Integrated data analysis for mineral exploration: A case study of clustering satellite imagery, airborne gamma-ray, and regional geochemical data suites

Partitioning cluster algorithms have proven to be powerful tools for data-driven integration of large geoscientific databases. We used fuzzy Gustafson-Kessel cluster analysis to integrate Landsat imagery, airborne radiometric, and regional geochemical data to aid in the interpretation of a multimethod database. The survey area extends over [Formula: see text] and is located in the Northern Cape Province, South Africa. We carefully selected five variables for cluster analysis to avoid the clustering results being dominated by spatially high-correlated data sets that were present in our database. Unlike other, more popular cluster algorithms, such as k-means or fuzzy c-means, the Gustafson-Kessel algorithm requires no preclustering data processing, such as scaling or adjustment of histographic data distributions. The outcome of cluster analysis was a classified map that delineates prominent near-to-surface structures. To add value to the classified map, we compared the detected structures to mapped geology and additional geophysical ground-truthing data. We were able to associate the structures detected by cluster analysis to geophysical and geological information thus obtaining a pseudolithology map. The latter outlined an area with increased mineral potential where manganese mineralization, i.e., psilomelane, had been located.

Download Full-text

Geochemical metallogenic potential based on cluster analysis: A new method to extract valuable information for mineral exploration from geochemical data

Applied Geochemistry ◽

10.1016/j.apgeochem.2020.104748 ◽

2020 ◽

Vol 122 ◽

pp. 104748 ◽

Cited By ~ 1

Author(s):

Shu-guang Zhou ◽

Ke-fa Zhou ◽

Jin-lin Wang

Keyword(s):

Cluster Analysis ◽

Mineral Exploration ◽

New Method ◽

Geochemical Data

Download Full-text

A Method to Identify Geochemical Mineralization on Linear Transects

Austrian Journal of Statistics ◽

10.17713/ajs.v49i4.1133 ◽

2020 ◽

Vol 49 (4) ◽

pp. 89-98 ◽

Cited By ~ 1

Author(s):

Dominika Mikšová ◽

Christopher Rieser ◽

Peter Filzmoser ◽

Simon Mose Thaarup ◽

Jeremie Melleton

Keyword(s):

Spatial Patterns ◽

Mineral Exploration ◽

Generalized Additive Models ◽

Complex Problem ◽

Additive Models ◽

Geochemical Data ◽

Data Sets ◽

Curvature Measure ◽

Concentration Data ◽

Sampling Transect

Mineral exploration in biogeochemistry is related to the detection of anomalies in soil, which is driven by many factors and thus a complex problem. Mikšová, Rieser, and Filzmoser (2019b) have introduced a method for the identification of spatial patterns with increased element concentrations in samples along a linear sampling transect. This procedure is based on fitting Generalized Additive Models (GAMs) to the concentration data, and computing a curvature measure from the pairwise log-ratios of these fits. The higher the curvature, the more likely one or both elements of the pair indicate local mineralization. This method is applied on two geochemical data sets which have been collected specifically for the purpose of mineral exploration. The aim is to test the technique for its ability to identify pathfinder elements to detect mineralized zones, and to verify whether the method can indicate which sampling material is best suited for this purpose.

Download Full-text

Integration of Satellite Imagery, Airborne Radiometric and Regional Geochemical Data Sets for Mineral Exploration

Near Surface 2010 - 16th EAGE European Meeting of Environmental and Engineering Geophysics ◽

10.3997/2214-4609.20144828 ◽

2010 ◽

Cited By ~ 1

Author(s):

D.G. Eberle ◽

H. Paasche

Keyword(s):

Satellite Imagery ◽

Mineral Exploration ◽

Geochemical Data ◽

Data Sets

Download Full-text

Automated integration of partially colocated models: Subsurface zonation using a modified fuzzy c -means cluster analysis algorithm

Geophysics ◽

10.1190/1.3374411 ◽

2010 ◽

Vol 75 (3) ◽

pp. P11-P22 ◽

Cited By ~ 25

Author(s):

Hendrik Paasche ◽

Jens Tronicke ◽

Peter Dietrich

Keyword(s):

