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WO1997034169A1 - Procede d'analyse de donnees dans le cadre d'explorations minerales - Google Patents

Procede d'analyse de donnees dans le cadre d'explorations minerales Download PDF

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Publication number
WO1997034169A1
WO1997034169A1 PCT/GB1997/000678 GB9700678W WO9734169A1 WO 1997034169 A1 WO1997034169 A1 WO 1997034169A1 GB 9700678 W GB9700678 W GB 9700678W WO 9734169 A1 WO9734169 A1 WO 9734169A1
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WIPO (PCT)
Prior art keywords
neural
mineral
survey
data
anomaly
Prior art date
Application number
PCT/GB1997/000678
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English (en)
Inventor
George Ravuama Bolt
Nicholas Francis Ryman-Tubb
Gavin John Daneel
Original Assignee
Neural Mining Solutions Pty. Ltd.
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Neural Mining Solutions Pty. Ltd. filed Critical Neural Mining Solutions Pty. Ltd.
Priority to AU19333/97A priority Critical patent/AU1933397A/en
Publication of WO1997034169A1 publication Critical patent/WO1997034169A1/fr

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Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01V—GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS
    • G01V11/00—Prospecting or detecting by methods combining techniques covered by two or more of main groups G01V1/00 - G01V9/00

Definitions

  • This invention relates to the exploration for minerals for example but not exclusively those containing metals
  • a method of locating a mineral deposit comprising the steps of i obtaining a plurality of data sets of indicators of physical or chemical conditions at a plurality of sites in an area known to contain the mineral, at least one of the indicators being an indicator of the mineral, ii training neural anomaly identifying means to identify anomalies in the data sets, iii obtaining a plurality of the indicators at a plurality of sites in a search area thought to contain the mineral; iv inputting the indicators obtained from the search area to the trained anomaly identifying means to obtain an indication of the location of the mineral deposit; and v visualising the indication
  • a model is trained to locate anomalies or clusters with data from an area known to contain a mineral deposit.
  • the model can thus reconcile particular features with the existence or absence of the mineral
  • the mechanism involved between the indicator and mineral need not be understood.
  • the data may involve a high degree of redundancy for example including data sets which gives no information about the sought mineral
  • At least one set of data should provide a positive or negative indicator of the existence of mineral.
  • Preferably a plurality of data sets provide an indication.
  • neural anomaly detectors and approximators are used.
  • An SOM is preferably used as the anomaly and clusters identifying means but other means such as ART could be used.
  • a preferred neural approximator is an MLP but others such as RBF's could be used. The precise means are not the subject of the invention. Those skilled in the art will have preferred means of their own or will develop suitable ones by reference to the text books hereinafter referred to or other reference works.
  • Fig 1 a map showing the results of an assay using plots for nine minerals of a first survey area.
  • Fig 2 is a map showing regions of anomaly located in the map of Fig 1.
  • Fig 3 shows the map of Fig 2 together with a nickel survey map
  • Fig 4 a contour plot of the area covered by Fig 1 where the data is restricted to a subject.
  • Fig 5 is a topological map resulting from cluster analysis of the first survey site.
  • Fig 6 is a geographical map showing the location of the clusters of Fig 5.
  • Fig 7 shows a plot of the existence of a cluster overlying the presence of silver
  • Fig 8 is a schematic of a display for exploring relationship.
  • Fig 9 is a neural correlation of data from the survey site A.
  • Fig 10 is a chart showing factors which influence gold assay level at site A.
  • Fig 11 is a chart showing the results of neural sensitivity analysis of site B
  • Fig 12 is a chart showing the factors which influence white core colour at site B
  • Fig 13 is a map showing the results of a neural fuzzy search for silver at site
  • Fig 14 is a map showing the results of a neural fuzzy search for areas containing high levels of gold and low levels of copper and nickel.
  • Fig 15 is a map of the geographical locations of areas of high copper assay at site B.
  • the EM data set contained up to twenty components for signal returns at 1 ms intervals
