10th International Workshop on Advanced Ground Penetrating Radar最新文献

Structural health monitoring of civil engineering structures using GPR detection of patch antenna resonance frequency changes 土木工程结构健康监测采用探地雷达探测贴片天线谐振频率变化

10th International Workshop on Advanced Ground Penetrating Radar Pub Date : 2019-09-08 DOI: 10.3997/2214-4609.201902599

T. Richard, D. Guilbert, A. Ihamouten, H. Gundel, C. Borderon, M. Latrach, Xavier Dérobert

引用次数: 0

The Gopher antenna: A new efficient design for ground penetrating radar 地鼠天线:一种高效的探地雷达新设计

10th International Workshop on Advanced Ground Penetrating Radar Pub Date : 2019-09-08 DOI: 10.3997/2214-4609.201902586

V. Voipio

引用次数: 4

Using GPR to Evaluate the Stratigraphic Condition of the Mosaic of the Dolphins in Delos Island, Greece, in order to Adopt the necessary Conservation measures 利用探地雷达对希腊Delos岛海豚镶嵌地层条件进行评价，以便采取必要的保护措施

10th International Workshop on Advanced Ground Penetrating Radar Pub Date : 2019-09-08 DOI: 10.3997/2214-4609.201902591

M. Manataki, C. Maris, A. Sarris, A. Vafidis

{"title":"Using GPR to Evaluate the Stratigraphic Condition of the Mosaic of the Dolphins in Delos Island, Greece, in order to Adopt the necessary Conservation measures","authors":"M. Manataki, C. Maris, A. Sarris, A. Vafidis","doi":"10.3997/2214-4609.201902591","DOIUrl":"https://doi.org/10.3997/2214-4609.201902591","url":null,"abstract":"Summary The mosaics of Delos island are a significant body of the ancient Greek art of the Hellenistic period that are inscribed to UNESCO’s World Heritage List. In this study, GPR systems with frequencies of 1600MHz, 500MHz and 250MHz are used to evaluate the stratigraphic condition of the mosaic at house of the Dolphins that decorates the peristyle impluvium. All the obtained data were processed in MATLAB and both Bscans and depth slices were used for the interpretation. The 1600MHz system performed the best and it was possible to identify the boundaries of the mosaic layers as well as problematic areas, such as bulges and high levels of moisture that may cause deterioration. The results obtained from the 500MHz and 250MHz are complimentary to the ones obtained from the high frequency system as the emitted signals can reach deeper levels. Structural characteristics of the cistern below the main mosaic floor were mapped in some detail including five stone arches. The overall performance of the GPR was satisfactory and the resulted information will be taken into account for the upcoming conservation plans.","PeriodicalId":162237,"journal":{"name":"10th International Workshop on Advanced Ground Penetrating Radar","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114082573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

3D Reconstruction of Tree Roots under Heterogeneous Soil Conditions using Ground Penetrating Radar 基于探地雷达的非均匀土壤条件下树根三维重建

10th International Workshop on Advanced Ground Penetrating Radar Pub Date : 2019-09-08 DOI: 10.3997/2214-4609.201902596

Abderrahmane Aboudourib, M. Serhir, D. Lesselier

引用次数: 0

Mathematic Physical Equivalency of 2D Pure Waves in Seismics and Electromagnetics 二维纯波在地震和电磁学中的数学物理等效性

10th International Workshop on Advanced Ground Penetrating Radar Pub Date : 2019-09-08 DOI: 10.3997/2214-4609.201902580

T. Qin, T. Bohlen

引用次数: 0

Anisotropie matrix-free tomography using discontinuous Galerkin of GPR data acquired between boreholes in Rustrel carbonates 利用在Rustrel碳酸盐岩井眼间获取的GPR数据的不连续伽辽金进行各向异性无基体层析成像

10th International Workshop on Advanced Ground Penetrating Radar Pub Date : 2019-09-08 DOI: 10.3997/2214-4609.201902594

M. Salcedo, S. Garambois, P. Bouteiller, J. Virieux

{"title":"Anisotropie matrix-free tomography using discontinuous Galerkin of GPR data acquired between boreholes in Rustrel carbonates","authors":"M. Salcedo, S. Garambois, P. Bouteiller, J. Virieux","doi":"10.3997/2214-4609.201902594","DOIUrl":"https://doi.org/10.3997/2214-4609.201902594","url":null,"abstract":"Summary An increasing number of Ground-Penetrating Radar (GPR) studies takes benefit from illumination capabilities provided by dense acquisitions between boreholes. Traveltime tomography, migration and full waveform inversion provide high-resolution images to be interpreted. Tomography is often performed under an isotropic assumption, although anisotropy may exist at different scales. A new tomography approach is investigated based on an anisotropic Eikonal solver for the forward problem and an adjoint formulation for inverse problem. The misfit gradient is computed directly without expressing the sensitivity matrix, leading to explicit contribution of anisotropic parameters. The parametrization of elliptical anisotropy based on vertical and horizontal velocities is preferred to a parametrization based on Thomsen parameters for a realistic synthetic example, inspired from a real example of GPR transmission tomography between two boreholes in a carbonate environment where an old gallery exists. The vertical velocity is nicely recovered while the anisotropy contribution stays small. However, the real application provides similar results either through a layered isotropic model or through a smoother anisotropic model. Geological information is, therefore, needed for further specific interpretation.","PeriodicalId":162237,"journal":{"name":"10th International Workshop on Advanced Ground Penetrating Radar","volume":"22 23 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134423206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Signal Processing For Tree-Trunk Investigation Using Ground Penetrating Radar 探地雷达树干探测信号处理

