جستجوی مقالات مرتبط با کلیدواژه « عدم‏قطعیت » در نشریات گروه « زمین شناسی »

In probability theory and statistics, Bayes' theorem , describes the probability of an event, based on prior knowledge of conditions that might be related to the event. One of the many applications of the theorem is Bayesian inference, a particular approach to statistical inference. When applied, the probabilities involved in the theorem may have different probability interpretations. With Bayesian probability interpretation, the theorem expresses how a degree of belief, expressed as a probability, should rationally change to account for the availability of related evidence. Bayesian inference is fundamental to Bayesian statistics. The purpose of this study is a modeling method based on hierarchical structure in prioritizing and providing appropriate solutions to reduce seismic hazards in the Alborz-Azerbaijan province. The basis of the work is based on using natural data and Bayesian statistics which is a powerful tool in modeling both uncertainty and randomness. The method can correctly show the values of peak ground acceleration (PGA) along with the quantities of its distribution function in the region. The input information for the method is seismic catalog from 1900 to 2020 and proper ground motion attenuation law. It should be noted that Iran strong motion network had limited data so that there was a gap of large earthquakes of data. This modeling contains a set of processes and rules for using and specifying variables and relationships between them. Based on the results of this study, conducted in the northern and north-western parts of Iran, using Iranian Seismological Center data (http://irsc.ut.ac.ir) that includes 11 stations in the study area, hazard maps were drawn for PGA over a period of next 50 and 475 years, with the highest acceleration in the Alborz region including Tehran and Zanjan and in the Azerbaijan region including Tabriz and Rasht. The correlation between estimated acceleration values by Bayesian method and the values observed by the accelerometer network of Iran Road, Housing and Urban Development Research Center was α=0.95. This indicates that the estimated maximum acceleration is in a good agreement with the observed maximum acceleration. According to the results, the southern part of Alborz (Tehran) and the north-western part of Iran (Tabriz) had the highest PGA. Then, the Bayesian method will give favorable results for probability seismic hazard analysis.The results confirm the uncertainty of different parameters of seismic acceleration. Therefore, all of these parameters calculated, indicate that in the west of the Caspian Sea (Rasht city) the lowest value was allocated. Then, Bayesian method with advantages such as considering the relationship between variables, conditions of uncertainty and high flexibility, has the necessary ability to analyze seismic risk in other parts of Iran. This method can also be used in construction projects. In carrying out such renovations, it is necessary to provide step-by-step protocols and rules guidance for application and specification of variables and relationships between them in designing and correcting the model.

In the variability of earthquake hazard analysis results, ground motion prediction equations play an important role. Selection of appropriate prediction relationships for the region can lead to stability and accuracy of earthquake hazard analysis results. In this study, different prediction relationships were investigated and analyzed for earthquake hazard analysis in Ahvaz city. These relationships were ranked based on the criteria of logarithmic probability, Euclidean distance and deviation information in different periods. Then the most efficient relationships were selected by data envelopment analysis (DEA) method on the basis of differences in the obtained results. Out of 67 possible relationships, 5 were identified as suitable relationships for earthquake hazard analysis in the Ahvaz urban area. Then, a special efficiency criterion was used to determine the weight of these relationships. The results of this study can help to reduce to a large extent the uncertainties involved in analyzing the seismic hazard of the area studied.