  • the EM data was pre-processed into seven components hereinafter referred to as ⁇ M special data' i Simple sum of EM 1 ms to 4 ms ii Simple sum of EM 5 ms to 8 ms iii Simple sum of EM 9 ms to 12 ms iv EM 1 ms signal
  • the 50x50 grid represents objects of about 300m square This window reflects the standard sized search employed by geologists
  • Cluster identification identifies global trends and patterns across the entire survey region.
  • a plot could depict the output map of the Self-Organising Map (SOM) neural computer employed to perform cluster identification.
  • SOM Self-Organising Map
  • An SOM is also known as a Kohonen network. Details may be found in 'Self Organisation and Associative Memory' by T. Kohonen Springer Verlag 3rd Edition 1989. On the basis of this document and known SOMs those skilled in the art will have little difficulty in devising a suitable SOM.
  • This may for example be a 2-D grid (not to be confused with the 2-D survey region) of 10x10 units. This grid may be divided into a number of separate regions; each region corresponding to a particular cluster identified. The number of units for a particular region indicates the level of generality for that cluster. For example, a cluster 1 could be assigned 2 units for data group "All Data Sets" This would indicate that it represents fairly unusual types of deposit characteristics.
  • anomaly feature plots are given for the top ten anomalies
  • the feature plot itself indicates the type of deposit within the anomaly Values for each survey component are conveniently normalised between 0 and 1 where 0 represents the lowest value and 1 the highest value in the data.
  • Neural correlation analysis using an approximator shows how different survey components influence each other The analysis may need only to be performed across a limited area of the survey region. This limitation could be defined by setting a small "Reality" value
  • MLP Multi Layer Perceptron
  • PERCEPTRON is a registered trademark.
  • One of the layers in the interpolated data set may be designed as the variable for which correlations are to be found This variable is the output target unit of the MLP
  • the remaining data layers in the data set provide input units After training the correlation of each input on the output is determined directly from the MLP by measuring the gradient of the relationship between the input and output.
  • Fuzzy searching locates regions of strong correlation
  • a search pattern may consist of three elements of the data sets First the values of interest of, for example, magnetic field, radioactive decay due to uranium and copper concentration in the ground The second is the flag indicating whether the value of interest is a maximum, minimum or equality Thirdly a weighting to be accorded to the data set is required A continuously valid degree or match between the search pattern and the grid points is obtained using a fuzzy matching neural computer The output may be presented in the form of a continuously valid degree of match across the region
  • Fuzzy neural searching allows specific patterns of survey data to be located and a measure of how "close" regions match the search pattern
  • the trained model may then be used to locate mineral
  • Figure 1 shows the results for each assay using plots These contour plots would normally be overlaid, in various combinations by a geologist to determine by observation any correlations and anomalies
  • Drill hole information gave northing, easting and elevation location, orientation, azimuth and maximum depth reached.
  • Lithological descriptions were divided into four sub-classes i Colours ii Physical properties iii Rock types iv Mineral constituents
  • the labels for each sub-class are given in Table 1.
  • Each two metre interval could be given any number of descriptor labels. For example, Grey Muscovite, Pale Green Muscovite, Sericite, Schist, and Quartz Veins.
  • the copper assay component was a numeric figure in units of ppm. All other components were represented by a '1' if that lithological feature was present otherwise '0'.
  • Neural anomaly detection is more powerful than simply finding localised regions where a single component from a survey dataset is particularly prominent For example, regions with higher levels of a certain mineral than ambient levels It is actually able to detect regions where multiple combinations of survey components are together anomalous with respect to the entire survey region. It is also able to perform the simple single component anomaly detection as well
  • the particular neural computing technique employed to perform anomaly detection was based upon a Self-Organising Map (SOM)
