Analysis Of Ground Penetrating Radar Data Research Articles

Modeling and advanced Ground Penetrating Radar techniques for evaluating pipe leakage and pavement distress

This study provides a comprehensive analysis of Ground Penetrating Radar (GPR) data processing techniques for detecting water leakage in urban pavements. By examining GPR images characterized by continuous and discontinuous high-amplitude reflections, the research identifies clear indicators of water saturation caused by leaking underground pipes. Computer modeling of GPR profiles enables effective comparisons with real data and helps in calibrating key GPR attributes such as energy, amplitude variance, and RMS amplitude. These attributes allow for the precise delineation of leakage zones in both synthetic and real GPR datasets collected in the Amazon region of Brazil. The Reflector Tilt Method (RTM) significantly enhances GPR data analysis, especially for detecting subsurface water leaks. RTM utilizes changes in the relative dielectric constant and electromagnetic wave velocity induced by water saturation in unconsolidated, horizontally stratified sand layers. By analyzing the tilt of reflections in GPR profiles, RTM differentiates between dry and saturated zones, providing insights into the geometry and extent of water infiltration. Notably, this study demonstrates how increased subsurface water saturation causes underlying reflectors to tilt. Using the tilt angle, we propose a novel technique to quantify the increase in the dielectric constant due to water saturation. This technique could also be adapted to detect leaks involving other fluids, as long as there is a strong contrast between the dielectric constant and the background. The integration of RTM improves the creation of accurate three-dimensional models of affected areas, enhancing leak detection and aiding in understanding water movement dynamics within the subsurface. The study underscores the importance of combining GPR attributes with RTM to generate detailed 3D models of leakage zones, which are essential for estimating the volume of infiltrated areas. Observations indicate significant water saturation near leaking pipes at shallow depths, with saturation levels decreasing rapidly as depth increases. By identifying 2D geophysical signatures of water leaks in public distribution networks, the research highlights the effectiveness of non-invasive GPR methods in mitigating risks such as soil erosion and surface collapse. This approach demonstrates the utility of GPR attributes and RTM as essential tools for assessing urban pavement integrity, enabling early detection of leaks and precise mapping of affected zones. The findings contribute significantly to the efficient and sustainable management of urban infrastructure, enhancing maintenance strategies and reducing potential hazards associated with undetected fluid leaks.

Enhancing full-waveform inversion of field GPR data: A source-independent approach with dynamic reference selection via SE-Wave-U-Net

Ground-penetrating radar (GPR) is a widely used nondestructive detection tool. Full-waveform inversion (FWI) offers a reliable algorithm for accurately imaging GPR data. Despite its potential, FWI’s effectiveness is often compromised by unknown excitation sources, which can lead to computational failures, diminished imaging accuracy, or both. Addressing this challenge, this paper introduces a convolution-type objective function designed to mitigate the detrimental effects of these unknown excitation sources on FWI imaging. Critical to the success of this function is the precise selection of reference traces. Further refining the capability for signal separation, we have integrated squeeze-and-excitation (SE) modules into the traditional Wave-U-Net architecture, culminating in the development of the SE-Wave-U-Net framework. This framework is adept at dynamically extracting accurate reference traces from field GPR data, which are essential for accurate FWI imaging. The efficacy, accuracy, and practical applicability of our methods are validated through extensive analysis of numerical, laboratory experiments, and field GPR data. Our algorithm not only establishes a high-precision computational framework for FWI but also enhances the reliability of GPR imaging in complex environments.

A Method to Detect Concealed Damage in Concrete Tunnels Using a Radar Feature Vector and Bayesian Analysis of Ground-Penetrating Radar Data

Many machine learning (ML)-based detection methods for interpreting ground-penetrating radar (GPR) data of concrete tunnels require extensive labeled damage-state data for model training, limiting their practical use in concealed damage detection of in-service tunnels. This study presents a probabilistic, data-driven method for GPR-based damage detection, which exempts the requirement in the training process of supervised ML models. The approach involves extracting a radar feature vector (RFV), building a Bayesian baseline model with healthy data, and quantifying damage severity with the Bayes factor. The RFV is a complex vector obtained by radargram data fusion. Bayesian regression is applied to build a model for the relationship between real and imaginary parts of the RFV. The Bayes factor is employed for defect identification and severity assessment, by quantifying the difference between the RFV built with new observations and the baseline RFV predicted by the baseline model with new input. The probability of damage is calculated to reflect the influence of uncertainties on the detection result. The effectiveness of the proposed method is validated through simulated data with random noise and physical model tests. This method facilitates GPR-based hidden damage detection of in-service tunnels when lacking labeled damage-state data in the model training process.

