深海油气地震勘探进展和展望

20世纪90年代后期以来，随着深海技术的进步，寻找新型大油气田的成功率明显上升，深海勘探进入一个新的纪元。本文较为详尽的综述了近几年国外在深海石油地震勘探中的关键技术和新发现，主要包括：（1）似地震反射面勘探技术探测天然气水合物；（2）混合地震反演技术；（3）区域多次二维叠前深度偏移技术，（4）地震和地质力学方法，（5）近偏移距离压制多次波，在将来的10年，海底检波器随地形排列等方面将成为新的深海油气勘探发展趋势。     

油气非地震勘探技术的发展趋势

提出未来非地震技术的两个主要发展方向：向高成熟区和复杂区的高精度非地震勘探技术和面向油气预测与油田开发的非地震岩性勘探技术；阐明了未来三维非地震技术及综合勘探技术对高精度、高效益油气勘探的重要性，指出了非地震技术在油气预测与油田开发中将占有重要地位，将发挥其特殊的作用，展现了油气非地震勘探技术广阔的应用前景。     

城市活断层的抗干扰高分辨率浅层地震勘探研究

首先，简要介绍了城市活断层探测的意义及世界各国开展城市活断层探测的基本情况。在简述抗干扰高分辨率浅层地震勘探基本原理的基础上，重点论述了城市活断层的抗干扰高分辨率浅层地震勘探的震源激发、数字地震仪性能、接收方式与接收条件、观测系统以及数据处理与资料解释等。研究表明，对于城市活断层的抗干扰高分辨率浅层地震勘探，在数据采集环节应采用具有线性或非线性变频扫描功能的可控震源和与其相匹配的地震仪器，以及小道间距、小偏移距、多接收道、短排列和高频检波器接收的工作方法；在数据处理与解释环节，要重视折射静校正技术、噪声压制技术、高精度速度分析技术、子波压缩技术、子波零相位化技术和叠前偏移技术等的应用。最后，给出了城市活断层的抗干扰高分辨率浅层地震勘探实例。     

东部煤炭数字地震勘探技术发展与关键问题讨论

东部煤炭数字地震勘探伴随着装备更新和技术进步留下一串闪光的发展足迹，数字地震技术的应用极大地提高了勘探能力和精度，也产生了巨大的经济效益与社会效益；对东部煤炭数字地震实际工作中的十大关键技术问题的讨论，既是对地震技术自身的进一步完善提高，也是数字地震技术向中西部推广应用可参的经验和借鉴.     

基于地震属性的煤层厚度预测模型及其应用

地震属性技术在岩性和构造解释等方面得到了越来越广泛的应用，特别是在煤、油气资源勘探中具有重要的作用.基于淮南矿区谢桥1区13 1煤层地震勘探资料，提取了28种地震属性数据；通过地震属性的分析，优选出平均峰值振幅、振幅的峰态、最大绝对振幅、瞬时频率斜率等4种地震属性作为煤层厚度预测模型基本参数，结合已知钻孔资料，利用多元多项式回归以及BP人工神经网络方法，求出了各属性与煤厚之间的多元多项式回归模型及人工神经网络模型，并对模型进行了误差分析和应用结果对比分析，反映出人工神经网络模型在煤厚预测中具有好的应用效果.     

地震勘探在煤田深部勘查中的应用

为了应对煤炭资源后备不足的现状,对于煤炭资源的勘探,我国加大了深部找煤及矿区外围找煤的力度,地震勘探作为地球物理勘探中一种重要的方法,在找煤、石油、天燃气等方面有着广泛的应用。本文用一个实例介绍了地震勘探在深部找煤中的具体方法及取得的地质效果。     

滩浅海地震勘探采集技术应用

胜利油田滩浅海地区由于特殊的地表条件和复杂多变的表层结构，既不同于陆上勘探也不同于海上勘探，尤其在两栖地带存在海陆两种施工方式，如何在地震波激发和接收方面采取措施确保资料一致性，及优化观测系统设计以拼接好过渡带的地震资料是施工任务的关键.为此，依托中石化地震勘探重点基础研究实验室，在地震波的激发、接收和观测系统优化设计方面作了大量研究试验工作，形成了一套适用于滩浅海地区的地震勘探技术.通过冀东和垦东等地区应用联合表层调查和检波器精确定位等技术，有效地提高了地震资料的信噪比，改进了成像精度，取得了很好的地震勘探效果.     

煤矿深部开采中的地球物理技术现状及展望

简要介绍了我国煤矿生产的发展趋势，说明地质构造及煤层顶、底板水问题是当前煤矿深部开采中所面临的主要地质问题.综合论述了煤矿深部开采中所用地球物理技术的发展现状，介绍了地震勘探技术、矿井直流电法、矿井瞬变电磁法及地质雷达等勘探技术的应用情况，并提出了综合地球物理勘探技术在煤矿深部开采中的发展趋势.     

