Share Your Geology Here

To draw magnetic structures in 3D

2009-11-10T01:19:00.001-08:00

Description:

MAG3D is a program to draw magnetic structures, described in a standard crystal data file, on a terminal screen. The picture can be rotated to obtain a satisfactory viewing angle and a high quality representation of the structure can finally be written in postscript format.

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 *

Classification:

Magnetic Structure Factors . . . . . . . Main Program

SUBROUTINE AROW3D(XP,YP,AL,ARDAT)

To draw 3D arrows

Arguments:

XP,YP is the position for the arrow in space 4 AL is the length in Angstoms(space 4) Arrow drawing data in ARDAT(4): can be filled in by subroutine ARTILT

PHI1 (ARDAT(1)) is the angle of rotation of the projected

: arrow from the x axis of the picture (in radians)

PHI2 (ARDAT(2)) is the angle of rotation of the minor axis of

: projected ellipses from the projected arrow direction.

FR (ARDAT(3)) is the perspective foreshortening factor in the

: direction parallel to the projection of the arrow

EX (ARDAT(4)) is the ratio of the minor to major axes of

: projected ellipses

Prerequisite calls:

MAG3DX to put the arrow definition in COMMON PICDEF

Notes:

Uses plotter space 5 to keep the transformation for the arrow rotation

Calls:

KANGA1 PLTRIN SPCSET

Called by:

MAG3D LABAXE

Common blocks used: /CONSTA/ to use PI TWOPI /PICDAT/ to use all members

* AROW3D improved by PJB March 1992 *

SUBROUTINE ARTILT(R,ARDAT)

To define the tilt parameters for 3d arrows

Arguments:

On entry: R is a unit vector parallel to the spin, on the orthogonal

: coordinate system of the picture.

On exit: Arrow drawing data in ARDAT(4)

PHI1 (ARDAT(1)) is the angle of rotation of the projected

: arrow from the x axis of the picture (in radians)

PHI2 (ARDAT(2)) is the angle of rotation of the minor axis of

: projected ellipses from the projected arrow direction.

FR (ARDAT(3)) is the perspective foreshortening factor in the

: direction parallel to the projection of the arrow

FT (ARDAT(4)) is the ratio of the minor to major axes of

: projected ellipses

Prerequisite calls:

MAG3DX to set the perspective transformation in COMMON PICDAT and the arrow definition in PICDEF

Calls:

PERSPC TESTOV UNIVEC

Called by:

MAG3D

Common blocks used: /PICDAT/ to use all members

* ARTILT NEW BY PJB MARCH 1992 *

SUBROUTINE ARROW(P,S,N)

Writes postscript output to plot an arrow in MAG3D

Calls:: DEGREE GMSCA
Called by:: ATLABS POSORT
Common blocks used: /VFRMTS/ to use all members /IOUNIT/ to use IPLO /LAYOUT/ to use all members /PICDEF/ to use CMPERA APERMB

* ARROW NEW BY PJB DECEMBER 1991 *

Classification:

Graphical Output . . . . . . . Utility

SUBROUTINE ATLAB(P,NAME)

Writes an atom name on the postscript output file

Arguments:: P gives the position and NAME the name
Calls:: GMSCA
Called by:: ATLABS
Common blocks used: /IOUNIT/ to use IPLO /PICDAT/ to use all members /PICDEF/ to use CMPERA

* ATLAB new by PJB March 1992 *

SUBROUTINE ATLABS(MODE)

Makes a key for the atom symbols in MAG3D

Arguments:: A multiple entry subprogram driven by MODE MODE = 1 Identify the inequivalent atoms MODE = 2 Arrange the space needed for the key MODE = 3 Write the key
Calls:: ARROW ATLAB ATOM LENGT
Called by:: MAG3D POSOUT
Common blocks used: /ARRAYS/ to use all members /ATMLAB/ to use all members /ATNAM/ to use ATNAME /PICDAT/ to use all members /PICDEF/ to use CMPERA PWIDTH PHGHT X0 Y0 /POSNS/ to use NATOM

* ATLABS new by PJB April 92 *

SUBROUTINE ATOM(R,N)

Writes postscript output to plot an atom in MAG3D

Calls:: GMSCA
Called by:: ATLABS POSORT
Common blocks used: /VFRMTS/ to use all members /IOUNIT/ to use IPLO /LAYOUT/ to use all members /PICDEF/ to use CMPERA

