Time-lapse borehole radar monitoring of an infiltration experiment in the vadose zone

Ground-penetrating radar (GPR) is an effective tool for imaging the spatial distribution of water content. An artificial groundwater recharge test was conducted in Nagaoka City in Japan, and time-lapse crosshole GPR data were collected to monitor the infiltration process in the vadose zone. Since electromagnetic wave velocities in the vadose zone are largely controlled by variations in water content, an increase in traveltime is interpreted as an increase in saturation. In the test zone, the infiltrated water penetrated downward with an average velocity of about 2.7 m/h. A finite-difference time-domain method using two-dimensional cylindrical coordinates is applied to simulate radargrams associated with the advancing wetting front and to quantify the effects of critical refraction. Standard zero-offset profiling for which all first-arrivals are assumed to be direct waves results in an underestimation of water content in the transition zone above the wetting front. As a result, correct velocity analysis requires identification of first-arriving critically refracted waves from the traveltime profile to accurately determine a water content profile.     

多震相走时和波形三维地震成象方法及在祁连山地壳结构研究中的应用

本文的理论方法是以几何射线理论为基础发展起来的、天然地震走时反演技术及天然地震层析成像技术。它存在着震源函数与介质参数的解耦问题。本研究采取了五种方法来改善反演结果。包括,利用,Pg,Pn等震相增大约束条件;用已有精度较高的人工地震测深结果作速度约柬：用波形反演来修改模型,把诸多物理量开发出来互为约束,以修改后的模型再作反演,使解的稳定性大大提高：采用最优化过程,选择遗传算法。可以进行震源定位,走时反演,波形反演：得到任意深度的速度分布及从地表到Moho面的速度剖面。用于在祁连山地区的结果表明,这些层析剖面对认识大地构造、重大深部事件动力学是很有益的。     

Stochastic estimation of facies using ground penetrating radar data

Explicitly defining large-scale heterogeneity is a necessary step of groundwater model calibration if accurate estimates of flow and transport are to be made. In this work, neural networks are used to estimate radar facies probabilities from ground penetrating radar (GPR) images, yielding stochastic facies-based models that honour the large-scale architecture of the subsurface. For synthetic GPR images, a neural network was able to correctly identify radar facies with an accuracy of approximately 90%. Manual interpretation of a set of 450 MHz GPR field data from the Borden aquifer resulted in the identification of four radar facies. Of these, a neural network was able to identify two facies with an accuracy of near 80% and one with an accuracy of 44%. The neural network was not able to identify the fourth facies, likely due to the choice of defining facies characteristics. Sequential indicator simulation was used to generate facies realizations conditioned to the radar facies probabilities. Numerical simulations indicate that significant improvements in the prediction of solute transport are possible when GPR is used to constrain the facies model compared to using well data alone, especially when data are sparse.This work was supported by funding to R. Knight under Grant No. DE-FG07–00ER15118-A000, Environmental Management Science Program, Office of Science and Technology, Office of Environment Management, United States Department of Energy (DOE). However, any opinions, findings, conclusions, or recommendations expressed herein are those of the authors and do not necessarily reflect the views of DOE. Further support was provided by a Stanford Graduate Fellowship to S. Moysey. The authors would also like to thank James Irving for his assistance with processing of the radar data.     

Bayesian frequency-domain blind deconvolution of ground-penetrating radar data

Enhancing the resolution and accuracy of surface ground-penetrating radar (GPR) reflection data by inverse filtering to recover a zero-phased band-limited reflectivity image requires a deconvolution technique that takes the mixed-phase character of the embedded wavelet into account. In contrast, standard stochastic deconvolution techniques assume that the wavelet is minimum phase and, hence, often meet with limited success when applied to GPR data. We present a new general-purpose blind deconvolution algorithm for mixed-phase wavelet estimation and deconvolution that (1) uses the parametrization of a mixed-phase wavelet as the convolution of the wavelet's minimum-phase equivalent with a dispersive all-pass filter, (2) includes prior information about the wavelet to be estimated in a Bayesian framework, and (3) relies on the assumption of a sparse reflectivity. Solving the normal equations using the data autocorrelation function provides an inverse filter that optimally removes the minimum-phase equivalent of the wavelet from the data, which leaves traces with a balanced amplitude spectrum but distorted phase. To compensate for the remaining phase errors, we invert in the frequency domain for an all-pass filter thereby taking advantage of the fact that the action of the all-pass filter is exclusively contained in its phase spectrum. A key element of our algorithm and a novelty in blind deconvolution is the inclusion of prior information that allows resolving ambiguities in polarity and timing that cannot be resolved using the sparseness measure alone. We employ a global inversion approach for non-linear optimization to find the all-pass filter phase values for each signal frequency. We tested the robustness and reliability of our algorithm on synthetic data with different wavelets, 1-D reflectivity models of different complexity, varying levels of added noise, and different types of prior information. When applied to realistic synthetic 2-D data and 2-D field data, we obtain images with increased temporal resolution compared to the results of standard processing.     