The goal of this study is to invert the seismic data directly to porosity as well as to quantify the associated uncertainty in one of the carbonate reservoirs located in southwestern Iran. Probabilistic inverse methods are able to present the model parameters as a posterior probability distribution function by combining the probability density function of the model prior information and the likelihood model. The likelihood density function is defined based on the noise model. The answer to the probabilistic inverse problem is a posterior distribution function that is not only consistent with the prior model but also is constrained to the seismic data. In this study, one of the sampling methods based on Markov chain Monte Carlo is used, which is able to generate realizations of the desired model parameter by sampling from the posterior distribution function. Unlike deterministic inverse methods, which provide only one answer for the model parameters, in probabilistic inverse methods the realizations generated from the posterior distribution function allow statistical analysis and model uncertainty quantification. The results of the implementation of the proposed method on synthetic seismic data showed that the estimation of noise variance has a significant effect on the results of probabilistic inversion and the uncertainty of the model realizations. The underestimation of the noise variance leads to fitting the noise on the data and subsequently generates artifacts on the output realizations. The overestimation of the noise variance provides smooth realizations with higher uncertainty. In the latter case, the reference porosity model is in the 95% confidence interval in contrast to the former case. Therefore, care should be taken in estimating the noise variance in probabilistic inverse methods. Considering a calibrated rock physics model for the carbonate reservoir under study, which is the main core in a direct inversion approach, the proposed algorithm was applied to the seismic traces adjacent to the four well logs. The uncertainty of the porosity was quantified in each well location. The correlation coefficient of the mean of the porosity realizations and the true porosity in four well locations were approximated about 79%, 63%, 51% and 67%. The consistency of the results obtained from the inversion with the observed porosity at the well locations indicates the good performance of the algorithm in estimating the porosity and its associated uncertainty. Due to the ability of the probabilistic inverse methods in a direct inverse of seismic data to the petrophysical properties, and their applicability in being performed in a parallel structure in processing clusters, these algorithms can be used in reservoir characterization of 2D and 3D data.

Assessment of data uncertainty increases our ability to study and accurate understanding of about real world, because awareness from uncertainty of resources and remove error for improving the obtained results is one of the most important items in the remote sensing science. Since the digital elevation model (DEM) extracted from different sensors and geometry, Therefore selection of the most suitable DEM that provide better ground conditions is the one of the main needs for experts in this field. The aim of this study to vertical accuracy assessment of the ASTER and SRTM digital elevation model compared to the elevation data from topographic maps that prepared by the Iran National Cartographic Center in the 12 city from Khuzestan province. In order to evaluation the accuracy of the data, the Mean Error (ME) index and Root Mean Square Error (RMSE) index are used. The result of evaluation ME index for the cities with topographic map for ASTER and SRTM data showed the value 6.08 and 2.34 meter, respectively. Also, the result of RMSE index for ASTER and SRTM data showed the value 6.94 and 3.46 meter, respectively. Therefore more, the result showed that in most cities of Khuzestan province the DEM derived from SRTM has a higher accuracy than the ASTER digital model; Since the SRTM data have a low uncertainty, Therefore in studies related to the extraction drainage network, drainage density, physiography of the watershed area, runoff or geology, it is suggested that SRTM elevation model be used.

Since all mining projects are subjected to uncertainty, risk analysis of these projects is essential. It is also possible to know the exact amount of minable reserve, due to its location in the underground and the operating limitations, when the entire reserve is extracted. Therefore, the effective parameters in the technical and economic evaluation of mining projects are highly diverse and all of them are subject to wide variations with respect to the quantitative and qualitative dimensions of the data. This process is caused by causes beyond human control, and sometimes, no matter how much it costs, full information is not possible. Hence, in all mining projects, in calculating income and expenditure figures and therefore economic calculations, they have to take into account the margin of confidence and are required to calculate risk. It would make more logical to consider the probabilistic changes of effective parameters in the economic analysis of mining projects. And taking into account the least and most likely occurrence of each row. When one of the most important issues with uncertainty is the price of metal, then uncertainty in the ultimate pit limit determination is raised. Since metal price prediction is uncertain, chaos theory and fractal geometry can be used to predict metal price. In this study, chaos theory and fractal geometry have been used to estimate the metal price correctly, since the metal price changes follow fractal and chaotic behavior. Therefore, if a correct estimate of the metal price is obtained, the ultimate pit limit will be close to reality and based on that the optimal pit limit can be obtained, which according to the principles of sustainable development leads to the metal mining at a lower price and the ultimate pit limit is more sustainable and has better environmental protection.

