电测深曲线的遗传算法反演

电测深曲线作为地下介质电阻率和深度的非线性函数，其解具有高度的非唯一性．常规的基于局部线性化的最优化反演方法易使解估计陷入局部极大值中，而且严重地依赖初始模型的选择．遗传算法作为一种全局最优化方法，对初始模型的依赖性大为减弱，且不易陷人局部极大值之中，从而能有效地解决这类非线性最优化问题．本文首次将遗传算法用于电测深解释并对实测曲线进行反演，效果很好，显示了遗传算法独特的优越性．     

实数编码遗传算法在大地电磁测深二维反演中的应用

作为全局非线性优化的新方法之一的遗传算法,近年来已从生物工程流行到大地电磁测深资料解释中.然而,大地电磁反演问题具有不适定性,解的非唯一性.通过结合求解不适定问题的Tikhonov正则化方法,本文采用实数编码遗传算法求解大地电磁二维反演问题.此算法在构建目标函数时引入正则化的思想,利用遗传算法求解最优化问题.常规的基于局部线性化的最优化反演方法易使解陷入局部极小值,而且严重的依赖初始模型的选择.与传统线性化的迭代反演方法相比,实数编码遗传算法能够克服传统方法的不足且能获得更好的反演结果.通过对大地电磁测深理论模型进行计算,结果表明:该算法具有收敛速度快、解的精度高和避免出现早熟等优点,可用于大地电磁资料解释.     

京津唐张地区速度结构和震源位置联合反演的遗传算法

震源参数和速度结构的联合反演是一个典型的非线性名参数最优化问题，常规的局部线性化反演方法往往易于陷入局部极值，且严重依赖于初始模型的选取。模拟生物界进化的遗传算法则是一种简单而高效的全局性搜索方法。     

基于不等式约束的三维电阻率探测混合反演方法

三维电阻率探测的线性反演和非线性反演中均存在着多解性的固有难题.电阻率线性反演方法的效率较高,但反演结果对初始模型的依赖性较强,易陷入局部极小;而非线性反演方法不依赖初始模型,但搜索效率极低,尚未见到关于三维电阻率非线性反演的文献.针对上述问题,融合线性与非线性反演方法的互补优势,提出了最小二乘法(线性方法)与改进遗传算法(非线性方法)相结合的混合反演方法的概念和思想.首先,提出了将介质电阻率变化范围作为不等式约束引入反演方程的思路,以实现压制多解性、提高可靠性的目标.提出了宽松不等式约束和基于钻孔推断的局部严格不等式约束的获取及定义方法.在此基础上,分别提出了基于不等式约束的最小二乘线性反演方法和遗传算法非线性反演方法.其次,对于遗传算法在变异搜索方向控制、初始群体产生等方面进行了改进,优化了其搜索方向和初始群体多样性.然后,提出了混合反演方法及其实现方案,利用改进遗传算法进行第一阶段反演,发挥其对初始模型的依赖程度低的优势,搜索到最优解附近的空间,输出当前最优个体;利用最小二乘法进行第二阶段反演,将遗传算法得到的当前最优个体作为初始模型,在最优解附近空间执行高效率的局部线性搜索,最终实现地电结构的三维成像.最后,开展了合成数据与实际工程算例验证,与传统最小二乘方法进行了对比,发现混合反演方法在压制多解性、摆脱初始模型依赖和提高反演效果方面有较好效果.     

多尺度逐次逼近遗传算法反演大地电磁资料

遗传算法是一种随机全局搜索算法,与常规的基于局部线性化的最优化方法相比对初始模型的依赖性大为减弱,但是存在着有效基因丢失和早熟收敛问题．采用多尺度逐次逼近反演思想而建立的多尺度逐次逼近遗传算法,能有效地解决上述问题．用该算法对大地电磁资料进行反演,理论曲线和实测资料的试算结果表明多尺度逐次逼近遗传算法能够自动反演地电参数．     

基于遗传算法CSAMT反演计算研究

提出了CSAMT方法测深的遗传算法反演,比较传统的线性反演方法,其对初始模型依赖性小,全局寻优能力强,适用于在地下电性参数比较模糊的条件下对测深曲线进行反演.本文设计多种理论模型进行模拟,证明该算法精度高,可行性强.     

基于遗传算法和统计学的电阻率测深二维反演研究

本文介绍了一种以遗传算法和统计学为基础的反演方法,该方法利用一维反演结果的统计学特征确定地层电性结构并设计初始模型,使用遗传算法修改模型,经过二维有限单元法正演达到拟合误差最小,从而完成电阻率测深数据的二维反演.这个方法的优点在于不需要解大型线性方程组,降低计算量,先判断地层电性结构,反演后给出用地电断面表示的直观的解释结果.理论模型试算和模型实验证明该方法效果较好.     