Tunnel Quality Inspection Based on GPR and Machine Learning

Abstract The development of non-destructive testing, particularly ground penetrating radar (GPR) technology, has gained popularity in underground detection due to its high resolution and non-destructive capabilities. However, the lack of intuitive imaging methods in GPR data analysis leads to poor interpretability, requiring skilled interpreters. To address this, the article suggests a machine learning-based algorithm for tunnel defect detection using GPR data. The YOLOv5 model network is used to train GPR data from actual road and railway tunnel inspections, and a CBAM attention mechanism is added to the original model to achieve high-precision radar scanning image defect recognition and localization. Compared with the original recognition model, the optimized recognition algorithm significantly improves recognition accuracy and greatly improves the efficiency of defect recognition compared to manual interpretation.

Research on the random generation model of 2D irregular aggregates in concrete based on fast grid region division method and its ground penetrating radar characteristics

Concrete can be regarded as a composite material with aggregates embedded in hardened slurries. The size, shape and distribution of aggregates directly affect multiple properties of concrete, including mechanical properties, ultrasonic field characteristics of concrete and ground penetrating radar (GPR) wave field characteristics. Therefore, conducting in-depth research on the random generation model of two-dimensional irregular aggregates is particularly important. This article introduces a fast grid region division method (FGRDM), which is an efficient reconstruction algorithm designed for two-dimensional concrete aggregate models. The algorithm takes into consideration the random micro-aggregate structure of concrete. A two-dimensional random distribution model of irregular aggregates in concrete was successfully established by generating concrete models with different particle size distributions and aggregate contents. Furthermore, the finite difference time domain method was used to analyze the GPR wave field of the random model. The significant influence of aggregate shape and distribution on the GPR wave field was discussed. Compared with existing two-dimensional irregular aggregate random generation models, the FGRDM proposed in this paper avoids computational redundancy and generates irregular aggregates at an increasingly fast speed. In the analysis of GPR data, electromagnetic waves are greatly affected by the shape and distribution of aggregates, highlighting the critical role of two-dimensional random distribution models in GPR data analysis. The paper uses FGRDM to generate random irregular aggregates to simulate the distribution of aggregates in actual concrete. Different diameter glass fiber reinforced polymer (GFRP) bars are inserted into the actual concrete and the numerical model to investigate the effect of aggregate size on the recognition of different diameter GFRP bars. The simulation results are generally consistent with the experimental results.

Numerical modelling of bridge deck reinforcement corrosion based on analysis of GPR data

AbstractThis study explores the impact of corrosion on Ground Penetrating Radar (GPR) responses through practical experiments and numerical modelling, focusing on rebar diameter reduction, corrosion product layer thickness, crack formation and corrosion product filling in vertical and transverse crack. Practical experiments involved GPR testing of reinforced concrete slab. By analyzing B-scans we identify areas with moderate and severe corrosion. Numerical modelling using the Finite Difference Time Domain (FDTD) Method to model GPR signal propagation in a concrete bridge deck with corrosion is applied. Key finding includes a significant 26.70% increase in reflected wave amplitude when corrosion product filling in vertical crack increased by 400%, highlighting its extensive effect on signal GPR propagation. Reduced rebar diameter led to a 9.79% amplitude decrease and a 0.06 ns arrival time delay. Increased corrosion product layer thickness primarily affected arrival time with a 0.06 ns extension but significantly amplified GPR signal amplitude. These findings offer insights for improving GPR based corrosion detection and assessment methods, leading to more robust systems for concrete bridge deck inspection and maintenance. This paper contributes to understanding how corrosion affects the signal that is detected by GPR. This information can be used to improve the way that we manage and assess corrosion in concrete bridge deck.

