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101.
Assessment and prioritization of soil erosion triggering factors using analytical hierarchy process and Taguchi method 总被引:1,自引:0,他引:1
The main aims of this study are assessing some factors which affect the soil erosion process and evaluating the results of an analytical hierarchy process(AHP) method using the Taguchi method. Firstly, the weights for those factors and levels affecting soil erosion were determined by the AHP method. The determination of weights was based on the opinions of academic researchers, experts, middle managers,and members of the Soil and Water Research Centers in Iran. Secondly, an L32 Taguchi orthogona... 相似文献
102.
利用磁梯度张量反演磁性目标参数的方法广泛应用于是磁性目标定位,本文提出了一种通过测量两点磁梯度张量对磁性目标进行在线定位的方法.首先利用磁矩指向相同的原则从张量方程的解中得到目标方向,其次利用参数之间的线性关系解算出目标距离,最后利用两点磁感应强度之差,结合Levenberg-Marquardt优化算法对距离做进一步修正.基于所设计的两点张量测量阵列的仿真实验表明:(1)在相同条件下,本文方法定位成功率为64.24%,而2015年吕俊伟改进的STAR方法为59.85%;(2)仪器测量噪声强度达到某一阈值时,一定区域内定位成功率随噪声强度的增加迅速下降;(3)优化算法迭代次数上限取10次即可;(4)其他条件不变时,随着基线长度的增加,定位成功率先升高后降低. 相似文献
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Aayush Agarwal Akash Chandra Shalivahan Shalivahan Roshan K Singh 《Geophysical Prospecting》2018,66(6):1215-1226
There is no meta‐heuristic approach best suited for solving all optimization problems making this field of study highly active. This results in enhancing current approaches and proposing new meta‐heuristic algorithms. Out of all meta‐heuristic algorithms, swarm intelligence is preferred as it can preserve information about the search space over the course of iterations and usually has fewer tuning parameters. Grey Wolves, considered as apex predators, motivated us to simulate Grey Wolves in the optimization of geophysical data sets. The grey wolf optimizer is a swarm‐based meta‐heuristic algorithm, inspired by mimicking the social leadership hierarchy and hunting behaviour of Grey Wolves. The leadership hierarchy is simulated by alpha, beta, delta and omega types of wolves. The three main phases of hunting, that is searching, encircling and attacking prey, is implemented to perform the optimization. To evaluate the efficacy of the grey wolf optimizer, we performed inversion on the total gradient of magnetic, gravity and self‐potential anomalies. The results have been compared with the particle swarm optimization technique. Global minimum for all the examples from grey wolf optimizer was obtained with seven wolves in a pack and 2000 iterations. Inversion was initially performed on thin dykes for noise‐free and noise‐corrupted (up to 20% random noise) synthetic data sets. The inversion on a single thin dyke was performed with a different search space. The results demonstrate that, compared with particle swarm optimization, the grey wolf optimizer is less sensitive to search space variations. Inversion of noise‐corrupted data shows that grey wolf optimizer has a better capability in handling noisy data as compared to particle swarm optimization. Practical applicability of the grey wolf optimizer has been demonstrated by adopting four profiles (i.e. surface magnetic, airborne magnetic, gravity and self‐potential) from the published literature. The grey wolf optimizer results show better data fit than the particle swarm optimizer results and match well with borehole data. 相似文献
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准确拾取P、S波震相到时是深入开展地震波研究工作的基础,本文改进了自动拾取参数优化函数算法和质量评估方案,引入了拾取到时优化方案,使用基于参数优化的频带-带宽拾取算法、AICD拾取算法和峰度拾取算法对腾冲地区7个宽频带地震台站记录的地震资料开展了地震P、S波到时自动拾取,对拾取结果进行了优化和质量判定.结果表明:经参数优化、拾取优化后,采用3种方法自动拾取的P、S波到时与人工拾取到时的时差在0.1 s内的记录占比分别达到74.66%、70.98%.这些参数值均优于算法改进前的同类参数,证明了优化方法的可靠性. 相似文献
107.
