Mapping of glaciotectonic deformation in an ice marginal environment with ground penetrating radar

A series of closely spaced parallel ground penetrating radar (GPR) profiles of glaciotectonic deformed glacio-fluvial sediments have been obtained in an ice marginal environment in Northwest Zealand, Denmark. The radar profiles can be differentiated into several radar facies with distinct reflection characteristics. The lithology and depositional environment of the radar facies is interpreted by correlation with information from profiles in gravel pits, geological maps and drill hole data. The radar facies include glaciotectonically disturbed glacio-fluvial sediments, sediments deposited penecontemporaneously with the deformations and sediments deposited post-tectonically. Several thrust planes with dip angles between 25° and 30° as well as major folds and minor faults have been interpreted from the GPR data. The deformation style of the deformed glacio-fluvial sediments is a thin-skinned pro-glacial thrust complex, with associated folding. The deformations have resulted in the present ridge morphology seen in the rim of the composite ridge. Syn- and post-tectonic sediments are deposited on top and in front of the deformed sediments, smoothing the ridge relief created by the thin-skinned thrust complex. A structural geological map constructed from the ground penetrating radar data reveals the extension of the individual radar facies in the thrust complex. Tectonic features such as thrust planes and folds can be followed throughout the mapped area.     

Inversion of multiple intersecting high-resolution crosshole GPR profiles for hydrological characterization at the Boise Hydrogeophysical Research Site

The integration of geophysical data into the subsurface characterization problem has been shown in many cases to significantly improve hydrological knowledge by providing information at spatial scales and locations that is unattainable using conventional hydrological measurement techniques. In particular, crosshole ground-penetrating radar (GPR) tomography has shown much promise in hydrology because of its ability to provide highly detailed images of subsurface radar wave velocity, which is strongly linked to soil water content. Here, we develop and demonstrate a procedure for inverting together multiple crosshole GPR data sets in order to characterize the spatial distribution of radar wave velocity below the water table at the Boise Hydrogeophysical Research Site (BHRS) near Boise, Idaho, USA. Specifically, we jointly invert 31 intersecting crosshole GPR profiles to obtain a highly resolved and consistent radar velocity model along the various profile directions. The model is found to be strongly correlated with complementary neutron porosity-log data and is further corroborated by larger-scale structural information at the BHRS. This work is an important prerequisite to using crosshole GPR data together with existing hydrological measurements for improved groundwater flow and contaminant transport modeling.     

An automatic recognition algorithm for GPR images of RC structure voids

Ground penetrating radar (GPR) is a powerful tool for detecting defects in and behind reinforced concrete (RC) structures. However, the traditional way of interpreting GPR data involves considerable manpower and is time-consuming. The aim of this study is to illustrate a new approach to recognize GPR images of RC structure voids automatically. Firstly, synthetic GPR images are created by FDTD method. As multiple waves caused by steel bars seriously interfere with the target echo signals, it is difficult to identify targets from the forward modeling images. According to the periodicity of multiple waves from steel bars, the predictive deconvolution method is used to suppress those waves and the outcome is preferable. Then, the support vector machine (SVM) algorithm is proposed to automatically recognize voids in GPR images. The automatic identification procedure includes four steps: 1) collecting training data, 2) extracting features from GPR images, 3) building the SVM model and 4) identifying the voids automatically. The results show that the proposed method provides a suitable tool to locate the cover depths and lateral ranges of the voids, and the trained SVM model gives a favorable outcome when noise (no more than 5%) is added to a synthetic GPR image.     

Internal sedimentary architecture and coastal dynamics as revealed by ground penetrating radar, Kachchh coast, western India

The coastline constitutes a very sensitive geomorphic domain which is constantly subjected to dynamic coastal processes and stores vital information regarding past sea level fluctuations. A ground-penetrating radar (GPR) survey was carried out along the northern coast of the Gulf of Kachchh which is one of the largest macrotidal inlets of the Arabian Sea, Western India. Our studies have delineated several radar surfaces and radar facies which reflect the internal architecture and sediment body geometry, which can be related to the processes acting along this coastline. Various radar facies, namely, beach ridge (Br), washover (Wo), coastal dune (Cd), swale (Sw), berm plain (Bp), and sandsheet facies (Ss) have been identified. The GPR studies successfully documented the subsurface presence of ancient beach ridge system towards the sea, and the coastal dunes towards the land side. The results are suggestive of signatures of changes in sea level and the coastline being prone to high energy events in the recent past. The GPR has been found to be an important non-invasive geophysical tool in the study of past coastal dynamics.     

