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Simulation is an art to model the reservoir’s structure in the petroleum industry, which transfers all possible data digitally to the reservoir management system’s suitable computation software. The present study attempts to construct a 3D structural model using the geostatistical method to access in situ oil volume and the best oil production position. The geological area is the Maroon oilfields located at Dezful Embayment in southwest of Iran. In this oilfield, the Asmari reservoir is divided into ten zones. Several steps such as griding, kriging, and experimental variogram were carried out to access all available data to make up the model. The evaluation revealed that zones one and five were the main oil-bearing layers since it contains about 60% of total oil volume in this field. Petrophysical parameter distribution indicated that zones four and seven are good candidates for oil production since they have higher porosity and net to growth thickness than other zones. From the seventh to the tenth zone, the oil volume decreased due to high water saturation in these areas. The best zones for the future program are located in the western part. This study shows that 3D modeling will find elongation-type anticline and reservoir direction and the best location for future drilling programs based on the interaction of the petrographic parameters. The results can be used in various steps during the lifetime of the different reservoirs around the globe.

Geochemical exploration of stream sediments is an important step for the identification of areas of interest with potential mineralization, particularly in the early stages of mineral exploration. A total of 407 sample points for 15 different geochemical traces were collected from Central Wales and classified into two groups: a training group consisting of 285 samples and a testing group consisting of 122 samples. Geospatial characterization of each parameter at stream level was performed using two different prediction models; the inverse distance weighting and the geostatistical kriging. Several variations of the IDW model was applied based on the power function and the number of the sample points, and the best one selected based on “root mean square prediction error” statistic. The same statistic was also used in the best-fitted semivariogram models including the circular, spherical, exponential and Gaussian for each geochemical parameter in Kriging. Finally, the mineral prospectivity map of the area was developed based on the geochemical accumulation index (GAI) using multivariate overlay analysis. The experimental results show that there is no single method that can be used independently to predict the spatial distribution of geochemical elements in streams. Instead, a combinatory approach of IDW and kriging is advised in order to generate more accurate predictions. The mineral prospectivity map based on GAI showed that most of the mineral-enriched streams were found in northwest, northeast, and south part of the study area which was also confirmed with the existing mining activities in the region.

Iron ore is a fundamental raw material for the world steel industry. For the success of mining projects, the average rate of the iron ore content ought to be acceptable to produce steel and cast iron. With irregular useful mineral contents, iron deposits address a problem for Algeria steel industry performance and it makes the operating cost very expensive. In this research, we used the geostatistical interpolation to evaluate the iron ore mixtures in Wenza open cast mine, northeast Algeria. We applied semi-variograms and kriging techniques to interpolate the needed values for mineral resource estimation. Then, we have integrated a mathematical approach to calculate and compare the grade of the sites in operation, with the orders grade. The results showed a significant variability of the iron content in the study area. The variogram revealed a nugget effect of 0.16, a range of 95.05 (m), and 17,329 kriged blocks of 5 × 5 × 7.5 m. We have used the output values of the geostatistical model as input data of the analytical model to set an operating mode of mining activities. Our study proposes to install out a homogenizing stock to correct the iron rate in Wenza deposit and to guarantee the quantity and quality required by the customer.

Geostatistical tools have been increasingly applied to compile spatial distribution maps of groundwater quality. Results of geostatistics can be helpful for decision-makers to carry out appropriate remedial actions to sustain the quality of groundwater sources. The main purpose of this paper is to assess the groundwater hydrogeochemistry of Tehran-Karaj plain aquifer, Iran, by a forward geostatistical method and an inverse geostatistical method called sequential Gaussian simulation. Seven important hydrogeochemical properties of the aquifer including total dissolved solids, sodium adsorption ratio, electrical conductivity, sodium, total hardness, chloride, and sulfate were analyzed and compiled geostatistically. Data were taken from 137 well samples in 2016. After data normalization, variography was compiled, and experimental variograms were plotted; then, the best theoretical model was fitted on each variogram based on the minimum residual sum of squares. Cross-validation was used to determine the accuracy of parameters related to the variograms. Estimation maps of the groundwater hydrogeochemistry were prepared, and the estimation variance map was drawn to assess the accuracy of estimation in each estimated point. Forward geostatistical methods are subjected to smoothing, while inverse geostatistical methods are not subjected to this problem. The results of this study revealed that the utilized inverse geostatistical methods called simulation algorithms are more accurate than forward methods. Eventually, estimation maps of each parameter, as well as error maps, were compiled, and critical regions have been proposed according to the simulated maps.

