重力梯度全张量数据三维共轭梯度聚焦反演

随着全张量重力梯度测量技术的日趋成熟和应用领域的不断扩大,重力梯度全张量数据的三维反演技术越来越受重视.本文利用剖分单元之间几何架构等效性,实现了重力梯度全张量场三维正演快速计算和导数矩阵优化存储.并将积分灵敏度、粗糙度和最小支撑泛函约束以及参考模型和模型参数界限约束引入到目标函数中,采用共轭梯度法进行反演迭代,实现了重力梯度全张量数据三维快速正反演计算.多种模型的反演试算表明:本文提出的反演算法的可靠性和稳定性较好,并且算法速度快、占用内存低且易于并行化.     

基于MPICH环境下的复杂重力异常体并行正演模拟

任意多面体重力异常正演公式常用于解决复杂几何形态地质体的正演问题。 本文以均匀物性多面体重力异常正演公式为基础, 应用有限元技术中的网格离散化思想, 以任意四面体为基本单元, 通过并行计算技术在MPICH环境下实现了任意连续空间物性分布复杂异常体网格模型的重力异常正演模拟, 通过并行处理可以有效加速正演计算速度。 本文研究结果对于联合重力异常场正演建模和开展复杂模型网格的重力场计算有一定参考意义。     

重磁数据三维物性反演方法进展

综述了重磁数据三维物性反演方法中的几个关键问题.主要包括正演快速算法、反演框架、约束因子讨论、反演算法实现等方面.正演快速算法主要讨论了等效存储几何格架技术、基于GPU加速的并行计算技术以及小波压缩技术.三维物性反演则是在最小二乘意义下使目标函数达到极小的线性或非线性反演.指出,对于特定地质问题需要谨慎选择不同且合适的约束方法乃至反演算法,才能达到好的效果.最后讨论了重磁数据三维物性反演较好的应用前景及发展方向.     

基于数据空间和稀疏约束的三维重力和重力梯度数据联合反演

随着重力和重力梯度测量技术的日趋成熟,基于重力和重力梯度数据的反演技术得到了广泛关注.针对反演多解性严重、计算效率低和内存消耗大等难点问题,本文开展了三维重力和重力梯度数据的联合反演研究,该方法结合重力和重力梯度两种数据,将L0范数正则化项加入到目标函数中,并在数据空间下采用改进的共轭梯度算法求解反演最优化问题.同时,本文摒弃了依赖先验信息的深度加权函数,引入了自适应模型积分灵敏度矩阵,用来克服因重力和重力梯度数据核函数随深度增加而衰减引起的趋肤效应问题.为了提高反演计算效率,本文又推导出基于规则网格化的重力和重力梯度快速正演计算方法.模拟试算表明,改进的共轭梯度法可以降低反演的迭代次数,提高反演的收敛速度;自适应模型积分灵敏度矩阵,可以有效解决趋肤效应,提高反演纵向分辨能力;数据空间和改进的共轭梯度算法结合,可以更好地降低反演求解方程的维度,避免存储灵敏度矩阵,有效地降低反演计算时间和内存消耗量.野外实例表明,该算法可以在普通计算机下快速地获得地下密度分布模型,表现出较强的稳定性和适用性.     

CPU/GPU协同计算在频率域二维全波形反演中的应用

全波形反演利用波场的运动学和动力学信息重建地下物理参数,是建立高精度速度模型的有效手段,巨大的计算量是制约其实用化的瓶颈之一。本文针对全波形反演中频率域正演的复杂计算问题,采用粗细结合的并行策略,将MPI技术应用于多炮间并行计算,同时利用GPU技术加速正演过程中大型稀疏线性代数方程组的求解,以提高频率域全波形反演的计算效率。通过理论模型验证本文方法的正确性和有效性,给出不同数据量与GPU计算效率的相关分析结论,提出频率域全波形反演CPU/GPU协同并行计算的制约瓶颈和发展方向。      

密度和速度随机分布共网格模型的重力与地震联合反演

针对重力与地震联合反演存在的问题,结合已有的研究成果,本文研究实现了速度和密度随机分布共网格单元模型的建模技术,以适应密度和速度剧烈变化的复杂模型及联合反演的计算要求.重力正演利用了该网格的二度半体模型,并进一步改进了地震走时的二维射线追踪计算方法,以适用于速度随机分布的网格介质.结合改进的模拟退火算法,实现了这种共网格条件下的重力与地震资料的同步联合反演.模型试验证明了重力与地震联合反演可以准确确定复杂物性界面的密度和速度结构,适用于物性界面不完全一致和物性变化剧烈的复杂模型,并且联合反演结果要优于单独的重力反演.带先验信息约束下的实际资料的联合反演,进一步证明了该方法的适用性和效果,可提高反演精度并减少多解性.     