  • the SOM is an unsupervised learning neural model In other words, it does not require to be told by an expert, during training, the output for example inputs Instead, it only takes a collection of example inputs During training, these are shown, without any explanation, to the SOM as data pattern examples For example, when training a SOM on the Survey A survey data, individual input patterns consisted of the nine chemical soil assay figures for each sample location
  • the SOM automatically forms a special topologically organised map (such that nearby output units in the grid represent similar input patterns) of these inputs on its two-dimensional output grid By this means, a much larger range of possible input patterns can be represented by the SOM than would be possible if the output grid was not topologically organised
  • a SOM is efficient in representing large complex datasets, such as survey results.
  • the final trained SOM actually represents a model of the structure of data seen during training.
  • a SOM was trained on the soil geochemistry assays from Survey A.
  • the nine- dimensional inputs (one input per chemical assay) were fed to the SOM as example inputs.
  • the SOM was then used to determine regions of anomaly within the Survey A dataset. For every soil sample, a numeric figure can be deterrnined using the SOM which reflects how anomalous its nine assays are with respect to the entire pattern of assays throughout the survey area.
  • Figure 2 shows the pattern of anomaly in the area surveyed, displayed as a contour plot based on the same co ⁇ ordinates as previously used in Figure 1. The contours represent increasingly anomalous regions within the survey site.
  • the neural anomaly tool automatically discovered six main areas of anomaly within the survey data. These six regions are marked 'A - T' on Fig 2. Other less unusual areas (but still anomalous) were also discovered and could perhaps provide lower priority leads to be later investigated by a geologist.
  • the tool can also provide information on the factors leading to the high degree of anomaly.
  • Information on the six main anomalous regions given in Fig. 2 is given in Table 2. Figures are given as percentages over the range of the assayed values of each rnineral, i.e. 0% equals its lowest value and 100% its highest value. Table 2
  • Regions A, C and D located in accordance with the invention corresponded to known areas of silver anomaly.
  • Regions of anomaly can be compared with original survey maps For example, region E has an anomalous proportion of nickel Figure 3 shows the anomaly map, as given above, together with the nickel survey map It can be seen how anomaly region E strongly correlates with the anomalous deposit of nickel.
  • Figure 4 shows a contour plot mapping the anomaly level over the survey site A for this particular selection of minerals.
  • Table 3 shows anomaly descriptions for each of the regions labelled on the anomaly map (A-E). It can be seen that the anomalous nickel plug (region D) and a known silver anomaly (A and to a lesser extent C) are clearly indicated. Gold anomalies are indicated at B and E.
  • Clustering is another analysis method which will be of great use to a geologist in analysing data from either a single survey or multiple surveys. It aids the geologist by extracting common types of geological deposits and their form. The tool automatically determines clustering across survey readings by using commercial neural computer techniques. It also supplies a numeric figure of importance for each cluster found A geologist can prioritise further detailed analysis within survey data using these important figures. Clustering is performed using a Self-Organising Map, as was used to determine anomalous regions However, it is employed in a different way As before, the SOM is trained by showing it survey data from various sampling points without explanation Again, training is totally automated without need for user guidance After training, the SOM is then analysed to determine clustering structures as formed in its topologically organised two-dimensional output map
  • the SOM used previously for anomaly detection in the survey site A was analysed for geological deposit clustering
  • the topological map of the SOM resulting from the cluster analysis is shown in Figure 5 It can be seen that six clusters were detected Note that this two-dimensional map does not relate to the geographical map of the survey site A.
  • the architecture of a SOM's output is arranged as a two-dimensional grid of neural processing units Each cluster corresponds to a specific class of geological deposit