Ground penetrating radar - based investigation of fracture stratigraphy and structural characterization in karstified carbonate rocks, Brazil

Fracture stratigraphy study for a subsurface sedimentary rock sequence can be a challenging and demanding task. Typically, the data obtained from seismic and well-logs are heavily impacted by resolution issues and are difficult to interconnect. In this work, we document and extract fracture properties (orientation, density, intensity, etc.) from a layered carbonate sequence for fracture stratigraphy characterization. High-resolution subsurface Ground Penetrating Radar (GPR) data images, coupled with drone and previously documented well-log profiles, were analyzed to achieve the structural characterization task. The studied outcrop is localized in the Potiguar Basin (Brazil), where the Cretaceous Jandaíra Formation carbonates are exposed for hundreds of meters. The sequence is subdivided into an upper packstone/grainstone portion and a lower wackestone bed package. We documented the higher fracture intensity/density in the lower bed package portion, highlighting that depositional texture and intra-bed stylolites control the higher fracture distribution in the sequence. Finally, a 3D conceptual model describing the overall results is presented. This model summarizes and shows the innovative fracture stratigraphy method based on the GPR data analysis.

Detecting Reinforced Concrete Rebars Using Ground Penetrating Radars

A new algorithm is developed to automatically detect rebar locations and diameters of reinforced concrete structures using the ground penetrating radar technique. The study uses two-way travel time and biquadratic equations to formulate electromagnetic wave speed in reinforced concrete structures where hyperbolic signatures are approximated. Leveraging an established algorithm, a computer code has been developed to offer automated analysis of ground-penetrating radar data obtained from survey grids. Four reinforced concrete slabs were designed, fabricated, and tested to validate the developed evaluation approach. The proposed methodology demonstrates outstanding signal processing proficiency and reliably and effectively identifies rebar information.

Wykorzystanie metody radaru penetrującego grunt w badaniu gleby zanieczyszczonej węglowodorami w obszarze Navodari - Rumunia

Ground penetrating radar (GPR) is a very useful geophysical method for use in hydrogeologic and near-surface mapping studies. It can be used to study contaminants in groundwater, subsurface faulting, and underground cavities (natural or man-made), all of which pose potentially dangerous geological hazards. The GPR technique is similar in principle to seismic reflection and sonar techniques. The propagation of the radar signal depends on the frequency-dependent electrical properties of the ground.Electrical conductivity of the soil or rock materials along the propagation paths introduces significant absorptive losses which limit the depth of penetration into the earth formations and is primarily dependent upon the moisture content and mineralization present.Reflected signals are amplified, and transformed to the audio-frequency range, recorded, processed, and displayed. From the recorded display, subsurface features such as soil/ soil, soil/rock, and unsaturated/saturated interfaces can be identified. In addition, the presence of floating hydrocarbons on the water table, the geometry of contaminant plumes, and the location of buried cables, pipes, drums, and tanks can be detected. The GPR data are presented as a two-dimensional depth profile along a scanned traverse line in which the vertical axis is two-way travel time measured in nanoseconds. The location of hydrocarbon contamination in the ground using the GPR method is based mainly on information taken from reflected signals. In the cases investigated in Romania contaminated sites (Navodari area), such signals were very rarely recorded. A long time after spillage, contamination takes the form of plumes with different size and distribution, which depends on the geological and hydraulic properties of the ground. The survey discussed in this paper was carried out using the GPR system-Noggin with two antennas (250 and 500mHz) Data collected were processed using software(EKKO_Project™ GPR Data Analysis) to produce 2D radargram in time scale. The presence of contaminant plumes as well as the water table are observed in the GPR sections at depths approximately of 0.5 to 1.5 m. In the GPR section, the oil contaminated layer exhibits discontinuous, subparallel, and chaotic high amplitude reflection patterns. Promising results were also obtained in the GPR survey where three obvious reflection patterns representing the top sand-silt layer, oil-contaminated zone and, the underlying thick soft clay were detected in all 2D radargrams of the GPR traverse lines.