The first and most important step in reducing damages and management of gully erosion, which leads to waste of water and soil resources, is the gully erosion susceptibility mapping. In present study, the potential of gully erosion in the Toroud-Najarabad basin were evaluated using the uncertainty of the theory of evidence method and linear multivariate regression as well as the combination of the above-mentioned methods. In view of this the gully erosion inventory map was prepared using various resources, and from 218 identified gullies, 70% (154 gullies) and 30% (64 gullies) were selected randomly for modeling and validation respectively. In the next step, 14 controling factors in the gully occurrence in the study area including topographic, geomorphometric, environmental and hydrologic parameters were determined for modeling. The results of multivariate regression model showed that land use parameters, slope and distance from the road had the greatest impact on gully occurrence. Susceptibility maps classified to five susceptibility classes of very low to very high. The area under curve (AUC) and Seed Cell Area Index (SCAI) were used to validate the susceptibility maps. The results of the validation showed that the combined model has a higher area under the curve (AUC) (0.933) toward the evidence-based (0.914) and multivariate regression (0.890) models, as well as, the SCAI value in the combined model showed that very low susceptibility to a very high susceptibility has a downward trend that indicating a proper separation of susceptibility classes in this model. Considering the excellent precision of the combined method proposed in this research, the results of this research can be used by decision makers and local managers to reduce losses and the methodology presented in this study can be used in similar areas to determine the areas that are susceptible to gully erosion.

Summary: In this study, a novel approach for linearized amplitude versus offset (AVO) inversion in a Bayesian framework is presented. Objective is to estimate the posterior distribution of three elastic parameters, P wave velocity, S wave velocity and rock density. The methodology is based on the convolutional model and a weak contrast linearized approximation of Zoeppritz equation for PP waves. In this study, assuming a priori Gaussian distribution for input parameters, and also, a Gaussian distribution for seismic misfit function, Bayesian equation also yields a Gaussian distribution for posterior parameters, which can be analytically computed. This analytic solution is a rather fast approach for inversion of elastic parameters along with their uncertainty distribution. This methodology is tested on both synthetic and field data sets and in both cases yields reasonable solutions. In the current study, assuming a weak contrast model for rock properties, a linearized AVO approximation of Zoeppritz equation is used in a Bayesian framework to invert prestack seismic data for the above-mentioned three elastic parameters. The methodology is tested on both synthetic and field data sets. The results show preferably good matches with the true data.

Introduction: Inversion of seismic AVO is a way to estimate elastic parameters from prestack seismic data. This technique can be solved in both nonlinear (Dahl and Ursin, 1991) and linear (Smith and Gildow, 1987) approaches. Lortzer and Berkhout (1993) also used the same methodology as presented here, but they used the relative contrast of elastic parameters instead of their absolute values.

Methodology and Approaches: Assuming a Gaussian distribution for elastic parameters and seismic noise, and also, a linearized formulation for forward modeling, distribution for posterior parameters will also be Gaussian. Based on this methodology, the mean and covariance matrices of prior distribution are estimated from well data. Then using the linearized formulation of AVO, the mean and covariance matrices of observed seismic data are estimated. Having the statistical parameters of prior and likelihood functions, the statistical parameters of posterior distribution is analytically yielded based on Bayesian formulation. The covariance matrix of posterior distribution gives an estimate of the uncertainty in the elastic parameters.

Results and Conclusions: A Bayesian AVO inversion method was proposed and tested on both synthetic and field data sets. In case of synthetic data, the estimated parameters fitted the true values almost exactly. The result for the field dataset was also reasonable and matched the well log data relatively well except in some locations where prestack seismic data were not preconditioned very well. The initial models used for this methodology does not need to be detailed at all and very simple initial models such as constant or linear values lead to good estimation of the posterior distribution. Therefore, this approach can be a good choice for generation of pseudowells where not a rich dataset is available.