改进的预处理共轭梯度快速算法在三维重力梯度数据反演中的应用（英文）

随着全张量重力梯度(FTG)测量技术的不断发展,重力梯度数据的三维反演技术在油气和矿产勘探中日益受到重视与关注。为了快速处理和解释大规模的高精度数据,图形处理器GPU(Graphics Processing Unit)和预处理分解技术(Preconditioning methods)在地球物理反演中的使用变得十分重要。本文结合对称逐次超松弛(SSOR)技术与不完全乔列斯基分解共轭梯度算法(ICCG)提出改进的预处理共轭梯度法,并考虑到方法预处理分解占用额外的时间,开发该算法的GPU并行算法来提高加速效果。然后通过含噪的模型数据反演来证明改进的并行预处理方法在三维全张量重力梯度数据反演中的适应性。由此,基于NVIDIATesla C2050 GPU的并行SSOR-ICCG算法和在2.0GHz CPU上的串行程序比较,达到了大约25倍的加速比。最后,我们将该算法应用于美国路易斯安那州南方Vinton盐丘的实测航空重力梯度数据反演中,反演出良好的反演结果,验证了该方法在三维全张量重力梯度数据快速反演中的优势和可行性。     

基于深度学习的重力异常与重力梯度异常联合反演

高效高精度的反演算法在重力大数据时代背景下显得尤为重要,受深度学习卓越的非线性映射能力的启发,本文提出了一种基于深度学习的重力异常及重力梯度异常的联合反演方法.文中首先提出了一种基于网格点几何格架的重力异常及重力梯度异常的空间域快速正演算法,这为本文深度学习反演算法的实现奠定了基础;其次对大量的不同密度模型进行正演计算获得样本数据集;然后设计了一种端到端的深度学习网络结构(GraInvNet),再利用样本数据对该网络结构进行训练;最后进行反演预测.组合模型试验表明,多维度数据联合反演相比单一分量反演其结果更“聚焦”,且与模型边界高度吻合,并且对于复杂模型的姿态与物性预测具有极为显著的优势,以及对于含噪声数据的反演,其质量也不会降低;Vinton岩丘实测重力数据也验证了文中方法的有效性;从而证明了深度学习在重力数据的高效高精度反演方面具有的巨大潜力.     

三维交错网格有限差分地震波模拟的GPU集群实现

有限差分实现简单、速度快,作为地震波场模拟一种有效数值方法,被广泛用于正演计算密集的波形反演和逆时偏移中.三维地震波正演模拟计算量大,一直以来制约着三维叠前逆时偏移和反演的工业化应用,GPU通用计算技术的产生及其内在的数据并行性有望改变这一现状.本文通过分析三维交错网格有限差分方法在GPU上的实施,利用片内共享存储器实现了三维地震波数值模拟的高效算法,取得了较单核CPU快79x～108x的加速比;通过区域分解技术将单GPU上不能计算的地质体模型沿Z轴方向进行粗粒度分解,采用消息传递接口交换边界数据,运用MPI+CUDA的方式实现了大尺度三维地震波场模拟,并着重分析了影响GPU并行计算效率的一些关键因素.大尺度三维地震波场模拟的加速实现,为促进叠前逆时偏移和波形反演技术的工业化转化提供了可能,因此具有重要的研究意义.     

基于剩余异常相关成像的重磁物性反演方法

将场源区剖分成长方体单元,通过采集的重磁数据反演出这些单元的密度或者磁化率变化,勾画出场源的分布图像,这种方式是重磁三维反演的重要方向.重磁相关成像通过计算测量的重磁异常与地下各点在测区上的重磁异常的归一化相关,显示出异常地质体的空间赋存状态和等效剩余重磁物性.该方法计算速度快,方法简单、稳定,但是反演的结果只是在-1到+1之间的等效物性,不能够直接反演剩余密度或者磁化率,并且无法引入已知的地质约束.本文通过对物性模型的正演和实测结果的残差进行相关成像,迭代更新物性模型实现对物性参数的反演过程.模型实验证明该方法相对相关成像不仅能提高分辨率,还能够得到真正的物性参数.     

