山区谷底沉陷预测模型及其参数反演

我国西部地区地势复杂,沟壑纵横,地下开采极易导致边坡失稳,引发采动滑坡。在地下采动沉降与滑坡体挤压上升的叠加影响下,谷底区域地表沉降值明显小于类似地质采矿条件下的平原地区。为准确预测山区谷底区域地表沉降值,基于简支梁的弹性变形理论,并借助概率密度函数建立了山区谷底区域地表沉陷预计修正模型,明确模型参数物理意义及其取值方法。依据修正模型,以实测值和预测值之差平方和最小为原则构建适应值函数,基于模拟退火粒子群算法提出新的模型参数反演方法,借助MATLAB语言编制了相应的参数反演程序。最后将研究成果应用于山西某矿,得到谷底区域预测结果中误差为73 mm,与实测值基本一致,取得了较好的工程实践效果。     

Deriving Optimal Exploration Target Zones on Mineral Prospectivity Maps

This paper describes a quantitative methodology for deriving optimal exploration target zones based on a probabilistic mineral prospectivity map. The methodology is demonstrated in the Rodalquilar mineral district in Spain. A subset of known occurrences of mineral deposits of the type sought was considered discovered and then used as training data, and a map of distances to faults/fractures and three band ratio images of hyperspectral data were used as layers of spatial evidence in weights-of-evidence (WofE) modeling of mineral prospectivity in the study area. A derived posterior probability map of mineral deposit occurrence showing non-violation of the conditional independence assumption and having the highest prediction rate was then put into an objective function in simulated annealing in order to derive a set of optimal exploration focal points. Each optimal exploration focal point represents a pixel or location within a circular neighborhood of pixels with high posterior probability of mineral deposit occurrence. Buffering of each optimal exploration focal point, based on proximity analysis, resulted in optimal exploration target zones. Many of these target zones coincided spatially with at least one occurrence of mineral deposit of the type sought in the subset of cross-validation (i.e., presumed undiscovered) mineral deposits of the type sought. The results of the study showed the usefulness of the proposed methodology for objective delineation of optimal exploration target zones based on a probabilistic mineral prospectivity map.     

Optimization of mobile radioactivity monitoring networks

In case of a nuclear accident, decision makers rely on high-resolution and accurate information about the spatial distribution of radioactive contamination surrounding the accident site. However, the static nuclear monitoring networks of many European countries are generally too coarse to provide the desired level of spatial accuracy. In the Netherlands, authorities are considering a strategy in which measurement density is increased during an emergency using complementary mobile measuring devices. This raises the question, where should these mobile devices be placed? This article proposes a geostatistical methodology to optimize the allocation of mobile measurement devices, such that the expected weighted sum of false-positive and false-negative areas (i.e. false classification into safe and unsafe zones) is minimized. Radioactivity concentration is modelled as the sum of a deterministic trend and a zero-mean spatially correlated stochastic residual. The trend is defined as the outcome of a physical atmospheric dispersion model, NPK-PUFF. The residual is characterized by a semivariogram of differences between the outputs of various NPK-PUFF model runs, designed to reflect the effect of uncertainty in NPK-PUFF meteorological inputs (e.g. wind speed, wind direction). Spatial simulated annealing is used to obtain the optimal monitoring design, in which accessibility of sampling sites (e.g. distance to roads) is also considered. Although the methodology is computationally demanding, results are promising and the computational load may be considerably reduced to compute optimal mobile monitoring designs in nearly real time.     

A heuristic cellular automata approach for modelling urban land-use change based on simulated annealing

This article presents a novel cellular automata (CA) approach to simulate the spatio-temporal process of urban land-use change based on the simulated annealing (SA) algorithm. The SA algorithm enables dynamic optimisation of the CA's transition rules that would otherwise be difficult to configure using conventional mathematical methods. In this heuristic approach, an objective function is constructed based on a theoretical accumulative disagreement between the simulated land-use pattern and the actual land-use pattern derived from remotely sensed imagery. The function value that measures the mismatch between the actual and the simulated land-use patterns would be minimised randomly through the SA process. Hence, a set of attribution parameters that can be used in the CA model is achieved. An SA optimisation tool was developed using Matlab and incorporated into the cellular simulation in GIS to form an integrated SACA model. An application of the SACA model to simulate the spatio-temporal process of land-use change in Jinshan District of Shanghai Municipality, PR China, from 1992 to 2008 shows that this modelling approach is efficient and robust and can be used to reconstruct historical urban land-use patterns to assist with urban planning policy-making and actions. Comparison of the SACA model with a typical CA model based on a logistic regression method without the SA optimisation (also known as LogCA) shows that the SACA model generates better simulation results than the LogCA model, and the improvement of the SACA over the LogCA model is largely attributed to higher locational accuracy, a feature desirable in most spatially explicit simulations of geographical processes.     