* ATOM new by PJB Dec 91 *

SUBROUTINE ELIPSE(X,Y,R,EX,ANG,ANG1,ANG2)

To draw an arc of an ellipse

Arguments:: X,Y are the coordinates of the centre. R is the length of the major axis EX is the ratio of the minor to the major axis ANG is the angle by which the major axis is inclined to the x-axis The arc is drawn from ANG1 to ANG2 measured counter clockwise from the x-axis.
Calls:: KANGA1 PLCONV
Common blocks used: /CONSTA/ to use TWOPI /PLTRAN/ to use NSPCE

* ELIPSE new by PJB Mar 92 *

SUBROUTINE INVPRS(P,Q)

Inverse perspective transformation

Description:: Given the x,y components of the 2D representation in P and the x(out of plane) component of the 3D vector in Q, fills in the other two components.
Called by:: OVERLA
Common blocks used: /PICDAT/ to use all members

* INVPRS new by PJB March 1992 *

SUBROUTINE LABAXE(P1,P2,AL,S,ID)

Labels the axes of a diagram

Calls:: AROW3D KANGA2 PLCONV SPCSET
Called by:: MAG3D
Common blocks used: /GRAYS/ to use all members /PICDAT/ to use all members /PLODAT/ to use CHUNIT

* LABAXE revised by PJB Apr 92 *

SUBROUTINE LAXIS(P1,P2,S,ID)

Puts arrows and labels to identify the axes of a postscript
picture

Calls:: DEGREE GMSCA
Called by:: POSORT
Common blocks used: /GRAYS/ to use all members /IOUNIT/ to use IPLO /PICDAT/ to use all members /PICDEF/ to use CMPERA FRLINE

* LAXIS new by PJB Apr 92 *

SUBROUTINE LINE(P1,P2)

Writes postscript output to plot a line in MAG3D

Calls:: GMSCA
Called by:: POSORT PSPOUT
Common blocks used: /IOUNIT/ to use IPLO /PICDAT/ to use all members /PICDEF/ to use CMPERA

* LINE new by PJB Dec 91 *

SUBROUTINE MAG3DX

To read the "X" cards for MAG3D

Calls:: ERRCHK ERRMES FINDCD IATOM RDNUMS RDREAL RDWORD
Called by:: MAG3D
Common blocks used: /ATNAM/ to use ATNAME /IOUNIT/ to use ITO /PICDAT/ to use all members /PICDEF/ to use CMPERA FRLINE APERMB /POSNS/ to use NATOM

* MAG3DX new by PJB Mar 92 *

FUNCTION MINZ(IQ,IP)

Determines what next to plot in MAG3D

Arguments:: IQ and IP mark the current positions in the arrays of atoms,lines and labels. IQ directly and IP with respect to the sorted arrays. IQ is given on entry and Ip is set in the subprogram.
Notes:: The function returns 1,2,or 3 to indicate that an atom, a line or an axis label should be plotted next. The return value 4 indicates the end.
Calls:: MINZ
Called by:: MINZ POSORT
Common blocks used: /ARRAYS/ to use all members

* MINZ new by PJB Mar 92 *

LOGICAL FUNCTION OVERLA(II,JJ,RAD,P)

Determines whether a line goes through an atom

Arguments:: II labels the atom in the arrays ARRAT, ARRAZ etc. JJ labesl a line in the arrays ALINE, BLINE etc. RAD is the radius of the atom. If OVERLA is TRUE ie line and atom intersect P is returned containing the perspective coordinates of the intersection point nearest to the viewer
Calls:: FACT GMADD GMSCA GMSUB INVPRS OVERLA PERSPC SCALPR UNIVEC VECPRD
Called by:: OVERLA POSORT
Common blocks used: /ARRAYS/ to use all members

* OVERLA new by PJB Mar 92 *

SUBROUTINE PERSPC(R,S,IS)

Perspective transformation

Arguments:: IS Indicates the space in which R is given IS=0 Orthogonal and no transformation IS=1 or 2 for real or reciprocal respectively, both with transformation
Prerequisite calls:: The common PICDAT must be set up to contain the perspective factors and any required transformation
Calls:: GMEQ GMPRD ORTHO
Called by:: MAG3D ARTILT OVERLA
Common blocks used: /PICDAT/ to use all members

* PERSPC new by PJB Jan 91 *

SUBROUTINE POSORT(NARRAS,NLINES)

