Sample Catchment Basin Research Articles

Enhancing Geological Understanding and Identifying Gold Anomalies in the Ailaoshan Orogen

AbstractThe Ailaoshan Orogen in the southeastern Tibet Plateau, situated between the Yangtze and Simao blocks, underwent a complex structural, magmatic, and metamorphic evolution resulting in different tectonic subzones with varying structural lineaments and elemental concentrations. These elements can conceal or reduce anomalies due to the mutual effect between different anomaly areas. Dividing the whole zone into subzones based on tectonic settings, ore cluster areas, or sample catchment basins (Scb), geochemical and structural anomalies associated with gold (Au) mineralization have been identified utilizing mean plus twice standard deviations (Mean + 2STD), factor analysis (FA), concentration‐area (C‐A) modeling of stream sediment geochemical data, and lineament density in both the Ailaoshan Orogen and the individual subzones. The FA in the divided 98 Scbs with 6 Scbs containing Au deposits can roughly ascertain unknown rock types, identify specific element associations of known rocks and discern the porphyry or skarn‐type Au mineralization. Compared with methods of Mean + 2STD and C‐A model of data in the whole orogen, which mistake the anomalies as background or act the background as anomalies, the combined methods of FA and C‐A in the separate subzones or Scbs works well in regional metallogenic potential analysis. Mapping of lineament densities with a 10‐km circle diameter is not suitable to locate Au deposits because of the delineated large areas of medium‐high lineament density. In contrast, the use of circle diameters of 1.3 km or 1.7 km in the ore cluster scale delineates areas with a higher concentration of lineament density, consistent with the locations of known Au deposits. By analyzing the map of faults and Au anomalies, two potential prospecting targets, Scbs 1 and 63 with a sandstone as a potential host rock for Au, have been identified in the Ailaoshan Orogen. The use of combined methods in the divided subzones proved to be more effective in improving geological understanding and identifying mineralization anomalies associated with Au, rather than analyzing the entire large area.

A comprehensive evaluation of OPTICS, GMM and K-means clustering methodologies for geochemical anomaly detection connected with sample catchment basins

The process of data-driven clustering to uncover geochemical anomalies linked to sample catchment basins (SCBs) includes a comprehensive framework to discern areas exhibiting unique geochemical attributes within a specified study area. The Ordering Points to Identify the Clustering Structure (OPTICS) method can serve as a robust methodology for detecting geochemical anomalies in SCBs. This is attributed to its capacity to effectively manage varying cluster densities, adaptively identify cluster numbers, exhibit resilience to noise, and display minimum sensitivity to parameters. A comparison was conducted in this research between the outcomes of the OPTICS clustering algorithm and two traditional clustering techniques, namely the Gaussian Mixture Model (GMM) and K-means clustering. In the following, the Expectation-Maximization (EM) technique is employed to train the GMM for clustering. Moreover, the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) as two validate statistical metrics implemented to ascertain the optimal number of components (clusters) belong to the GMM. It should be noted that the effectiveness of the clustering algorithms was further assessed using the Calinski-Harabasz (CH) index and the success-rate curves. OPTICS, a density-based clustering approach, was confirmed to be more effective than K-means and GMM for identifying MVT PbZn anomalies in Varcheh district, western Iran. Furthermore, the specified anomalies show a geo-spatial correspondence with the geological facts, and it has been observed that strong anomalies are more discoverable in close proximity to MVT PbZn occurrences. This work suggests a novel anomaly detection approach based on OPTICS, which exhibits superior performance and data-modeling efficiency. The main emphasis is on effectively distinguishing geochemical anomalies from sample data originating from populations with uncertain distributions.

