For a large data set there could be millions of pairs (or more) and the associated pair comparison file would be very large. On the other hand, with the variogram grid in the example above there are only 32 grid cells regardless of the number of pairs contained in a particular grid cell. Herein lies the computational saving of the variogram grid approach. It is not necessary that every pair is stored in a variogram grid cell; each variogram grid cell stores only a small set of summary statistics which represent all of the pairs contained within that cell.

Variogram Model

The variogram model mathematically specifies the spatial variability of the data set and the resulting grid file. The interpolation weights, which are applied to data points during the grid node calculations, are direct functions of the variogram model.

NUGGET EFFECT: quantifies the sampling and assaying errors and the short scale variability (i.e. spatial variation occurring at distance closer than the sample spacing).

SCALE (C): is the vertical scale for the structured component of the variogram. Each component of a variogram model has its own scale.

SILL: is the total vertical scale of the variogram (Nugget Effect + Sum of all component Scales). Linear, Logarithmic, and Power variogram models do not have a sill.

LENGTH: is the horizontal range of the variogram. (Some variogram models do not have a length parameter; e.g., the linear model has a slope instead.)

VARIANCE: is the mean squared deviation of each value from the mean value. Variance is indicated by the dashed horizontal line in the diagram shown above.

The Inverse Distance to a Power gridding method is a weighted average interpolator, and can be either an exact or a smoothing interpolator.

With Inverse Distance to a Power, data are weighted during interpolation such that the influence of one point relative to another declines with distance from the grid node. Weighting is assigned to data through the use of a weighting power that controls how the weighting factors drop off as distance from a grid node increases. The greater the weighting power, the less effect points far from the grid node have during interpolation. As the power increases, the grid node value approaches the value of the nearest point. For a smaller power, the weights are more evenly distributed among the neighboring data points.

Normally, Inverse Distance to a Power behaves as an exact interpolator. When calculating a grid node, the weights assigned to the data points are fractions, and the sum of all the weights are equal to 1.0. When a particular observation is coincident with a grid node, the distance between that observation and the grid node is 0.0, and that observation is given a weight of 1.0, while all other observations are given weights of 0.0. Thus, the grid node is assigned the value of the coincident observation. The Smoothing parameter is a mechanism for buffering this behavior. When you assign a non-zero Smoothing parameter, no point is given an overwhelming weight so that no point is given a weighting factor equal to 1.0.

One of the characteristics of Inverse Distance to a Power is the generation of "bull’s-eyes" surrounding the position of observations within the gridded area. You can assign a smoothing parameter during Inverse Distance to a Power to reduce the "bull’s-eye" effect by smoothing the interpolated grid.

Inverse Distance to a Power is a very fast method for gridding. With less than 500 points, you can use the All Data search type and gridding proceeds rapidly.

Kriging Standard Deviations

Enter a path and file name into the Output Grid of Kriging Standard Deviations field in the Kriging Advanced Options dialog to produce an estimation standard deviation grid. If this edit control is empty then the estimation standard deviation grid is not created.

The Kriging standard deviation grid output option greatly slows the Kriging process. This is contrary to what you may expect since the Kriging variances are usually a by-product of the Kriging calculations. However, Surfer uses a highly optimized algorithm for calculating the node values. When the variances are requested, a more traditional method must be used, which takes much longer.

There are several cases where a standard deviation grid is incorrect or meaningless. If the variogram model is not truly representative of the data, the standard deviation grid is not helpful to your data analysis. Also, the Kriging standard deviation grid generated when using a variogram model estimated with the Standardized Variogram estimator or the Autocorrelation estimator is not correct. These two variogram estimators generate dimensionless variograms, so the Kriging standard deviation grids are incorrectly scaled. Similarly, while the default linear variogram model will generate useful contour plots of the data, the associated Kriging standard deviation grid is incorrectly scaled and should not be used. The default linear model slope is one, and since the Kriging standard deviation grid is a function of slope, the resulting grid is meaningless.

Kriging Type

There are two most common Kriging types: Point Kriging  and Block Kriging. Both Point Kriging and Block Kriging generate an interpolated grid. Point Kriging estimates the values of the points at the grid nodes. Block Kriging estimates the average value of the rectangular blocks centered on the grid nodes. The blocks are the size and shape of a grid cell. Since Block Kriging is estimating the average value of a block, it generates smoother contours (block averaging smooths). Furthermore, since Block Kriging is not estimating the value at a point, Block Kriging is not a perfect interpolator. That is even if an observation falls exactly on a grid node, the Block Kriging estimate for that node does not exactly reproduce the observed value.

When a Kriging standard deviation grid is generated with Block Kriging, the generated grid contains the Block Kriging standard deviations and not the Point Kriging standard deviations.