The synthesis of travelling ionospheric disturbance (TID) signatures in HF radar observations using ray tracing

Characteristic signatures are often observed in HF radar range-time-intensity plots when travelling ionospheric disturbances (TIDs) are present. These signatures, in particular the variation of the F-region skip distance, have been synthesised using a ray tracing model. The magnitude of the skip variation is found to be a function of the peak electron density perturbation associated with the TID and radar frequency. Examination of experimental observations leads to an estimate of the peak electron density perturbation amplitude of around 25% for those TIDs observed by the CUTLASS radar system. The advantage of using the skip variation over the radar return amplitude as an indicator of density perturbation is also discussed. An example of a dual radar frequency experiment has been given. The investigation of the effect of radar frequency on the observations will aid the optimisation of future experiments.     

网格剖分方式对大地电磁数据反演的影响

随着计算机技术的飞速发展,大地电磁测深数据的各种反演方法得到空前发展。对于反演过程中的网格剖分问题,前人研究有限。本文就此进行研究,确定影响网格剖分方式的因素,找到一种合适的网格剖分方法。为此,设计一个复杂模型,对正演模拟得到的数据加入随机噪声作为反演的输入数据,选择不同的网格剖分方式,使用DASOCC与NLCG反演方法,以相同参数分别进行反演,对所得结果进行对比分析。结果显示,在设定的模型及参数条件下,针对DASOCC反演方法,纵向及横向网格均不加密,反演效果比较理想,即横向采用一个测点对应一个网格的剖分方式,纵向采用表层加密,100 m以下后一网格为前一网格厚度1.1倍的剖分方式;针对NLCG反演方法,纵向与DASOCC反演方法相同,采用不加密网格的剖分方式得到的结果较好,而横向则建议采用一个测点对应一个网格,并在2个测点之间插入一个网格的剖分方式。     

Worth of secondary data compared to piezometric data for the probabilistic assessment of radionuclide migration

A common approach for the performance assessment of radionuclide migration from a nuclear waste repository is by means of Monte-Carlo techniques. Multiple realizations of the parameters controlling radionuclide transport are generated and each one of these realizations is used in a numerical model to provide a transport prediction. The statistical analysis of all transport predictions is then used in performance assessment. In order to reduce the uncertainty on the predictions is necessary to incorporate as much information as possible in the generation of the parameter fields. In this regard, this paper focuses in the impact that conditioning the transmissivity fields to geophysical data and/or piezometric head data has on convective transport predictions in a two-dimensional heterogeneous formation. The Walker Lake data based is used to produce a heterogeneous log-transmissivity field with distinct non-Gaussian characteristics and a secondary variable that represents some geophysical attribute. In addition, the piezometric head field resulting from the steady-state solution of the groundwater flow equation is computed. These three reference fields are sampled to mimic a sampling campaign. Then, a series of Monte-Carlo exercises using different combinations of sampled data shows the relative worth of secondary data with respect to piezometric head data for transport predictions. The analysis shows that secondary data allows to reproduce the main spatial patterns of the reference transmissivity field and improves the mass transport predictions with respect to the case in which only transmissivity data is used. However, a few piezometric head measurements could be equally effective for the characterization of transport predictions.     

3-D inversion of borehole-to-surface electrical data using a back-propagation neural network

The “fluid-flow tomography”, an advanced technique for geoelectrical survey based on the conventional mise-à-la-masse measurement, has been developed by Exploration Geophysics Laboratory at the Kyushu University. This technique is proposed to monitor fluid-flow behavior during water injection and production in a geothermal field. However data processing of this technique is very costly. In this light, this paper will discuss the solution to cost reduction by applying a neural network in the data processing. A case study in the Takigami geothermal field in Japan will be used to illustrate this. The achieved neural network in this case study is three-layered and feed-forward. The most successful learning algorithm in this network is the Resilient Propagation (RPROP). Consequently, the study advances the pragmatism of the “fluid-flow tomography” technique which can be widely used for geothermal fields. Accuracy of the solution is then verified by using root mean square (RMS) misfit error as an indicator.