1,721,150 research outputs found
Synthetic and Field Examples of GPR profile improvement using two Phase Detection Techniques
No full textTecnologie geofisiche innovative per le indagini archeologiche: applicazioni al sito di Medinet Madi (Egitto)
No full textAssessment of a deep learning framework for time-lapse seismic monitoring
No full textIn the context of CO2 storage, cost-effective monitor methods are essential to ensure safe and long-term storage. This work explores the use of seismic time-lapse monitoring, combined with deep learning (DL) techniques, to assess potential leakage and migration pathways. The goal is to develop a cost-effective monitoring method while guaranteeing the safety of storage operations. To this end, we propose a Siamese Neural Network (SNN)-based framework to analyse shot gathers, designed to detect and localize changes within the storage complex. We aim to address the challenges of working with large seismic datasets, enabling the identification of significant events with high confidence, while avoiding the need for event-by-event processing. This framework can allow experts to rely on semi-automatic detections while ensuring human evaluation for interpreting and validating the results.
The proposed SNN architecture processes pairs of shot gather from baseline and monitor surveys in a cross-well configuration. It uses two identical neural networks with shared weights to encode the shot gathers into latent feature embeddings, which are then compared to identify similarities and detect changes. By transforming the data into a shared latent space, the model focuses on capturing relevant patterns while filtering out irrelevant variations, ensuring robust and accurate comparisons. When the SNN detects changes between the baseline and the monitor surveys, it highlights the regions where these changes occur. This approach is particularly effective for identifying subtle but important changes in seismic data, such as those caused by CO2 migration, which alters the velocity and density of the subsurface. Even in noisy data, the SNN can detect these variations, thanks to its ability to learn features that are highly sensitive to small but meaningful changes. The SNN architecture is scalable and can be adaptable to various seismic monitoring tasks, requiring minimal preprocessing. The proposed framework harnesses the power of deep learning to provide insights into the dynamics of the storage complex, with a focus on identifying changes in time-lapse seismic data related to localized variations. The proposed migration detection tool offers a cost-effective and reliable solution to the modern challenges of gas storage monitoring. This study aims to enable operators to identify and address problems promptly, thereby minimising the impact of potential leakages
Review of multi-offset GPR applications: Data acquisition, processing and analysis
No full textGPR and reflection seismics share common physical and methodological bases but are sensitive to different subsurface physical properties. The peculiarities of the electromagnetic case impact data acquisition, processing and interpretation. We review multi-offset techniques in GPR applications focusing on similarities and differences through examples taken from different subsurface and target conditions.
GPR multi-offset data acquisition methods basically involve common-offset and common midpoint geometries: accuracy and work load are the main factors that drive the choice, together with effectiveness of the solution for the objectives of the study.
Multi fold data processing algorithms can bring remarkable signal-to-noise ratio enhancement and offer the opportunity to extract additional information from field data. Velocity field and related dielectric constants distribution, attenuation and related conductivity variations, changes in the GPR response with offset are some of the examples. Coherent noise suppression and velocity analysis are key features in GPR multi fold processing sequences and we review the relevant methods with examples of application in addition to technical aspects.
Multi-channel acquisitions, full wave-form inversion, pre-stack depth migration, azimuthal and polarimetric analysis, are among the many topics in current and future research that are briefly reviewed to provide some highlights of the forthcoming developments in GPR methods
VS and VP Determination via Joint Inversion of Rayleigh Waves and Refraction Travel Times by means of Pareto Criterion and Evolutionary Algorithms
No full textIntegrated seismic tomography and ground-penetrating radar (GPR) for the high-resolution study of burial mounds (tumuli)
No full textThe study primarily aims at providing adequate imaging resolution of large and prominent targets of
archaeological interest, such as pyramids and tumuli, at all depth levels. We implemented an integrated
seismic tomography and georadar (STG) technique to perform high-resolution imaging and characterization
of tumuli (burial mounds). We tested the proposed technique on a preserved late Bronze Age
burial mound in northern Italy, for which STG succeeded in performing an accurate 3-D reconstruction of
the structure and stratigraphy as proved by later archaeological excavations. We completed two transmission
seismic tomography measurements, at present ground level and at 1.5 m elevation, with a 24-
channel seismograph and 15 angular separation between geophones. The ground-penetrating radar
(GPR) dataset encompasses 12 250 MHz radial profiles and 12 common mid point gathers for velocity
analysis. Shallow layers of the mound are successfully imaged by GPR, whilst the structure of the deep
central part of the tomb is reconstructed from seismic traveltime inversion. In particular, GPR images
lenses and layers of sediments forming the external part of the tumulus, evidences of a looting attempt,
peripheral structures associated with later exploitation of the mound (furnaces) and, in the external
sector of the tumulus, the top of the deep layer of silty sediments covering the funeral chamber.
Tomographic results reveal seismic velocity anomalies of potential archaeological interest at ground
level, which were successively validated by archaeological excavations. The integration of GPR and
tomographic datasets is an effective strategy to overcome the imaging and interpretation problems
related to the structure of such peculiar funeral monuments. STG can be applied to a virtually unlimited
dimensional range and requires a limited data acquisition, processing and inversion effort. The results of
the study allowed the identification of the funeral chamber and a detailed imaging of layering and
structural details
Morfobatimetria e immagini acustiche dei fondali marini della riviera di Miramare (Golfo di Trieste)
No full textJoint inversion of surface wave dispersion curves and reflectiontravel times via multi-objective evolutionary algorithms
No full textDue to the character of seismic energy generation and propagation, shallow high-resolution seismic-reflection surveys often fail in the identification of the shallowest horizons and, due to the limited offsets, accuracy of velocity analyses is often not very high.
In recent years, Rayleigh wave dispersion analysis have proved to have good potential also for near-surface applications but dispersion curve inversion and related uncertainty evaluation pose serious problems to a completely stand-alone application.
In order to overcome these problems a joint inversion scheme is proposed, which is based on the identification of the Pareto front, performed in the framework of a Multi-Objective Evolutionary Algorithm (MOEA). Seismic data considered to design the two objectives are the Rayleigh wave dispersion curve and reflection travel times.
We initially analyse a set of synthetic cases and evaluate the obtained results. A significant improvement of the retrieved models is observed as long as reflection travel times are added to the dispersion curve alone.
Furthermore, the proposed methodology also provides relevant indications about the consistency of the overall inversion process. In fact, the distribution of the models in the objective space, the trend of the objectives over the passing generations and the evolution of the Pareto front can provide useful information to evaluate the provisional tentative interpretation (number of strata and reflector identification) inherently adopted for the data inversion.
On the basis of the results obtained from the tests on the synthetic datasets, the analyses of a field dataset are interpreted as possible evidence of lateral heterogeneities
Prospezioni geofisiche magnetometriche, sismiche e Ground Penetrating Radar sul tumulo di Sant’Osvaldo (UD)
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