Application of a feedforward neural network in the search for Kuroko deposits in the Hokuroku district, Japan

A feedforward neural network with one hidden layer and five neurons was trained to recognize the distance to kuroko mineral deposits. Average amounts per hole of pyrite, sericite, and gypsum plus anhydrite as measured by X-rays in 69 drillholes were used to train the net. Drillholes near and between the Fukazawa, Furutobe, and Shakanai mines were used. The training data were selected carefully to represent well-explored areas where some confidence of the distance to ore was assured. A logarithmic transform was applied to remove the skewness of distance and each variable was scaled and centered by subtracting the median and dividing by the interquartile range. The learning algorithm of annealing plus conjugate gradients was used to minimize the mean squared error of the scaled distance to ore. The trained network then was applied to all of the 152 drillholes that had measured gypsum, sericite, and pyrite. A contour plot of the neural net predicted distance to ore shows fairly wide areas of 1 km or less to ore; each of the known deposit groups is within the 1 km contour. The high and low distances on the margins of the contoured distance plot are in part the result of boundary effects of the contouring algorithm. For example, the short distances to ore predicted west of the Shakanai (Hanaoka) deposits are in basement. However, the short distances to ore predicted northeast of Furotobe, just off the figure, coincide with the location of the Nurukawa kuroko deposit and the Omaki deposit, south of the Shakanai-Hanaoka deposits, seems to be on an extension of short distance to ore contour, but is beyond the 3 km limit from drillholes. Also of interest are some areas only a few kilometers from the Fukazawa and Shakanai groups of deposits that are estimated to be many kilometers from ore, apparently reflecting the network's recognition of the extreme local variability of the geology near some deposits.     

西藏甲玛铜多金属矿床深部斑岩矿体找矿突破及其意义

甲玛铜多金属矿是西藏冈底斯成矿带中东段勘查程度最高、成矿元素与矿体类型复杂的超大型斑岩-矽卡岩型矿床。通过4年多、近350 个钻孔的验证,不仅实现了矽卡岩矿体的找矿突破,同时在0-40线深部发现了产于二长花岗斑岩与石英闪长玢岩中的斑岩钼（铜）矿体,建立了“四位一体”的找矿勘查模式。斑岩矿体赋存标高一般处于4 600 m以下,矿体走向NW-SE,倾向NE,近直立,矿体垂向延伸大于350 m;斑岩矿石以发育细脉-浸染状、网脉状构造为特征;矿石中的矿石矿物主要为黄铜矿与辉钼矿,少见斑铜矿;脉石矿物主要为石英。初步查明:与铜矿化有关的含矿岩体主要为偏中性的石英闪长玢岩,蚀变以典型的细粒热液黑云母交代角闪石斑晶和基质而成的黑云母化蚀变为主;与钼矿化有关的含矿岩体主要为二长花岗斑岩,蚀变以硅化为主,次为绿帘石化、泥化和钾化。斑岩体与碳酸盐岩接触带常产出厚度超过200 m的巨厚矽卡岩矿体,且在岩体一侧有内矽卡岩产出。甲玛深部斑岩矿体的发现不仅证实了“斑岩-矽卡岩型”的矿床成因观点,而且完善了甲玛矿床成矿模式与勘查模型。     

基于粒子群优化的岩土工程反分析研究

岩土工程优化反分析本质上看是一个典型的复杂非线性函数优化问题,采用全局优化算法是解决这个问题的理想途径,但由于优化反分析中多次调用正分析的特点使得整个算法的计算效率很低。为了提高优化反分析的计算效率,把一种计算效率更高的新型仿生算法--粒子群优化引入岩土工程反分析领域,提高反分析的计算效率。在此基础上,结合有限元数值分析技术,提出了一种新的岩土工程优化反分析算法--粒子群优化反分析。并通过一个简单算例验证了该法的有效性。     

