Detrital zircon provenance of Neoproterozoic to Cenozoic deposits in Iran: Implications for chronostratigraphy and collisional tectonics

Ion-microprobe U–Pb analyses of 589 detrital zircon grains from 14 sandstones of the Alborz mountains, Zagros mountains, and central Iranian plateau provide an initial framework for understanding the Neoproterozoic to Cenozoic provenance history of Iran. The results place improved chronological constraints on the age of earliest sediment accumulation during Neoproterozoic–Cambrian time, the timing of the Mesozoic Iran–Eurasia collision and Cenozoic Arabia–Eurasia collision, and the contribution of various sediment sources of Gondwanan and Eurasian affinity during opening and closure of the Paleotethys and Neotethys oceans. The zircon age populations suggest that deposition of the extensive ~ 1 km-thick clastic sequence at the base of the cover succession commenced in latest Neoproterozoic and terminated by Middle Cambrian time. Comparison of the geochronological data with detrital zircon ages for northern Gondwana reveals that sediment principally derived from the East African orogen covered a vast region encompassing northern Africa and the Middle East. Although most previous studies propose a simple passive-margin setting for Paleozoic Iran, detrital zircon age spectra indicate Late Devonian–Early Permian and Cambrian–Ordovician magmatism. These data suggest that Iran was affiliated with Eurasian magmatic arcs or that rift-related magmatic activity during opening of Paleotethys and Neotethys was more pronounced than thought along the northern Gondwanan passive-margin. For a Triassic–Jurassic clastic overlap assemblage (Shemshak Formation) in the Alborz mountains, U–Pb zircon ages provide chronostratigraphic age control requiring collision of Iran with Eurasia by late Carnian–early Norian time (220–210 Ma). Finally, Cenozoic strata yield abundant zircons of Eocene age, consistent with derivation from arc magmatic rocks related to late-stage subduction and/or breakoff of the Neotethys slab. Together with the timing of foreland basin sedimentation in the Zagros, these detrital zircon ages help bracket the onset of the Arabia–Eurasia collision in Iran between middle Eocene and late Oligocene time.     

Intra-survey reservoir fluctuations – implications for quantitative 4D seismic analysis

During the time taken for seismic data to be acquired, reservoir pressure may fluctuate as a consequence of field production and operational procedures and fluid fronts may move significantly. These variations prevent accurate quantitative measurement of the reservoir change using 4D seismic data. Modelling studies on the Norne field simulation model using acquisition data from ocean-bottom seismometer and towed streamer systems indicate that the pre-stack intra-survey reservoir fluctuations are important and cannot be neglected. Similarly, the time-lapse seismic image in the post-stack domain does not represent a difference between two states of the reservoir at a unique base and monitor time, but is a mixed version of reality that depends on the sequence and timing of seismic shooting. The outcome is a lack of accuracy in the measurement of reservoir changes using the resulting processed and stacked 4D seismic data. Even for perfect spatial repeatability between surveys, a spatially variant noise floor is still anticipated to remain. For our particular North Sea acquisition data, we find that towed streamer data are more affected than the ocean-bottom seismometer data. We think that this may be typical for towed streamers due to their restricted aperture compared to ocean-bottom seismometer acquisitions, even for a favourable time sequence of shooting and spatial repeatability. Importantly, the pressure signals on the near and far offset stacks commonly used in quantitative 4D seismic inversion are found to be inconsistent due to the acquisition timestamp. Saturation changes at the boundaries of fluid fronts appear to show a similar inconsistency across sub-stacks. We recommend that 4D data are shot in a consistent manner to optimize aerial time coverage, and that additionally, the timestamp of the acquisition should be used to optimize pre-stack quantitative reservoir analysis.     

Deep neural network application for 4D seismic inversion to changes in pressure and saturation: Optimizing the use of synthetic training datasets

In this work, we tackle the challenge of quantitative estimation of reservoir dynamic property variations during a period of production, directly from four-dimensional seismic data in the amplitude domain. We employ a deep neural network to invert four-dimensional seismic amplitude maps to the simultaneous changes in pressure, water and gas saturations. The method is applied to a real field data case, where, as is common in such applications, the data measured at the wells are insufficient for properly training deep neural networks, thus, the network is trained on synthetic data. Training on synthetic data offers much freedom in designing a training dataset, therefore, it is important to understand the impact of the data distribution on the inversion results. To define the best way to construct a synthetic training dataset, we perform a study on four different approaches to populating the training set making remarks on data sizes, network generality and the impact of physics-based constraints. Using the results of a reservoir simulation model to populate our training datasets, we demonstrate the benefits of restricting training samples to fluid flow consistent combinations in the dynamic reservoir property domain. With this the network learns the physical correlations present in the training set, incorporating this information into the inference process, which allows it to make inferences on properties to which the seismic data are most uncertain. Additionally, we demonstrate the importance of applying regularization techniques such as adding noise to the synthetic data for training and show a possibility of estimating uncertainties in the inversion results by training multiple networks.     