基于L1范数的瞬变电磁非线性反演

瞬变电磁反演存在高度的非线性特征,常用的最小二乘等线性反演方法往往对初始模型高度依赖,并且极易陷入局部最优解.本文基于观测数据与模拟数据的L1范数建立目标函数,采用模拟退火非线性全局最优化方法实现瞬变电磁一维反演.初始模型完全随机产生,通过指数函数退温机制模拟系统能量最小实现迭代,通过接收概率函数评价当前模型,实现局部最优解的跳出,最终实现全局最优化求解.通过数值算例发现,无论给定的反演层数等于还是大于设计模型,都可以获得较好的反演效果,因而可以在反演初始就设计较多的层数,实现反演模型的自动拟合;同时,利用含噪声数据反演进一步验证算法的稳定性.最后,对实测数据进行了反演测试,结果与钻孔编录基本一致,表明提出的基于L1范数的模拟退火反演可用于实测数据处理.     

一种新的地球物理反演方法——模拟原子跃迁反演法

详细研究了一般地球物理反问题的迭代优化求解过程与物理学中原子跃迁过程的对应关系，建立了反演问题中模型空间、初始模型、局部极值模型、最优化模型等与原子的态空间、定态、激发态、基态等的对应关系. 在此基础上，模拟了物理学中原子从激发态向基态跃迁的物理过程，建立了一种与原子跃迁过程相对应的非线性随机跃迁数学模型和模型解跃迁搜索准则，导出了适用于一般地球物理资料的模拟原子跃迁的非线性反演算法. 用理论测试函数对这种新的反演方法进行了数值试验，结果表明该方法具有解不依赖于初始模型、收敛速度快等优点.     

大地电磁测深资料的二次函数逼近非线性反演

将二次函数逼近非线性优化首次应用于大地电磁测深反演问题,该反演方法利用二次函数有唯一最小值的特点进行逼近大地电磁反演模型,从而避免了常规的迭代反演过程中陷入局部极小问题,实现了对目标函数求全局极小,较好地解决了非唯一性问题;同时该方法不用求灵敏度矩阵,且对初始模型无任何要求。通过理论模型检验、井旁MT点反演结果与测井曲线的对比及MT测线的反演电阻率深度剖面与地震测线的时间剖面对比均表明,本文方法取得较好的应用效果。     

基于混合遗传算法的斜测电离图参数反演

斜向探测是获取电离层状态信息的重要手段之一,对斜测电离图的反演可以得到电离层的相关结构参数.遗传算法是一种有效的并得到普遍应用的反演方法,该算法的求解不依赖于初值的选择,可以有效地减少反演问题解的非唯一性,但也存在“过早收敛”和局部搜索能力差等缺陷,从而导致反演精度下降,影响反演结果的可靠性.本文提出将基于模拟退火的混合遗传算法应用到斜测电离图的参数反演中,该算法不仅把握总体能力强,且具有较强的局部搜索能力,是遗传算法和模拟退火算法的优势互补.为了验证该算法反演结果的可靠性和稳定性,首先分别采用遗传算法、模拟退火算法和混合遗传算法对合成的电离图进行反演,反演参数包括临界频率,最大电子浓度和半厚度.通过对三种算法反演结果的对比,得出混合遗传算法的反演结果最接近真实值,需要的迭代次数也远远小于其他两种算法;通过改变种群大小和总迭代次数来判断参数值的改变对三种算法反演结果的影响,得出混合遗传算法有效地降低了参数的选取对反演结果的影响.然后用这三种反演算法对实测电离图进行反演,并将它们的反演结果与斜测链路中点的实际垂测数据进行比较,结果显示混合遗传算法84.62%的反演结果可以控制在误差范围之内,高于遗传算法（76.93%）和模拟退火算法（65.38%）.这些都表明了混合遗传算法的反演结果具有较强的可靠性,在反演的寻优能力和稳定性上要明显优于遗传算法和模拟退火算法,对实测电离层图的反演具有很强的借鉴意义和应用价值.     

Joint parameter estimation from magnetic resonance and vertical electric soundings using a multi‐objective genetic algorithm

Magnetic resonance sounding (MRS) has increasingly become an important method in hydrogeophysics because it allows for estimations of essential hydraulic properties such as porosity and hydraulic conductivity. A resistivity model is required for magnetic resonance sounding modelling and inversion. Therefore, joint interpretation or inversion is favourable to reduce the ambiguities that arise in separate magnetic resonance sounding and vertical electrical sounding (VES) inversions. A new method is suggested for the joint inversion of magnetic resonance sounding and vertical electrical sounding data. A one‐dimensional blocky model with varying layer thicknesses is used for the subsurface discretization. Instead of conventional derivative‐based inversion schemes that are strongly dependent on initial models, a global multi‐objective optimization scheme (a genetic algorithm [GA] in this case) is preferred to examine a set of possible solutions in a predefined search space. Multi‐objective joint optimization avoids the domination of one objective over the other without applying a weighting scheme. The outcome is a group of non‐dominated optimal solutions referred to as the Pareto‐optimal set. Tests conducted using synthetic data show that the multi‐objective joint optimization approximates the joint model parameters within the experimental error level and illustrates the range of trade‐off solutions, which is useful for understanding the consistency and conflicts between two models and objectives. Overall, the Levenberg‐Marquardt inversion of field data measured during a survey on a North Sea island presents similar solutions. However, the multi‐objective genetic algorithm method presents an efficient method for exploring the search space by producing a set of non‐dominated solutions. Borehole data were used to provide a verification of the inversion outcomes and indicate that the suggested genetic algorithm method is complementary for derivative‐based inversions.     