  • Cluster 4 is clearly the most important Clusters which are, to some degree, similar in terms of geological deposit share nearby regions on the topographic map. Clusters which are very dissimilar are assigned regions spaced far apart. In Figure 5, clusters 1 and 5 are similar, but 1 and 6 are very dissimilar.
  • Table 4 shows the typical soil geochemistry assays for each of the six clusters detected. Percentage values range from the minimum assayed value (0%) to the largest assay (100%).
  • Neural analysis also provides a level of importance for each cluster. This will aid a geologist in visualising the relative merits of each cluster with respect to each other and their relevance in the survey. Important figures for the clusters are given in Table 5. Table 5
  • cluster '4' is the most important, followed by clusters T and '5'
  • the geographical location of a specific type of deposit can be examined by choosing a cluster from the above table then displaying an overlaid survey map Figure 6 shows the geographical location of the six clusters in the survey site The contours highlight
  • Figure 7 shows an overlaid contour map of silver analysis with Cluster 3 It can be seen how this cluster typifies the deposition of silver within the survey area A
  • the neural computing technique used employed auto-associative neural models. These are unsupervised learning systems which do not need prior information of example known correlations or given any external user guidance during training. Instead, the auto-associative neural model merely takes survey datasets and automatically leams embedded relationships between the various survey components chose for correlation analysis.
  • the neural model Once the neural model has completed traming, it is then analysed to determine any possible mixture of correlations. It can be thought that the neural computer becomes a complete model of the survey data.
  • sensitivity analysis is performed to extract these learned relationships. It also allows complex exploration of the relationships between components of the survey datasets. Traditional correlation analysis allows only two components to be studied for their effect on each other. Neural sensitivity analysis allows the simultaneous study of inter-relationships between multiple components to be understood.
  • the actual value of the relationship strength for one particular component should be considered independently from those for other components. Where many components influence the target component, strength values will all be fairly small. However, in the case where the target component is only influenced by a few other components, strength values will be much larger The actual numeric size of values is not particularly important. Instead, it is the relative value of strengths.
  • Figure 12 shows the factors influencing core samples which are coloured "White".
  • the important positive factors are: Alteration (physical property), Quartz, Carbonate, Chrysocola, Calcite.
  • a geologist will often want to study the occurrence of specific combinations of minerals, rock types, physical properties, assayed levels etc. These interests will also arise from use of the previous neural analysis tools leading to interesting facts being determined about the survey data. These may prompt the geologist into performing some detailed searching through the survey datasets based on knowledge gained.
  • neural computers with their powerful pattern recognition capabilities are able to accept a "vague description" from the geologist and perform a neural fuzzy search.
  • the neural computing techniques employed to perform neural fuzzy searching are completely unsupervised and automatic. No user guidance is required once the search parameters are supplied by the geologist.
  • the geologist is able to search for regions specifying a range of requirements for individual survey components for example "Copper Assay” or Quartz:
  • the result of the neural search is the degree of match between the vague search description supplied by the geologist and every location within the survey region. This can be displayed using a contour plot showing the geographical location of matching regions within the survey site.
  • Figure 13 shows a contour map illustrating the distribution of high silver deposits within the geographical map of the site.
  • Fig 15 shows a plan view of the geographical location of matching regions. Again, contours indicate areas (1) with a high degree of match and area (2) with very poor matching. The plan view means that the actual degree of match represents the entire drill hole.
  • the neural search indicates that the bottom right corner of the region most closely matches the search pattern.