基于粒子群算法与混合罚函数法的有限元优化反演模型及应用

岩土工程优化反分析是一个典型的复杂非线性函数优化问题,采用全局优化算法是解决这个问题的理想途径。针对常规反演方法应用于岩土工程参数反演时搜索效率低的缺点,结合粒子群算法和遗传算法的特点,充分考虑二者的互补性,提出一种效率较高的全局优化算法,以测点的实测值与计算值建立一种新的评价函数,将多目标优化问题转化为单目标优化问题,用混合罚函数法将约束问题变为无约束问题,构建了一种新的目标函数,将有限元程序ABAQUS作为一个模块嵌入到优化算法程序中,编制了有限元优化反演分析程序。并给出了应用实例验证了该法的有效性和实用性,是一种可行的参数反演方法,可应用于实际工程中复杂岩土介质初始应力场反演、渗流场以及位移反分析     

Opaque mineralogy and magnetic properties of selected banded iron‐formations,Hamersley Basin,Western Australia

The oxide mineralogy and rock magnetic properties of unmineralised banded iron‐formations in selected portions of four drillholes in the Hamersley Basin, Western Australia are reviewed. In all four drillholes, petrographic studies indicate that primary euhedral to subhedral hematite is partially replaced by magnetite as a result of subsolidus reduction. All drillholes show partial recrystallisation of the secondary magnetite, suggesting that early subsolidus reduction was probably a regional event occurring during prograde metamorphism. Incomplete replacement of primary hematite by magnetite within and between sedimentary band structures indicates that equilibration in the magnetite stability field was not reached even at the mesoband scale. Subsequent subsolidus oxidation of magnetite and the formation of a second‐generation hematite are documented in only two of the drillholes. Goethite‐filled veins and thick selvages of goethite around some veins reflect movement of circulating oxidising fluids. The absence of goethite and second‐generation hematite in two of the drillholes indicates that subsolidus oxidation is not a regional event, but very much localised. Rapid changes in down‐hole susceptibility measurements correlate directly with detailed petrographic results as susceptibility readings change with the hematite/magnetite ratio on a mesoband scale. Acquisition of the main remanence correlates with the formation of hematite as the primary oxide phase followed by partial replacement by magnetite as a result of subsolidus reduction, supporting regional models requiring pre‐folding remanence. The strong orientation of the primary hematite parent parallel to band structures in the banded iron‐formations has influenced the direction of crystallisation remanent magnetisation during subsolidus reduction to the magnetite daughter. The strong planar alignment has also produced a planar magnetic fabric and marked anisotropy of magnetic susceptibility. A natural remanent magnetisation overprint and reduction in anisotropy of magnetic susceptibility are only recorded in samples that have undergone subsolidus oxidation and the recognition of localised post‐metamorphic oxidation overprinting can also explain ore deposit models requiring post‐folding remanence. The relative timing of and between oxidising fluid events is not known, but both petrographic and rock magnetic evidence to date suggests that there was at least one and probably two post‐folding oxidising events in the area of study.     

位移反分析的粒子群优化-高斯过程协同优化方法

针对采用随机全局优化技术进行岩土工程位移反分析存在数值计算量大、效率低的问题,将粒子群优化算法与高斯过程机器学习技术相结合,提出了位移反分析的粒子群优化-高斯过程协同优化方法。该方法利用全局寻优性能优异的粒子群优化算法进行寻优的基础上,采用高斯过程机器学习模型不断地总结历史经验,预测包含全局最优解的最有前景区域,通过提高粒子群搜索效率并降低适应度评价次数,进而有效地降低位移反分析过程中的数值计算工作量。多种测试函数的数学验证和工程算例的研究结果表明该方法是可行的,与传统方法相比较,可显著地降低位移反分析的计算耗时。     

山东乳山蓬家夼金矿矿体变化特征及深部成矿预测

蓬家夼金矿是山东乳山地区新发现的金矿类型,受荆山群中的层间滑动角砾岩系控制,沿胶莱盆地边缘莱阳组砾岩与荆山群副变质岩的构造接触带分布。矿体严格受层间滑动构造控制。在横、纵剖面上均呈连续的透镜状,每５０～１５０ｍ出现一次膨缩。矿体厚大部位产于层间滑动断层产状由陡（４５°～５０°）变缓（１５°～２０°）处。在上述认识的基础上,结合物化探方法,我们对蓬家夼金矿Ⅰ号矿体成功进行了预测。     

