井间探地雷达数据的非源时域波形反演

X. Meng, S. Liu
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引用次数: 3

摘要

近年来,波形反演因其能提供亚波长图像而成为最受欢迎的方法之一。在野外数据反演中,必须对源小波进行估计。传统的方法是将源小波作为一个新的未知参数加入到反演中,并通过迭代进行更新。当反演结果与真实模型相同时,估计的源小波与真实源小波相同。该方法可用于合成数据的反演。但在现场数据信噪比不高的情况下,其性能不佳,需要进行大量的干预。在本文中,我们实现了一种与源无关的时域波形反演。该算法的目标函数包括建模数据与来自字段数据的参考轨迹的卷积,以及字段数据与来自建模数据的参考轨迹的卷积。这样,源小波的影响就被消除了。对于合成数据的反演,同时更新了介电常数和电导率,结果表明该算法是有效的。最后,将该算法应用于现场数据。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Source-independent time-domain waveform inversion of cross-hole GPR data
Recently, waveform inversion is one of the most popular methods because it can provide sub-wavelength images. In the inversion of field data, the source wavelet must be estimated. Traditionally, the source wavelet is added in the inversion as a new unknown parameter and updated with iterations. When the results of the inversion are the same as the true models, the estimated source wavelet is same as the true source wavelet. The method is useful in the inversion of synthetic data. But it does not perform well and needs a lot of intervention if the signal-to-noise ratio of field data is not high. In this paper, we realize a source-independent time-domain waveform inversion. The object function of this algorithm consists of the convolution of the modeled data with a reference trace from the field data, and the convolution of the field data with a reference trace from the modeled data. In this way, the effects of the source wavelets are removed. For the inversion of synthetic data, the permittivity and conductivity are simultaneously updated and the results obtained show that the algorithm is effective. Finally, we apply the algorithm to field data.
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