3D ground-penetrating radar data analysis and interpretation using attributes based on the gradient structure tensor

In near-surface geophysics, ground-penetrating radar (GPR) surveys are routinely used in a variety of applications including those from archaeology, civil engineering, hydrology, and soil science. Thanks to recent technical developments in GPR instrumentation and antenna design, 3D surveys comprising several hundred thousand traces can be performed daily. Especially in complex environments such as sedimentary systems, analyzing and interpreting the resulting GPR volumes is a time-consuming and laborious task that is still largely performed manually. In the past few decades, several data attributes have been developed to guide and improve such tasks and assure a higher degree of reproducibility in the resulting interpretations. Many of these attributes have been developed in image processing or computer vision and are routinely used, for example, in reflection seismic data interpretation. Especially in sedimentary systems, variations in the subsurface are accompanied by variations of GPR reflections in terms of the amplitudes, continuity, and geometry in view of the dip angle and direction. A promising tool to analyze such structural features is known as the gradient structure tensor (GST). To date, the application of the GST approach has been limited to a few 2D GPR examples. Thus, we take the basic idea of GST analysis and introduce and evaluate the corresponding attributes to analyze 3D GPR data. We apply our GST approach to one synthetic and two field data sets imaging diverse sedimentary structures. Our results demonstrate that our set of GST-based attributes can be efficiently computed in three dimensions and that these attributes represent versatile measures to address different typical interpretation tasks and, thus, help for an efficient, reproducible, and more objective interpretation of 3D GPR data.

Thickness regression for backfill grouting of shield tunnels based on GPR data and CatBoost & BO-TPE: A full-scale model test study

Ground penetrating radar (GPR) is a vital non-destructive testing (NDT) technology that can be employed for detecting the backfill grouting of shield tunnels. To achieve intelligent analysis of GPR data and overcome the subjectivity of traditional data processing methods, the CatBoost & BO-TPE model was constructed for regressing the grouting thickness based on GPR waveforms. A full-scale model test and corresponding numerical simulations were carried out to collect GPR data at 400 and 900 MHz, with known backfill grouting thickness. The model test helps address the limitation of not knowing the grout body condition in actual field detection. The data were then used to create machine learning datasets. The method of feature selection was proposed based on the analysis of feature importance and the electromagnetic (EM) propagation law in mediums. The research shows that: (1) the CatBoost & BO-TPE model exhibited outstanding performance in both experimental and numerical data, achieving R2 values of 0.9760, 0.8971, 0.8808, and 0.5437 for numerical data and test data at 400 and 900 MHz. It outperformed extreme gradient boosting (XGBoost) and random forest (RF) in terms of performance in the backfill grouting thickness regression; (2) compared with the full-waveform GPR data, the feature selection method proposed in this paper can promote the performance of the model. The selected features within the 5–30 ns of the A-scan can yield the best performance for the model; (3) compared to GPR data at 900 MHz, GPR data at 400 MHz exhibited better performance in the CatBoost & BO-TPE model. This indicates that the results of the machine learning model can provide feedback for the selection of GPR parameters; (4) the application results of the trained CatBoost & BO-TPE model in engineering are in line with the patterns observed through traditional processing methods, yet they demonstrate a more quantitative and objective nature compared to the traditional method.

Wavelet Analysis of GPR Data for Belowground Mass Assessment of Sorghum Hybrid for Soil Carbon Sequestration

Among many agricultural practices proposed to cut carbon emissions in the next 30 years is the deposition of carbon in soils as plant matter. Adding rooting traits as part of a sequestration strategy would result in significantly increased carbon sequestration. Integrating these traits into production agriculture requires a belowground phenotyping method compatible with high-throughput breeding (i.e., rapid, inexpensive, reliable, and non-destructive). However, methods that fulfill these criteria currently do not exist. We hypothesized that ground-penetrating radar (GPR) could fill this need as a phenotypic selection tool. In this study, we employed a prototype GPR antenna array to scan and discriminate the root and rhizome mass of the perennial sorghum hybrid PSH09TX15. B-scan level time/discrete frequency analyses using continuous wavelet transform were utilized to extract features of interest that could be correlated to the biomass of the subsurface roots and rhizome. Time frequency analysis yielded strong correlations between radar features and belowground biomass (max R −0.91 for roots and −0.78 rhizomes, respectively) These results demonstrate that continued refinement of GPR data analysis workflows should yield an applicable phenotyping tool for breeding efforts in contexts where selection is otherwise impractical.