Sorts the arrows and atoms for postscript output of MAG3D

Arguments:: NARRAS is the number of atom/arrows to be plotted NLINES is the number of lines to be plotted
Calls:: ARROW ATOM GMEQ JGMEQ LAXIS LINE MINZ OVERLA SORTX
Called by:: POSOUT
Common blocks used: /ARRAYS/ to use all members /PICDAT/ to use all members

SUBROUTINE POSOUT(NARS,NLINES)

Drives the postscript output for MAG3D

Arguments:: NARRAS is the number of atom/arrows to be plotted NLINES is the number of lines to be plotted
Calls:: ATLABS FILNOM LENGT NOPFIL POSORT PSPROC UPONE
Called by:: MAG3D
Common blocks used: /CARDRC/ to use ICRYDA /IOUNIT/ to use LPT ITO IPLO LUNI /NTITL/ to use NTITLE /PICDAT/ to use all members /PICDEF/ to use CMPERA FRLINE PWIDTH PHGHT X0 Y0 /TITLE/ to use all members /SCRACH/ to use MESSAG /WHEN/ to use all members

* POSOUT new by PJB Apr 92 *

SUBROUTINE PSPOUT(LUNO,CHARS,N)

Formats and prints CHARS on LUNO in lines only breaking at spaces

Calls:: LINE
Called by:: PSPROC

SUBROUTINE PSPROC(LUN)

Print postscript arrow procedures on unit LUN

Calls:: PSPOUT
Called by:: POSOUT
Common blocks used:

Kriging

2009-11-06T20:44:00.001-08:00

Kriging is a geostatistical gridding method that has proven useful and popular in many fields. This method produces visually appealing maps from irregularly spaced data. Kriging attempts to express trends suggested in your data, so that, for example, high points might be connected along a ridge rather than isolated by bull's-eye type contours.
Kriging is a very flexible gridding method. You can accept the Kriging defaults to produce an accurate grid of your data, or Kriging can be custom-fit to a data set by specifying the appropriate variogram model.

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 3x3, 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.

How To Import CSV to Database using Oasis Montaj

2009-11-06T20:37:00.001-08:00

Importing Collar Data (CSV/text format)

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:

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

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

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

VARIOGRAM

2009-11-06T00:05:00.001-08:00

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.

Inverse Distance

2009-11-05T23:14:00.001-08:00

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.

How to convert data point into Polyline or Polygon in Mapinfo

2009-10-28T19:00:00.001-07:00

1. First prepare your data. The data must contains easting (x), northing(y) and Join. The Join is a unique attribute that represents how the points connect to others. Point with the same join will a line. See my data below.

2. Now create point using the data. Go to Table|Create Point. An the result is something like this.

3. Now its time to convert the points into polygon. Make sure you already installed Encom Discover. I use current Discover version (version 11) but you can do this using any previous

Discover version. Go to Discover|Table utilities|Build Objects from Tables.
4. On the Build Objects from Table window, specify the file format you are using. In this example I use Column delimited x,y Polylines (from table), and make sure you check the Convert closed polylines to region option. On the next window, specify the table you want to convert, east and north field and also the object delimiter (join).

And here is the final conversion result.

Before conversion

After Conversion.

How to Create Gridded surface using Encom Discover 9.0

2009-10-28T18:23:00.000-07:00

This tutorial shows how gridded surfaces and contour plans are created and used in Discover. A topographic dataset containing spot heights is used as the data source.
An Exercise in Surface Modelling and Analysis Elevation data, stored as a series of spot height points, are located in the table called SPOT HEIGHTS in the \Discover Tutorial \Surfaces folder. The objectives of this tutorial are to interpolate a surface grid and generate a contour plan, create a profile, determine grid slope and aspect, perform sun-shading and clip the grid to a region.

Step 1 – Configure Grid Handlers
Discover can create surface grids in 3 widely used grid formats: Geosoft *GRD, ER Mapper *.ERS and Surfer *.GRD. Apart from creating grids in these formats, Discover can also import grids created in other gridding software applications including the following:
•Geosoft
•ER Mapper
•Surfer
•Vertical Mapper
•ESRI ASCII
•Other ASCII grids
•USGSDEM
•Minex
•BIL
Discover supplies a number of Grid Handler files which enable it to create or import grids in any of these formats.
Select Discover>Configuration>Grid Handlers and make sure that the following grid handlers are listed. Check the Use MapInfo Grid Handlers (when possible) box and highlight either the ERMapper, Geosoft or Surfer grid to be the default output grid type. The default output grid type can be overwritten when a new grid is created. Click OK.