Global climate change affects weather patterns around the world. Rainfall variability, especially in arid regions, can increase or decrease drastically in the space and time domains. To determine the spatial and temporal continuity of rainfall phenomena, geostatistical models and variograms are key tools for estimating and modeling the space and time dependence in a data set. The basic steps in any regional modeling method involve obtaining the experimental variogram and fitting a model to the experimental variogram. Theoretical matching is performed for the experimental variogram considering the sill, nugget, and other features, which reflect the regional characteristics of the phenomena concerned. In this study, a product-sum space-time-standardized variogram procedure is proposed and used to identify the spatial and temporal structures of annual rainfall in the western part of Saudi Arabia. The standardized variogram is, in fact, the reduced scale of the original form without the loss of the characteristics of the underlying phenomena. The results of the space-time-standardized experimental and variogram models indicate that the average annual spatial variograms have a continuous structure based on an exponential model with a small nugget. The average annual temporal variograms have a continuous structure based on an exponential model with a large nugget. These results confirm the high variation in the rainfall pattern in the time domain and low variation in the spatial domain.

History matching was usually conducted on pilot wells for calibrating reservoir parameters in static model. How to complete matching work with limited geological data is a big challenge, especially for coalbed methane (CBM) reservoir with high heterogeneity. In this paper, a new workflow was proposed to overcome it. Study area lies in Surat Basin, Australia, and the targets for six pilot CBM wells are low rank coal. Totally, five parameters, including permeability, gas content, gas saturation, fracture porosity and variogram length, were selected for testing their uncertainties. For the former two parameters, the correlations with depth were established for predicting their distribution separately, and then, percentile (P10, P90) methods were used to define their high and low variation ranges. For the later three parameters, experience methods were used to define their high and low variation ranges. Then, experimental design method was adopted to generate 43 realizations of simulation cases with aforementioned five parameters. For gas rate, water rate and bottom hole pressure of each well, 10 more better matching cases were selected separately, and then compared to find the common cases. After combing all the common cases of each well together, a general understanding about geological conditions was achieved. It was found that all cases have high gas saturation and most cases have high fracture porosity and high permeability. These results agree with previous regional geological studies.

The objective of this study was to investigate five nutrient factors of the forest topsoil (0–20 cm), including pH, soil organic matter (SOM), total nitrogen (STN), available phosphorus (AP), and available potassium (AK). This study adopted the revised Nemerow index method to conduct a comprehensive quantitative evaluation on the soil nutrition contents, and using geostatistical method to address the spatial variability of soil properties in hilly and mountainous regions, southern China. Data were analyzed both statistically and geostatistically on the basis of semivariogram. Our results indicate that the soil is generally acidic, containing rich SOM and STN, but lacks AP and AK. Most of the sub-indexes of fertility were at grade III, showing a middle level. According to the coefficient of variations (CV), the five soil nutrient factors can be ranked in decreasing order as follows: AP > SOM > AK > STN > pH. There were differences between the sub-indexes of fertility, which could be ranked as follows: PSOM > PAK > PSTN > PpH > PAP. The semi-variation functions for the five soil nutrient factors studied here show some spatial structure features, over a range of 23–274 km. All nutrient factors had different nugget-to-sill ratios in each region, which varied from 10 to 50%, suggesting a strong or medium correlation. Thus, it can be concluded that the nutrient spatial distribution of the study area was the result of the combined action of structural factors and random factors, and the factors affecting the soil nutrients of the research area are highly complex. We will develop more targeted research plans about soil nutrients of study area in the next time.