Improved preconditioned conjugate gradient algorithm and application in 3D inversion of gravity-gradiometry data

With the continuous development of full tensor gradiometer (FTG) measurement techniques, three-dimensional (3D) inversion of FTG data is becoming increasingly used in oil and gas exploration. In the fast processing and interpretation of large-scale high-precision data, the use of the graphics processing unit process unit (GPU) and preconditioning methods are very important in the data inversion. In this paper, an improved preconditioned conjugate gradient algorithm is proposed by combining the symmetric successive over-relaxation (SSOR) technique and the incomplete Choleksy decomposition conjugate gradient algorithm (ICCG). Since preparing the preconditioner requires extra time, a parallel implement based on GPU is proposed. The improved method is then applied in the inversion of noisecontaminated synthetic data to prove its adaptability in the inversion of 3D FTG data. Results show that the parallel SSOR-ICCG algorithm based on NVIDIA Tesla C2050 GPU achieves a speedup of approximately 25 times that of a serial program using a 2.0 GHz Central Processing Unit (CPU). Real airborne gravity-gradiometry data from Vinton salt dome (southwest Louisiana, USA) are also considered. Good results are obtained, which verifies the efficiency and feasibility of the proposed parallel method in fast inversion of 3D FTG data.     

基于GPU混合反演的隧道电阻率超前探测成像研究

隧道施工期超前探测对于避免突涌水灾害的发生具有重要作用,为满足隧道三维电阻率超前探测快速化解译与成像的要求,本文提出了一种基于GPU并行的蚁群算法与最小二乘方法相结合的混合反演算法.该方法结合线性反演与非线性反演的优点,利用蚁群算法全局搜索能力强的优点为最小二乘反演提供较优的初始模型,以克服最小二乘算法容易陷入局部最优的缺点,提高了隧道三维电阻率反演成像的精度.同时,基于蚁群算法的天然并行性,提出了CUDA环境下的GPU并行策略,实现了三维电阻率反演的快速化成像.其次,开展了基于GPU混合反演的数值算例,与传统最小二乘线性反演进行了对比,基于GPU并行计算的混合反演计算效率得到了显著提高,对含水构造的位置、形态有较好的反映,压制了三维反演的多解性.最后开展了物理模型试验,结果表明基于GPU混合反演探测的低阻异常体与实际含水构造的位置较为相符,发现基于GPU加速的混合反演方法在提高探测精度与加快反演速度方面具有显著优势,为三维电阻率混合反演方法在隧道超前探测实际工程中的应用奠定了基础.     

Optimization of a precise integration method for seismic modeling based on graphic processing unit

General purpose graphic processing unit(GPU) calculation technology is gradually widely used in various fields.Its mode of single instruction,multiple threads is capable of seismic numerical simulation which has a huge quantity of data and calculation steps.In this study,we introduce a GPU-based parallel calculation method of a precise integration method(PIM) for seismic forward modeling.Compared with CPU single-core calculation,GPU parallel calculating perfectly keeps the features of PIM,which has small bandwidth,high accuracy and capability of modeling complex substructures,and GPU calculation brings high computational efficiency,which means that high-performing GPU parallel calculation can make seismic forward modeling closer to real seismic records.     

可控源音频大地电磁数据的反演方法

从反演方程、构造目标函数和求解三方面对用于可控源音频大地电磁法(CSAMT)的实用反演方法中的四种进行了描述．水平层状地层CSAMT法资料的直接反演法首次尝试了一维空间的全资料CSAMT反演，效果较好，但该方法尚难应用于2D、3D复杂介质中；奥克姆反演方法既考虑了横向的光滑函数，又考虑了纵向的光滑函数，得到比较光滑的横向、纵向变化的背景电性结果，但有可能把一些小构造光滑掉．快速松驰反演算法和共轭梯度算法由于计算速度快，占内存少而被用于三维反演中，二者相比，快速松驰算法在求解雅可比矩阵时只做一次正演计算，在更新模型时解小型方程组，所以在速度上更胜一筹．在后三种算法中，由于复杂电性结构无解析解，正演计算都采用数值计算．数值计算的可靠性、速度影响着反演算法的有效性，这方面的研究也将是2D、3D复杂电性结构反演的研究方向之一.     

基于矢量有限元的带地形大地电磁三维反演研究

研究了基于矢量有限元方法的大地电磁带地形三维反演算法并开发了三维反演计算程序代码.在大地电磁场正演数值模拟方面,采用并行直接稀疏求解器PARDISO且无需进行散度校正的快速正演方案,对典型地形模型,在中等规模计算条件下,与双共轭梯度法(BICG)计算结果比较,发现PARDISO比BICG快10倍以上;通过理论模型试算,并与前人的有限元法计算结果对比,验证了带地形三维正演计算程序的正确性.在反演方面,本研究基于共轭梯度方法编写了大地电磁带地形三维反演代码,为了避免直接求取雅可比矩阵,将反演中的雅可比矩阵计算问题转为求解两次“拟正演”问题,进而将PARDISO的快速正演方案应用于“拟正演”问题的求解,以提高反演计算效率.利用开发的反演算法对多个带地形地电模型的合成数据进行了三维反演,反演结果能很好地重现理论模型的电性结构,验证了本文开发的三维反演算法的正确性和可靠性.最后,利用该算法反演了某矿区大地电磁实测数据,反演得到的三维电性结构清晰地反映了研究区的地电特征,将反演结果与该区已有地质资料结合进行解释,应用效果明显,进一步验证了本文算法的有效性.     