GWOKM: A novel hybrid optimization algorithm for geochemical anomaly detection based on Grey wolf optimizer and K-means clustering

Identifying the geochemical signatures of valuable mineral deposits using regional geochemical data from stream sediments is a challenging task due to the intricate characteristics of geological formations. Our team is currently investigating the potential of unsupervised clustering analysis (CA) and hybridization with the grey wolf optimizer (GWO) in developing multi-element geochemical models using stream sediment data. To cluster the geochemical data and uncover any unusual patterns, we opted to use the K-means (KM) algorithm due to its straightforward implementation, fast computation speed, and capacity to handle the large datasets. Despite its benefits, the KM method also has notable limitations, such as the random selection of cluster centroids. This can result in higher systematic uncertainty in unsupervised geochemical modeling and longer computation times. To mitigate this concern, we have introduced a new hybrid approach, grey wolf optimizer with K-means so-called the GWOKM algorithm to enhance the delineation of multi-elemental patterns in stream sediment geochemical data. This method incorporates the grey wolf optimization algorithm with KM to optimize the identification of both anomalies and backgrounds using factor analysis and sample catchment basin modeling techniques. This approach was utilized to detect anomalous multi-elemental geochemical patterns indicative of porphyry and skarn copper deposits in the Baft area, Kerman belt, Iran. Upon comparison of the geochemical models derived from KM and GWOKM clustering methods, the latter outperformed the former, as evidenced by its higher prediction rate. The outcomes affirm the efficacy of unsupervised KM clustering analysis (CA) as a means of breaking down geochemical anomaly-background populations. Moreover, the integration of clustering methods with optimization algorithms has proven to be successful for enhancing the credibility of mineralized areas, which could be advantageous in future exploration phases. Based on the results, the GWOKM approach generates more reliable and efficient geochemical anomaly targets.

Is the pathway length of sediments relevant to assess the background value in stream sediment geochemical exploration?

The sample catchment basin approach (SCBA) is one of the methods mainly used to assess the expected chemical composition of a stream sediment sample considering the geolithological variability of the catchment basin upstream of its collection point. In the original SCBA, the catchment surface considered for the purpose is limited to the area between two subsequent collected samples; in the true sample catchment basin approach (TSCBA), the catchment area of each collected sample is limited upstream only by the actual watershed. In both approaches, the dilution is generally assessed in relation to the extension of the single geolithological units falling within the sample catchment area used.However, it makes sense to think that when multiple geolithological units are outcropping upstream of a sampling location, the sediments originating from the ones closer to the collection point (which have traveled a shorter distance) could have a major influence on the geochemical composition of the samples. It is plausible that the degree of dilution occurring during the transport from the source to a given point could also be a function of the distance traveled by the sediments along the fluvial pattern. This also holds in the case of mineralization occurring within the catchment: the farther a mineralization is from a sampling point, the more its geochemical signal should be diluted at the sampling site.In the present study, using the TSCBA, a modification of the existing algorithm is proposed to include both the extension of the geological units and their position relative to the catchment outlet (represented by the sampling point).The territory around Feshark village (Isfahan, central Iran), where stream sediment and rock samples have been collected and analyzed, has been used as the study area. The geochemical background values (corresponding to the expected geochemical values following dilution) for each stream sediment sample were determined using the modified algorithm. The results were compared with those obtained by applying the original equation to the same dataset. The results demonstrated that the new equation could more accurately estimate background (expected) concentrations at the basin outlet, showing a better performance of the new method compared to the original one.

Incorporating the genetic and firefly optimization algorithms into K-means clustering method for detection of porphyry and skarn Cu-related geochemical footprints in Baft district, Kerman, Iran

For the purpose of multi-element geochemical modeling of stream sediment data, we investigate the use of unsupervised clustering analysis (CA). For the various reasons including straightforward implementation, fast computation, and scaling to big dataset, the K-means (KM) algorithm was implemented to cluster the geochemical data to reveal anomalous populations linked to porphyry and skarn Cu deposits in Baft district, NE Iran. However, one of the biggest disadvantages of KM method is the randomly selection of cluster centroids which may increase the systemic uncertainty in unsupervised geochemical modeling and also run time. To address this, genetic (GA) and firefly (FA) metaheuristic optimization algorithms were incorporated into the KM method (namely GKM and FKM models) for determination of cluster centroids, and then, optimal definition of geospatial patterns of anomaly-background classes using clr-transformed values of stream sediment geochemical data. To do so, sample catchment basins (SCBs) of anomalous geochemical dataset were used to spatially map the anomaly populations derived from KM, GKM and FKM methods. We have used the advantages of quantitative evaluation measures namely normalized density index (NDI) and success-rate curves to assess the effectiveness of multi-element geochemical anomaly in determining exploratory targets. The results not only affirms that CA is a useful method to decompose the geochemical anomaly-background populations, but also hybridization of clustering methods via optimization algorithms can efficiently increase the certainty of mineralized zones to be used in further exploration stages.

