We have developed a stochastic inversion procedure for common-offset ground-penetrating radar (GPR) reflection measurements. Stochastic realizations of subsurface properties that offer an acceptable fit to GPR data are generated via simulated annealing optimization. The realizations are conditioned to borehole porosity measurements available along the GPR profile or equivalent measurements of another petrophysical property that can be related to the dielectric permittivity, as well as to geostatistical parameters derived from the borehole logs and the processed GPR image. Validation of our inversion procedure is performed on a pertinent synthetic data set and indicates that our method is capable of reliably recovering strongly heterogeneous porosity structures associated with surficial alluvial aquifers. This finding is largely corroborated through application of the methodology to field measurements from the Boise Hydrogeophysical Research Site near Boise, Idaho, USA.

中文翻译：

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共偏移距探地雷达反射数据的条件随机反演

我们已经开发了一种用于共偏移量探地雷达 (GPR) 反射测量的随机反演程序。通过模拟退火优化生成地下特性的随机实现，提供可接受的 GPR 数据拟合。这些实现取决于沿 GPR 剖面可用的钻孔孔隙度测量值或与介电常数相关的另一种岩石物理特性的等效测量值，以及从钻孔测井和处理后的 GPR 图像导出的地质统计参数。我们的反演程序的验证是在相关的合成数据集上进行的，并表明我们的方法能够可靠地恢复与地表冲积含水层相关的强非均质孔隙结构。