水文地质参数反演的Hooke-Jeeves粒子群混合算法

水文地质参数寻优结果的好坏会直接影响到地下水数值模拟的精度,而参数寻优结果很大程度上取决于寻优方法的选择。粒子群算法是一种基于群智能的随机全局寻优方法,算法的缺陷是后期搜索效率低劣。基于随机寻优算法的混合策略,引入有效的约束处理手段和粒子群算法惯性因子的动态非线性调整技术,有机融合粒子群算法与Hooke-Jeeves方法,提出一种适用于水文地质参数反演的HJPSO混合算法。应用研究表明,HJPSO混合算法在参数反演计算中求解精度高、收敛速度快、寻优性能强,是一种值得推广的水文地质参数识别方法。     

金川铜镍矿床隐伏富铜矿体成因研究及其深部找矿意义

金川矿床赋存于富橄榄石超镁铁质岩中,是目前世界第三大在采铜镍硫化物矿床.经过多年开采,如何在深、边部找到新的替代资源问题显得越来越重要.通过对金川矿床富铜隐伏矿体的矿体特征、矿石特征和矿石特殊地球化学特性等方面进行分析研究,特别是从空间立体对其矿化规律进行总结,并与其附近Ⅱ1#主矿体进行对比,指出该类型矿体既有岩浆熔离作用的特点,又有后期改造作用的特征,其形成经历了三个阶段:富含Cu、PGE岩浆深部熔离-脉动贯入、构造活化富集和后期热液叠加.同时探讨了来源于地幔深部的高镁玄武质岩浆,在深部岩浆房和阶段岩浆房熔离分异过程中,富镍岩浆和矿浆之间存在富铜岩浆.此外,Pb同位素表明,该隐伏富铜矿体形成时间为8亿年左右,早于块状特富矿;Pb、S同位素表明,该矿体主要来源于地幔,但曾被少量地壳物质混入.最后指出铜镍硫化矿床的成因是复杂多样的,在金川矿床深边部寻找新型矿体,尤其是富铜矿体的前景很大,而F_6断层可能是富铜隐伏矿体岩浆通道和Ⅱ1#主矿体岩浆的深部侵位通道之一.     

Optimization of nonconventional wells under uncertainty using statistical proxies

The determination of the optimal type and placement of a nonconventional well in a heterogeneous reservoir represents a challenging optimization problem. This determination is significantly more complicated if uncertainty in the reservoir geology is included in the optimization. In this study, a genetic algorithm is applied to optimize the deployment of nonconventional wells. Geological uncertainty is accounted for by optimizing over multiple reservoir models (realizations) subject to a prescribed risk attitude. To reduce the excessive computational requirements of the base method, a new statistical proxy (which provides fast estimates of the objective function) based on cluster analysis is introduced into the optimization process. This proxy provides an estimate of the cumulative distribution function (CDF) of the scenario performance, which enables the quantification of proxy uncertainty. Knowledge of the proxy-based performance estimate in conjunction with the proxy CDF enables the systematic selection of the most appropriate scenarios for full simulation. Application of the overall method for the optimization of monobore and dual-lateral well placement demonstrates the performance of the hybrid optimization procedure. Specifically, it is shown that by simulating only 10% or 20% of the scenarios (as determined by application of the proxy), optimization results very close to those achieved by simulating all cases are obtained.     

基于Surpac的铜陵新桥矿床露天开采境界优化

矿床的境界优化就是要设计一个最为接近理想状态的模型,使矿山企业利润最大化,在满足经济和边坡角几何约束的条件下,设计一个最终开采境界.基于三维地质矿业软件,可以将三维可视化技术与矿床的后期开采、生产进度编排进行紧密的结合,完成矿体形态与工程实体的衔接,其不同于传统境界优化的方法,可以在三维空间内进行境界优化的应用和研究.本研究选取SURPAC三维地质矿业软件系统,结合铜陵新桥矿床的实际需求,基于三维数字矿山模型,使用L-G算法规范的数学逻辑结构对露天采场进行境界优化.其结果可以为扩大经济效益提供详实的数据基础,并为新桥矿床开展更为合理的开采生产安排提出合理化建议.     