Multivariate analysis of dilution-corrected residuals to improve the interpretation of geochemical anomalies and determine their potential sources: The Mingardo River case study (Southern Italy)

Stream sediment geochemical prospecting is historically acknowledged as a reliable tool to investigate the influence of geology and to assess the presence of natural and/or anthropogenic anomalies within river catchment basins at different scales. Several methods have been developed during decades to seek for geochemical anomalies especially those considered with a high potential for mineral resources. In the last decades, the Sample Catchment Basin (SCB) method, based on the use of dilution-corrected residuals (DCRs), has been developed, improved, and extensively used in mineral exploration, mostly at regional scale. In this paper the method was applied to a small catchment basin in southern Italy (Mingardo River) with the purposes of testing its reliability and improving the interpretation of geochemical patterns. The SCBs method was performed by using as input both unprocessed (raw) and centered log-ratio (clr) transformed data, and the obtained DCRs were classified through the concentration-area (C-A) method. After verifying the inadequacy of raw data, Principal component analysis (PCA) and Hierarchical Cluster Analysis (HCA) on PCA loadings were used to address and improve the interpretation processes in a multivariate perspective.The method has proved to be effective in identifying anomalies associated with lithological layers that are often not reported on the medium scale and which, however, have the ability to condition the geochemical patterns of the basin to which they belong. The geochemical footprint of anthropic presence (road, villages, etc.) was also detected although its impact on the study area is limited.

Optimization of geochemical anomaly detection using a novel genetic K-means clustering (GKMC) algorithm

Due to the complicated characteristics of regional geochemical data from stream sediments as a result of the complexity of geological features, detection of multi-elemental geochemical footprints of mineral deposits of interest is a challenging task. As a way to address this, a hybrid genetic algorithm-based technique, namely genetic K-means clustering (GKMC) algorithm, is proposed here for optimum delineation of multi-elemental patterns (both anomaly and background) in stream sediment geochemical data. To do so, factor analysis and sample catchment basin modeling were coupled with GKMC and traditional K-means clustering (TKMC) methods for identification of anomalous multi-elemental geochemical footprints of deposits of porphyry copper and skarn copper in the 1:100,000 scale Varzaghan map sheet, northwest Iran. Based on higher prediction rate, it can be inferred that the model derived by GKMC is superior to the one derived by TKMC. In addition, the strong anomaly classes of the GKMC and TKMC models predict, respectively, ~83% and ~66% of the porphyry/skarn Cu deposits in ~22% and ~36% of the study district. Thus, the geochemical anomaly targets derived by the GKMC method are more reliable than those generated by the TKMC method. This revealed that the GKMC algorithm is an efficient and robust tool for recognizing multi-element geochemical anomalies for mineral exploration.

The use of unmixing technique in stream sediment geochemical exploration

This research has aimed at employing unmixing method as a sediment tracing technique and its use in stream sediment geochemical exploration. Sediment tracing could be applied to determine the relative contributions of source units (Cij) to the metal contents of stream sediment samples. This is the first attempt to use the “fingerprinting” technique for estimating the background values of stream sediment samples.In catchment basin analysis of stream sediment data, the relative area sizes of lithological units (Xij) were used as the proxies for their contributions to the metal contents of stream sediment samples. The Hughes mixing model was solved using the particle swarm optimization (PSO) method to assign the contributions of 7 lithological units to 69 sample catchment basins. The results revealed some similarities between the estimated Cij values and the relative area sizes of lithological units (Xij) in all catchment basins, demonstrating the representativeness of area sizes as the proxies for the relative contributions of upstream sources to the metal content of stream sediments. The background values at the basin outlet were estimated using both Xij and Cij values. The results, therefore, revealed the superiority of the mixing model in estimating the background values at the basin outlet for most of the elements. The mixing model accurately estimated the elemental concentrations of Fe, Mo, Sb, Th, Ti, Y, and Yb; however, the unmixing approach failed to accurately predict the metal contents of elements such as Cr, Ni, Pb, Li, and Zn.

