Characteristic analysis?1981: Final program and a possible discovery

McCammon, Richard B.; Botbol, J.M.; Sinding-Larsen, Richard; Bowen, Roger W.

doi:10.1007/bf01030076

Cited by 48 publications

(15 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This approach (Cassard et al, 2012;Tourlière et al, 2012) is not illustrated here. In simple cases, it may allow dispensing with classification, becoming comparable to the "characteristic analysis" approach (McCammon, 1983).…”

Section: Resultsmentioning

confidence: 99%

“…A technique consisting in applying a grid with included attributes over a study area was previously described by McCammon et al (1983) for generating predictive maps, but rather than using their Characteristic Analysis method of data processing of a logical type, CBA uses statistical ranking of coded cells for identifying the different associations/environments contained in the map.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cell Based Associations: A procedure for considering scarce and mixed mineral occurrences in predictive mapping

Tourlière

Pakyuz-Charrier

Cassard

et al. 2015

Computers & Geosciences

View full text Add to dashboard Cite

International audienceCell Based Association is an innovative mineral favorability procedure designed to answer special needs of the mining industry in data wise critical situations where usual favorability methods may not yield satisfactory results. Those situations relate to input data quality (e.g. clustered points, mixed and scarce data, approximate location) or some assumptions that are considered unreasonable (e.g. map areas relevance, conditional independence).The principle of CBA consists in replacing polygons of geological units with a square cell grid (hence the ‘cell-based’). Each cell contains a range of units (‘association’) that are binary coded in terms of their presence (1) or absence (0) within study area. The loss of resolution inherent to this procedure is compensated by the enriched information contained in each cell owing to the notion of (lithological) association.Lithological associations are considered as binary spectra and as such are classified using Ascendant Hierarchical Clustering (AHC) thus obtaining a synthetic map of lithological associations. The prospectivity map shows as favourable the cells of the same AHC classes that the ones including mineral occurrences.It was observed that CBA can distinguish between different ore deposit varieties from a blended mineral occurrences data set. CBA can theoretically include any spatialized data (e.g. geophysics, structural data) as an extra variable to specify classification and narrow favourable areas. Doing so would make it an independent favorability mapping procedure and is still under development.Cell size in a grid is a critical parameter of the procedure; it must be compatible with the looked-for phenomena and should have a sufficient lithological variability.In addition to its use for producing favorability maps, a CBA-derived map could help in understanding the background information contained in geological maps. CBA can also be applied to other fields, such as agriculture and urban planning

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cell Based Associations: A procedure for considering scarce and mixed mineral occurrences in predictive mapping

Tourlière

Pakyuz-Charrier

Cassard

et al. 2015

Computers & Geosciences

View full text Add to dashboard Cite

show abstract

“…Subjective methods have been generally formulated as follows: (i) formulate genetic models, (ii) relate geological observations to genetic processes, and (iii) estimate subjective probabilities of mineral occurrence. Objective (mathematical) methods attempt to maximally use various existing mineral occurrence data and quantified geological variables (Botbol et al 1978;Chung and Agterberg 1980;Agterberg 1988;McCammon et al 1983;Singer and Kouda 1988). An objective approach generally consists of three major steps: (i) quantification of geological variables, (ii) estimation of mathematical models, and (iii) extrapolation of the estimated models to identify target areas.…”