Step 2 – Generate a Surface Grid
Open the table SPOT HEIGHTS. Choose Map>View Entire Layer. From the Discover menu, select Surfaces menu. The Surfaces menu is added to the MapInfo menu bar.
Choose Surfaces>Create Grid…. Select the SPOT HEIGHTS table to be gridded and click OK. Note that if only a portion or subset of the displayed data points is to be gridded, an additional Selection option appears in the dialog list.

The Gridding Tool consists of a series of tab dialogs for grid parameter set-up that create a surface grid. Accompanying each dialog box is a preview window of the grid as it is created.
The Gridding Tool consists of a series of tab dialogs for grid parameter set-up that create a surface grid. Accompanying each dialog box is a preview window of the grid as it is created.

On the Input tab of the Gridding dialog, select Elevation from the list of available fields to grid. The image appears on the right hand side of the dialog. Above the grid preview are 6 buttons and a pull-down list. These buttons control the display properties of the grid in the Preview window. Ensure that the Apply histogram equalisation button is selected and select pseudocolor from the drop down list of colour tables.
Right-mouse click in the Preview window to display the pop-up menu:

On the Method tab, set the Method to Triangulation. The gridding methods provided use different algorithms in order to interpolate values for grid cells from and between the input data points. Triangulation uses the Delaunay triangulation method which creates triangles between all the data points. Grid cell values are then assigned based on the coplanar values of the triangle in which each grid cell is located. This gridding method is best suited to datasets which need to honour the original data input points as accurately as possible: e.g. elevation data.
Other types of datasets such as geochemical data can be gridded using different interpolation methods such as Minimum Curvature or Inverse Distance Weighting.
The Grid Geometry tab specifies the grid cell size which may be altered manually if required. The extents of the data to be gridded can also be altered under the Data Coverage options.
On the Output tab save the grid in a Geosoft format [.GRD] named TOPO_GRID.TAB. Click the OK button and Discover will save the grid and open it up into a new map window or the map window containing the SPOT HEIGHTS data.
Step 3 – Contour Elevation Grid
Select Surfaces>Contour a Grid and the Contour a Grid dialog appears. Choose TOPO_GRID as the Grid to contour, check the Contour Smoothing box and the Specify minimum/maximum values to contour between box, leaving the range as 0 and 1200. Click on Output Contour Table and save the table as GRID_CONTOURS in the Discover Tutorial\Surfaces folder.

Click OK and Discover adds the new contour table to the TOPO_GRID grid map window. You can use the Surfaces>Label Contour Lines menu option to add contour labels to your contours.
Close the GRID CONTOURS table on completion of this exercise.

Plotting Structural Measurements in ArcGis

2009-10-27T18:05:00.000-07:00

1. 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.

How to import Excel Table using ArcGis

2009-10-24T19:44:00.000-07:00

To connect to an Excel table so you can use it in ArcCatalog and ArcMap, follow these instructions:

Close ArcCatalog before starting any of this.

In your Excel table highlight the rows/columns you want in your table, including the field names. This is done so you can possibly have more than one table within an Excel document.

From Worksheet Menu Bar, choose Insert > Name > Define. Give it a name.

Click OK and Save your document.
Open your system Control Panel and choose Administrative Tools > Data Sources (ODBC)
On User DSN tab, click Add…
Highlight Driver do Microsoft Excel Driver (*.xls) and click Finish.
Fill in the Data Source Name with any name you wish. It doesn’t have to be the name of the xls file you are working with.
Click Select Workbook…
Browse and select your Excel document.
In ODBC Microsoft Excel Setup window, click OK.
In ODBC Data Source Administrator, click OK.
Start ArcCatalog.
In the catalog tree, click Database Connections.
Double-click Add OLE DB Connection.
Highlight Microsoft OLE DB Provider for ODBC Drivers. Click Next >>
In Use data source name click the down arrow. You should find the name you set in step 8. Choose it.
Click Test Connection to see if things are working OK.
Click OK. A new entry under Database Connections should be listed by the name of OLE DB Connection.
Open OLE DB Connection (or you may rename it first).
There should be a table listed by the name you assigned in Step 3 above.