Todays, sediment fingerprinting have been increasingly used to determine source contributions of sediments, but there is a need to continue refining procedure especially with respect to uncertainty analysis during modeling. In this research, we applied a Monte Carlo fingerprinting framework to exploring of uncertainty associate to source contributions of sand dune sediments in the Jazmurian region, Kerman province. In order to, after selecting optimum composite fingerprints using a two-stage statistical procedure including Kruskal-Wallis H test and discriminant function analysis, Collins mixing model was solved 10000 times in the MATLAB with 95% confidence levels (percentiles 2.5% and 97.5%). Among of 10 tracers, four tracers including Cr, Ni, Li and Co were selected as optimum composite fingerprints and then they entered as modeling procedure. Full uncertainty (0-100%) calculated for some sources especially Qs and Qal, and also for some sediment samples, Qt recognized as main source. Variability on the source for sand dune sediment samples is due to winds with multiple directions. The effectiveness of this technique to sand dune sediment fingerprinting suggests that they will be useful in other desert regions with active dune fields for recognition of their sources, quantifying source contributions and studying uncertainty of source contributions.

Design aspects of the mining projects, including resource geological modelling, mining methodology, mineral processing and production rates have a significant impact on the project economics and overall value. To generate a resource model, typically tonnes, grade and the tonnes/grade above the cut-off are applied as the economic criterion, which are not adequate alone. Geometallurgy combines geological and metallurgical information to provide spatially-based predictive model for mineral processing plants. This novel field has been introduced to integrate the various parameters such as hardness, grindability, recovery, liberation, concentrations, mineral texture, etc. Geometallurgical modeling requires to develop matrix an x-y-z plot where two of the axes represent geological factors (e.g., rock type and alteration) and the third axis represents critical parameters (hardness, texture, liberation degree of ore, distribution of penalty elements, etc.) that plays an important role for deposit domaining. The spatial distribution of response parameters is determined based on the primary parameters, which must consider nonlinear relationships and conversion scales between the experimental, pilot or industrial modes. The proposed algorithm represents a “value chain” approach in geometallurgical model compared to the common concept of mine planning evaluations.

Shear wave velocity (Vs) is a basic engineering soil property implemented in evaluating the soil shear modulus. Due to a few limitations، sometimes it is preferable to determine Vs indirectly by in situ tests، such as standard penetration test (SPT). However، inaccuracies in measurement or estimation of the influencing parameters have always been a major concern، and thus various statistical approaches have been proposed to subdue the effect of such inaccuracies in predictions of future events. In this article، an innovative approach based on robust optimization has been utilized to enumerate the effect of such uncertainties. In order to assess the merits of the proposed approach a database containing 326 data points of case histories from Adapazari، Turkey were gathered from renowned references. The identification technique used in this article is based on the robust counterpart of the least square problem which is a second order cone problem and is efficiently solved by interior point method. A definition of uncertainty based on frobenius norm of the data is introduced and examined against correlation coefficient of various correlation parameters and optimum values are determined. Finally the results of new correlation are compared with those utilizing a commonly used statistical method and the advantages and possibilities of the proposed correlation over the conventional method are highlighted