2.5D forward modeling and inversion of frequency-domain airborne electromagnetic data

Frequency-domain airborne electromagnetics is a proven geophysical exploration method. Presently, the interpretation is mainly based on resistivity—depth imaging and one-dimensional layered inversion; nevertheless, it is difficult to obtain satisfactory results for two- or three-dimensional complex earth structures using 1D methods. 3D forward modeling and inversion can be used but are hampered by computational limitations because of the large number of data. Thus, we developed a 2.5D frequency-domain airborne electromagnetic forward modeling and inversion algorithm. To eliminate the source singularities in the numerical simulations, we split the fields into primary and secondary fields. The primary fields are calculated using homogeneous or layered models with analytical solutions, and the secondary (scattered) fields are solved by the finite-element method. The linear system of equations is solved by using the large-scale sparse matrix parallel direct solver, which greatly improves the computational efficiency. The inversion algorithm was based on damping least-squares and singular value decomposition and combined the pseudo forward modeling and reciprocity principle to compute the Jacobian matrix. Synthetic and field data were used to test the effectiveness of the proposed method.     

The inversion of density structure by graphic processing unit (GPU) and identification of igneous rocks in Xisha area

Organic reefs, the targets of deep-water petroleum exploration, developed widely in Xisha area. However, there are concealed igneous rocks undersea, to which organic rocks have nearly equal wave impedance. So the igneous rocks have become interference for future exploration by having similar seismic reflection characteristics. Yet, the density and magnetism of organic reefs are very different from igneous rocks. It has obvious advantages to identify organic reefs and igneous rocks by gravity and magnetic data. At first, frequency decomposition was applied to the free-air gravity anomaly in Xisha area to obtain the 2D subdivision of the gravity anomaly and magnetic anomaly in the vertical direction. Thus, the distribution of igneous rocks in the horizontal direction can be acquired according to high-frequency field, low-frequency field, and its physical properties. Then, 3D forward modeling of gravitational field was carried out to establish the density model of this area by reference to physical properties of rocks based on former researches. Furthermore, 3D inversion of gravity anomaly by genetic algorithm method of the graphic processing unit (GPU) parallel processing in Xisha target area was applied, and 3D density structure of this area was obtained. By this way, we can confine the igneous rocks to the certain depth according to the density of the igneous rocks. The frequency decomposition and 3D inversion of gravity anomaly by genetic algorithm method of the GPU parallel processing proved to be a useful method for recognizing igneous rocks to its 3D geological position. So organic reefs and igneous rocks can be identified, which provide a prescient information for further exploration.     

The inversion of density structure by graphic processing unit （GPU） and identification of igneous rocks in Xisha area

Organic reefs, the targets of deep-water petroleum exploration, developed widely in Xisha area. However, there are concealed igneous rocks undersea, to which organic rocks have nearly equal wave impedance. So the igneous rocks have become interference for future exploration by having similar seismic reflection characteristics. Yet, the density and magnetism of organic reefs are very different from igneous rocks. It has obvious advantages to identify organic reefs and igneous rocks by gravity and magnetic data. At first, frequency decomposition was applied to the free-air gravity anomaly in Xisha area to obtain the 2D subdivision of the gravity anomaly and magnetic anomaly in the vertical direction. Thus, the distribution of igneous rocks in the horizontal direction can be acquired according to high-frequency field, low-frequency field, and its physical properties. Then, 3D forward modeling of gravitational field was carried out to establish the density model of this area by reference to physical properties of rocks based on former researches. Furthermore, 3D inversion of gravity anomaly by genetic algorithm method of the graphic processing unit(GPU) parallel processing in Xisha target area was applied, and 3D density structure of this area was obtained. By this way, we can confine the igneous rocks to the certain depth according to the density of the igneous rocks. The frequency decomposition and 3D inversion of gravity anomaly by genetic algorithm method of the GPU parallel processing proved to be a useful method for recognizing igneous rocks to its 3D geological position. So organic reefs and igneous rocks can be identified, which provide a prescient information for further exploration.