A simultaneous perturbation stochastic approximation algorithm for coupled well placement and control optimization under geologic uncertainty

Development of subsurface energy and environmental resources can be improved by tuning important decision variables such as well locations and operating rates to optimize a desired performance metric. Optimal well locations in a discretized reservoir model are typically identified by solving an integer programming problem while identification of optimal well settings (controls) is formulated as a continuous optimization problem. In general, however, the decision variables in field development optimization can include many design parameters such as the number, type, location, short-term and long-term operational settings (controls), and drilling schedule of the wells. In addition to the large number of decision variables, field optimization problems are further complicated by the existing technical and physical constraints as well as the uncertainty in describing heterogeneous properties of geologic formations. In this paper, we consider simultaneous optimization of well locations and dynamic rate allocations under geologic uncertainty using a variant of the simultaneous perturbation and stochastic approximation (SPSA). In addition, by taking advantage of the robustness of SPSA against errors in calculating the cost function, we develop an efficient field development optimization under geologic uncertainty, where an ensemble of models are used to describe important flow and transport reservoir properties (e.g., permeability and porosity). We use several numerical experiments, including a channel layer of the SPE10 model and the three-dimensional PUNQ-S3 reservoir, to illustrate the performance improvement that can be achieved by solving a combined well placement and control optimization using the SPSA algorithm under known and uncertain reservoir model assumptions.     

Well placement optimization subject to realistic field development constraints

This work considers the well placement problem in reservoir management and field development optimization. In particular, it emphasizes embedding realistic and practical constraints into a mathematical optimization formulation. Such constraints are a prerequisite for the wider use of mathematical optimization techniques in well placement problems, since constraints are a way to incorporate reservoir engineering knowledge into the problem formulation. There are important design limitations that are used by the field development team when treating the well placement problem, and these limitations need to be articulated and eventually formalized within the problem before conducting the search for optimal well placements. In addition, these design limitations may be explicit or implicit. In this work, various design limitations pertaining to well locations have been developed in close collaboration with a field operator on the Norwegian Continental Shelf. Moreover, this work focuses on developing constraint-handling capability to enforce these various considerations during optimization. In particular, the Particle Swarm Optimization (PSO) algorithm is applied to optimize for the well locations, and various practical well placement constraints are incorporated into the PSO algorithm using two different constraint-handling techniques: a decoder procedure and the penalty method. The decoder procedure maps the feasible search space onto a cube and has the advantage of not requiring parameter tuning. The penalty method converts the constrained optimization problem into an unconstrained one by introducing an additional term, which is called a penalty function, to the objective function. In contrast to the penalty method, only feasible solutions are evaluated in the decoder method. Through numerical simulations, a comparison between the penalty method and the decoder technique is performed for two cases. We show that the decoder technique can easily be implemented for the well placement problem, and furthermore, that it performs better than the penalty method in most of the cases.     

http://www.sciencedirect.com/science/article/pii/S1674987114001637

In this study, Imperialistic Competitive Algorithm(ICA) is utilized for locating the critical failure surface and computing the factor of safety(FOS) in a slope stability analysis based on the limit equilibrium approach. The factor of safety relating to each trial slip surface is calculated using a simplified algorithm of the Morgenstern-Price method, which satisfies both the force and the moment equilibriums. General slip surface is considered non-circular in this study that is constituted by linking random straight lines.To explore the performance of the proposed algorithm, four benchmark test problems are analyzed. The results demonstrate that the present techniques can provide reliable, accurate and efficient solutions for locating the critical failure surface and relating FOS. Moreover, in contrast with previous studies the present algorithm could reach the lower value of FOS and reached more exact solutions.     

Well placement optimization with the covariance matrix adaptation evolution strategy and meta-models