A precise earthquake location and location error estimation is a crucial element in many seismological applications such as local earthquake tomography, seismicity and seismic hazard assessment. Location error estimates may also be crucial to establish whether the hypocenter trend of an earthquake sequence really marks the seismogenic structure or simply reflects ill-conditioning of the location process.So far many methods have been introduced to locate earthquakes. Earthquake location methods have undergone many changes by Geiger’s (1912) principles. One of the first programs based on Geiger’s principles is Hypo71 (Lee and Lahr, 1972), which has already been used in many studies. The basic theory of Geiger (1912) is using Taylor series expansion of the travel time function of source to station. In order to simplify the earthquake location problem, Geiger used only the first term of Taylor series expansion that led to a straight-line equation. Therefore, they are known as linearized relationships. Using the linearized relationships results in a decrease in the accuracy of earthquake location due to losing the higher terms of Taylor series; it may lead to failure in determining the location of earthquakes using a suboptimal network, e.g. where earthquakes are located outside the seismic network. Because of the nonlinearity of the earthquake location problem, all of the algorithms and methods based on theses linearized relationships solve the earthquake location equation iteratively. Thurber (1985) showed that when the depth of earthquake is smaller than the closest distance to station, determining the focal depth is not possible in the linearized methods. Furthermore, for using the higher terms of Taylor series, it is necessary to calculate higher degree derivatives, which are very complex, and sometimes impossible, using a three-dimensional velocity model. However, the non-linear earthquake location problem can also be solved directly by a range of probabilistic algorithms (Tarantola and Valet, 1982). Tarantola and Valet (1982) presented a method that determined the location of earthquakes with fully non-linear relationships without any need to calculate the partial derivatives. The basic theory of nonlinear probabilistic method to determine the location of the earthquakes was introduced by Tarantola and Valet (1982) and Tarantola (1987).Reporting a reliable uncertainty for the location of an earthquake is one of the most important parts of earthquake location, so that presenting the epicenter and depth for an earthquake without the uncertainty is completely meaningless. Moreover, knowing the uncertainty of a location is very important in many other studies such as seismicity and tomography. Thus all the methods and algorithms designed to earthquake location; present an uncertainty for the depth and epicenter of the location. Calculation of uncertainty in an entire earthquake location problem, such as Hypo71 (Lee and Lahr, 1972) based on Geiger’s principles and NonLinLoc (Lomax et al., 2000) is by calculation of a covariance matrix. The basic premise in these methods is that theuncertainties of the observed arrival times and their relationship with the predicted travel times are assumed to be Gaussian (bell-shaped). A bell-shaped error in the time of receipt will be achieved only if the error is observed at the time and is calculated from a random and independent model. However, apart from errors that result from picking the seismic phases (in arrival times); the biggest error in an earthquake location is given by the seismic network. Bondar et al. (2004) identified four main network criteria for epicenter accuracy: (1) the number of phases used in per location; (2) the distance to the closest station; (3) the azimuthal gap; and (4) the secondary azimuthal gap.Thus, many studies are done to find optimal conditions for the use of a network station, e.g. Chatelain et al. (1980), Kissling et al. (1988), Gomberg et al. (1990). Based on the relocation of explosions, Bondar et al. (2004) introduced four characteristics for an optimal seismic network to achieve a location within a 95% confidence level and under 5 km error in depth and epicenter: (1) there are 10 or more stations, all within 250 km, (2) an azimuthal gap of less than 110, (3) a secondary azimuthal gap of less than 160, and (4) at least one station within 30 km.Another source of related errors is to use an inappropriate velocity model of the seismic waves to predict the travel times from source to stations.In this work, to investigate the calculation of uncertainty in different location methods, we compared the performances of nonlinear and linear earthquake location methods with synthetic data by simulation of three clusters of earthquakes in Central Alborz region where the location problem was ill-conditioned. Comparisons were made between the non-linear probabilistic algorithm named NonLinLoc and linear location method known as Hypo71. We studied the performance of these algorithms under different suboptimal network conditions including primary and secondary (largest azimuthal gap by removing single station) azimuthal gaps, an inappropriate velocity model, phase-reading error and the distance to the nearest station using various synthetic tests in the same network-geometry conditions of the real earthquake sequences in Mosha, Firuzkuh and Qom regions.We found out that in the suboptimal network conditions, the location error estimates from Hypo71 were, in general, less accurate than NonLinLoc's and NonLinLoc solutions were more reliable. For earthquakes occurred inside a dense seismic network, we concluded that linearized methods produced lower quality location error estimates with no overall bias in the hypocentral coordinates compared to non-linear methods.

