s. mojeddifar

Sineqan mine, 25 km away from Delijan city (Markazi province), has shown important evidences of iron oxide ores containing copper, gold and rare earth elements (REE) in the early stages of exploration. In the present study, a low-grade sample of the mine from the albite zone containing 0.69% of copper and 223, 186 and 55 g/t of cerium, lanthanum and neodymium was processed and compared with the results of elements correlation in geochemical samples. Cerium, neodymium and lanthanum showed the highest correlation with each other and with the thorium. The correlation coefficient of light REEs with phosphorus was negative. The results of ammonium sulfate leaching showed that the occurrence of REE in the sample is not as adsorption on clay minerals and the study of the correlation coefficient of REE with aluminum and sodium in geochemical samples also confirmed this result. The results of leaching with H2SO4 showed that selective leaching of copper is possible. The low efficiency of leaching with H2SO4, HCl and HNO3 and the higher efficiency of 96% for leaching with cerium and lanthanum under acidic baking conditions showed that a significant part of REEs is in the form of refractory minerals. The concentration ratio of REEs in the magnetic concentrate was close to iron. According to the correlation analysis of elements in geochemical samples, the correlation of calcium with REE was in the range of -0.2to-0.3. In the flotation test, although the grade of calcium increased 3.5 times in the flotation concentrate, this was not the case for REEs. The results of this research can be used in similar cases in the early stages of exploration and processing feasibility studies.

Separating hydrothermal alteration zones is a main challenge in remote sensing science. The results showed that application of conventional methods based on spectral properties could not exactly separate hydrothermal alterations. Therefore, this research attempted to identify hydrothermal alteration zones in the study area by combining principal component analysis and concentration-area fractal model. The developed model was evaluated by confusion matrix, commission and omission error and total accuracy. The commission and omission error of phyllic zone is 4.51 and 9.13 percent, respectively, and the omission error of argillic zone is equal to 54.16 percent. The results indicated that concentration-area fractal model is able to identify phyllic, argillic and propylitic zones in the study area. Because of the same spectral signature of phyllic and argillic minerals, the concentration-area fractal model could not exactly discriminate these two alteration zones.

Hydrocarbon explorations usually are performed based on seismic inversion techniques in which there exists computational complexity. Therefore, application of simpler methods such as probabilistic neural network could be considered to decrease uncertainties of the results. The present research used a probabilistic neural network to characterize the sand reservoir of F3 block in North Sea. This algorithm applied the seismic attributes of energy, similarity and instantaneous amplitude as input parameters to estimate porosity distribution of the F3 reservoir. Calculating the likelihood probability is dependent on the smoothing parameter. Therefore, the cross validation technique was used to determine the smoothing parameter that equals to 0.21. This paper considered 16 porosity classes from 0.22 to 0.3 as output of probabilistic algorithm. This algorithm calculated the posterior probability for every point in reservoir to determine the class of each point. The maximum posterior probability was selected as the final output. The obtained results were compared with the linear equation driven regression model for acoustic impedance and porosity values. The comparison showed that the developed network could detect gas-bearing region. Also, the confusion matrix was used to validate the results and the total accuracy parameter was calculated as 0.7587 and 0.4623 for probabilistic neural network and linear regression, respectively. Therefore, Bayesian neural network could be introduced as an effective tool to explore the hydrocarbon-bearin layers because of computational complexity of seismic inversion techniques.

This research used prestack seismic data in Penobscot to detect gas-bearing layers using AVO analysis. AVO technique is not easy based on Zoeppritz equations then this work applied Shuey approximation to calculate gradient (G) and intercept (Rp) parameters using least square and Tikhonov solutions. Model covariance matrix was calculated for least square and Tikhonov technique to obtain the variance of estimation of Rp and G. The variance of estimation of least square method is 0.24 and 15.08 for RP and G, respectively. Also, it is equal to 0.08 and 0.14 for Rp and G in Tikhonov solution. Therefore, the results of Tikhonov are more reliable than least square solution. Multiplication of intercept and gradient showed the strong and positive values in gas layers. The reflection coefficient of shear wave identified gas-bearing layers by a positive value. The difference of P-wave and S-wave profile appeared relatively high negative values in time range of 2000 to 2020 ms. L30 well which was drilled in cross line 1153 showed that there is gas accumulation in time range of 2000 to 2036 ms. Therefore, AVO attributes could identify gas-bearing layers.

The study area is located in the south of Markazi province, in Khomein city, in the Sanandaj- Sirjan zone and in the lead and zinc metal belt of Malayer-Isfahan. The research was conducted on 170 samples of 33 boreholes in Haft-Savaran area to identify Pb anomaly. Studies have shown that original and modified singularity models provide acceptable results for geochemical anomalies, but the results of the weighted singularity model were not significant in the two- and three-dimensional studies. Therefore, two-dimensional and three-dimensional integration of singularities are effective in separating the position and spreading the anomalies.Modified singularity model could provide better results than the original singularity method due to the separation of weak anomalies. Coefficient of areal association also indicated a high correlation between original and modified singularity models. Finally, it was found that Pb mineralization has a SW- NE trend in the region.