The amount of hydrocarbon recovered can be considerably increased by finding optimal placement of non-conventional wells. For that purpose, the use of optimization algorithms, where the objective function is evaluated using a reservoir simulator, is needed. Furthermore, for complex reservoir geologies with high heterogeneities, the optimization problem requires algorithms able to cope with the non-regularity of the objective function. In this paper, we propose an optimization methodology for determining optimal well locations and trajectories based on the covariance matrix adaptation evolution strategy (CMA-ES) which is recognized as one of the most powerful derivative-free optimizers for continuous optimization. In addition, to improve the optimization procedure, two new techniques are proposed: (a) adaptive penalization with rejection in order to handle well placement constraints and (b) incorporation of a meta-model, based on locally weighted regression, into CMA-ES, using an approximate stochastic ranking procedure, in order to reduce the number of reservoir simulations required to evaluate the objective function. The approach is applied to the PUNQ-S3 case and compared with a genetic algorithm (GA) incorporating the Genocop III technique for handling constraints. To allow a fair comparison, both algorithms are used without parameter tuning on the problem, and standard settings are used for the GA and default settings for CMA-ES. It is shown that our new approach outperforms the genetic algorithm: It leads in general to both a higher net present value and a significant reduction in the number of reservoir simulations needed to reach a good well configuration. Moreover, coupling CMA-ES with a meta-model leads to further improvement, which was around 20% for the synthetic case in this study.     

Proposed algorithm for optimization of directional additional exploratory drill holes and computer coding

Block model estimated based on the drill hole information gathered during exploration stage is not appropriate for short- and medium-term mine planning. Additional information prior to mining is required for various purposes, that the source of this information is drill holes. In most situations, reducing the overall uncertainty on the estimation is the major objective of additional drilling and collecting samples from them. The topic of selecting the optimum location for additional exploratory drill holes has been of interest to mining engineers and geostatisticians in the second half of last century, but all of them are not able to optimize directional drill holes because they are based on the 2D space and do not consider the 3D extension of drill holes and block model. This study introduces a methodology to optimize the dip and location of additional exploratory drill holes based on the defined objective function with considering 3D extension of ore body and drill holes. Direct search simulated annealing used that is efficient and robust method for solving the nonlinear unconstrained global optimization problems. This method is very useful in the case that drilling the drill holes with different dips has been obligated due to topography condition and ore body situation. A case study in gold resource illustrates the method.     

基于椭球滑动体假定和三维简化JANBU法的边坡稳定分析

采用一般椭球体模拟三维土坡的滑动体,利用三维简化JANBU法计算其安全系数,该法假定各个条柱底面的滑动方向在水平面上的投影相等,借此可用一个变量来表达各个底滑面的滑动方向,简便易行。此外,综合借鉴粒子群优化算法和和声算法的优点,提出了改进的粒子群算法,并利用对2个三维土坡的最危险滑动体进行了搜索,与已有结果进行了对比分析,表明方法是可行有效的。     

新疆哈密市黄山基性-超基性岩带铜镍矿床地质特征及矿床成因

黄山基性-超基性岩带铜镍矿床为东天山重要铜镍成矿带,受近东西向康古尔韧性剪切带控制,分布有黄山西、黄山东和香山3个中—大型铜镍矿床。矿体赋存于华力西晚期贫硅、贫碱、富镁铁超基性岩体中下部及相变部位,主要为隐伏矿体,呈板状、透镜状,成群分布。主要含镍钴金属硫化物为镍黄铁矿和紫硫镍矿,含铜矿物为黄铜矿。铜镍矿体与基性-超基性岩体紧密伴生,为同一母岩浆——上地幔岩浆上升侵位、结晶和重力分异之产物,属典型的岩浆熔离—贯人式铜镍矿床。     

Streeter-Phelps模型参数估计的遗传算法

提出Streeter-Phelps模型参数估计的新方法--遗传算法(Genetic Algorithm),它不同于常规参数估计方法,其优点在于,从多个初始点开始寻优,并采用交迭和变异运算避免过早地收敛到局部最优解,可获得全局最优解,且不受初始值影响.该方法不必求导计算,编程简单快捷.给出了实例计算及与其他方法相比较的结果.     

基于改进差分进化算法的阵列侧向测井反演

针对阵列侧向测井反演受给定初值影响的问题,将阵列侧向测井反演问题转化为无约束的非线性全局优化问题,提出了一种基于改进差分进化反演的新方法。在30次随机计算中改进差分进化算法与差分进化算法相比,其寻优成功率可以达到93.3%,平均进化代数为差分进化代数的1/8,其最优适应度也仅为差分进化最优适应度的一半。通过对阵列侧向模型进行反演计算,表明该算法满足电测井反演的需求。改进差分进化方法可有效地解决传统优化方法反演结果对初值的依赖性问题。