Use of a trench adjacent to a shallow foundation as a mitigation measure for hazards associated with reverse faulting     

Sadra  Vahid  Ghalandarzadeh  Abbas  Ashtiani  Mehdi 《Acta Geotechnica》2020,15(11):3167-3182

Acta Geotechnica - Evidence from recent earthquakes reminds us that fault-induced permanent ground displacement has a devastating effect on structures in addition to damage caused by wave...  相似文献   

Artificial recharge efficiency assessment by soil water balance and modelling approaches in a multi-layered vadose zone in a dry region     

Mojtaba Pakparvar  Hossein Hashemi  Meisam Rezaei  Wim M. Cornelis  Gholamali Nekooeian  Sayyed Ahang Kowsar 《水文科学杂志》2018,63(8):1183-1202

To assess recharge through floodwater spreading, three wells, approx. 30 m deep, were dug in a 35-year-old basin in southern Iran. Hydraulic parameters of the layers were measured. One well was equipped with pre-calibrated time domain reflectometry (TDR) sensors. The soil moisture was measured continuously before and after events. Rainfall, ponding depth and the duration of the flooding events were also measured. Recharge was assessed by the soil water balance method, and by calibrated (inverse solution) HYDRUS-1D. The results show that the 15 wetting front was interrupted at a layer with fine soil accumulation over a coarse layer at the depth of approx. 4 m. This seemed to occur due to fingering flow. Estimation of recharge by the soil water balance and modelling approaches showed a downward water flux of 55 and 57% of impounded floodwater, respectively.  相似文献   

Separation of magnetic anomalies into induced and remanent magnetization contributions     

Jamaledin Baniamerian  Shuang Liu  Xiangyun Hu  Maurizio Fedi  Mahak Singh Chauhan  Mahmoud Ahmed Abbas 《Geophysical Prospecting》2020,68(7):2320-2342

Inversion of magnetic data is complicated by the presence of remanent magnetization, and it provides limited information about the magnetic source because of the insufficiency of data and constraint information. We propose a Fourier domain transformation allowing the separation of magnetic anomalies into the components caused by induced and remanent magnetizations. The approach is based on the hypothesis that each isolated source is homogeneous with a uniform and specific Koenigsberger ratio. The distributions of susceptibility and remanent magnetization are subsequently recovered from the separated anomalies. Anomaly components, susceptibility distribution and distribution of the remanent and total magnetization vectors (direction and intensity) can be achieved through the processing of the anomaly components. The proposed method therefore provides a procedure to test the hypotheses about target source and magnetic field, by verifying these models based on available information or a priori information from geology. We test our methods using synthetic and real data acquired over the Zhangfushan iron-ore deposit and the Yeshan polymetallic deposit in eastern China. All the tests yield favourable results and the obtained models are helpful for the geological interpretation.  相似文献   

Lithology-dependent seismic anisotropic amplitude variation with offset correction in transversely isotropic media     

Amir Abbas Babasafari  Deva Prasad Ghosh  Ahmed Mohammed Ahmed Salim  Masoumeh Kordi 《Geophysical Prospecting》2020,68(8):2471-2493

Analysis of amplitude variation with offset is an essential step for reservoir characterization. For an accurate reservoir characterization, the amplitude obtained with an isotropic assumption of the reservoir must be corrected for the anisotropic effects. The objective is seismic anisotropic amplitude correction in an effective medium, and, to this end, values and signs of anisotropic parameter differences (Δδ and Δε) across the reflection interfaces are needed. These parameters can be identified by seismic and well log data. A new technique for anisotropic amplitude correction was developed to modify amplitude changes in seismic data in transversely isotropic media with a vertical axis of symmetry. The results show that characteristics of pre-stack seismic data, that is, amplitude variation with offset gradient, can be potentially related to the sign of anisotropic parameter differences (Δδ and Δε) between two layers of the reflection boundary. The proposed methodology is designed to attain a proper fit between modelled and observed amplitude variation with offset responses, after anisotropic correction, for all possible lithofacies at the reservoir boundary. We first estimate anisotropic parameters, that is, δ and ε, away from the wells through Backus averaging of elastic properties resulted from the first pass of isotropic pre-stack seismic inversion, on input data with no amplitude correction. Next, we estimate the anisotropic parameter differences at reflection interfaces (values and signs of Δδ and Δε). We then generate seismic angle gather data after anisotropic amplitude correction using Rüger's equation for the P-P reflection coefficient. The second pass of isotropic pre-stack seismic inversion is then performed on the amplitude-corrected data, and elastic properties are estimated. Final outcome demonstrates how introduced methodology helps to reduce the uncertainty of elastic property prediction. Pre-stack seismic inversion on amplitude-corrected seismic data results in more accurate elastic property prediction than what can be obtained from non-corrected data. Moreover, a new anisotropy attribute (ν) is presented for improvement of lithology identification.  相似文献   

Predicting groundwater level of wells in the Diyala River Basin in eastern Iraq using artificial neural network          下载免费PDF全文

