ORCID Profile
0000-0002-4704-6036
Current Organisations
CSIRO
,
University of Sydney
,
University of New South Wales
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Publisher: Springer Science and Business Media LLC
Date: 29-05-2014
Publisher: Elsevier BV
Date: 10-2023
Publisher: MDPI AG
Date: 29-05-2022
DOI: 10.3390/MIN12060689
Abstract: Prediction of geochemical concentration values is essential in mineral exploration as it plays a principal role in the economic section. In this paper, four regression machine learning (ML) algorithms, such as K neighbor regressor (KNN), support vector regressor (SVR), gradient boosting regressor (GBR), and random forest regressor (RFR), have been trained to build our proposed hybrid ML (HML) model. Three metric measurements, including the correlation coefficient, mean absolute error (MAE), and means squared error (MSE), have been selected for model prediction performance. The final prediction of Pb and Zn grades is achieved using the HML model as they outperformed other algorithms by inheriting the advantages of in idual regression models. Although the introduced regression algorithms can solve problems as single, non-complex, and robust regression models, the hybrid techniques can be used for the ore grade estimation with better performance. The required data are gathered from in situ soil. The objective of the recent study is to use the ML model’s prediction to classify Pb and Zn anomalies by concentration-area fractal modeling in the study area. Based on this fractal model results, there are five geochemical populations for both cases. These elements’ main anomalous regions were correlated with mining activities and core drilling data. The results indicate that our method is promising for predicting the ore elemental distribution.
Publisher: Elsevier BV
Date: 03-2015
Publisher: Springer International Publishing
Date: 2023
Publisher: Springer Science and Business Media LLC
Date: 07-03-2014
Publisher: Copernicus GmbH
Date: 03-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-1429
Abstract: & & A significant issue in all geochemical anomaly classification methods is uncertainty in the identification of different populations and allocation of s les to those populations, including the critical category of geochemical anomalies or patterns that are associated with the effects of mineralisation. This is a major challenge where the effects of mineralisation are subtle. There are various possible sources of such uncertainty, such as (i) gaps in coverage of geochemical s ling within a study area (ii) errors in geochemical data analysis, spatial measurement, interpolation (iii) misunderstanding of geological and geochemical processes (iv) fuzziness or vagueness of the threshold between geochemical background and geochemical anomalies. In this research, the well-established concentration-area (C-A) and the newly established concentration-concentration (C-C) fractal models were applied to centered-logratio (clr) transformed data, and highly correlated elements of Cu-Te, respectively. Such models were applied to the available till s les (2578 s les) collected by the Geological Survey of Sweden (SGU) from 75% of the country area, to generate the Cu volcanic massive sulfide (VMS) geochemical anomaly classified map and define the highly promising areas for further exploration. However, to be confident more about the robustness of each class recognised by the thresholds obtained from the C-A and C-C log-log plots, Monte Carlo simulation (MCSIM) was applied to each class to simulate a higher number of values per class and consider the relevant error propagation. Under the MCSIM approach, the P50 value (the average 50& sup& th& /sup& percentile of the multiple simulated distributions represents a neutral probability in decision-making) is defined as the expected & #8216 return& #8217 . The uncertainty is calculated, in this approach, as 1/(P90-P10) for which P10 (lower decile) and P90 (upper decile) are the average 10& sup& th& /sup& and 90& sup& th& /sup& percentiles of the multiple simulated values, associated with each class. The most reliable classes are those with high returns and low risks. Based on the results obtained, C-A could not provide robust enough results since in the defined classes, the risk was almost equal or even higher than the return, however, the C-C model provided high returns and very low uncertainties, demonstrating the robustness of C-C compared to C-A. This approach can improve the quality of the decision-making in choosing the most robust classification models, and consequently getting more reliable results.& &
Publisher: Springer Science and Business Media LLC
Date: 29-08-2013
Publisher: Springer Science and Business Media LLC
Date: 08-2023
Publisher: Elsevier BV
Date: 2021
Publisher: Bulletin of the Mineral Research and Exploration
Date: 06-2016
DOI: 10.19111/BMRE.89526
Publisher: Polish Academy of Sciences Chancellery
Date: 03-2013
Abstract: Determination of the vertical distribution of geochemical elemental concentrations is of fundamental importance in mineral exploration. In this paper, eight mineralized boreholes from the Nowchun Cu-Mo porphyry deposit, SE Iran, were used to identify of the vertical distribution directional properties of Cu and Mo values using number-size (N-S) fractal model. The vertical distributions of Cu and Mo in the mineralized boreholes show a positively skewed distribution in the former and a multimodal distribution in the latter types. Elemental threshold values for the mineralized boreholes were computed by fractal model and compared with the statistical methods based on the data obtained from chemical analysis of s les. Elemental distributions are not normal in these boreholes and their median equal to Cu and Mo thresholds. The results of N-S fractal analysis reveal that Cu and Mo values in mineralized boreholes are multifractals in nature. There are at least three geochemical populations for Cu and Mo in the boreholes and Cu and Mo thresholds have ranges between 0.07%-0.3% and 50-200 ppm, respectively. The results obtained by N-S fractal model were compared with geological observations in the boreholes. Major Cu and Mo enrichment correlated by monzonitic rocks and high amounts of observed Cu and Mo ores (Chalcopyrite and molybdenite) in the boreholes.