Cluster Analysis ◽

Cluster Algorithm ◽

Spatial Information ◽

Expert Knowledge ◽

Structural Information ◽

Geophysical Data ◽

Data Sets ◽

S Wave ◽

Cluster Algorithms ◽

Velocity Models

Partitioning cluster analyses are powerful tools for rapidly and objectively exploring and characterizing disparate geophysical databases with unknown interrelations between individual data sets or models. Despite its high potential to objectively extract the dominant structural information from suites of disparate geophysical data sets or models, cluster-analysis techniques are underused when analyzing geophysical data or models. This is due to the following limitations regarding the applicability of standard partitioning cluster algorithms to geophysical databases: The considered survey or model area must be fully covered by all data sets; cluster algorithms classify data in a multidimensional parameter space while ignoring spatial information present in the databases and are therefore sensitive to high-frequency spatial noise (outliers); and standard cluster algorithms such asfuzzy [Formula: see text]-means (FCM) or crisp [Formula: see text]-means classify data in an unsupervised manner, potentially ignoring expert knowledge additionally available to the experienced human interpreter. We address all of these issues by considering recent modifications to the standard FCM cluster algorithm to tolerate incomplete databases, i.e., survey or model areas not covered by all available data sets, and to consider spatial information present in the database. We have evaluated the regularized missing-value FCM cluster algorithm in a synthetic study and applied it to a database comprising partially colocated crosshole tomographic P- and S-wave-velocity models. Additionally, we were able to demonstrate how further expert knowledge can be incorporated in the cluster analysis to obtain a multiparameter geophysical model to objectively outline the dominant subsurface units, explaining all available geoscientific information.

Download Full-text

The detection of potassic alteration by gamma‐ray spectrometry—Recognition of alteration related to mineralization

Geophysics ◽

10.1190/1.1444884 ◽

2000 ◽

Vol 65 (6) ◽

pp. 2001-2011 ◽

Cited By ~ 30

Author(s):

Robert B. K. Shives ◽

B. W. Charbonneau ◽

K. L. Ford

Keyword(s):

British Columbia ◽

Case History ◽

Gamma Ray ◽

Mineral Exploration ◽

Yukon Territory ◽

Geochemical Data ◽

Gamma Ray Spectrometry ◽

Case Histories ◽

Massive Sulfides ◽

Porphyry Cu

Canadian case histories document the use of airborne and ground gamma‐ray spectrometry to detect and map potassium alteration associated with different styles of mineralization. These include: volcanic‐hosted massive sulfides (Cu‐Pb‐Zn), Pilley’s Island, Newfoundland; polymetallic, magmatic‐hydrothermal deposits (Au‐Co‐Cu‐Bi‐W‐As), Lou Lake, Northwest Territories; and porphyry Cu‐Au‐(Mo) deposits at Mt. Milligan, British Columbia and Casino, Yukon Territory. Mineralization in two of these areas was discovered using airborne gamma‐ray spectrometry. In each case history, alteration produces potassium anomalies that can be distinguished from normal lithologic potassium variations by characteristic lows in eTh/K ratios. Interpretations incorporating airborne and ground spectrometry, surficial and bedrock geochemistry and petrology show that gamma‐ray spectrometric patterns provide powerful guides to mineralization. This information complements magnetic, electromagnetic, geological, and conventional geochemical data commonly gathered during mineral exploration programs.

Download Full-text

Identification of thermally mature TOC-rich layers in shale formations using an effective machine learning approach

Interpretation ◽

10.1190/int-2020-0184.1 ◽

2021 ◽

pp. 1-50

Author(s):

Adewale Amosu ◽

Yuefeng Sun

Keyword(s):

Test Data ◽

Gamma Ray ◽

Geochemical Data ◽

Support Vector ◽

Data Sets ◽

Data Set ◽

Shale Formations ◽

Shale Formation ◽

Using Data ◽

Unconventional Resource

We develop a support vector machine (SVM) method that relies on core-measured data as well as gamma ray, deep resistivity, sonic and density wireline well log data in identifying thermally mature TOC-rich layers at depth intervals with missing geochemical data in unconventional resource plays. We first test the SVM method using the Duvernay shale formation data. The SVM method successfully classifies the TOC data set into TOC-rich, TOC-poor classes and the Tmax data set into thermally mature and thermally immature classes, when optimal features are selected. To further test the SVM approach, we generate depth-separated training and test data sets from a well in the Duvernay shale formation and successfully use the approach to identify thermally mature TOC-rich intervals. We also demonstrate the successful cross-basin application of the SVM approach in predicting TOC using data from the Barnett and Duvernay shale formations as the training and test data sets respectively.