Abdulrahman Th Mohammad  Qassem H Jalut  Nadia L Abbas 《地下水科学与工程》2020,8(1):87-96

Al-Mansourieh zone is a part of Al-Khalis City within the province of Diyala and located in the Diyala River Basin in eastern Iraq with a total area about 830 km2.Groundwater is the main water source for agriculture in this zone.Random well drilling without geological and hydraulic information has led the most of these wells to dry up quickly.Therefore,it is necessary to estimate the levels of groundwater in wells through observed data.In this study,Alyuda NeroIntelligance 2.1 software was applied to predict the groundwater levels in 244 wells using sets of measured data.These data included the coordinates of wells(x,y),elevations,well depth,discharge and groundwater levels.Three ANN structures(5-3-3-1,5-10-10-1 and 5-11-11-1)were used to predict the groundwater levels and to acquire the best matching between the measured and ANN predicted values.The coefficient of correlation,coefficient determination(R2)and sum-square error(SSE)were used to evaluate the performance of the ANN models.According to the ANN results,the model with the three structures has a good predictability and proves more effective for determining groundwater level in wells.The best predictor was achieved in the structure 5-3-3-1,with R2 about 0.92,0.89,0.84 and 0.91 in training,validation,testing and all processes respectively.The minimum average error in the best predictor is achieved in validation and testing processes at about 0.130 and 0.171 respectively.On the other hand,the results indicated that the model has the potential to determine the appropriate places for drilling the wells to obtain the highest level of groundwater.  相似文献   

Geochemical Prospectivity Mapping Through a Feature Extraction–Selection Classification Scheme     

Zekri  Hamid  Cohen  David R.  Mokhtari  Ahmad Reza  Esmaeili  Abbas 《Natural Resources Research》2019,28(3):849-865

Natural Resources Research - Machine learning (ML) schemes can enhance success in geochemical prospectivity mapping. This study has examined the effectiveness of several feature extraction or...  相似文献   

Interior solutions of fluid sphere in f(R,T) gravity admitting conformal killing vectors     

M. Zubair  I. H. Sardar  F. Rahaman  G. Abbas 《Astrophysics and Space Science》2016,361(7):238

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Empirical assessment of rockfall and debris flow risk along the Karakoram Highway,Pakistan     

Ali  Sajid  Haider  Rashid  Abbas  Wahid  Basharat  Muhammad  Reicherter  Klaus 《Natural Hazards》2021,106(3):2437-2460

Natural Hazards - The Karakoram Highway links north Pakistan with southwest China. It passes through unique geomorphological, geological and tectonic setting. This study focused 200-km-long section...  相似文献   

Investigation of Failed Slopes Based on a New Method of Back Analysis in Rock Masses: A Case Study     

Taherynia  Mohammad Hossein  Koopialipoor  Mohammadreza  Azizzadeh  Abbas  Fahimifar  Ahmad  Ghyasvand  Soheil 《Geotechnical and Geological Engineering》2021,39(7):5119-5133

Geotechnical and Geological Engineering - Small variation in shear strength parameters results in remarkable changes in the safety factor (SF) of a rock slope. In this regard, rock mass strength of...  相似文献   

Burden prediction in blasting operation using rock geomechanical properties     

Mohammad Rezaei  Masoud Monjezi  Saeed Ghorbani Moghaddam  Farhad Farzaneh 《Arabian Journal of Geosciences》2011,5(5):1031-1037

Burden prediction is a vital task in the production blasting. Both the excessive and insufficient burden can significantly affect the result of blasting operation. The burden which is determined by empirical models is often inaccurate and needs to be adjusted experimentally. In this paper, an attempt was made to develop an artificial neural network (ANN) in order to predict burden in the blasting operation of the Mouteh gold mine, using considering geomechanical properties of rocks as input parameters. As such here, network inputs consist of blastability index (BI), rock quality designation (RQD), unconfined compressive strength (UCS), density, and cohesive strength. To make a database (including 95 datasets), rock samples are used from Iran’s Mouteh goldmine. Trying various types of the networks, a neural network, with architecture 5-15-10-1, was found to be optimum. Superiority of ANN over regression model is proved by calculating. To compare the performance of the ANN modeling with that of multivariable regression analysis (MVRA), mean absolute error (E _a), mean relative error (E _r), and determination coefficient (R ²) between predicted and real values were calculated for both the models. It was observed that the ANN prediction capability is better than that of MVRA. The absolute and relative errors for the ANN model were calculated 0.05 m and 3.85%, respectively, whereas for the regression analysis, these errors were computed 0.11 m and 5.63%, respectively. Moreover, determination coefficient of the ANN model and MVRA were determined 0.987 and 0.924, respectively. Further, a sensitivity analysis shows that while BI and RQD were recognized as the most sensitive and effective parameters, cohesive strength is considered as the least sensitive input parameters on the ANN model output effective on the proposed (burden).  相似文献