Section: Target Delineationmentioning

confidence: 99%

General Framework of Quantitative Target Selections

Pan¹

2018

Handbook of Mathematical Geosciences

View full text Add to dashboard Cite

Mineral target selection has been an important research subject for geoscientists around the world in the past three decades. Significant progress has been made in development of mathematical techniques and estimation methodologies for mineral mapping and resource assessment. Integration of multiple data sets, either by experts or statistical methods, has become a common practice in estimation of mineral potentials. However, real effect of these methodologies is at best very limited in terms of uses for government macro policy making, resource management, and mineral exploration in commercial sectors. Several major problems in data integration remain to be solved in order to achieve significant improvement in the effect of resource estimation. Geoscience map patterns are used for decision-making for mineral target selections. The optimal data integration methods proposed so far can be effectively applied by using GIS technologies. The output of these methods is a prognostic map that indicates where hidden ore bodies may occur. Issues related to randomness of mineral endowment, intrinsic statistical relations, exceptionalness of ore, intrinsic geological units, and economic translation and truncation, are addressed in this chapter. Moreover, a number of specific important technical issues in information synthesis are also identified, including information enhancement, spatial continuity, data integration and target delineation. Finally, a new concept of dynamic control areas is proposed for future development of quantification of mineral resources. G. Pan ( ✉ )China Hanking Holdings,

show abstract

“…Ponderação Multivariada A ponderação multivariada consiste em atribuir pesos para cada critério diagnóstico a partir da análise de componentes principais (ACP), consistindo numa técnica de classificação de objetos (Harff & Davis 1990). Dentre as variantes deste método estatístico para a exploração, podem ser destacados os trabalhos de McCammon et al (1983), que enfoca a análise característica, e Pan & Porterfield (1995), onde a definição de alvos baseia-se na comparação entre as variáveis existentes em células-padrão (depósitos conhecidos) e as variáveis distribuídas regionalmente (área total avaliada).…”

Section: Quantificação De Favorabilidade a Etapaunclassified

Alguns Aspectos Da Avaliação De Favorabilidade Em Geologia Exploratória

Rostirolla¹

1997

RBG

View full text Add to dashboard Cite

SOME ASPECTS OF FAVORABILITY EVALUATION IN EXPLORATION GEOLOGY This paper deals with a method of modeling and estimating the favorability in petroleum and mineral exploration. It discusses natural systems analyses, deposit modeling and geomathematical techniques. The basic methodology consists in weighting the diagnostic variables of mineralization and accumulation processes, in order to characterize the necessity and sufficiency of these variables to deposit prediction. The available geological, geochemical and geophysical data are spatially integrated in the selected area, in order to establish the importance of ali variables. Exploration data are integrated in a raster form, using simple geocoded information procedures, according two methods of statistical analysis, a conditional probability approach and a multivariate (principal components) analysis. The modeling and complementary procedures are demonstrated in the Recôncavo Basin, Northeast Brazil, for petroleum, and in the Itaiacoca Formation of Ribeira Belt, Southeast Brazil, for Pb-Zn deposits.RESUMO Neste trabalho é discutida uma metodologia para avaliação de favorabilidade em exploração mineral e de petróleo. A estratégia consiste em ponderar as variáveis diagnosticas dos processos mineralizadores ou acumuladores, procurando quantificar seus graus de necessidade e suficiência com base em técnicas geomatemáticas e de modelagem de depósitos. Os dados exploratórios são combinados espacialmente na área selecionada, com o objetivo de estabelecer a importância das variáveis. As informações geológicas, geoquímicas e geofísicas são representadas em forma matricial, sendo integradas através de sistemas de informações geográficas. Estes mapas são combinados, com os ponderadores sendo computados de dois modos: análise probabilística condicional e análise multivariada (componentes principais). As duas técnicas de ponderação e as demais atividades foram demonstradas na Bacia do Recôncavo, nordeste do Brasil, para petróleo e na Formação Itaiacoca do Cinturão Ribeira, sul do Brasil, para depósitos de Pb-Zn tipo Mississipy Valley.Palavras-chave: exploração mineral, petróleo, geomatemática, probabilidade condicional, modelos de depósitos.

show abstract

Characteristic analysis?1981: Final program and a possible discovery

Cited by 48 publications

References 2 publications

Cell Based Associations: A procedure for considering scarce and mixed mineral occurrences in predictive mapping

Cell Based Associations: A procedure for considering scarce and mixed mineral occurrences in predictive mapping

General Framework of Quantitative Target Selections

Alguns Aspectos Da Avaliação De Favorabilidade Em Geologia Exploratória

Contact Info

Product

Resources

About