The numerical integration required for point-to-block variogram calculations necessary for Block Kriging are carried out using a 3×3, two-dimensional Gaussian-Quadrature.

Kriging References

For a detailed derivation and discussion of Kriging see Cressie (1991) or Journel and Huijbregts (1978). Journel (1989) is, in particular, a concise presentation of geostatistics (and Kriging). Isaaks and Srivastava (1989) offer a clear introduction to the topic, though it does not cover some of the more advanced details. For those who need to see computer code to really understand an algorithm, Deutsch and Journel (1992) includes a complete, well-written, and well-documented source code library of geostatistics computer programs (in FORTRAN). Finally, a well-researched account of the history and origins of Kriging can be found in Cressie (1990).

Abramowitz, M., and Stegun, I. (1972), Handbook of Mathematical Functions, Dover Publications, New York.

Cressie, N. A. C. (1990), The Origins of Kriging, Mathematical Geology, v. 22, p. 239-252.

Cressie, N. A. C. (1991), Statistics for Spatial Data, John Wiley and Sons, Inc., New York, 900 pp.

Deutsch, C.V., and Journel, A. G. (1992), GSLIB – Geostatistical Software Library and User’s Guide, Oxford University Press, New York, 338 pp.

Isaaks, E. H., and Srivastava, R. M. (1989), An Introduction to Applied Geostatistics, Oxford University Press, New York, 561 pp.

Journel, A.G., and Huijbregts, C. (1978), Mining Geostatistics, Academic Press, 600 pp.

Journel, A.G. (1989), Fundamentals of Geostatistics in Five Lessons, American Geophysical Union, Washington D.C.

 At times, it is necessary to analyze map profiles with LoggerPC4.2 for skyline analysis. If the profiles can be identified and digitized with ArcMap, the X, Y, and Z (elevation) coordinates as well as the profile name can be exported as a dBase file and then imported into LoggerPC 4.2. This can be done in a batch format which means that all profiles can be digitized into one file and LoggerPC 4.2 will break them into the individual profiles for analysis. A text file format can also be used, although it is a more complicated process. A variation of this process can be used to generate point coordinates for helicopter analysis in Logcost. For the helicopter coordinates for landings and unit centroids, you do not need the line feature class.

The task is to get the X, Y, and Z values at certain points along a line feature class and to digitize them into a point feature class.

 Given:

Using ArcMap

Grid created from a 10 or 30 meter DEM

Contour shapefile. Not needed for helicopter.

Line shapefile (the profiles) for LoggerPC. Not needed for helicopter.

Must be done:

1. The user must create an empty point shapefile. (Use ArcCatalog).
   1. Within ArcCatalog, select the file menu, new, shapefile, name (type in the name you want to use), for feature type select point.
   2. The description will say unknown coordinate system. Select edit and either import a coordinate system from another point shape file in the project, or select predefined coordinate system that applies to the project area.
   3. Select OK.
2. The user must add three items to the attribute table of the point shapefile.
   1. In ArcMap, use the + to add the new point shapefile to the project. Right click on the new point shapefile then left click on open attribute table. Left click options, left click on add field.
   2. Referring to the table below, type the name, precision and scale as shown in the table. You will need to do this three times for the three table names (X_Coord, Y_Coord, and Elevation). It is also necessary to add another field called Name where you can name groups of points which are profiles, so you can keep track of the profiles. The type for Name is text. Make the field width 8. These fields are important because the result of all this will be a single table with the coordinates of all the points. Once you are done creating the fields, close the attribute table.

1. The point shapefile must be populated with data – digitize a point at each intersection of the profile lines within the line shapefile, and the contour layer. For helicopter, you will not have the profile line shape file and you don’t need the contour shape file.
   1. The line shape file is a file where you have drawn all your map profiles. To populate the point shapefile with data, have it displayed along with the contour and point shapefile.
   2. Click editor, select the project, then OK. On the target bar make sure the point shapefile is selected. Click editor. Click snapping. Make sure nothing is clicked except the edge and end of the profile shapefile, and the edge of the contour shapefile. Close the snapping screen. Click edit, click options, set the snapping tolerance you desire and make sure it is in mapping units not pixels. Click OK. Select the pencil tool and digitize the end point of the profile then each place it intersects a contour, then the other endpoint. Open the attribute table, select the records or points you want to name, right click on the note field heading, left click on calculate values, in the larger box type your label in double quotes (“ “), click OK, close the attribute table, stop editing and save edits.
2. When all the points are digitized in, the point shapefile must be given 3D geometry.
   1. If you don’t have the 3D analyst tool bar, click tools, extensions, 3D-Analyst, close. Then right click in the gray area at the top of the screen and click on 3D-Analyst. This brings in the toolbar.
   2. To get the following box, click on 3D-Analyst, click on convert, click on features to 3D. The input features box is where you select your point shapefile, the raster or TIN surface is where you select your DEM file. DEM stands for digital elevation model.