Making 3D GIS Videos

2009-09-27T03:04:00.000-07:00

Making 3D GIS Videos! The ArcScene Interface

Things needed:
> ArcGIS 9.0 with extensions licensed
> Data: elevation (DEM), aerial photographs, buildings, roads, etc
> 3 button mouse

1. Start ArcScene: Start> All Programs> ArcGIS> ArcScene
2. Add your data by using the yellow plus sign button on the toolbar. It will probably take a while to add aerial photos – be patient 
Note: your DEM layer doesn’t need to be turned on if you have aerial photographs.
3. Make your data 3D … Right click on the layer go to properties & the base height tab

4. Select Obtain Height for layer from surface – your DEM should automatically be there (otherwise you need to browse for it).
5. Change the Z unit Conversion to exaggerate the hills and valleys – the larger the number the steeper the hills.
6. Repeat for all of your data in the scene (e.g. aerial photographs, roads, etc)
7. If you are using aerial photographs you can improve their quality by going to the rendering tab in the properties menu. At the bottom of the window slide the “Quality Enhancement for raster images” closer to High. (but not all the way because it will take a long time render).

How to calculate X, Y and Z values for points

2009-09-27T02:37:00.000-07:00

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).
a. Within ArcCatalog, select the file menu, new, shapefile, name (type in the name you want to use), for feature type select point.
b. 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.
c. Select OK.
2. The user must add three items to the attribute table of the point shapefile.
a. 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.
b. 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.

Name Type Precision Scale
X_Coord Float 12 6
Y_Coord Float 13 6
Elevation Short Integer
Name Text Length = 8

3. 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.
a. 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.
b. 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.
4. When all the points are digitized in, the point shapefile must be given 3D geometry.
a. 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.
b. 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…)
LINK

Share Your Geology Here

To draw magnetic structures in 3D

Description:

Input:

Output:

Notes:

Running the program:

Calls:

Common blocks used:

*** MAG3D Postscript arrows included C110 ***

Classification:

To draw 3D arrows

Arguments:

Prerequisite calls:

Notes:

Calls:

Called by:

Common blocks used:

*** AROW3D improved by PJB March 1992 ***

To define the tilt parameters for 3d arrows

Arguments:

Prerequisite calls:

Calls:

Called by:

Common blocks used:

*** ARTILT NEW BY PJB MARCH 1992 ***

Writes postscript output to plot an arrow in MAG3D

Calls:

Called by:

Common blocks used:

*** ARROW NEW BY PJB DECEMBER 1991 ***

Classification:

Writes an atom name on the postscript output file

Arguments:

Calls:

Called by:

Common blocks used:

*** ATLAB new by PJB March 1992 ***

Makes a key for the atom symbols in MAG3D

Arguments:

Calls:

Called by:

Common blocks used:

*** ATLABS new by PJB April 92 ***

Writes postscript output to plot an atom in MAG3D

Calls:

Called by:

Common blocks used:

*** ATOM new by PJB Dec 91 ***

To draw an arc of an ellipse

Arguments:

Calls:

Common blocks used:

*** ELIPSE new by PJB Mar 92 ***

Inverse perspective transformation

Description:

Called by:

Common blocks used:

*** INVPRS new by PJB March 1992 ***

Labels the axes of a diagram

Calls:

Called by:

Common blocks used:

*** LABAXE revised by PJB Apr 92 ***

Puts arrows and labels to identify the axes of a postscript picture

Calls:

Called by:

Common blocks used:

*** LAXIS new by PJB Apr 92 ***

Writes postscript output to plot a line in MAG3D

Calls:

Called by:

Common blocks used:

*** LINE new by PJB Dec 91 ***

To read the "X" cards for MAG3D

Calls:

Called by:

Common blocks used:

*** MAG3DX new by PJB Mar 92 ***

Determines what next to plot in MAG3D

* MAG3D Postscript arrows included C110 *

* AROW3D improved by PJB March 1992 *

* ARTILT NEW BY PJB MARCH 1992 *

* ARROW NEW BY PJB DECEMBER 1991 *

* ATLAB new by PJB March 1992 *

* ATLABS new by PJB April 92 *

* ATOM new by PJB Dec 91 *

* ELIPSE new by PJB Mar 92 *

* INVPRS new by PJB March 1992 *

* LABAXE revised by PJB Apr 92 *

Puts arrows and labels to identify the axes of a postscript
picture

* LAXIS new by PJB Apr 92 *

* LINE new by PJB Dec 91 *

* MAG3DX new by PJB Mar 92 *

* MINZ new by PJB Mar 92 *

* OVERLA new by PJB Mar 92 *

* PERSPC new by PJB Jan 91 *

* POSOUT new by PJB Apr 92 *