Anomaly delineation of porphyry copper deposits of Hanza Region through geochemical data analyses and multispectral remote sensing

Hanza region is located in the southern part of Urumieh–Dokhtar Metallogenic belt in southeastern Iran. This region includes six known porphyry copper deposits and it is considered as an ore- bearing region from geochemical point of view. The aim of this research is to examine effective processing techniques in the analysis of stream sediment geochemical datasets and ASTER satellite images. The processing methods have led to identification of eight new prospective areas. These methods are aimed at providing univariate geochemical maps. The stream sediment geochemical mapping for Cu and Mo was performed by the sample catchment basin approach. The results derived from this approach have been mapped in four classes associated with the first quartile, third quartile and threshold values obtained from Median Absolute Deviation method. False-colour composite and band ratio techniques were adopted as two well-known methods for the processing of an ASTER scene spanning the study area. Eight new targets for possible mineralization have been resulted from geochemical data analyses. Image processing techniques on the ASTER multispectral data have also revealed widespread hydrothermal alterations associated with the known porphyry copper deposits and the new prospects.

Mapping of single- and multi-element geochemical indicators based on catchment basin analysis: Application of fractal method and unsupervised clustering models

The study aimed to recognize significant single- and multi-elemental geochemical indicators associated with porphyry and skarn copper deposits in the Varzaghan district, NW Iran, from stream sediment geochemical data. In this regard, because of the diversity and complexity of geological processes that influence dispersion patterns of geochemical elements in different areas, sample catchment basin (SCB) modeling of stream sediments was applied. To judge the capability of each single-element geochemical indicator to predict exploration targets of mineral deposits of the type sought, prediction-area plot analysis was employed. Then, concentration-area fractal modeling was performed on the SCB models to delineate geochemical anomalies of each indicator element. Next, a Cu-Au-Mo-Bi association was considered to reflect multi-element indicator of porphyry-skarn Cu mineralization. Different populations of the SCB multi-element geochemical indicators were delineated by three unsupervised clustering methods, namely K-means, K-medoids and fuzzy c-means. Silhouette Width and Xie-Beni validation indices were calculated for selecting the optimal cluster number to optimize delineation of significant anomalies. Finally, for quantitative evaluation of the efficiency of single- and multi-element indicator anomalies in delineating exploration targets, we applied normalized density and success-rate curve based on the known Cu occurrences in the study area.

Comparison of efficiency of techniques for delineating uni-element anomalies from stream sediment geochemical landscapes

This paper compares analyses of raw geochemical data and processed stream sediment geochemical data (i.e., background-corrected and dilution-corrected via the sample catchment basin approach (SCBA)). Both sets of analyses, based on multifractal approach, aimed to identify geochemical anomalies in three study regions in Iran with different geological characteristics and mineralization styles. The results of both analyses were validated for robustness by using the concept of normalized density (ND), which simultaneously considers both mineralized locations and area of anomalous zones. The comparison of the results emphasizes the efficiency of background and dilution correction procedures, which deal with stream sediment geochemical landscapes in the search for mineral deposits. This paper also presents results of discrete (catchment-based) and continuous (interpolation-based) field modelling of stream sediment geochemical dataset per region study. Although interpolation-based field modelling is rapid, time efficient and simple for delineation of geochemical anomalies, the results of discrete-based field modelling of geochemical anomalies are more superior. Therefore, regardless of the mineralization styles and geological settings, the SCBA to identify geochemical anomalies for mineral exploration is efficient.