In many countries of the world, groundwater is the main source of water in arid and semiarid regions. The scarcity of water is one of the main issues in Morocco. The coastal aquifer system of Rmel-Oulad Ogbane is recognized as one of the most important aquifers in Morocco and is very well known for their role in industrial, economic, and social development. However, this role is confronted to climate change impacts and heavy abstraction rates leading to a major decline in the groundwater levels and may eventually cause a deficit water balance of the aquifer as well as a degradation of the freshwater quality by seawater intrusion. The objective of this research is to identify and evaluate the distribution and spatial changes of regionalized variables on reservoirs and groundwater resources using geostatistical analysis in Geographic Information System (GIS) software. The prediction of these variables was performed using an interpolation method: ordinary kriging in a GIS. The normality test and trend analysis were applied to each variable to select the appropriate semivariogram model (SVM) and check the results using cross-validation (CV). Hence, several kriged maps of reservoirs and water resources have been produced to be exploited by the decision maker. The studied variables related to reservoirs and hydrodynamic data have a strong spatial dependence, which show correlations in specific direction, while the hydrochemical data are mainly related to groundwater mechanisms, such as advective-diffusive transport, without any autocorrelation between data.

Groundwater is cornerstone of global food production, and its quality plays a vital role in crop development. Success of agriculture in Pakistan relies heavily on groundwater because of reduced surface water supplies and rainfalls. The current study was conducted to assess the spatio-temporal quality of groundwater in the Lower Chenab Canal (LCC) east area with emphasis on characterizing groundwater quality zones for irrigation applications. Groundwater samples from 289 observation wells were collected and chemically analyzed. A geostatistical approach using kriging interpolation was applied to express spatial variation and minimize estimation error in data. Three water quality parameters, electrical conductivity (EC), sodium absorption ratio (SAR), and residual sodium carbonate (RSC), were utilized to develop groundwater quality maps for two seasons including pre-monsoon and post-monsoon. To describe the spatial autocorrelation, suitable semivariogram models were identified and cross validated. The exponential model gave the best results for EC and RSC, whereas the spherical model was found to be the best fit for SAR. The overlay analysis was performed to create composite water quality maps according to which 40% LCC area is showing good groundwater quality for irrigation, 49% is exhibiting marginal quality, and 10% unsuitable during pre-monsoon and post-monsoon seasons. Groundwater quality is 100% good for Sultanpur irrigation subdivision, and only 3.75% is good for Paccadala subdivision. The results of the study may provide guidelines for the planners/policy makers to devise area-specific strategies for sustainable groundwater use.

Lack of data in time and location domain is a main problem in statistical seismology. Therefore, several methods have been developed to make synthetic catalogs to compensate these uncertainties. The approaches introduced in this paper try to decrease these uncertainties. A statistical tool has been determined to study the lack of data in time domain and revise a magnitude-frequency relationship in one of the very high-seismic regions in the world, northwest of Iran (44.00 to 48.00 E and 35.60 to 40.00 N). A geostatistical simulation method has also been utilized to reduce the uncertainties in the spatial domain. Variogram tool has been introduced as a spatial tool to assign a spatial regression among data in various directions. A geostatistical simulation produced a representative, synthetic catalog with 10,707 events to reduce spatial uncertainties (the number of data in the earthquake catalogs was 307 events). The synthetic database was classified by a geographical information system (GIS) based on simulated magnitudes to reveal the underlying seismicity patterns. Although some regions with high seismicity correspond to known faults, as far as seismic patterns are concerned, the new method could highlight locations of interest that have not been previously determined. It also suggests some previously unrecognized lineation and clusters in likely future strain release.

Mining activities pose a potential risk of metal contamination around mining sites. On May 6, 2010, a tailings dam failure of the Mazraeh copper mine near Ahar in East Azerbaijan province, Iran, released vast amounts of mine wastes. To better understand the magnitude of copper contamination in the waste-affected soils, it is important to assess the spatial distribution of soil copper content at unsampled points. A total of 30 soil samples and their surficial sediments together with the 6 uncontaminated control samples (0–10 and 10–30 cm) were collected along the stream flow that joined Ahar-Chai River. Some of soil properties as well as total copper concentration were determined in all samples. The mean value of the latter in the surface contaminated soils was found to be approximately two times more than controls. Furthermore, the mean concentration of copper in the surface loaded material was 10 times more than the soils. High copper concentrations were observed in surficial sediments of the soils near the broken tailings dam. The Inverse Distance Weighting (IDW) method was employed in data analysis. The spherical and Gaussian semivariogram models were properly fitted to the data of copper contents in soils and surficial sediments.