A Voronoi neighborhood-based search heuristic for distance/capacity constrained very large vehicle routing problems

Local search heuristics for very large-scale vehicle routing problems (VRPs) have made remarkable advances in recent years. However, few local search heuristics have focused on the use of the spatial neighborhood in Voronoi diagrams to improve local searches. Based on the concept of a k-ring shaped Voronoi neighbor, we propose a Voronoi spatial neighborhood-based search heuristic and algorithm to solve very large-scale VRPs. In this algorithm, k-ring Voronoi neighbors of a customer are limited to building and updating local routings, and rearranging local routings with improper links. This algorithm was evaluated using four sets of benchmark tests for 200–8683 customers. Solutions were compared with specific examples in the literature, such as the one-depot VRP. This algorithm produced better solutions than some of the best-known benchmark VRP solutions and requires less computational time. The algorithm outperformed previous methods used to solve very large-scale, real-world distance constrained capacitated VRP.     

正则化偏移成像的全局优化快速算法

目前,偏移后的地震剖面往往只是一个地质构造图像,还不能为后续的岩性分析和油气储层属性的提取提供更精确的信息.为了得到高分辨率真振幅的图像,建议采用正则化偏移成像方法.针对本问题数据规模大和正演算子矩阵稀疏的特点,提出采用一种新的算法--无记忆拟牛顿-模拟退火法对偏移算子方程进行求解.该方法综合了无记忆拟牛顿法优良的局部...     

A hybrid computational approach to formulate soil deformation moduli obtained from PLT

In this study, new empirical equations were developed to predict the soil deformation moduli utilizing a hybrid method coupling genetic programming and simulated annealing, called GP/SA. The proposed models relate secant (E_s), unloading (E_u) and reloading (E_r) moduli obtained from plate load–settlement curves to the basic soil physical properties. Several models with different combinations of the influencing parameters were developed and checked to select the best GP/SA models. The database used for developing the models was established upon a series of plate load tests (PLT) conducted on different soil types at various depths. The validity of the models was tested using parts of the test results that were not included in the analysis. The validation of the models was further verified using several statistical criteria. A traditional GP analysis was performed to benchmark the GP/SA models. The contributions of the parameters affecting E_s, E_u and E_r were analyzed through a sensitivity analysis. The proposed models are able to estimate the soil deformation moduli with an acceptable degree of accuracy. The E_s prediction model has a remarkably better performance than the models developed for predicting E_u and E_r. The simplified formulations for E_s, E_u and E_r provide significantly better results than the GP-based models and empirical models found in the literature.     

引入退火机制的智能单粒子算法在复杂边坡最危险滑动面搜索中的应用

复杂边坡的安全系数可能存在多个局部极小值点,如何确定边坡的最小安全系数是复杂边坡稳定性分析中的一个关键问题.本文结合简化Bishop法,采用一种新的启发式全局优化算法--智能单粒子算法(ISP0)来搜索复杂边坡的最危险滑动面.为帮助该算法快速跳出局部极值点,本文将模拟退火(SA)机制引入到智能单粒子算法中,结合了两种算...     

基于稳健模拟退火-单纯形混合算法的微震定位研究

为提高矿井微震的定位精度,通过模拟试验对比分析常规定位及新方法在定位求解中的适用范围及优缺点,研究反演未知变量数目、检波器的密度、波速等对各自定位方法的影响。基于单纯形法的优点,并结合模拟退火法的全局收敛性,提出采用稳健的模拟退火-单纯形法进行微震定位。研究发现,该方法不必直接搜索发震时刻,只需通过稳健方法预先估算,利用较少的检波器就可进行微震定位,且基本不受初始波速影响,从而大大减少搜索步数,有效地提高定位精度及收敛速度。以柿竹园矿人工爆破定位测试试验为例,重点分析比较模拟退火-单纯形法、稳健模拟退火-单纯形法及其他定位方法的空间绝对距离及目标函数误差,认为检波器数量较多且可获得理想的P波信息时,应用非线性最小二乘法能求出可信的结果;其余定位方法的三维定位误差及目标函数误差相差不大,但稳健模拟退火-单纯形法目标函数误差最小,稳定性最高,可在矿井微震定位中推广使用。