(With ArcMap, use the 3D Analyst extension and Convert and Features to 3D…)

(With ArcMap, use the 3D Analyst extension and Convert and Features to 3D…)

Calculating the values:

With the attribute table for the dddfeat1 (or another number besides 1) shapefile open, calculate these values: X_Coord, Y_Coord and Elevation. At this point you should be in the edit mode with the dddfeat file set as the selected and target layer.

Calculating the values:

With the attribute table for the dddfeat1 (or another number besides 1) shapefile open, calculate these values: X_Coord, Y_Coord and Elevation. At this point you should be in the edit mode with the dddfeat file set as the selected and target layer.

To get the following screens, once in the point shape file attribute table, right click on the column header (X_Coord, Y_Coord, Elevation), then select calculate fields.

For the X_Coord calculation, use

For the Y_Coord calculation use.

..

For the Elevation calculation in feet, use

Export for use with Excel:

You should end up with a table that looks similar to this:

Now you can export the table to either a .dbf or a .txt file and open it with MS Excel.

If you export the file in the .dbf format, it is not necessary to load it into Excel. You can convert the file directly with LoggerPC 4.2.

The necessary fields for the dbf format are:

Id X_Coord YCoord Elevation Name

There can be other fields, and they can be in any order. The LoggerPC 4.2 conversion routine will sort things out.

The process is more complicated with the .txt format. The fields must be

“FID_”,”Id”,”X_Coord”,”Y_Coord”,”Elevation”,”Name”

After exporting the txt file from ArcMap to Excel, arrange the fields so that they are in this order and delete any other fields or columns. Save the file as a comma delimited text file by selecting save as then within the save as type select CSV (comma delimited)(*.csv). This will save a file that looks like a spreadsheet in that it has an Excel icon. Close the spreadsheet and rename the file with a .txt extension.

For the ArcMap export routine, from within the attribute table, select options then export.

MS Excel will read a .dbf file.

For the helicopter option in Logcost, you can use meters for X and Y coordinates and feet for elevation. With Excel you use the copy and past special then values to place the data into Coord Maker within Logcost.

Note that once you convert the X and Y coordinates to feet, they can’t be used as coordinates in a GIS system. For LoggerPC, we are only interested in the distance between the terrain points on a profile. For this reason, converting the coordinates to feet is legitimate.

Loggerpc GIS Conversion:

Within LoggerPC 4.2, select file and Convert GIS files to get this screen.

Select the Dbase option. Select the input file and the output directory and a prefix for the names if desired. Make sure that the input units match the units that came over from GIS. It will probably be meters for X and Y and feet for elevation. Then select run and the conversion will take place. You can then open the individual profiles within LoggerPC 4.2.

LInk

Open the new *.txt file in excel and save a location file (Drillhole_structures_north_east.csv) and an attribute file (Drillhole_structures_attributes.csv) in *.csv format. Each entry should have a unique and corresponding ID number (SID). The structural data needs to have the strike in the form of the right-hand rule.

2. Add XY data (Drillhole_structures_north_east.csv) into ArcGis (Tools>Add XY Data).

3. Join the attribute file (Drillhole_structures_attributes.csv) to the location file using the SID number (right click on file >joins and relates > joins….).

4. Export the joined file as a shape file (right click on file > Data > Export Data…).

5. Select the symbol type (right click on file > Properties > Symbology).

6. Rotate the symbols (right click on file > Properties > Symbology > Advanced > Rotation). To plot the symbols with the correct orientation, the orientation data will have to be given in the form of the right-hand-rule.

This document describes how load data into a geodatabase after it has been created from a data model template, or personal geodatabase.
Note: You need to set the Spatial Reference while running the Schema Wizard o when creating the geodatabase. You will need to specify a spatial reference so that the feature classes created can be used with your data. There is a separate document that describes how to specify the spatial reference while running the Schema Wizard. This process is important because the spatial reference cannot be changed after the schema is created. This document assumes that you have a database created that will fit your data.