Geological facies modeling is a crucial problem for reservoir characterization as it affects the reservoir heterogeneities and fluid flow performance prediction. The main purpose of this research is to adopt a stochastic simulation to construct 3D lithofacies models of the tidal/estuarine depositional environment of the upper sandstone member in south Rumaila oil field, located in Iraq. Based on core measurements, the upper sandstone member has three main lithofacies: sand, shaly sand, and shale. Literature review indicates that the formation is encompassed of mainly sandstone with some inter-bedded shale zones. To reconstruct the 3D lithofacies model, the sequential indicator simulation (SISIM) was adopted to build the categorical image, pixel by pixel, considering the nonparametric condition distribution. Specifically, SISIM depends on the variogram to address and model the variation between any two spatial points from the available data. Therefore, 12 different variograms were constructed given the three lithofacies in four different azimuth directions: 0°, 45°, 90°, and 135°.The resulting lithofacies models in the four selected azimuth directions have shown frequent tidal lithofacies channeling and indicate an approximate matching with the original description of the formation depositional environment of the tidal-dominated and sand-rich environment. The generated lithofacies model in 135° direction has sand channels prevailing towards the southeast shoreline of the reservoir. The created lithofacies model also preserves the reservoir complexity and heterogeneity because it was created using a high-resolution gridding system with approximately two million grids. Additionally, the resulting tidal lithofacies model ensures reservoir heterogeneity as the petrophysical properties are then distributed given each lithofacies with distinct indicator variograms.

This study was performed in order to establish new parameters to recognize the depositional microfacies using grain size distribution (GSD) data from laser diffraction analysis. A total of 938 samples were collected from five sedimentary microfacies, and 18 characteristic parameters of these samples were developed by statistical analysis, fractal theory, fingerprint methods, and variogram function. The samples were divided into a training data set and testing data set. The training data set was used to establish a discriminative database with five depositional microfacies; the testing data set was used to verify whether these characteristic parameters can identify their depositional microfacies according to the discriminative database. Out of the 50 samples in the testing data set, 47 samples were correctly recognized within their depositional environment and only three samples of point bar were shared with natural levee using the random forest (RF) method. The analysis results indicate the fine grain size and general characteristic of GSD have more significant influences on depositional environment classification than other parameters. The flood plain and fan-between depression environments had the most similar hydrodynamic conditions among the five considered depositional microfacies. Therefore, the 18 characteristic parameters of GSD are useful tools to identify depositional microfacies for hydrogeological parameter studies.

Descriptive statistics, correlation, regression, and geostatistical modeling are applied to assess the trace elements of groundwater and their spatial distribution at the Rangpur district of Bangladesh. A total number of 47 water samples have been collected from wells at depth ranging from 10 to 53 m. The descriptive statistics results show that the mean concentrations of iron (Fe), manganese (Mn), and barium (Ba) have exceeded the permissible limits and those concentrations are alarming to human health and their surrounding environments. Furthermore, Mn, Zn, Al, and Ba concentrations reveal the highly positive skewed and are considered to be extreme. The statistical results demonstrate that groundwater trace element quality is mainly related to natural/geogenic sources followed by anthropogenic sources in the study area even though they show significant correlations among them. The multiple regression models are developed for prediction of each trace element of groundwater samples. The spatial analysis of groundwater trace elements is performed by geostatistical modeling. The cross validation results reveal that kriging models are produced to show the most accurate spatial distribution maps for all trace elements except Ba concentration. The semivariogram models have demonstrated that most of the elements have shown moderate to strong spatial dependence suggesting less agronomic/residential area influences. The findings of the multiple regression model and the correlation matrix are also consistent with the spatial analysis results. It is anticipated that outcomes of this study will provide insights for decision makers taking adaptive measures for groundwater trace element monitoring in Rangpur district, Bangladesh.