Publisher: Springer Science and Business Media LLC
Date: 22-10-2018
Publisher: Elsevier BV
Date: 11-2012
Publisher: Copernicus GmbH
Date: 25-03-2022
DOI: 10.5194/EGUSPHERE-EGU22-14
Abstract: & & A common problem in geochemical exploration projects is the limited number of collected s les due to budgetary, time, and other constraints. Therefore, to study spatial mineralisation patterns using available s les in both s led and uns led areas, the interpolation of the available data is essential to assign estimates to uns led areas. Because interpolation estimates are based on the data available only within the search window, in continuous field geochemical modelling such interpolations using any single method are often the main source of uncertainty. Error propagation analysis needs to be considered to evaluate interpolation errors& #8217 effects in geochemical anomaly detection. One method for analysing the propagation of errors in models and evaluating their stability is Monte Carlo Simulation (MCSIM). In this method, the P50 (median) value (called & #8216 return& #8217 ) and the uncertainty value (called & #8216 risk& #8217 ) are calculated. Here the uncertainty is calculated as 1/(P90-P10) for which P10 (lower decile) and P90 (upper decile) are the average 10th and 90th percentiles of the multiple simulated values, correspond to each element. We have applied this method to Swedish till data, collected throughout the country by the Geological Survey of Sweden. The main concern is whether to evaluate if the s les are sufficient and representative of the target elements concentrations for geochemical studies. To address this concern, the s ling uncertainty in a statistical sense (not geochemical) per element was studied using the return-risk matrix. This matrix was applied to volcanogenic massive sulfide (VMS) target elements, then subsequently to the s les per bedrock. Therefore, a large number of simulated values (e.g., 5,000, which is higher than the number of the s les, i.e., 2,578) was generated using MCSIM. Where the quantified return is low or negative, and the quantified uncertainty is high, particularly higher than its relevant return, additional s ling is required to achieve the minimum required spatial continuity in the data or the stability of the later applied classification models. This affects the certainty of the models generated in the study area. In the Sweden data, all the elements assessed have relatively high returns and low uncertainty, demonstrating the stability of the parameters. The process was subsequently applied to s les separated into the main lithological categories or geological domains to determine if the stability in the patterns is affected by rock type (and associate natural variability in the background). In Swedish till s les, the statistical s ling quality is acceptable in the bedrocks of Exotic Terranes, Archean, Baltoscandian, and Idefjorden. However, it is not acceptable in the Palaeoproterozoic units and the Eastern Segment, due to the risks being higher than the returns, which may increase the error propagation effect on the interpolated map and efficiency of the classes obtained by different classification models.& &
Publisher: Elsevier BV
Date: 05-2021
Publisher: Elsevier BV
Date: 10-2017
Publisher: Copernicus GmbH
Date: 15-05-2023
DOI: 10.5194/EGUSPHERE-EGU23-1585
Abstract: A key task in the analysis of exploration geochemical data is the selection and application of efficient classification models to discriminate mineralization-related signals from other processes affecting variation in element concentrations. Similarly in environmental or urban geochemistry one objective is to separate geochemical patterns associated with anthropogenic contamination from geogenic processes. To classify geochemical maps into background or anomalous s les or regions, a variety of mathematical and statistical models have been developed. In this study various fractal modelling has been applied to centered logratio transformed Cu, Ba, Mn, Pb, Zn, In, As, Au, and Ag contents of soil s les from