How does sampling density affect mineralization detection in stream sediment geochemical exploration? A case study from NW of Iran

Sampling density is an important parameter in different stages of exploration programs such as stream sediment-based geochemical exploration. Some researchers have suggested the effects of different sampling densities in regional exploration. However, a few researchers have addressed the effect of stream sediment sampling density on anomaly delineation. This research aims at investigating the effect of sampling density in delineating ore-related geochemical signals in stream sediment exploration through sensitivity analysis. A dataset from northwestern Iran with known copper occurrences was employed, and the available data was systematically resampled into different proportions to simulate different sampling densities and analyzed using the true sample catchment basin approach. The results showed a positive correlation between the number of outlining known deposits and sampling density, implying that a higher sampling density increases the chance of outlining new potential zones. Moreover, when sampling density increases, the total size of the delimited anomalous area decreases. The estimated background values ( M j ) for upstream lithologic units varies heavily with sampling density. This is critical as separating anomalous basins depends on M j . M j variation has been more emphasized for lithologies with high estimated background content, displaying heteroscedastic behavior.

Improved detection of anomalous catchment basins by incorporating drainage density in dilution correction of geochemical residuals

In exploration for mineral deposits using stream sediment samples, a phenomenon known as dilution of metal content originating from mineralized sources should be taken into account. Dilution occurs when stream sediments originating from upstream mineralized and non-mineralized areas are mixed during transport and deposition. In the sample catchment basin approach, dilution correction is accomplished by employing Hawkes's equation (1976). Catchment area size is the correction factor used in the Hawkes's equation to correct geochemical residuals for dilution. However, the upstream area of each stream sediment sample is representative of the hydrological response and dilution capability of a drainage basin. In the present research, the Hawkes's equation was modified by incorporating drainage density of catchment basin to correct geochemical residuals for dilution. The existing and new equations were applied to stream sediment data from Iran. The results were compared using known occurrences of Au mineralization as validation points. This showed the superiority of the new technique using drainage density as a factor for correcting geochemical residuals against dilution.

The true sample catchment basin approach in the analysis of stream sediment geochemical data

The Sample Catchment Basin Approach (SCBA) is one of the techniques widely employed in the analysis of stream sediment geochemical data and delineation of anomalous catchments. However, this method fails to take into account the real catchment basin boundaries of each sample by incorporating only the incremental area between two adjacent samples. In other words, the SCBA incorrectly assumes that the geochemistry of every sample catchment within a drainage is independent from upstream sample catchment(s) feeding into this drainage. The chemical composition of sediment at the basin outlet originates from the whole basin upstream and not the incremental area as postulated in the SCBA. Consequently, the calculated background values for various lithologies and the background value for the element of interest at the basin outlet might be far from reality.This study used a True Sample Catchment Basin Approach (TSCBA), that reflects the true catchment boundary of every stream sediment sample, and in which all calculations are carried out on the premise that this boundary and the true area affect the composition of each sample. The results obtained from the application of both the SCBA and TSCBA to a gold endowed study area in western Iran clearly illustrated the superiority of the TSCBA over the SCBA. In addition, this study demonstrated the advantage of using the modified dilution correction equation of Mokhtari and Garousi Nezhad (2015), as compared to the existing Hawkes’s equation commonly used for dilution correction of residual values.

Recognition of significant multi-element geochemical signatures of porphyry Cu deposits in Noghdouz area, NW Iran

Diversity and complexity of geological processes in different areas result in different element associations for prospecting a certain mineral deposit-type sought. In this regard, because element associations are affected by the diversity of geological characteristics of different areas, it is important to analyze and recognize significant geochemical signatures that represent the deposit-type sought. This paper aims to recognize significant multi-element geochemical signatures of porphyry-Cu deposits in the Noghdouz area, Iran, using stream sediment data. For this, we used factor analysis and two modeling methods of geochemical anomalies, sample catchment basin and contour map. Then, to recognize significant geochemical signatures of the deposit-type sought and evaluate the anomaly mapping methods, we adapted prediction-area (P-A) plot, normalized density, and success rate curve. By using these processes, we recognized the best geochemical signature of the deposit-type sought in the study area. The proposed methods in this paper can efficiently be used in other areas to recognize significant geochemical signatures of different types of mineral deposits.