Downflow amplitude decrease of ground penetrating radar reflections in base surge deposits

Ground penetrating radar (GPR) data were collected parallel to a flow direction in the base surge deposits of the Ubehebe hydrovolcanic field (Death Valley, California). These data showed a downflow amplitude decrease of the reflections that was displayed by computing Fourier transforms and the average square of the sample points of the traces. This trend probably reflects some aspects of the lateral facies variation of base surge deposits, and could be explained by the concomitant downflow increase in sorting and decrease in grain size with a consequent reduction of the differences between the beds, which, in turn, causes a reduction of the reflected energy. These results suggest that, although the GPR profiles are traditionally used merely as 2-D images of the subsurface stratigraphy, they could be useful in obtaining information about the heterogeneous distribution of subsurface physical properties. The downflow decrease of the amplitude of the signal could be used as a flow direction indicator when the position of the vent is unknown, although other geologic information and field constraints are probably necessary.     

Bedload transport of small rivers in Malaysia

Numerous time-consuming equations, based on the relationship between the reliability and representativeness of the data utilized in defining variables and constants, require complex parameters to estimate bedload transport. In this study the easily accessible data including flow discharge, water depth, water surface slope, and surface grain diameter （ds0） from small rivers in Malaysia were used to estimate bedload transport. Genetic programming （GP） and artificial neural network （ANN） models are applied as complementary tools to estimate bed load transport based on a balance between simplicity and accuracy in small rivers. The developed models demonstrate higher performance with an overall accuracy of 97% and 93% for ANN and GP, respectively compared with other traditional methods and empirical equations.     

GPR reflection characteristics and depositional models of mud volcanic sediments—Wushanting mud volcano field, southwestern Taiwan

Ground penetrating radar (GPR) survey was conducted in the Wushanting mud volcano field (Yanchao, Kaohsiung) using a 500 MHz antennae, which allowed high-resolution imaging of subsurface structures. Seven GPR reflection characteristics are recognized. Sigmoid GPR reflection patterns resulted from a recent mud lobe deposited above an underlying older mud lobe front. Contorted GPR facies resulted from recent mud flow which encountered obstacles. Subparallel reflections resulted from mud volcano deposits of limited flowability, low velocity and gentle gradient. Hummocky reflection patterns are formed by interfingering of recent mud lobes building onto low land. Disrupted GPR facies were due to lateral breaks of continuity from mud cracks, which, according to field observation, can provide channels for erosion and form deeper erosion gullies. GPR time slices of different depths are rendered as a three-dimensional model. Approximately orbicular GPR reflection characteristics can indicate arcuate stacked mud lobe fronts of different periods. Some depositional models to explain GPR reflection characteristics can be founded upon observations of recent sedimentary phenomena. The models of this study may be applied to paleoenvironments and the depositional evolution of mud volcanoes in similar geological settings.     

GPR reflection characteristics and depositional models of mud volcanic sediments—Wushanting mud volcano field,southwestern Taiwan

Ground penetrating radar (GPR) survey was conducted in the Wushanting mud volcano field (Yanchao, Kaohsiung) using a 500 MHz antennae, which allowed high-resolution imaging of subsurface structures. Seven GPR reflection characteristics are recognized. Sigmoid GPR reflection patterns resulted from a recent mud lobe deposited above an underlying older mud lobe front. Contorted GPR facies resulted from recent mud flow which encountered obstacles. Subparallel reflections resulted from mud volcano deposits of limited flowability, low velocity and gentle gradient. Hummocky reflection patterns are formed by interfingering of recent mud lobes building onto low land. Disrupted GPR facies were due to lateral breaks of continuity from mud cracks, which, according to field observation, can provide channels for erosion and form deeper erosion gullies. GPR time slices of different depths are rendered as a three-dimensional model. Approximately orbicular GPR reflection characteristics can indicate arcuate stacked mud lobe fronts of different periods. Some depositional models to explain GPR reflection characteristics can be founded upon observations of recent sedimentary phenomena. The models of this study may be applied to paleoenvironments and the depositional evolution of mud volcanoes in similar geological settings.     