Integrated use of georadar, electrical resistivity, and SPT for site characterization and water content estimative

Geophysical methods are potent tools for geotechnical site characterization in a nondestructive way. They improve the extrapolation of punctual data from direct survey methods, allowing a fast and cost-effective evaluation of large areas. Ground Penetrating Radar (GPR) and DC electrical resistivity (ER) are the most requested methods for geotechnical and geoenvironmental applications. Their use, however, is usually uncoupled, with no sharing of information from one method to another to improve data interpretation. This case study illustrates the development of protocols and scripts in R© programming language for ER and GPR data analysis with Standard Penetration Tests (SPT) data to produce more accurate information on subsurface conditions concerning lithology, water content, and groundwater table (GWT) position. The SPT data were used to associate resistivity ranges with different soil lithologies and GPR pulse velocities for estimating the soil water content. Estimated water content values aided in interpreting ER data and locating the groundwater table. The contacts between layers in the radargrams allowed the refinement of the ER model, rendering 3D volumes for each soil layer in situ.

High‐Resolution Geological Information from Crosshole Ground Penetrating Radar in Clayey Tills

AbstractHeterogeneous glacial deposits dominate large parts of the Northern Hemisphere. In these landscapes, high‐resolution characterization of the geology is crucial for understanding contaminant transport. Geological information is mostly obtained from multiple boreholes drilled during a site investigation, but such point‐based data alone do not always provide the required resolution to map small‐scale heterogeneity between boreholes. Crosshole ground penetrating radar (GPR) is suggested as a tool for adding credible geological information between boreholes at contaminated site investigations in industrial sites where infrastructure, such as electrical installations, can pose a challenge to other geophysical methods. GPR data are sensitive to the dielectric permittivity and the bulk electrical conductivity, which can be related to the distribution of water content and sand/clay occurrences. Here we present a detailed crosshole GPR dataset collected at an industrial contaminated site in a clay till setting. The data are processed using a novel inversion approach where information on changes in the velocity and attenuation of the radar signal are obtained independently. The GPR results are compared to borehole logs, grain size analyses, and relative permeability data from the site. The GPR data analysis provided valuable information on the understanding of the lateral geological variability. A silt layer with a thickness of a few decimeters, likely important for flow characterization, was confirmed and resolved by GPR data. Our findings suggest that crosshole GPR has the potential for contributing with high‐resolution geological information by filling the data gap between boreholes, thereby becoming a relevant tool in contaminated site investigations.

Identification of the Damages and Abnormal Objects in Tibetan Stone Walls Based on GPR Data Analysis

ABSTRACT Based on the amplitude attribute analysis of ground penetrating radar (GPR) data, this paper applies GPR to the identification of internal damages and abnormal objects in stone walls of Tibetan ruins. The corresponding relationship between the characteristics of internal damages and abnormal objects and the radar data is explored through the Tibetan stone walls (TSWs) simulation test. The identification of location and size of the damages and abnormal objects in TSWs is realized based on the Mahalanobis distance abnormal data discrimination method. Using the normal distribution and the noncentral chi-square distribution, the identification law of types of the damages and abnormal objects is constructed, which takes root mean square (RMS) amplitude and interface reflection coefficient as the characteristic values. The field detection results of TSWs at three Tibetan sites are taken as an extension and supplement to the simulation results, and a set of identification atlas for the location, size and type of the internal damages and abnormal objects in TSWs is established. The application results of the identification atlas were verified by the field detection and the results show that the atlas has a high accuracy, which can significantly improve the efficiency of the identification, and can provide a basis for the performance research, protection and maintenance of TSWs.

Improving 3D Metric GPR Imaging Using Automated Data Collection and Learning-Based Processing

Ground Penetrating Radar (GPR) is one of the most important non-destructive evaluation (NDE) devices to detect subsurface objects (i.e., rebars, utility pipes) and reconstruct the underground scene. There are two challenges for GPR-based inspection, which are GPR data collection and 3D subsurface object imaging. To address these challenges, we first propose a robotic solution that automates the GPR data collection process with a free motion pattern. It facilitates the 3D metric GPR imaging by tagging the pose with GPR measurement in real-time. Moreover, to improve the 3D GPR imaging, we introduce a learning-based GPR data analysis method, which includes a noise removal module to clear the background noise in raw GPR data and a Convolutional Recurrent Neural Network (CRNN) to estimate the dielectric value of subsurface medium in each GPR B-scan data. We use both field and synthetic data to verify the proposed methods. Experimental results demonstrate that our proposed methods can achieve higher performance and faster processing speed in 3D GPR imaging than baseline methods.