Blended acquisition along with efficient spatial sampling is capable of providing high-quality seismic data in a cost-effective and productive manner. While deblending and data reconstruction conventionally accompany this way of data acquisition, the recorded data can be processed directly to estimate subsurface properties. We establish a workflow to design survey parameters that account for the source blending as well as the spatial sampling of sources and detectors. The proposed method involves an iterative scheme to derive the survey design leading to optimum reflectivity and velocity estimation via joint migration inversion. In the workflow, we extend the standard implementation of joint migration inversion to cope with the data acquired in a blended fashion along with irregular detector and source geometries. This makes a direct estimation of reflectivity and velocity models feasible without the need of deblending or data reconstruction. During the iterations, the errors in reflectivity and velocity estimates are used to update the survey parameters by integrating a genetic algorithm and a convolutional neural network. Bio-inspired operators enable the simultaneous update of the blending and sampling operators. To relate the choice of survey parameters to the performance of joint migration inversion, we utilize a convolutional neural network. The applied network architecture discards suboptimal solutions among newly generated ones. Conversely, it carries optimal ones to the subsequent step, which improves the efficiency of the proposed approach. The resultant acquisition scenario yields a notable enhancement in both reflectivity and velocity estimation attributable to the choice of survey parameters. 相似文献
108.
Polarimetric data is an additional source of information in PSI technique to improve its performance in land subsidence estimation. The combination of polarimetric data and radar interferometry can lead to an increase in coherence and the number of PS pixels. In this paper, we evaluated and compared the dual polarized Sentinel-1A (S1A) and TerraSAR-X (TSX) data to improve the PSInSAR algorithm. The improvement of this research is based on minimizing Amplitude Dispersion Index (ADI) by finding the optimum scattering mechanism to increase the number of PSC and PS pixels. The proposed method was tested using a dataset of 40 dual-pol SAR data (VV/VH) acquired by S1A and 20 dual-pol SAR data (HH/VV) acquired by TSX. The results revealed that using the TSX data, the number of PS pixels increased about 3 times in ESPO method than using the conventional channels, e.g., HH, and VV. This increase in S1A data was about 1.7 times in ESPO method. In addition, we investigated the efficiency of the three polarimetric optimization methods i.e. ESPO, BGSM, and Best for the dual polarized S1A and TSX data. Results showed that the PS density increased about 1.9 times in BGSM and about 1.5 times in Best method in TSX data. However, in S1A data, PS density increased about 1.1 times in BGSM. The Best method was not successful in increasing the PS density using the S1A data. Also, the effectiveness of the method was evaluated in urban and non-urban regions. The experimental results showed that the method was successful in significantly increasing the number of final PS pixels in both regions. 相似文献
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110.
Multi-tier interactive genetic algorithms for the optimization of long-term reservoir operation 总被引:1,自引:0,他引:1
Genetic algorithms (GAs) are well known optimization methods. However, complicated systems with high dimensional variables, such as long-term reservoir operation, usually prevent the methods from reaching optimal solutions. This study proposes a multi-tier interactive genetic algorithm (MIGA) which decomposes a complicated system (long series) into several small-scale sub-systems (sub-series) with GA applied to each sub-system and the multi-tier (key) information mutually interacts among individual sub-systems to find the optimal solution of long-term reservoir operation. To retain the integrity of the original system, over the multi-tier architecture, an operation strategy is designed to concatenate the primary tier and the allocation tiers by providing key information from the primary tier to the allocation tiers when initializing populations in each sub-system. The Shihmen Reservoir in Taiwan is used as a case study. For comparison, three long-term operation results of a sole GA search and a simulation based on the reservoir rule curves are compared with that of MIGA. The results demonstrate that MIGA is far more efficient than the sole GA and can successfully and efficiently increase the possibility of achieving an optimal solution. The improvement rate of fitness values increases more than 25%, and the computation time dramatically decreases 80% in a 20-year long-term operation case. The MIGA with the flexibility of decomposition strategies proposed in this study can be effectively and suitably used in long-term reservoir operation or systems with similar conditions. 相似文献