This paper presents a comparison of ordinary kriging and simple kriging estimation methods in Chehelkureh copper deposit, SE Iran. The sample data used in this study consist of exploration drilling data, suitably composited, and the dataset is irregularly spaced and has an almost lognormal distribution. Simple kriging produced a result that is “smoother,” and results show that simple kriging can be less accurate than ordinary kriging. The model obtained by ordinary kriging is more accurate, and future economic decision by ordinary kriging results was more reliable.

In this study, Saudi Arabia is divided into five geographical regions: northwestern, southwestern, western, central, and finally, eastern with central. The study shows the possibility of constructing and formatting rainfall as a regional variable depending on the locality. Two techniques derived from the point cumulative semivariogram (PCSV) model are used to predict rainfall in the five regions. These are, namely, the trigonometric point cumulative semivariogram (TPCSV) method and the classical weighing average method (classical point cumulative semivariogram (CPCSV)). These methods are applied to data from 21 weather stations in the five regions for a 35-year period from 1970 to 2005 in the majority of stations. It is found that the absolute error between observed values and the predicted values using TPCSV and CPCSV does not exceed 5 and 13 %, respectively, except in the central region, where the absolute error is 18 % using both methods. Regarding the spatial rainfall variation in the region, the strength of correlation is calculated in terms of angles among the sites using the TPCSV technique, the smaller the angle the stronger the correlation. The standard weighing function (SWF) is used to find the weight of rainfall records at each site, which expresses the effect of locality on rainfall variation. The SWF approach reflects the homogeneity of rainfall around the pivot sites in all regions except the central. High-correlation angles (85, 90, and 88°) among the sites of the region explain the weakness of correlation among this set of station, where the error of prediction both method in this region exceeds 18 %. PCSV succeeds in explaining the spatial rainfall variation and finding the weight of rainfall phenomena for each site in all five regions, while successfully predicting rainfall.

The scarcity of water is one of the main issues in Saudi Arabia. In particular, the extreme climate in the form of less frequent rainfall affects the groundwater availability. Moreover, groundwater has been depleted by the increase in population. In this research, the spatial distribution of groundwater quality has been developed, and the prediction of groundwater chemical parameters has been made using geostatistical analysis in geographic information system (GIS) software. The study area is Madrakah village as the recharge area of Wadi Usfan located in the western region of Saudi Arabia. Ordinary kriging method was applied to map the spatial distribution of the groundwater chemistry. Most of the groundwater is not suitable for drinking purposes. Groundwater chemical parameters are decreasing toward the eastern part of Madrakah village. In predicting groundwater chemistry distribution maps, data transformation has been executed to reduce the skewness on most of the chemical parameters. The best semivariogram model for every parameter varies based on the root mean square error (RMSE) criterion. The groundwater chemical parameters, i.e., Na+, Mg2+, Cl−, conductivity, salinity, and total dissolved solid (TDS), have a strong spatial dependence, while, NO3 − and temperature have a moderate and weak spatial dependence, respectively.

This paper presents an application of median indicator kriging in the analysis of iron mineralisation data from the Chadormalu iron mine in Yazd province, Iran. The sample data used in this study consist of exploration drilling data, suitably composited. The data set is sparse, irregularly spaced, and has a bimodal distribution. It is important to use an estimation method that deals with the nature of the distribution appropriately. Since none of the grade distributions are normal or log-normal, a non-linear estimator is ideal. The indicator kriging method is considered to provide a better way of estimating an element with a bimodal distribution. Indicator semivariograms were modelled for nine cut offs, the deciles. Semivariograms were standardised by dividing each semivariogram by the total sill, and a single spherical structure plus nugget was fitted in each case. All data were treated as point data in three-dimensional space rather than block data. The grade tonnage curves were calculated on the assumption that each estimate is representative of a block of size 20 m by 20 m by 12.5 m centred on the location of the datum.