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  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • General Life Sciences & Earth Sciences (AREA)
  • General Physics & Mathematics (AREA)
  • Geophysics (AREA)
  • Geophysics And Detection Of Objects (AREA)

Abstract

L'invention concerne un procédé de recherche d'un dépôt de minéraux qui consiste à: (i) obtenir une pluralité d'ensembles de donnéees d'indicateurs d'états physiques ou chimiques sur une pluralité de sites, dans une zone connue renfermant le minéral, au moins un des indicateurs étant un indicateur de minéral; (ii) effectuer l'apprentissage d'un dispositif d'identification d'anomalies neuronales afin d'identifier des anomalies dans les ensembles de données; (iii) obtenir une pluralité d'indicateurs sur une pluralité de sites dans une zone de recherche censée contenir le minéral; (iv) introduire les indicateurs, obtenus dans la zone de recherche, dans le dispositif d'identification des anomalies ayant subi un apprentissage pour obtenir une indication de l'endroit du dépôt de minéraux; et (v) visualiser l'indication.
PCT/GB1997/000678 1996-03-12 1997-03-12 Procede d'analyse de donnees dans le cadre d'explorations minerales WO1997034169A1 (fr)

Priority Applications (1)

Application Number Priority Date Filing Date Title
AU19333/97A AU1933397A (en) 1996-03-12 1997-03-12 Method for analysing data in the exploration for minerals

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Application Number Priority Date Filing Date Title
GB9605227.9 1996-03-12
GB9605227A GB2303475B (en) 1996-03-12 1996-03-12 Exploration for minerals

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WO1997034169A1 true WO1997034169A1 (fr) 1997-09-18

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2162615C1 (ru) * 2000-04-14 2001-01-27 Кистеров Кирилл Всеволодович Способ поиска золоторудных тел
CN110060173A (zh) * 2019-04-27 2019-07-26 烟台市牟金矿业有限公司 一种深部金矿床成矿找矿方法

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US6236942B1 (en) 1998-09-15 2001-05-22 Scientific Prediction Incorporated System and method for delineating spatially dependent objects, such as hydrocarbon accumulations from seismic data
US6574565B1 (en) 1998-09-15 2003-06-03 Ronald R. Bush System and method for enhanced hydrocarbon recovery
GB0007156D0 (en) 2000-03-23 2000-05-17 Oxford Medical Image Analysis Improvements in or relating to processing data for interpretation
US7991717B1 (en) 2001-09-10 2011-08-02 Bush Ronald R Optimal cessation of training and assessment of accuracy in a given class of neural networks

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2234589A (en) * 1989-07-25 1991-02-06 Amoco Corp Locating subterranean features
EP0539018A1 (fr) * 1991-10-25 1993-04-28 Texaco Development Corporation Interprétation de données aéromagnétiques par un réseau neural
EP0561492A2 (fr) * 1992-03-16 1993-09-22 Texaco Development Corporation Procédé pour estimer la perméabilité de formations à partir de mesures dans un puits utilisant des réseaux neuronaux
WO1993019426A1 (fr) * 1992-03-25 1993-09-30 Western Mining Corporation Limited Procede de detection et de cartographie de mineraux et autres elements geologiques a l'aide de spectrometres d'imagerie aeroportes
US5373486A (en) * 1993-02-03 1994-12-13 The United States Department Of Energy Seismic event classification system

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
GB2234589A (en) * 1989-07-25 1991-02-06 Amoco Corp Locating subterranean features
EP0539018A1 (fr) * 1991-10-25 1993-04-28 Texaco Development Corporation Interprétation de données aéromagnétiques par un réseau neural
EP0561492A2 (fr) * 1992-03-16 1993-09-22 Texaco Development Corporation Procédé pour estimer la perméabilité de formations à partir de mesures dans un puits utilisant des réseaux neuronaux
WO1993019426A1 (fr) * 1992-03-25 1993-09-30 Western Mining Corporation Limited Procede de detection et de cartographie de mineraux et autres elements geologiques a l'aide de spectrometres d'imagerie aeroportes
US5373486A (en) * 1993-02-03 1994-12-13 The United States Department Of Energy Seismic event classification system

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
RU2162615C1 (ru) * 2000-04-14 2001-01-27 Кистеров Кирилл Всеволодович Способ поиска золоторудных тел
CN110060173A (zh) * 2019-04-27 2019-07-26 烟台市牟金矿业有限公司 一种深部金矿床成矿找矿方法
CN110060173B (zh) * 2019-04-27 2021-05-18 烟台市牟金矿业有限公司 一种深部金矿床成矿找矿方法

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Publication number Publication date
GB9605227D0 (en) 1996-05-15
GB2303475A (en) 1997-02-19
GB2303475B (en) 1997-07-09
AU1933397A (en) 1997-10-01

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