Studying ionospheric responses to magnetic storms depending on the local time of the storm main phase onset

A morphological analysis of vertical sounding data obtained in Irkutsk from 2003 to 2008 has been performed. The AE index was used to determine the geomagnetic activity level, and the storm main phase onset was registered based on the D _st index. The ionospheric response to a magnetic storm was estimated based on the relative deviation of the critical frequency and altitude of the ionospheric F2 region from the median values. Superstrong magnetic storms and storms without positive initial phases were not considered when the data were selected. We found that positive ionospheric disturbances, which were accompanied by an increase in the F2 region maximum altitude, predominated between the storm initial phase and main phases during all considered magnetic storms. Between these storm phases, negative disturbances were only registered at night. Predominance of positive ionospheric disturbances over negative ones can be related to the selection of storms for studies.     

基于牛顿迭代法和遗传算法的CSAMT近场校正

由于可控源音频大地电磁法（CSAMT）采用人工场源在大大增加信号强度的同时也带来了在近区产生非平面波效应的问题,其表现特征为在双对数坐标中卡尼亚电阻率曲线呈45°上升,即所谓的近场效应.本文首先提出了基于牛顿迭代法求解视电阻率方程的近场效应校正方法,通过对二层、三层理论模型试算验证了该方法的有效性;为进一步增强对噪声的压制能力,本文进一步引入了遗传反演的思想,将全频段误差最小作为目标函数,采用自适应正则化方法引入模型参数梯度最小作为稳定因子,由遗传算法求解得到校正后视电阻率,通过对理论模拟数据加随机噪声的校正结果,表明该方法在实现近场校正的同时能剔除噪声干扰,得到的结果与理论模拟曲线吻合度较高.通过对内蒙古曹四夭钼矿区实测CSAMT数据处理结果表明,牛顿法和遗传算法均能明显校正CSAMT因近场效应引起的假高阻异常,能更好地反映地电结构特征.     

Variations in the horizontal correlation radius of the ionosphere during a magnetospheric substorm

A change in the correlation radius of the ionosphere during the magnetospheric substorm of February 14, 2011, which is considered to be 500 km at midlatitudes, has been estimated. The vertical sounding (VS) data from the St. Petersburg and Sodankyla (Finland) observatories, as well as the data of oblique incidence sounding (OIS) at the Sodankyla-St. Petersburg path with a length of 790 km, have been analyzed. A specific feature of the experiment consisted in that the signals of a VS transmitter from Sodankyla were synchronously received at the receiving point on the OIS path in St. Petersburg. The OIS path reflection point is located at a distance of ～400 km from the VS reflection point. Ionograms typical of the VS and OIS signal reflection points in the ionosphere, the distance between which was slightly smaller than the correlation radius of the ionosphere (500 km), and the data of the Sodankyla and St. Petersburg ionosondes have been compared. It has been indicated that a horizontal correlation radius of 400 km can only be considered acceptable during three disturbance phases: the initial phase before the reconfiguration of the ionosphere; the explosion phase (the disturbance maximum), when only the sporadic Es layer is the reflecting ionospheric layer; and the recovery phase, when a disturbance already ceases and the ionosphere returns to its initial undisturbed state. During other disturbance phases, the correlation radius (if it exists) is much smaller than 400 km.     

Curve‐Number/Green–Ampt mixed procedure for streamflow predictions in ungauged basins: Parameter sensitivity analysis

A procedure combining the Soil Conservation Service‐Curve Number (SCS‐CN) method and the Green–Ampt (GA) infiltration equation was recently developed to overcome some of the drawbacks of the classic SCS‐CN approach when estimating the volume of surface runoff at a sub‐daily time resolution. The rationale of this mixed procedure, named Curve Number for Green–Ampt (CN4GA), is to use the GA infiltration model to distribute the total volume of the net hyetograph (rainfall excess) provided by the SCS‐CN method over time. The initial abstraction and the total volume of rainfall given by the SCS‐CN method are used to identify the ponding time and to quantify the hydraulic conductivity parameter of the GA equation. In this paper, a sensitivity analysis of the mixed CN4GA parameters is presented with the aim to identify conditions where the mixed procedure can be effectively used within the Prediction in Ungauged Basin perspective. The effects exerted by changes in selected input parameters on the outputs are evaluated using rectangular and triangular synthetic hyetographs as well as 100 maximum annual storms selected from synthetic rainfall time series. When applied to extreme precipitation events, which are characterized by predominant peaks of rainfall, the CN4GA appears to be rather insensitive to the input hydraulic parameters of the soil, which is an interesting feature of the CN4GA approach and makes it an ideal candidate for the rainfall excess estimation at sub‐daily temporal resolution at ungauged sites. Copyright © 2013 John Wiley & Sons, Ltd.