An efficient 2-D joint full-waveform inversion method for electromagnetic and seismic data in a layered medium background is developed. The joint inversion method based on the integral equation (IE) method is first proposed in this paper. In forward computation, the IE method is employed, which usually has smaller discretized computation domain and less cumulative error compared with the finite-difference method. In addition, fast Fourier transform is used to accelerate the convolution between Green's functions and induced sources due to the shift invariance property of the layered Green's functions in the horizontal direction. In the inversion model, the cross-gradient function is incorporated into the cost function of the separate inversion to enforce the structure similarity between electric conductivity and seismic-wave velocity. We use the improved variational Born iteration method and two different iteration strategies to minimize the cost function and reconstruct the contrasts. Several typical models in geophysical applications are used to validate our joint inversion method, and the numerical simulation results show that joint inversion can improve the inversion results when compared with those from the separate inversion.

Complex seismic data sets benefit from adaptive sparse transformation methods to improve resolution and fidelity. The data-driven tight frame (DDTF) is a fast dictionary learning method that can simplify the sparse representation process and efficiently update the compact frame. However, the DDTF-based method is computationally intensive and relatively ineffective in accurately characterizing high-dimensional data sets with weak features and high noise levels. A new adaptive patch-selection method based on a simple framework for the contrastive learning of visual representations (SimCLR) is designed to further accelerate and improve the dictionary training process in the DDTF. The variance of all of the available patches is first calculated; then, the cosine similarity between the patch with the maximum variance and the other patches is calculated. Finally, the patches with large similarities are used for the filter bank training process. This method can efficiently track seismic events. The efficacy of applying the trained filter bank for seismic data recovery is tested by using the Monte Carlo-DDTF and SimCLR-DDTF methods. The numerical results demonstrate that the SimCLR-DDTF method can intelligently select training patches with more effective information, greatly improve the training efficiency of the DDTF, and obtain high-quality denoising and interpolation results.

To deal with remaining noise in a single domain, a transformation domain hybrid technique that employs a processing step-based adaptive denoising scheme is proposed to identify and remove noise while preserving the target seismic signals for practical engineering applications. The adaptive algorithm uses a higher order statistic to stack the wavelet-based coefficients of the seismic signals in the preprocessing step. It then whitens the noise and recovers the processed signals in the wavelet coefficient environment without the need to choose an arbitrary threshold. Automatic thresholding is employed to identify the high-correlation signal and stack this signal using the higher order statistic. The complex block thresholding filter captures the features of the remaining mode elements by iteratively using the wavelet and curvelet basis functions in the curvelet domain in the postprocessing step, with the goal of removing the noise with disordered correlations in the rotational directions. Seismic records of karst topography and hidden sinkhole detections, which are used for the evaluation of foundation stability in constructional engineering, are analyzed to determine the signal-to-noise ratio and fidelity of this scheme. A comparison of the performances between different denoising methods in the single domain is presented using synthetic seismic waveforms that are contaminated with different types of noise to validate the efficiency of this hybrid denoising scheme. Processing field data demonstrates the effectiveness of this hybrid denoising scheme compared with electrical resistivity imaging.

A new mise-a-la-masse (MALM)-based induced-polarization (IP) method is developed for heavy-metal contamination monitoring, in which the charged source in a nonperfect conductor and contact ...

非线性、非平稳探地雷达数据常掺杂各种复杂噪声源, 其对精确提取弱反射波信号、识别绕射波双曲线同相轴特征具有严重影响, 忽略噪声影响给探地雷达探测数据全波形偏移成像及后续解译造成较大误差. 采用传统阈值函数的曲波变换去噪需要根据数据噪声水平人为确定合理阈值控制系数. 对此, 本文开展自适应阈值函数的曲波变换去噪算法研究. 引入块状复数域阈值函数算法, 分析传统阈值函数曲波变换去噪的效果随阈值控制系数变化的规律; 利用高阶统计量理论, 对曲波变换系数在尺度、方向上进行相关性叠加, 通过相关性统计量自适应确定有效信号在曲波变换系数分布尺度、旋转方向, 由此确定清除噪声成分阈值范围, 构建统计量自适应阈值函数曲波变换去噪算法. 针对包含随机噪声、相关噪声合成探地雷达数据及实测探地雷达数据, 采用传统阈值函数曲波变换去噪与本文提出去噪算法处理结果对比分析, 检验了本文算法的有效性及可行性. 研究成果对复杂探地雷达数据精确推断解译具有指导意义.

井筒电磁法作为一种高效的地球物理勘探技术特别适合我国地形复杂地区(沙漠、高山等)的油气资源勘探. 地形起伏区域对井筒电磁响应的观测具有严重影响, 但到目前为止人们对三维井筒电磁地形效应特征的研究十分有限. 本文提出基于区域划分的积分方程法模拟带地形频率域井筒电磁系统响应, 与基于偏微分方程的有限差分、有限单元法相比, 该方法能更高效地模拟地形响应. 首先根据地形起伏情况定义感应数, 将地形条件下目标体的井筒电磁场模拟区域划分为参考模型、背景介质及目标体介质分布子区域, 针对各子区域的模拟计算特点, 配置Anderson算法、稳定型双共轭梯度-快速傅里叶积分方程算法, 从而获得三维地形频率域井筒电磁场响应. 通过将计算结果与半空间模型的Anderson算法解析解、带山谷地形模型的其他已发表的三维边界积分方程结果进行对比, 检验了本文算法的精度及高效性. 最后, 系统分析了山谷地形对井筒电磁地井观测系统电磁场响应的影响特征. 本文研究结果对三维井筒电磁地形效应的识别和校正具有指导意义.