Download Full-text

Cluster analysis for large data sets: applications to individual aerosol particles from the mid-pacific

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100132078 ◽

1992 ◽

Vol 50 (2) ◽

pp. 1488-1489

Author(s):

Thomas W. Shattuck ◽

James R. Anderson ◽

Neil W. Tindale ◽

Peter R. Buseck

Keyword(s):

Cluster Analysis ◽

Chemical Reactivity ◽

Large Data ◽

Large Data Sets ◽

Particle Analysis ◽

Data Sets ◽

Halogen Chemistry ◽

Complete Study ◽

Components Analysis ◽

Automated Scanning

Individual particle analysis involves the study of tens of thousands of particles using automated scanning electron microscopy and elemental analysis by energy-dispersive, x-ray emission spectroscopy (EDS). EDS produces large data sets that must be analyzed using multi-variate statistical techniques. A complete study uses cluster analysis, discriminant analysis, and factor or principal components analysis (PCA). The three techniques are used in the study of particles sampled during the FeLine cruise to the mid-Pacific ocean in the summer of 1990. The mid-Pacific aerosol provides information on long range particle transport, iron deposition, sea salt ageing, and halogen chemistry.Aerosol particle data sets suffer from a number of difficulties for pattern recognition using cluster analysis. There is a great disparity in the number of observations per cluster and the range of the variables in each cluster. The variables are not normally distributed, they are subject to considerable experimental error, and many values are zero, because of finite detection limits. Many of the clusters show considerable overlap, because of natural variability, agglomeration, and chemical reactivity.

Download Full-text

EROICA: Exploring regions of interest with cluster analysis in large functional magnetic resonance imaging data sets

Concepts in Magnetic Resonance ◽

10.1002/cmr.a.10053 ◽

2003 ◽

Vol 16A (1) ◽

pp. 50-62 ◽

Cited By ~ 9

Author(s):

M. Jarmasz ◽

R.L. Somorjai

Keyword(s):

Magnetic Resonance Imaging ◽

Cluster Analysis ◽

Magnetic Resonance ◽

Functional Magnetic Resonance Imaging ◽

Regions Of Interest ◽

Data Sets ◽

Imaging Data ◽

Functional Magnetic Resonance ◽

Resonance Imaging ◽

Magnetic Resonance Imaging Data

Download Full-text

Mineral Exploration using CSAMT data: Application to Longmen region metallogenic belt, Guangdong Province, China

Geophysics ◽

10.1190/geo2012-0115.1 ◽

2013 ◽

Vol 78 (3) ◽

pp. B111-B119 ◽

Cited By ~ 28

Author(s):

Xiangyun Hu ◽

Ronghua Peng ◽

Guiju Wu ◽

Weiping Wang ◽

Guangpu Huo ◽

...

Keyword(s):

Large Scale ◽

Southern China ◽

Mineral Exploration ◽

Geochemical Data ◽

Data Application ◽

Metallogenic Belt ◽

Fe Content ◽

Geophysical Surveys ◽

Occam’S Inversion ◽

Integrated Interpretation

A controlled-source audio-frequency magnetotelluric (CSAMT) survey has been carried out to investigate potential iron (Fe) and polymetallic (Pb-Zn-Cu) deposits in Longmen region, which is one of the main metallogenic belts in southern China. Conducting geophysical surveys in this area is quite difficult due to mountainous terrain, dense forest, and thick vegetation cover. A total of 560 CSAMT soundings were recorded along twelve surveying lines. Two-dimensional Occam’s inversion scheme was used to interpret these CSAMT data. The resulting electric resistivity models showed that three large-scale highly conductive bodies exist within the surveying area. By integrated interpretation combined with available geologic, geophysical, and geochemical data in this area, three prospective mineral deposits were demarcated. Based on the CSAMT results, a borehole penetrating approximately 250-m depth was drilled at the location of 470 m to the northwest end of line 06, defined with a massive pyrite from the depth of 52–235 m with 7%–16% Fe content, as well as locally high-grade Pb-Zn- and Ag-Ti-bearing ores.

Download Full-text