Geostatistical simulations have been recently widely used in the geological and mining investigations. Variogram, the fundamental tools of geostatistics, can identify the spatial distribution of the regionalized variable within the area. One of the important issues of geostatistical simulation in seismotectonics is producing uncertainty maps, which could be applicable to predict earthquake parameters through the site locations especially for civil structures like bridges. It can help engineers to design the structure of interest better. Earthquake parameters as for example seismic fault and surface wave magnitude (Ms) have significant impact on the feasibility study of the civil structures. In this research, a method is presented to produce uncertainty maps for seismic fault and surface wave magnitude, Ms. For this aim, information related to surface wave magnitude and fault trace in Zagros region (SW of Iran) has been collected. Then, the relationships between them through the site location have been investigated and analyzed by conditional geostatistical simulation. In order to quantify the uncertainty of each parameter, the uncertainty formula after generating the E-type maps has been provided and discussed. Finally, in “Talgah Bridge” site, these uncertainty maps were produced to interpret the impact of the surface wave magnitude and fault trace in this specific civil structure.

A geostatistical approach based on ordinary kriging is presented for the evaluation and the augmentation of an existing rain gauge network. The evaluation is based on estimating the percentage of the area that achieves a targeted level of acceptable accuracy. The variances of kriging estimation erros at un-gauged locations were assumed to be normally distributed. Kriging estimation erros with a probability that equals to or exceeds a given threshold value of acceptance probability were assumed to have satisfactory accuracies. The percentage of the area that achieved the targeted probability of acceptance is delineated and used to judge the overall performance of the existing rain gauge network. A study area in northern Oman located in Sohar governorate is selected as the pilot case. The area has 34 rain gauges and it is characterized by a terrain surface that varies from coastal plain to mountains. For a threshold value of 0.85, and 0.90 of acceptance probability, the existing network achieved area of acceptable probability of 88.71 and 77.72 %, respectively. For a success criterion of 80 %, the existing rain gauge network indicated acceptable performance for acceptance probability threshold of 0.85 and inadequate performance is noticed in the case of probability threshold of 0.90, which necessitated further network augmentation. A sequential algorithm for ranking and prioritization of the existing rain gauges is used to classify the existing rain gauges into base and non-base rain gauges. The base rain gauge network for mean annual rainfall comprised about 29 of the existing rain gauges. The identified non-base rain gauges were sequentially relocated to achieve higher levels of percentage of area with acceptable accuracy. The percentage of area with acceptable accuracy increased from 88.71 % for the original rain gauge network to about 94.51 % for the augmented network by adding four rain gages at probability acceptance threshold of 0.85. It also increased from 77.72 % for the existing network to 90.50 % for the augmented rain gauge network at acceptance threshold of 0.9.

Soil properties like pH, organic carbon (OC), available nitrogen (AN), available phosphorus (AP), and available potassium (AK) vary spatially from a field to a larger region scale and determine the soil fertility. This study addressed the spatial variability of soil properties in Brahmaputra plains, northeastern India using geostatistical method. For this, a total of 767 soil samples from a depth of 0–25 cm at an approximate interval of 1 km were collected over the entire Bongaigaon district of Assam. Data were analyzed both statistically and geostatistically on the basis of semivariogram. Soil properties showed large variability with greatest variation was observed in AP (86 %) where as the smallest variation was in pH (19 %). The semivariogram for all soil properties were best fitted by exponential models and showed a highest (2.7 km) range for OC and lowest (1.2 km) for AP. The nugget/sill ratio indicates a strong dependence for pH (12 %), moderate spatial dependence for available nutrients (53–72 %) and a weak spatial dependence for OC (77 %). Evaluation of spatial maps indicated that except for AN due to high root mean square error (61.8), kriging could successfully interpolate other soil properties. Soil pH highly negatively correlated with OC (−0.330**) and AN (−0.228**) and highly positive correlated with AP (0.334**) and AK (0.164**). A highly significant correlation was also found between OC and AN (0.490**).