Converting Data to a Different Format
Converting data from one format to another is a common task, usually done at the beginning of a project. You might receive data in Interchange (e00) format and have to import that data to a coverage, then import that into the geodatabase. Or, you might export data from a shapefile into a geodatabase, or you may import from one geodatabase to another.
ArcCatalog makes it easy to change a data source’s format. Right-click the data source whose format you want to change and point to Export. A list of the data converters that are appropriate for the selected data source will appear.
Importing a geodatabase feature class
You can use the Feature class to Geodatabase tool in ArcCatalog and ArcToolbox to import feature classes from one geodatabase to another or to import features from one feature class into a new feature class in the same geodatabase. This tool creates simple feature classes only and does not preserve object identity.
When you use this tool to import a custom or network feature class, a new simple feature class is created and the geometry and attributes of each feature are imported. If the input feature class has any subtypes, default values, relationships, and so on, they are not imported along with the features.

Copying and moving geodatabase data (limited in ArcView)
In addition to the Feature class to Geodatabase tool, ArcCatalog contains tools to directly move and copy data between geodatabases while preserving object identity, subtypes, relationships, network connectivity, and so on. With this method of copying data, you can copy entire feature datasets or individual feature classes between geodatabases.

When the data is copied, the copy has all the behavior of the original data; any attribute domains referenced in the original geodatabase are copied along with the feature class or table. If the feature class or table participates in a relationship class, then that relationship class, along with the feature class or table it is related to through that relationship class, is also copied. For example, if you copy a feature class with feature-linked annotation, the feature-linked annotation is automatically copied with the feature class.
If you are copying a feature class into an existing feature dataset either in the same geodatabase or in another geodatabase, then the spatial reference of the feature class and the feature dataset must match. If the spatial references do not match, you will not be able to copy the data.
You can move feature classes and relationship classes in and out of, or between, feature datasets in the same geodatabase by dragging and dropping them in ArcCatalog. When moving a feature class into a feature dataset, the feature class and the feature dataset must have the same spatial reference.
If you copy or move a network feature class, all the feature classes that participate in the network, and the geometric network itself, are also copied or moved with the feature class.
How to import shapefiles
Importing shapefiles using default values
In the ArcCatalog tree, right-click the shapefile you want to import into your geodatabase.
Point to Export.
Click Shapefile to Geodatabase Wizard.
The wizard appears with the input shapefile field already populated with the shapefile you selected in ArcCatalog.
Click Next.
Navigate to the database or database connection into which you want to import the shapefile, or type its path.
To import the shapefile into an existing feature dataset in the database, click the first option and then click the feature dataset’s name from the dropdown list.
Or, to import the shapefile into a new feature dataset, click the second option and type the new feature dataset’s name.
Or, to import the shapefile as a standalone feature class, click the third option.
Type a name for the new feature class.
Click Next.
Click the first option to accept the default parameters.
Click Next.
Review the options you specified for your data import operation. If you want to change something, you can go back through the wizard by clicking the Back button.
When satisfied with your options, click Finish to import the shapefile into the database.
Tips
Shapefiles can also be imported into geodatabases by clicking the geodatabase and using the Import menu. In this case, the destination database is prepopulated, and you must browse for—or type the name of—the shapefile.
If you select multiple shapefiles from the contents view of ArcCatalog and click Import/Shapefile to Geodatabase, the tool will automatically be set in batch mode with all of the input shapefiles prepopulated.

Importing shapefiles using custom values
Follow steps 1 through 8 for ‘Importing shapefiles using default values’ (see above).
Click the second option to import the shapefile defining custom parameters.
Click Next.
Type custom spatial index grid values if you do not want to use the defaults. (Only one index grid is used in Personal geodatabases, while ArcSDE geodatabases use up to three.)
Click Next.
Review the names in the Corrected Fields column. Click a name and then type a new one if you do not want to use the default.
Double-click in the Delete Field column and then click Yes if you do not want to include one of the original fields in the new feature class.
Click Next.
Review the summary of the coordinate system that will be used.
Click Change if you want to modify any of the shapefile’s coordinate system parameters.
Click on one of the buttons to change the default coordinate system by one of the following methods:
Selecting a preexisting one
Importing a coordinate system from a shapefile, coverage, or feature class
Defining a new one
Modifying the default coordinate system’s parameters
Click the X/Y Domain tab and modify the default parameters.
Repeat the previous step with the Z Domain and M Domain tabs, if present.
Click OK.
Click Next.
Review the summary of the parameters used to import the shapefile.
To change a parameter, navigate back to the appropriate panel by clicking Back.
Click Finish.

Tips
Changes to the field names are proposed when the original field names are invalid in the database. For example, when a field name contains an invalid character such as a hyphen, the hyphen is replaced by an underscore in the corrected field name. An error message indicating why the original name was corrected appears in the Original Error column.
You can click the Revert button to change the corrected field names back to their original values as automatically corrected by the Import wizard.
For details on creating new coordinate systems, see ArcGIS help topics for Creating feature datasets or Using the Extract Data Wizard.