Real Depth-Correction in Ground Penetrating RADAR Data Analysis for Bridge Deck Evaluation

When ground penetrating radar (GPR) is used for the non-destructive evaluation of concrete bridge decks, the rebar reflection amplitudes should be corrected for rebar depths to account for the geometric spreading and material attenuation of the electromagnetic wave in concrete. Most current depth-correction methods assume a constant EM wave velocity in the entire bridge deck and correct GPR amplitudes based on the two-way travel time (TWTT) instead of the actual rebar depth. In this paper, we proposed a depth-correction algorithm based on the real rebar depths. To compare different depth-correction methods, we used gprMax software to simulate GPR signals in four models with various dielectric constants and conductivity. The comparison shows that the TWTT-based depth-correction method tends to over-correct GPR amplitudes so that underestimates the deterioration level of concrete decks at certain locations. Two depth-based correction methods are proposed that use migrated amplitudes and further normalize the corrected amplitude by rebar depth (attenuation rate). These methods are then applied to GPR data collected on two bridges, and the results were validated by other NDE methods and chloride concentration test.

Simulation of GPR B-Scan Data Based on Dense Generative Adversarial Network

Urban subsurface infrastructures, e.g., pipelines and roads, are aging with the expansion of modern cities. Benefiting from the capability of nondestructive detection, ground penetrating radar (GPR) has been widely applied to underground objects or disasters detection, and GPR B-scan images are employed by manual interpretation. While, this way of high subjectivity and uncertainty inevitably results in failure of detection. Meanwhile, the shortage of labelled images greatly impedes the automatization and intelligentization of underground disaster detection based on GPR. Many data simulation techniques, e.g., forward modelling, were used to augment images for training; however, the generated forward images were not similar enough to the real B-scan data, which makes recognition a challenging task. To address this problem, we proposed a novel B-scan image simulation method based on generative adversarial network to generate synthetic images for training detection networks. Our network utilizes DenseNet as the backbone network of generator to extract image features, and a weighted total variation regularization term to regularize the loss function of the network. The comparison and ablation experiments verified that our network could generate simulation images with high similarity to real GPR B-scan images. We believe that this work contributes to the intelligent processing and analysis of GPR data, and improves the efficiency of underground disaster detection.

Subsurface Propagation Velocity Estimation Methods in Ground-Penetrating Radar: A review

Velocity estimation is one of the most crucial tasks in ground-penetrating radar (GPR) surveys. It is an integral part of GPR data analysis for interpreting GPR-generated images of subsurface media. Error in velocity estimation leads to wrong interpretations of underground scenarios. Since the beginning of GPR development, researchers have proposed various velocity estimation procedures. In this article, a survey of different subsurface propagation velocity estimation techniques is presented. Based on the survey methods, GPR environment, and mathematical models involved, different available techniques are presented in a classified manner. A few techniques are experimentally validated and analyzed for their performance. Finally, the impact of wave velocity on GPR imaging is demonstrated on practical GPR measurement data.

Joint Investigation with Ground Penetrating Radar and Infrared Thermography as a Diagnostic Support for the Restoration of Two Wall Mosaics in the Church of St. Mary of the Admiral in Palermo, Italy

The church of S. Mary of the Admiral in Palermo, known as “La Martorana” and very famous for its Byzantine mosaics, has been a World Heritage site since 2015. The mosaic system of the church includes several groups of figures and scenes from the life of the Virgin Mary. From the western part of the ancient church only two mosaics survive, detached from their original position, and are now located in two internal chapels. On the occasion of several restoration works, these two mosaic panels were investigated with non-invasive techniques, in order to provide diagnostic support to the restoration and consolidation interventions. The investigations were aimed at detecting any air pockets that could cause the detachment of the tesserae or of possible differences between cement mortars under the tesserae. For this purpose, the integrated use of two non-invasive techniques namely infrared thermography (IRT) and ground penetrating radar (GPR) was considered. The joint analysis of IRT and GPR data allowed the interpretative uncertainties inherent in each technique to be reduced. Furthermore, for both techniques differentiated analyses were performed for layers at different depths under the mosaic surface. The results of these analyses were found to be more reliable regarding GPR data, compared to infrared thermography, the latter being more influenced by the reflectivity of the tesserae. However, the results partially confirmed the restorers’ diagnosis, also allowing the identification of further critical areas that could be affected by deterioration or compositional differences in the layers supporting the mosaics