A modified equation for the downstream dilution of stream sediment anomalies

Mixing stream sediments originating from mineralization surface with eroded materials from background areas leads to downstream dilution in metal content of mineralization-sourced sediments. This phenomenon has a negative effect on delineation of anomalous catchment basins. In order to eliminate the dilution effect from the chemical analysis of stream sediments data, Hawkes (1976) proposed the equation “CmAm=Aa(Ca−Cb)+CbAm” through which he calculated the metal concentration at the mineralization surface. Hawkes's equation was a great advancement in interpretation of stream sediment geochemical datasets.However, Hawkes makes some simplifying assumptions to derive his formula from the mass balance equation including the hypothesis that the total sediment produced at a basin surface is delivered to the basin outlet. By this approach, an equation is obtained in which a linear relation is set between the sediment delivery and the area size of the basin. Some of the Hawkes’s assumptions are inevitable, but the aforementioned one is not in general true. In the present research, a new equation is derived by employing the concept of sediment delivery ratio (SDR) and its introduction into the mass balance equation, used by Hawkes (1976). SDR that represents the gap between the gross erosion in a catchment basin and the amount of sediment delivered at the basin outlet is negatively related to the basin size, and, as the basin size increases, the rate of deposition at the basin outlet decreases.SDR plays the main role in our work to attain a modified formula in which the dilution is related to the basin size by a power function. The modified equation “CmAm1+n=CaAa1+n−Cb(Aa−Am)1+n” is a general form of Hawkes's equation where power equals 1 (n=0) corresponds to Hawkes's equation. The new equation was applied to test the data presented by Hawkes, and it emerged that, in his study, n=0 delivers closer results to the reality in the first case study, but n=−0.25 and−0.5 delivers closer Cu values to the actual value in the second case. Similarly, more acceptable results were achieved for Mo if n=−0.25 in the second case. Additionally, the sample catchment basin was tested on the stream sediment dataset in the west of Iran, where orogenic gold occurrences were recognized to exist. Employing the modified equation with those three n values has resulted in repositioning of some catchment basins in terms of their favorability.

Identifying geochemical anomalies associated with Cu mineralization in stream sediment samples in Gharachaman area, northwest of Iran

The aim of this paper was to delineate Cu anomalies in the eastern part of Gharachaman 1:100,000 sheet, which was located in Sahand–Bazman magmatic belt, northwest of Iran. The sample catchment basin (SCB) technique was applied on stream sediment data to prospect Cu and its related pathfinder at Gharachaman area; then the concentration–area (C–A) multifractal model was performed on positive Cu and Mo residuals to separate different population and mapping anomalies. The spatial distributions of residuals Cu and Mo, based on threshold values via C–A multifractal analysis, showed high anomaly in the northeast and southeast of the studied area, where intrusive rocks were underlain by the basaltic breccias. Furthermore, principle component analysis (PCA) was conducted on the results of SCB technique in order to determine multi-element geochemical signatures that reflected the presence of mineralization. As a result, four components were defined which could explain nearly 83% of the variability. Meanwhile, PC3 represented a Cu–Mo association and reflected a multi-element mineralization related to the hydrothermal Cu deposit occurrence. Finally, the C–A analysis was performed on positive scores of PC3. The PC3 scores map displayed less anomalous areas than uni-element of residual Cu map, probably due to the characteristic of PCA method that eliminated false anomalies. Studies proved that using this hybrid method (SCB and PCA) could be a useful technique to recognize the stream sediment anomalies in the preliminary stages of prospecting concealed mineral deposits.

Different spatial methods in regional geochemical mapping at high density sampling: An application on stream sediment of Romagna Apennines, Northern Italy