A Neuro-Genetic Network for Predicting Uniaxial Compressive Strength of Rocks

Uniaxial Compressive Strength (UCS) is considered as one of the most important parameters in designing rock structures. Determination of this parameter requires preparation of rock samples which is costly and time consuming. Moreover discrepancy of laboratory test results is often observed. To overcome the drawbacks of traditional method of UCS measurement, in this paper, predictive models based on neuro-genetic approach and multivariable regression analysis have been developed for predicting compressive strength of different type of rocks. Coefficient of determinatoin (R2) and the Mean Square Error (MSE) were calculated for comparison of the models’ efficiency. It was observed that accuracy of the neuro-genetic model is significantly better than regression model. For the neuro-genetic and regression models, R2 and MSE were equal to 95.89 % and 0.0045 and 77.4 % and 1.61, respectively. According to sensitivity analysis for neuro-genetic model, Schmidt rebound number is the most effective parameter in predicting UCS.     

Application of artificial intelligence techniques for predicting the flyrock distance caused by blasting operation

Flyrock arising from blasting operations is one of the crucial and complex problems in mining industry and its prediction plays an important role in the minimization of related hazards. In past years, various empirical methods were developed for the prediction of flyrock distance using statistical analysis techniques, which have very low predictive capacity. Artificial intelligence (AI) techniques are now being used as alternate statistical techniques. In this paper, two predictive models were developed by using AI techniques to predict flyrock distance in Sungun copper mine of Iran. One of the models employed artificial neural network (ANN), and another, fuzzy logic. The results showed that both models were useful and efficient whereas the fuzzy model exhibited high performance than ANN model for predicting flyrock distance. The performance of the models showed that the AI is a good tool for minimizing the uncertainties in the blasting operations.     

Application of two fuzzy models using knowledge-based and linear aggregation approaches to identifying flooding-prone areas in Tehran

Flooding is one of the most problematic natural events affecting urban areas. In this regard, developing flooding models plays a crucial role in reducing flood-induced losses and assists city managers to determine flooding-prone areas (FPAs). The aim of this study is to investigate on the prediction capability of fuzzy analytical hierarchy process (FAHP) and Mamdani fuzzy inference system (MFIS) methods as two completely and semi-knowledge-based models to identify FPAs in Tehran, Iran. Six flooding conditioning factors including density of channel, distance from channel, land use, elevation, slope, and water discharge were extracted from various geo-spatial datasets. A total of 62 flooding locations were identified in the study area based on the existing reports and field surveys. Of these, 44 (70%) floods were randomly selected as training data and the remaining 18 (30%) cases were used for the validation purposes. After the data preparation step, data were processed by means of two statistical (FAHP) and soft computing (MFIS) methods. Unlike most statistical and soft computing approaches which use flooding inventory data for both training and evaluation of models, only conditioning factor was involved in data processing and inventory data were used in the current study to assess models prediction accuracy. Also, the efficiency of two approaches was evaluated by pixel matching (PM) and area under curve to validate the prediction capability of models. The prediction rate for MFIS and FAHP was 89% and 84%, respectively. Moreover, according to the results obtained from PM, it was found out that about 90% of known flooding locations fell in high-risk areas, whereas it was 83% for FAHP, indicating that flooding susceptibility map of MFIS has higher performance.

     

Time effects on dynamic soil properties under random excitation conditions

Two types of clayey soils, a kaolinite and a bentonite, were tested using a resonant column apparatus under random excitation conditions. The concept of root mean square (rms) strain was utilized for the purpose of strain calculations during random loading. The conventional estimator of the transfer function was used for random vibration analysis. The effect of confinement duration (at a constant pressure) on dynamic soil properties, namely damping and shear modulus, was evaluated. The results indicate that for both cohesive soils, the effect of time was less pronounced during random vibration than sinusoidal loading at the same rms strain. This effect is however more pronounced when the peak shearing strain of sinusoidal loading is considered. Furthermore, time effects were more pronounced at low strain levels than at high strain levels.     

Recent developments of the Middle East catalog

This article summarizes a recent study in the framework of the Global Earth model (GEM) and the Earthquake Model of the Middle East (EMME) project to establish the new catalog of seismicity for the Middle East, using all historical (pre-1900), early and modern instrumental events up to 2006. According to different seismicity, which depends on geophysical, geological, tectonic, and seismicity data, this region is subdivided to nine subregions, consisting of Alborz–Azerbaijan, Afghanistan–Pakistan, Saudi Arabia, Caucasus, Central Iran, Kopeh–Dagh, Makran, Zagros, and Turkey (Eastern Anatolia; after 30° E). After omitting the duplicate events, aftershocks, and foreshocks by using the Gruenthal method, and uniform all magnitude to Mw scale, 28,244 main events remain for the new catalog of Middle East from 1250 B.C. through 2006. The magnitude of completeness (Mc) was determined as 4.9 for five out of nine subregions, where the least values of Mc were found to be 4.2. The threshold of Mc is around 5.5, 5.0, 4.5, and 4.0, for the time after 1950, 1963, 1975, and 2000, respectively. The average of teleseismic depths in all regions is less than 15 km. Totally, majority of depth for Kopeh–Dagh and Central Iran, Zagros, and Alborz–Azerbaijan, approximately, is 15, 13, and 11 km and for Afghanistan–Pakistan, Caucasus, Makran, Turkey (after 30° E), and Saudi Arabia is about 9 km.