Ricker子波视主频的变化对混沌振子检测效果的影响

针对地震勘探资料中存在加性强随机噪声干扰，已经提出用混沌振子系统处理的方法技术.为完善该方法，需要研究诸如地震勘探同相轴完整程度、组成同相轴的地震子波变化等对系统检测效果的影响.本文以Ricker子波模拟地震子波，研究Ricker子波视主频变化的相应检测效果.研究结果：随着Ricker子波视主频的升高，检测SNR逐渐变差，在高频部分SNR的变化明显比低频部分缓慢；在通常地震勘探频率范围内，混沌振子系统检测SNR有可能部分补偿由于地震波传播引起的幅度降低；为检测由畸变子波构成的同相轴提供一定的可能.     

地震解释技术现状及发展趋势

本文以我国塔里木油田石油地球物理勘探实例为基础，概述了石油勘探过程中地震解释技术类型、特征、现状和发展趋势.本文认为，在地震勘探技术飞速发展的今天，地球物理学家及地质学家希望获得的地震信息，应当是能够直接反应地下岩石物理特性或油气水的分布，而利用常规的地震解释技术是很难做到这些；随着石油勘探的进一步深化，一些新的地震解释技术涌现出来，并在油气勘探与开发过程中发挥着巨大作用.未来的石油勘探将会面临前所未有的困难，新情况、新问题将层出不穷，地震解释技术也同样面临着考验，因此，只有立足在现有的成熟解释技术之上，并不断探索新的技术与思路，才能与未来的石油勘探步伐相一致.     

A two‐phase automatic static correction method

Statics are an effective approach to correct for complex velocity variations in the near surface, but so far, to a large extent, a general and robust automatic static correction method is still lacking. In this paper, we propose a novel two‐phase automatic static correction method, which is capable of handling both primary wave statics (PP statics) and converted‐wave statics (S‐wave statics). Our method is purely data driven, and it aims at maximizing stacking power in the target zone of the stack image. Low‐frequency components of the data are analysed first using an advanced genetic algorithm to estimate seed statics and the time structure for an event of interest, and then the original full‐band data are further aligned via the back‐and‐forth coordinate descent method using the seed statics as initial values and the time structure for event alignment guidance. We apply our new method to two field datasets, i.e., one for 2D PP static correction and the other for 3D S‐wave static correction.     

基于遗传算法的CSAMT最小构造反演

利用遗传算法进行不考虑近场校正的全场资料CSAMT反演研究.遗传算法属于全局最优化方法,具有对初始模型依赖小,不易陷入局部极值的优点,然而,当未知数较多时,多解性仍是该方法的瓶颈.为了减小多层反演的多解性,在反演中引入最小构造约束,针对CSAMT的遗传算法反演问题定义了最小构造目标函数,经过模型试验找到了其具体表达式,并找到了适合CSAMT资料反演的拉格朗日乘子的最佳取值μ=0.5,实现了基于遗传算法的CSAMT最小构造反演.利用H、A、K、Q和HKH、KHA模型对方法进行了数值试验,在无噪和加入10％噪声情况下,反演结果与模型一致;加入20％噪声后,反演仍取得良好结果,与理论模型基本吻合.将该方法用于水平层状地层和横向变化地层的实测资料反演,结果与地质资料吻合.不同的计算实例表明了该方法的有效性.     

1D elastic full‐waveform inversion and uncertainty estimation by means of a hybrid genetic algorithm–Gibbs sampler approach

Stochastic optimization methods, such as genetic algorithms, search for the global minimum of the misfit function within a given parameter range and do not require any calculation of the gradients of the misfit surfaces. More importantly, these methods collect a series of models and associated likelihoods that can be used to estimate the posterior probability distribution. However, because genetic algorithms are not a Markov chain Monte Carlo method, the direct use of the genetic‐algorithm‐sampled models and their associated likelihoods produce a biased estimation of the posterior probability distribution. In contrast, Markov chain Monte Carlo methods, such as the Metropolis–Hastings and Gibbs sampler, provide accurate posterior probability distributions but at considerable computational cost. In this paper, we use a hybrid method that combines the speed of a genetic algorithm to find an optimal solution and the accuracy of a Gibbs sampler to obtain a reliable estimation of the posterior probability distributions. First, we test this method on an analytical function and show that the genetic algorithm method cannot recover the true probability distributions and that it tends to underestimate the true uncertainties. Conversely, combining the genetic algorithm optimization with a Gibbs sampler step enables us to recover the true posterior probability distributions. Then, we demonstrate the applicability of this hybrid method by performing one‐dimensional elastic full‐waveform inversions on synthetic and field data. We also discuss how an appropriate genetic algorithm implementation is essential to attenuate the “genetic drift” effect and to maximize the exploration of the model space. In fact, a wide and efficient exploration of the model space is important not only to avoid entrapment in local minima during the genetic algorithm optimization but also to ensure a reliable estimation of the posterior probability distributions in the subsequent Gibbs sampler step.     