Geochemical mapping is a fundamental tool for environmental monitoring and land management. For this reason, regional-, national- and global-scale geochemical mapping projects have been carried out in various countries since the late 1960s. A high density regional stream sediment geochemical survey was carried out over an area of 4125km2 in the Romagna Apennines (Northern Apennines) by collecting 770 samples (1 sample per 5km2). The <0.180mm fraction was analysed for 30 elements by means of X-ray fluorescence spectrometry.The area has a complex geology dominated by sedimentary rocks, and characterised by various geological units, which belong to the Ligurian, Tosco-Umbrian and Padano-Adriatic palaeogeographical dominions. In the study area, the industrial sites and the largest towns are located on the plains, the agricultural areas are in the hills, and there is a wooded mountainous area in the upper reaches of the main streams.Various mapping techniques were used for presenting and interpreting the data: proportionally sized dots, Exploratory Data Analysis (EDA)-based symbols, Inverse Distance Weighted (IDW) interpolation and Sample Catchment Basin (SCB) mapping approach. Proportional dot maps indicate the lithological control of the geological units. EDA maps demonstrate that it might be easier to extract some information with relevant symbols and class divisions; moreover, the subdivision of a whole data set in separate populations, related to a specific grouping variable, highlights anomalous areas, which would not be visible in a general presentation. Finally, the comparison of IDW interpolation and SCB mapping techniques illustrates that significant anomalies related to geological and anthropogenic sources are better modelled when the geochemical landscape is represented as a discrete surface rather than a continuous surface. However, this study shows how the concentration of chemical elements cannot be presented properly with a standardised mapping technique in an area characterised by multiple factors. Indeed, the use of various mapping techniques highlights interesting features for understanding the effects of local geology or human impact.

VYHODNOCENÍ INDEXU PŘEDCHOZÍCH SRÁŽEK U VYBRANÝCH KLIMATOLOGICKÝCH STANIC

The water retention capacity of a landscape, usually measured for a catchment basin, is a very important and decisive characteristic to identify the runoff amount from the catchment area and, in consequence, for antierosion and flood protection measures. Besides, creating water reserves in the landscape and keeping the water in them is also rather important.Soil humidity contributes to the calculation of potential water retention through modelling the runoff amount and peak discharge from the catchment basin within an area not larger than 5–10 km2. This method is based on curve number values (CN), which are tabulated according to hydrological characteristics of soils, land use, vegetation cover, tillage, antierosion measures and soil humidity, estimated as a 5-day sum of preceding precipitation values. This estimation is known as the antecedent precipitation index and it is divided into 3 degrees – I, II, III. Degree I indicates dry soil but still moist enough to till, whereas degree III means that the soil is oversaturated by water from preceding rainfall. Degree II is commonly used in this context as the antecedent precipitation index. The aim of this paper is to obtain real antecedent precipitation index values in given climatological stations (Brno, Dačice, Holešov, Náměšť nad Oslavou, Strážnice, Telč – Kostelní Myslová, Velké Meziříčí, Znojmo – Kuchařovice) for the period of years 1961 – 2009. Daily precipitation sums higher than 30 mm were considered to be the best candidate for such precipitation value since this occurs approximately once a year in studied areas. The occurence of these sums was also analysed for each month within the growing season (April to October). The analysed data was tabulated by climatological stations in order to check the real occurence of all antecedent precipitation index degrees within the studied period.Finally, the effects of different antecedent precipitation index values on the potential water retention capacity of the sample catchment basin were calculated to compare the results.

Weighted drainage catchment basin mapping of geochemical anomalies using stream sediment data for mineral potential modeling

In stream sediment geochemical exploration, anomalies can be recognized by ‘undiluting’ concentrations of indicator elements in every stream sediment sample catchment basin (SCB) as a function of topographic, geomorphologic, and geologic factors. However, this SCB modeling, like contour mapping, of stream sediment geochemical anomalies depends on sampling locations and sampling density. These arbitrary aspects of stream sediment sampling can render SCB or contour mapping of stream sediment anomalies inefficient. In this paper, instead of evaluating the relative exploration importance of each sample per SCB, we evaluated the relative exploration importance of samples per natural drainage catchment basin (DCB). Accordingly, we developed a new fuzzy weighting scheme for each DCB based on the distribution of anomalous and background samples in each DCB. In this new approach of weighted drainage catchment basin (WDCB) mapping of stream sediment geochemical anomalies, individual DCBs are given fuzzy weights representing their relative importance for prospecting the deposit-type sought. Hence, a map of WDCB can be used directly as a geochemical evidence layer in fuzzy-based mineral prospectivity mapping. In this regard, we demonstrated that the prediction rate of prospectivity map obtained by using WDCB approach with respect to known mineral deposit occurrences is higher than that of prospectivity map obtained by using a SCB or contour map.