Subsurface models of hydrocarbon reservoirs are coarse and of low resolution when compared with the actual geologic characteristics. Therefore, the understanding of the three-dimensional architecture of reservoir units is often incomplete. Outcrop analogues are commonly used to understand the spatial continuity of reservoir units. In this study, a Late Jurassic outcrop analogue for the Arab-D reservoir of central Saudi Arabia was used to build a high-resolution model that captures fine geologic details. Subsurface reservoir lithofacies were matched with those from the studied outcrop, and porosity values derived from published core and well log data from the Ain Dar, Uthmanyah, and Shudgum areas of the Ghawar Field, eastern Saudi Arabia, were then applied to the equivalent lithofacies in the outcrop. Maximum, minimum, and average subsurface porosity for each lithofacies were distributed in the facies model using a geostatistical algorithm to produce nine porosity models for the field data. Several realisations were run to visualise the variability in each model and to quantitatively measure the uncertainty associated with the models. The results indicated that potential reservoir zones were associated with grainstone, packstone, and some wackestone layers. Semivariogram analysis of the lithofacies showed good continuity in the N-S direction and less continuity in the E-W direction. The high-resolution lithofacies models detected permeability barriers and isolated low porosity bodies within the potential reservoir zones. This model revealed the porosity distribution in areas smaller than one cell in the subsurface model and highlighted the uncertainty associated with several aspects of the model.

A comparative study on two interpolator methods for Bouguer anomaly mapping was performed in the El Kef-Ouargha region, Tunisia. In particular, we compare the results of minimum curvature gridding and kriging. We undertake a geostatistical study using both anisotropic and isotropic analysis for studding the spatial variability and kriging the random variable: “Bouguer anomaly”. The model parameters of each method are calculated to compare the results. The anisotropic analysis most closely approximates the measured data. Consequentially, the Bouguer anomaly and vertical gradient maps using anisotropic kriging have the best correlation with the geological map, best represent density distributions at depth and most closely approximate the measured anomaly amplitudes.

The most of shallow earthquakes are followed, just after the main shock, by increased residual seismicity known as “aftershocks” or “aftershock sequences”. Because of their disparity in time and space, aftershock sequences are more or less obvious and their productivity is spread out in time. Several studies have been regularly proposed to explain or to understand the mechanisms of the occurrence and the behaviour of these small earthquakes. In a theoretical context, many factors can induce the aftershock triggering: residual friction, subcritical crack growth, pore fluid flow etc. Just after the occurrence of the most destructive main shock of the 21 May 2003 Boumerdes (Algeria) earthquake, a wide sequence of aftershocks was recorded at different geographical locations and with various magnitudes. Based on the fact that the region of Boumerdes (40 km east of the capital Algiers) did not develop major earthquakes in the past, a geostatistical investigation of the data for this aftershock sequence is a valuable input for better seismogeological identification of this area. In the present analysis, after an overview of the geological factors in the likely occurrence of the earthquake, fundamental statistical parameters were chosen: the b value from the Gutenberg–Richter law, the p factor of the extracted respectively from the b value and the fractal variogram defined as a graphic tool to describe the continuity or the roughness of data. Jointly to the geostatistical parameters provided by the variogram like the fractal dimension. The main objective of the calculation and interpretation of these parameters is oriented towards a better understanding of the seismicity of the region of Boumerdes (Algeria) now classified as seismogenic zone.

The valuation of a mining project depends upon the accuracy of geological block model. Sampling density, estimation method, and proper block size mainly affect the accuracy of estimated block. This paper aims to answer three questions: (1) which estimation method is more accurate, (2) what is the relation between sampling density and block size, and (3) what the optimum block size is. Conditional Gaussian simulation (CGS) was used to generate a hypothetical deposit, considered as a real block model. A range of different block dimensions were estimated by ordinary kriging, inverse squared distance, and nearest neighbor methods based on tow-simulated drilling grids database. The comparison of estimated and real block grades reveals that increasing the sampling density results the similar outcomes of geostatistics and deterministic interpolation methods. Furthermore, it was deduced that sampling density could not be a viable alternative in choosing appropriate block dimension and the variogram rang a was suggested as an affective parameter in block size selection. Then a geometrical formula was developed to obtain the block size based on the variogram range. The increment in project value that a mine planner can expected from the additional information of the dense drilling grid was also calculated and it was concluded that the block size obtained based on the suggested formula results acceptable information value. Finally, the database of Chador Malu iron ore mine which is located in 180 km northeast of Yazd city in the central part of Iran were used to validate the suggested formula.