Abstract:

Full waveform inversion(FWI)has emerged as a highly effective approach for obtaining accurate and high-resolution S-wave velocity structure of the shallow subsurface. However, there exist several challenges when applying FWI in the field. The first challenge is the issue of local minima that arises when inaccurate initial models are used, especially when inverting shallow surface seismic data dominated by high-frequency and strongly dispersive surface waves. These local minima are caused by the non-linear misfit function that represents the differences between the measured and simulated data. Moreover, defining an appropriate minimization criterion to reduce the sensitivity of the inversion results to errors in the recorded seismic wave amplitudes is another significant challenge. Amplitude errors can arise from various factors, such as different coupling effects between seismic sources, receivers, and the ground, or variations in the strength of seismic sources excited at different shot locations. If the amplitude information of the recorded seismic wavefield is inaccurate, the reliability of the FWI inversion results will be effected negatively.

To overcome these problems, we propose a novel misfit function that incorporates exponential-phase coherency, thereby eliminating the reliance on amplitude information from the measured and simulated data. This new misfit function is designed to measure the coherency between the measured and simulated data based on exponential phase. It achieves a balance in extracting valuable information from various amplitude components of the recorded seismic wavefield, such as surface waves, reflections, and scattering waves. The method for computing this coherency is inspired by Phase-Weighted Stacking(PWS)method to detect weak but correlated seismic signals. In PWS, the exponential phase of the data is computed to obtain a phase-dependent coherency that is independent of amplitude. This correlation is then used to enhance the stacking of signals with similar instantaneous phases.

By utilizing the adjoint-state method, we efficiently calculate the gradient of the misfit function with respect to the model and conduct a thorough analysis of its shape and characteristics. To demonstrate the robustness of our proposed misfit function against random noise, we perform experiments by using simulated data contaminated with varying levels of noise. The results demonstrate that the misfit function based on exponential-phase coherency remains highly robust and reliable, even in the presence of significant random noise. This robustness is particularly crucial in practical applications where noise contamination is a common challenge.

To evaluate the performance of FWI employing exponential-phase coherency in a real field environment, we employ seismic data collected at an archaeological site as a benchmark dataset. This dataset presents a complex and challenging scenario due to the presence of complex subsurface structures. In addition to the inherent noise in the data, we introduce additional random noise to assess the robustness of our proposed misfit function.

The inversion results obtained with our novel FWI approach exhibit an impressive resemblance to the known velocity structure of the archaeological site. These results are further validated through independent archaeological excavations, which confirms the accuracy and reliability of our imaging technique. The success of using our method in accurately reconstructing subsurface features under challenging field conditions underscores the significant potential of FWI based on exponential-phase coherency to enhance the accuracy and reliability of shallow subsurface imaging in practical scenarios.

Key words: near-surface imaging, full-waveform inversion, objective function, random noise

摘要：

全波形反演(FWI)已成为获取浅地表高分辨率S波速度(V_S)的有效方法。为了克服FWI在野外应用中的一些挑战, 文中引入了一种基于指数相位相关性的目标函数, 该函数不依赖于测量和模拟数据的振幅信息。文中采用伴随状态方法高效计算目标函数相对于模型的梯度, 并分析了新目标函数的形状。使用受随机噪声污染的模拟数据, 证明了该目标函数对随机噪声的鲁棒性。此外, 使用在考古遗址上采集的地震数据作为基准数据集, 测试了基于指数相位相关性的全波形反演方法在真实野外环境中的性能, 同时还添加了随机噪声以进一步评估目标函数对噪声的鲁棒性。文中所得反演结果与该遗址已有的速度结构非常相似, 并已通过独立的考古挖掘验证。因此, 基于指数相位相关性目标函数的FWI具有在实际野外情况下显著提高浅地表成像准确性的潜力。

关键词: 浅地表成像, 全波形反演, 目标函数, 随机噪声