量子遗传算法在大地电磁反演中的应用

量子遗传算法（QGA）以量子理论为基础,通过利用量子位编码代替经典遗传算法的二进制位编码,利用量子旋转门定向更新种群来代替传统方法中种群的选择、交叉和变异过程,使得算法具有一定的内在并行运算能力和量子的隧道效应,从而加快了搜索速度,改善了收敛速度,并具有更强的全局寻优能力.本文针对地球物理反演问题的非线性、多极值特点提出一套实现方案,通过理论模型和实测数据试验对比研究,表明量子遗传方法在大地电磁反演中的寻优质量和效果明显优于传统遗传算法.     

Anisotropy from full waveform inversion of multicomponent seismic data using a hybrid optimization method

Seismic waveforms contain valuable information about the media, but the waveform inversion is a non‐linear problem. We present a waveform inversion method that combines a local optimization method with a genetic algorithm to determine the anisotropic parameters of a horizontally stratified medium. Synthetic seismograms for a horizontally stratified anisotropic medium are calculated using the reflectivity technique. In the initial stage of the inversion, the global space‐sampling properties of the genetic algorithm are used to direct the search to the region close to the global solution. This solution is then further improved using a conjugate‐gradient method. The numerical experiments performed with noisy synthetic data show that our hybrid optimization method satisfactorily reconstructs the anisotropic parameters at a reasonable computing cost while the range of slowness is adequate. We found that (i) for small‐angle data neither single‐ nor multiple‐component data are sufficient to determine the anisotropic parameters uniquely; (ii) for medium‐angle data the multiple‐component data are sufficient to determine the anisotropic parameters exactly whereas the single‐component data are not sufficient; and (iii) for wide‐angle data, either single‐ or multiple‐component data are sufficient to determine the anisotropic parameters accurately.     

Short‐period static correction without Normal Move Out†

Static correction is a common step in a seismic data proccessing flowchart for land data. Here we propose a new algorithm for automatic short‐period static correction. The algorithm is based on the assumption that seismic events after short‐period static correction should be locally plane nearly everywhere. No other assumptions are made. Therefore the proposed method does not require a preliminary velocity analysis. The algorithm consists in two main parts: evaluation of second spatial differences of trajectories and subsequent regularized integration of these differences. The proposed method proves its robustness and shows results comparable with conventional residual static correction based on improving common‐midpoint stacking. In contrast to the conventional residual static, the proposed algorithm can estimate short‐period statics in complex cases where common‐midpoint stacking fails because of non‐hyperbolic events.     

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.     

关于-遗传算法收敛性的注记

遗传算法是一种受到广泛注意的全局优化算法,已经在包括地震工程的很多领域中获得应用.本文将结合这一算法的实际操作步骤,对简单遗传算法的不收敛性和包含最优个体保护策略的遗传算法的收敛性给出一个简要的证明.     

Estimation of Nash's IUH parameters using stochastic search algorithms

In rainfall–runoff studies, it is often necessary to change the duration of a given unit hydrograph. Nash's Instantaneous Unit Hydrograph (IUH) is an ideal method that eliminates the hydrograph duration. This paper presents the results of the application of search algorithms, namely a genetic algorithm and hill climbing, to develop the IUH that minimizes the error between the observed and generated hydrographs. Also the performance of these methods has been compared with that of the classical method used for estimation of IUH, namely the method of moments. The genetic algorithm is a popular search procedure for function optimization that applies the mechanics of natural genetics and natural selection to explore a given search space. Hill climbing is an optimization technique that belongs to the family of local search and algorithms can be used to solve problems that have many solutions, with some solutions better than others. The results obtained from both the genetic algorithm and hill climbing algorithm for estimation of Nash's IUH parameters were compared with the results obtained by the method of moments for storms from two river basins that are located in different climatic regions. It was found that both the genetic algorithm and hill climbing provided improved and consistent results. Copyright © 2008 John Wiley & Sons, Ltd.     

基于粗粒度并行遗传算法的隔震层参数优化

针对基于经典遗传算法的隔震层参数优化方法效率不高的问题,提出一种基于粗粒度并行遗传算法的隔震层参数优化方法。利用Python的多进程机制和Python与ETABS的交互,实现CPU各核同时调用ETABS并进行遗传操作,最后通过一个隔震工程的实例进行验证。结果表明：采用粗粒度并行遗传算法进行隔震层参数优化,与原设计结果相比,优化后的隔震结构性能更优;同时,用10核CPU计算,与经典遗传算法相比,该方法既能准确得出全局最优解,又可显著提高优化效率,加速比约为6,可基本满足隔震工程设计的及时性需求,具有较好的工程应用价值。