the Geochemical Atlas of Cyprus. Areas with contamination have previously been shown not to display normal fractal behavior for values exceeding lithology-dependent background populations. Therefore, two new fractal methods & #8211 concentration-concentration (C-C) and concentration-distance from centroid-points (C-DC) & #8211 were applied to discriminate anthropogenic from geogenic anomalies. One of the strongest indicators of proximity to major Cu mineralization is In. The C-C model displays broad similarity between the Cu-In pairing and the raw Cu, and between its reverse in the In-Cu pairing and raw In. Of the five populations that emerge from the Cu-In fractal model, the first two (regional background and weakly anomalous) are largely restricted to the Circum-Troodos Sedimentary Succession units. The moderately anomalous population extends across all the basalts and north from the Troodos Ophiolite (TO) across the fanglomerates and more recent alluvium-colluvium that contains material shedding north off the TO. It is noted that the strongest anomalies are at the boundary between the sheeted dyke complex and basalts and on one of the major NE-trending structures that cut across the TO, but where there are only a small number of minor Cu mineralization occurrences. In the C-DC model, the centroids used to model the spatial variation of the soil geochemistry were the known mineral deposits. The Cu C-DC model delivers just two populations that are lithologically-controlled. The first spans the ultramafic TO core and the Pakhna Formation carbonates (the two extremes in the raw data geochemical compositions), and all other units, including the TO mafics, Mamonia Terrain, and the fanglomerates and alluvium-colluvium areas in the second population. The In C-DC model is somewhat similar to the In-Cu C-C model, but the second major population is more restricted to the sections of the basalts containing known Cu mineralization as well as a restricted zone in the sheeted dykes in western TO. Applying the C-DC model to the transformed scores, there are three main populations evident. The highest one contains all the known Cu mines and mineral deposits, as well as a number of NE-trending zones that cut across the sheeted dykes on the western and the eastern sides of the TO, and which also appear to follow the major sinistral faults that transect the TO.
Publisher: MDPI AG
Date: 06-03-2023
DOI: 10.3390/MIN13030370
Abstract: Mineral resource classification is an important step in mineral exploration and mining engineering. In this study, copper and molybdenum resources were classified using a combination of the Turning Bands Simulation (TBSIM) and the Concentration–Volume (C–V) fractal model based on the Conditional Coefficient of Variation (CCV) for Cu realizations in the Masjed Daghi porphyry deposit, NW Iran. In this research, 100 scenarios for the local variability of copper were correspondingly simulated using the TBSIM and the CCVs were calculated for each realization. Furthermore, various populations for these CCVs were distinguished using C–V fractal modeling. The C–V log–log plots indicate a multifractal nature that shows a ring structure for the “Measured”, “Indicated”, and “Inferred” classes in this deposit. Then, the results obtained using this hybrid method were compared with the CCV–Tonnage graphs. Finally, the results obtained using the geostatistical and fractal simulation showed that the marginal parts of this deposit constitute inferred resources and need more information from exploration boreholes.
Publisher: Springer Science and Business Media LLC
Date: 24-12-2014
Publisher: Elsevier BV
Date: 03-2015
Publisher: Elsevier BV
Date: 02-2015
Publisher: SPIE-Intl Soc Optical Eng
Date: 26-08-2016
Publisher: Elsevier BV
Date: 09-2013
Publisher: Elsevier BV
Date: 2015
Publisher: Elsevier BV
Date: 10-2021
Publisher: Elsevier BV
Date: 12-2021
Publisher: Springer Science and Business Media LLC
Date: 29-10-2014
Publisher: Elsevier BV
Date: 2021
Location: Australia
No related grants have been discovered for Behnam Sadeghi.