10th International Workshop on Advanced Ground Penetrating Radar Pub Date : 2019-09-08 DOI: 10.3997/2214-4609.201902601

I. Giannakis, F. Tosti, Livia Lantini, Daniel Egyir, A. Alani

引用次数: 3

Data Augmentation in feature-space with Generative Adversarial Networks, applied to GPR-based Buried Threat Detection 基于生成对抗网络的特征空间数据增强，应用于基于gpr的隐藏威胁检测

10th International Workshop on Advanced Ground Penetrating Radar Pub Date : 2019-09-08 DOI: 10.3997/2214-4609.201902604

Jordan M. Malof, Daniel Reichman, L. Collins

{"title":"Data Augmentation in feature-space with Generative Adversarial Networks, applied to GPR-based Buried Threat Detection","authors":"Jordan M. Malof, Daniel Reichman, L. Collins","doi":"10.3997/2214-4609.201902604","DOIUrl":"https://doi.org/10.3997/2214-4609.201902604","url":null,"abstract":"Summary In this work we consider the problem of developing algorithms for automatic buried threat detection (BTD) in ground penetrating radar (GPR) data. Many such algorithms are supervised, and perform best when they can be trained on large quantities of labeled threat and non-threat GPR data, respectively. Unfortunately, such data is costly to collect, and therefore relatively scarce. One approach to mitigate this problem is data augmentation, in which novel training data is created by applying transformations to existing data. Prior work has shown that augmentation can indeed improve the training of GPR-based BTD algorithms. In this work, we explore the use of Generative Adversarial Networks (GANs) for data augmentation. GANs can be trained to generate novel, but highly realistic, data after training on a real-world dataset. GANs have yielded impressive results on many types of data, but they are notoriously difficult to train. In this work, we propose an approach, entitled featureGAN, that mitigates some of the challenges training GANs. We show that augmentation using featureGAN yields improved detection performance, and yields better performance than some naive alternative augmentation strategies. We also propose a metric for quantifying the success of GAN training, called the q-metric, which was crucial to achieving good results.","PeriodicalId":162237,"journal":{"name":"10th International Workshop on Advanced Ground Penetrating Radar","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130641113","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Full 3D Ground Penetrating Radar Imaging of Vertical Structures 垂直结构的全三维探地雷达成像

10th International Workshop on Advanced Ground Penetrating Radar Pub Date : 2019-09-08 DOI: 10.3997/2214-4609.201902597

G. Gennarelli, G. Ludeno, C. Noviello, F. Soldovieri, I. Catapano

引用次数: 0

Quasi-Measurement Pitch Change: A New Framework for Machine Learning on GPR 准测量螺距变化:探地雷达机器学习的新框架

10th International Workshop on Advanced Ground Penetrating Radar Pub Date : 2019-09-08 DOI: 10.3997/2214-4609.201902581

R. Ogura, T. Miyata

{"title":"Quasi-Measurement Pitch Change: A New Framework for Machine Learning on GPR","authors":"R. Ogura, T. Miyata","doi":"10.3997/2214-4609.201902581","DOIUrl":"https://doi.org/10.3997/2214-4609.201902581","url":null,"abstract":"Summary Ground-penetrating radar (GPR) is a geophysical method for non-destructive inspection of underground infrastructure. The main impediment to machine-learning-based classification of GPR data is gathering enough labeled data. Previous work done to solve this problem generated pseudo-GPR data through the numerical simulations done with expensive GPU clusters. In this paper, we propose a simple yet effective framework for machine learning with GPR data without enormous computational cost. The key idea of our method is quasi-measurement pitch changes (QMPC) that can obtain several times the amount of pseudo-data from real measurements. QMPCs are based on a simple sub-sampling procedure from real data, and no interpolation is applied for the pseudo-data. Thus, special hardware like GPU clusters are not required and no artifacts are produced by such an interpolation. Moreover, using QMPCs for test data allows us to apply ensemble learning at the inference phase of machine learning. The experimental results for the classification problem of buried objects clearly show that our framework can drastically improve accuracy with additional labeled data and without significantly increasing computational cost.","PeriodicalId":162237,"journal":{"name":"10th International Workshop on Advanced Ground Penetrating Radar","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114856306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0