3D imaging of a reservoir analogue in point bar deposits in the Ferron Sandstone, Utah, using ground-penetrating radar

Most existing reservoir models are based on 2D outcrop studies; 3D aspects are inferred from correlation between wells, and so are inadequately constrained for reservoir simulations. To overcome these deficiencies, we have initiated a multidimensional characterization of reservoir analogues in the Cretaceous Ferron Sandstone in Utah. Detailed sedimentary facies maps of cliff faces define the geometry and distribution of reservoir flow units, barriers and baffles at the outcrop. High‐resolution 2D and 3D ground‐penetrating radar (GPR) images extend these reservoir characteristics into 3D to allow the development of realistic 3D reservoir models. Models use geometric information from mapping and the GPR data, combined with petrophysical data from surface and cliff‐face outcrops, and laboratory analyses of outcrop and core samples. The site of the field work is Corbula Gulch, on the western flank of the San Rafael Swell, in east‐central Utah. The outcrop consists of an 8–17 m thick sandstone body which contains various sedimentary structures, such as cross‐bedding, inclined stratification and erosional surfaces, which range in scale from less than a metre to hundreds of metres. 3D depth migration of the common‐offset GPR data produces data volumes within which the inclined surfaces and erosional surfaces are visible. Correlation between fluid permeability, clay content, instantaneous frequency and instantaneous amplitude of the GPR data provides estimates of the 3D distribution of fluid permeability and clay content.     

Application of ground‐penetrating radar to the identification of subsurface piping in blanket peat

Natural soil pipes are common and significant in upland blanket peat catchments yet there are major problems in finding and defining the subsurface pipe networks. This is particularly important because pipeflow can contribute a large proportion of runoff to the river systems in these upland environments and may significantly influence catchment sediment and solute yields. Traditional methods such as digging soil pits are destructive and time‐consuming (particularly in deep peat) and only provide single point sources of information. This paper presents results from an experiment to assess the use of ground‐penetrating radar (GPR) to remotely sense pipes in blanket peat. The technique is shown to be successful in identifying most of the pipes tested in the pilot catchment. Comparison of data on pipes identified by GPR and verified by manual measurement suggests that pipes can be located in the soil profile with a depth accuracy of 20 to 30 cm. GPR‐identified pipes were found throughout the soil profile; however, those within 10–20 cm of the surface could not be identified using the 100 or 200 MHz antennae due to multiple surface reflections. Generally pipes smaller than 10 cm in diameter could not be identified using the technique although modifications are suggested that will allow enhanced resolution. Future work would benefit from the development of dual‐frequency antennae that will allow the combination of high‐resolution data with the depth of penetration required in a wetland environment. The GPR experiment shows that pipe network densities were much greater than could be detected from surface observation alone. Thus, GPR provides a non‐destructive, fast technique which can produce continuous profiles of peat depth and indicate pipe locations across survey transects. Copyright © 2002 John Wiley & Sons, Ltd.     

路基病害识别算法与应用研究

本文通过对支持向量机原理以及路基病害特征的讨论和分析,提出了基于支持向量机的探地雷达回波信号识别算法;利用所提出的算法对探地雷达实测数据进行处理,试验结果表明该算法的识别效果优于神经网络算法,并且该算法克服了神经网络的过学习和局部极小值的缺点,是一种适合路基病害识别的高效算法.     

Step-frequency radar applied on thin road layers

In the field of road construction and maintenance, the need for information on the thickness of very thin road layers is not satisfied by means of commercial pulse GPR, due to the inability of such devices to operate over ranges of several gigahertz. As a result, research has focused on the design of a step-frequency radar technique, able to work with very high-frequency synthetic pulses.An ultrawide band antenna, belonging to the family of Vivaldi antennas, has been developed for road applications. It has been created using stripline technology and yields a band width greater than one decade. During an initial step, this antenna was tested on various bituminous concrete samples with a network analyzer. Different parameters were studied, including band width, offset between antennas, and height and shape of the frequency-dependent pulse.A second step involved GPR dynamic measurements. A customized software program enabled recording data from the network analyzer. Several radar profiles were developed from selected road construction and maintenance test sites (e.g. the Circular Pavement Fatigue Test Track, composed of a number of known structures). Results show improved resolution when compared to a commercial impulse GPR system.     