Seismic vulnerability assessment is a multi attribute decision making problem based on geospatial information where the weight and importance of each of the criteria is determined by experts. Geospatial information deals with some uncertainties. One of the ways for integrating this information is evidential reasoning theory. The theory is based on independent assumption of information sources which is not correct in many cases including geospatial information. This paper propose a new method of intelligent decision making based on cautious conjunctive rule of combination for seismic vulnerability assessment in Tehran by assuming activation of the north fault. Also assumed that activation of this fault does not affect on activation of other faults in Tehran. This combination rule does not need independent assumption of information sources and can be used on data sources that have overlapping information and uncertainty.

Porosity parameter is an important reservoir property that can be obtained by studying the well core. However, all wells in a field do not have a core. Additionally, in some wells such as horizontal wells, measuring the well core is practically impossible. However, for almost all wells, log data is available. Usually these logs are used to estimate porosity. The porosity value obtained from this method is influenced by factors such as temperature, pressure, fluid type, and amount of hydrocarbons in shale formations. Thus it is slightly different from the exact value of porosity. Thus, estimates are prone to error and uncertainty. One of the best and yet most practical ways to reduce the amount of uncertainty in measurement is using various sources and data fusion techniques. The main benefit of these techniques is that they increase confidence and reduce risk and error in decision making. In this paper, in order to determine porosity values, data from four wells located in Azadegan oil field are used. First, multilayer neural network and multiple linear regressions are used to estimate the values and then the results of these techniques are compared with a data fusion method (Bayesian theory). To check if it would be possible to generalize these three methods on other data, the porosity parameter of another independent well in this field is also estimated by using thesetechniques. Number of input variables to estimate porosity in both the neural network and the multiple linear regressions methods is 7, and in the data fusion technique, a maximum of 7 input variables is used. Finally, by comparing the results of the three methods, it is concluded that the data fusion technique (Bayesian theory) is a considerably more accurate technique than multilayer neural network, and multiple linear regression, when it comes to porosity value estimation; Such that the results are correlated with the ground truth greater than 90%.

Porosity is one of the main variables in evaluating the characteristics of an oil field. Petrophysical data are normally used to determine these variables. Measurements obtained from well logs، containes some errors and uncertainty. This porosity is influenced by different factors، such as temperature، pressure، fluid type، clay content and the and amount of hydrocarbons. One of the best، and yet most practical ways to reduce the amount of uncertainty in porosity measurement is using various sources of data and data fusion techniques. Data fusion increase certainty and confidence and reduce risk and error in decision making. In this research، the porosity is estimated in 4 wells of Azadegan oil field، with data fusion method (Bayesian theory). To check the ability of generalization of the method، the porosity was also estimated in one other well of this field. A maximum of 7 input variables were used to estimate porosity in this new approach. The results showed that data fusion technique is more powerfull than traditional tecniques for porosity estimation. According to the results، this method has higher credibility than traditional techniques that show 0. 7 to 0. 8 regressions with log data but data fusion technique showed solidarity over 0. 9 with log data.

Nowadays different methods of soft computing to reduce time and calculation content are widely used in oil and gas industry. One of the main applications of these methods is prediction of the results of different processes in oil industry which their estimation with usual methods is too difficult and has either no single response or their response finding need more time and cost due to their nonlinear of the related problems. Because of much uncertainty on information which used in simulators، the results of these simulation models may have lot errors so production data (Pressure، Production Rate، Water Oil Ratio (WOR)، Gas Oil Ratio (GOR) and etc.) during reservoir life is used to historical accommodation between simulator results and actual data. The main purpose of this study is investigation and feasibility study of a usual method of artificial intelligence in oil industry، which is based on the soft computing. In this study، Artificial Neural Network (ANN) is used to make a predicting model for bottom hole pressure and for one of the fractured oil reservoirs with the seven years history of production. Some unconditional parameters such as fracture porosity، horizontal and vertical fracture permeability، height of matrix and matrix-fracture dual porosity were applied as input data of the networks، and pressure was applied as an output in network making. Applied data in network making is achieved from the 50 runs with simulator. The conclusion of this study showed that predicting model of ANN with error less than 4% and reduces the time of process، has a good ability to history matching.