基于对抗式残差密集深度神经网络的CT稀疏重建

针对计算机断层成像稀疏重建过程中产生条状伪影的问题,本文提出一种基于对抗式残差密集深度神经网络的CT图像高精度稀疏重建方法.设计一种耦合残差连接、密集连接、注意力机制和对抗机制的UNet网络,以含条状伪影图像和高精度图像作为训练样本,通过大规模训练数据,对该网络进行训练,使其具有压制条状伪影的能力.首先,利用滤波反投影...     

THE DELINEATION OF THREE-DIMENSIONAL SHALLOW GEOMETRY OF ACTIVE FAULT BASED ON TLS AND GPR: A CASE STUDY OF AN NORMAL FAULT ON THE NORTH MARGIN OF MAOYABA BASIN IN LITANG,WESTERN SICHUAN PROVINCE

It is crucial to reveal the surface traces and activity of active faults by obtaining high-precision microtopography and three-dimensional shallow geometry. However, limited by the traditional geological investigation methods in the field and geological condition factors, the measurement method on microtopography and shallow geometry of active fault is badly insufficient. In this study, the TLS and GPR are firstly used comprehensively to delineate the microtopography and shallow geometry of the normal fault scarp on the north margin of Maoyaba Basin in Litang. Firstly, the vertical displacements of two landforms produced by the latest two periods of normal faulting and the two-dimensional GPR profiles are obtained separately. Secondly, the three-dimensional measurement method of active fault based on TLS and GPR is preliminarily established. On this basis, three-dimensional model of fault scarp and three-dimensional images of subsurface geometry are also obtained. These data all reveal a graben structure at normal fault scarps. Thirdly, the fusion and interpretation of three-dimensional data from the surface and subsurface are realized. The study results show:1)the vertical displacements of the T₁ and T₂ terraces by the normal fault movement is 1.4m and 5.7m, the GPR profile shows a typical fault structure and indicates the existence of small graben structure with a maximum width of about 40m in the shallow layer, which further proves that it is a normal fault. 2)the shallow geometry of the normal fault scarp can be more graphically displayed by the three-dimensional radar images, and it also makes the geometry structure of the fault more comprehensive. The precise location and strike of faults F₁ and F₂ on the horizontal surface are also determined in the three-dimensional radar images, which further proves the existence of small graben structure, indicating the extensional deformation characteristics in the subsurface of the fault scarps. Furthermore, the distribution of small graben structure on the surface and subsurface is defined more precisely. 3)the integrated display of microgeomorphology and shallow geometry of normal fault scarp is realized based on the three-dimensional point cloud and GPR data. The fusion of the point cloud and GPR data has obvious advantages, for the spatial structure, morphological and spectral features from the point cloud can improve the recognition and interpretation accuracy of GPR images. The interpreted results of the GPR profiles could minimize the transformation of the surface topography by the external environment at the most extent, restore the original geomorphology, relocate the position and trend of faults on the surface and constrain the width of deformation zones under the surface, the geological structure, and the fault dislocation, etc. In a word, the TLS and GPR can quickly and efficiently provide the spatial data with multi-level and multi-visual for non-destructive inspection of the microgeomorphology and shallow structure for the active fault in a wide range, and for the detection of active fault in the complex geological environments, and it is helpful to improve the accuracy and understanding of the investigation and research on microtopography and shallow geometry of active faults. What's more, it also offers important data and method for more comprehensive identification and understanding of the distribution, deformation features, the behaviors of active faults and multi-period paleoseismicity. Therefore, to continuously explore and improve this method will significantly enhance and expand the practicability and application prospects of the method in the quantitative and elaborate studies of active faults.     

探地雷达图像的正演合成与偏移处理

本文讨论了射线追踪法实现二维地电断面的探地雷达图像的正演合成问题,其中包括二维地电断面的数字拟合、多层连续界面介质中高频电磁波的反射与透射统一的运动学边界方程;根据Huygen's-Fresnel原理导出了均匀介质中二维管状体模型的衍射雷达波的计算公式;给出了用射线追踪法合成的二维地电模型的探地雷达图像;用衍射波公式合成了二维管状模型的雷达图像,并与物理模拟作了比较.在此基础上,用Kirchhoff积分法对数字模拟与物理模拟记录的雷达图像作了偏移处理,并给出了实例.     

Stochastic analysis of effective rate constant for heterogeneous reactions

A probability density function (pdf) formulation is applied to a heterogeneous chemical reaction involving an aqueous solution reacting with a solid phase in a batch. This system is described by a stochastic differential equation with multiplicative noise. Both linear and nonlinear kinetic rate laws are considered. An effective rate constant for the mean field approximation describing the change in mean concentration with time is derived. The effective rate constant decreases with increasing time eventually approaching zero as the system approaches equilibrium. This behavior suggests that a possible explanation for the observed discrepancy between laboratory measured rate constants on uniform grain sizes and field measurements may in part be caused by the heterogeneous distribution of grain sizes in natural systems. This work was supported in part by the US Department of Energy under the DOE/BES Program in the Applied Mathematical Sciences, Contract KC-07-01-01, and the Environmental Management Science Program, Office of Biological and Environmental Research. This work made use of shared facilities supported by SAHRA (Sustainability of Semi-Arid Hydrology and Riparian Areas) under the STC Program of the National Science Foundation under agreement EAR-9876800. Los Alamos National Laboratory is operated by the University of California for the US Department of Energy under contact W-7405-ENG-36.     

Remigration of ground-penetrating radar data

Optimal electromagnetic wave propagation velocities and subsurface images for ground-penetrating radar (GPR) data can be specified by using an imaging scanning method. In addition to time-migrating the unmigrated GPR section, we remigrate the already time-migrated section by a one-step remigration operator using different velocities. This creates many time-migrated images for different constant migration velocities. In this way, the computation time for time-migration is very much reduced. Time-migrated reflector images `propagate' when the constant migration velocity is continuously changed. For this `propagation' there exists a wave-equation-type partial differential equation. Each time-migrated section can thus be viewed as a snapshot for a certain migration velocity. The time-migrated reflector images behave like `waves', called image waves. This is applied to real GPR data acquired over a concrete body within which a steel cable frame is buried. The method produces a quick velocity scan to find a reliable migration velocity leading to the best time-migrated image.     

探地雷达在探测隐伏活动断层中的应用

以东昆仑断裂带东部采用探地雷达探测隐伏活动断层为例。 在浅覆盖区, 利用合理的采集参数和数据处理流程; 雷达剖面图像能够清晰地显示出隐伏断层的形态特征和岩土分层; 结合沉积序列, 可以分析和评价断层的活动性。 实验证明, 探地雷达是一种有效探测浅覆盖区隐伏活动断层的方法。     

An englacial image and water pathways of the Fourcade glacier on King George Island, Antarctic Peninsula, inferred from ground-penetrating radar

The distribution of small fractures and water content of the Fourcade glacier on King George Island, Antarctica, was investigated in November 2006 and December 2007 by two ground-based (470-and 490-m-long profiles) and one helicopter-borne (470-m-long profile) ground-penetrating radar (GPR) surveys using 50-, 100-, and 500-MHz antennas. Radar images in the pre-migrated GPR sections are characterized by a smooth ice surface and irregular bed topography, numerous diffraction hyperbolas in the ice and at the g...     

Short-term prediction of influent flow in wastewater treatment plant

Predicting influent flow is important in the management of a wastewater treatment plant (WWTP). Because influent flow includes municipal sewage and rainfall runoff, it exhibits nonlinear spatial and temporal behavior and therefore makes it difficult to model. In this paper, a neural network approach is used to predict influent flow in the WWTP. The model inputs include historical influent data collected at a local WWTP, rainfall data and radar reflectivity data collected by the local weather station. A static multi-layer perceptron neural network performs well for the current time prediction but a time lag occurs and increases with the time horizon. A dynamic neural network with an online corrector is proposed to solve the time lag problem and increase the prediction accuracy for longer time horizons. The computational results show that the proposed neural network accurately predicts the influent flow for time horizons up to 300 min.