Search Results (60)

The South China block (SCB) is characterized by complex tectonics, large-scale lithospheric deformation, and extensive mineralization in its southeastern region. However, the geodynamic processes and mechanisms driving mineralization remain controversial, partly due to the lack of information on its fine crustal structure. The resolution of crustal seismic tomography is relatively low due to the uneven distribution of local earthquakes in South China. In this study, we conduct a joint inversion of Bouguer gravity and seismic travel-time data to investigate the detailed 3-D P-wave velocity (Vp) structure of the crust beneath the SCB. Our results show the following: (1) strong lateral heterogeneities exist in the crust, which reflect the surface geology and tectonics well; (2) the Vp patterns at different depths beneath the Yangtze block are almost consistent, but those beneath the Cathaysia block vary significantly, which might be related to the lithosphere thinning in the Mesozoic; (3) decoupling between the upper crust and the lower crust occurs at ~20 km depth beneath the eastern SCB; (4) the Vp patterns vary beneath different metallogenic belts; and (5) distinct low-Vp anomalies exist in the lower crust beneath mineral deposits. These results suggest that the deep mineralization is closely associated with the lithospheric thinning and upwelling thermal flow in the Mesozoic beneath the eastern SCB. Our Vp tomographic result also strongly supports the viewpoint that the mineralization mechanism varies for different metallogenic belts. Full article

The Carpathian orogen represents a natural laboratory for the study of geodynamic interactions between lithospheres of different ages. The ancient Archean Cratons, such as the East European Craton, and Proterozoic platforms like the Scythian and Moesian platforms collided with the younger Tisza and Dacia mega-units, resulting in the formation of the current architecture of the Carpathian Mountains. To better understand how the lithospheric structure on Romanian territory changes from the East European Craton to younger European microplates, we use earthquake data recorded at the permanent broadband seismic stations of the Romanian National Seismic Network (RSN). Applying the multiple filter technique, we examine the dispersion of Rayleigh wave group velocities for earthquakes located within a 4000 km radius of the epicenter. Travel time tomography, conducted through fast marching surface tomography, helps us to construct group velocity maps for periods between 30 and 80 s. Our findings highlight a low-velocity body in front of the Vrancea slab, indicating asthenospheric upwelling due to slab verticalization. Full article

As a significant geological hazard in large–scale engineering construction, deep subsurface voids demand effective and precise detection methods. Cross–hole radar tomography overcomes depth limitations by transmitting/receiving electromagnetic (EM) waves between boreholes, enabling the accurate determination of the spatial distribution and EM properties of subsurface cavities. However, conventional inversion approaches, such as travel–time/attenuation tomography and full–waveform inversion, still face challenges in terms of their stability, accuracy, and computational efficiency. To address these limitations, this study proposes a deep learning–based imaging method that introduces the concept of travel–time fingerprints, which compress raw radar data into structured, low–dimensional inputs that retain key spatial features. A large synthetic dataset of irregular subsurface cavity models is used to pre–train a UNET model, enabling it to learn nonlinear mapping, from fingerprints to velocity structures. To enhance real–world applicability, transfer learning (TL) is employed to fine–tune the model using a small amount of field data. The refined model is then tested on cross–hole radar datasets collected from a highway construction site in Guizhou Province, China. The results demonstrate that the method can accurately recover the shape, location, and extent of underground cavities, outperforming traditional tomography in terms of clarity and interpretability. This approach offers a high–precision, computationally efficient solution for subsurface void detection, with strong engineering applicability in complex geological environments. Full article

Accurate measurement of atmospheric turbulent fluctuations is critical for understanding environmental dynamics and improving models in applications such as wind energy. Advanced remote sensing technologies are essential for capturing instantaneous velocity and temperature fluctuations. Acoustic tomography (AT) offers a promising approach that utilizes sound travel times between an array of transducers to reconstruct turbulence fields. This study presents a systematic evaluation of the time-dependent stochastic inversion (TDSI) algorithm for AT using synthetic travel-time measurements derived from large-eddy simulation (LES) fields under both neutral and convective atmospheric boundary-layer conditions. Unlike prior work that relied on field observations or idealized fields, the LES framework provides a ground-truth atmospheric state, enabling quantitative assessment of TDSI retrieval reliability, sensitivity to travel-time measurement noise, and dependence on covariance model parameters and temporal data integration. A detailed sensitivity analysis was conducted to determine the best-fit model parameters, identify the tolerance thresholds for parameter mismatch, and establish a maximum spatial resolution. The TDSI algorithm successfully reconstructed large-scale velocity and temperature fluctuations with root mean square errors ($RMSE\mathrm{s}$) below 0.35 m/s and 0.12 K, respectively. Spectral analysis established a maximum spatial resolution of approximately 1.4 m, and reconstructions remained robust for travel-time measurement uncertainties up to 0.002 s. These findings provide critical insights into the operational limits of TDSI and inform future applications of AT for atmospheric turbulence characterization and system design. Full article

Gradient damage processes in cementitious materials are generally produced by chemical and/or physical processes that travel from outside to inside. Depending on the type of damage, it can cause different effects such as decreased porosity, cracking, or steel corrosion in the case of carbonation, or increased porosity, micro-cracks, expansion, and spalling (also present in thermal damage) in the case of external attack by sulphates or acid attack. Therefore, estimating the boundaries of this damage is an essential task for concrete quality assessment. The first objective of this work was to use neural networks (NNs) for ultrasound tomographic reconstruction of concrete samples in order to estimate the advance front in gradient damage. Unlike the usual X-ray tomography, ultrasound tomography is affected by diffraction, among other factors. NNs can learn to compensate for these effects; however, they require a large amount of training data to achieve accurate results. In the case of cement-based materials, obtaining and measuring a real training database could be complicated, expensive, and time-consuming. For this purpose, a training process using simulated measurements was carried out. The second objective of this work was to demonstrate the feasibility of training neural networks through simulations, which reduces costs. Finally, the trained neural network for tomographic reconstruction was evaluated using real cylindrical concrete specimens. Each specimen consisted of an outer cylinder, representing externally exposed cement, and an inner cylinder, simulating the unaffected core. The Structural Similarity Index (SSIM) was used as a metric to assess the reconstruction accuracy, achieving values of 0.95 for simulated signals and up to 0.82 for real signals. Full article

Velocity gradient is an important parameter for interpreting tomographic velocity field and identifying geological boundaries. It is usually translated from the results of seismic travel-time tomography. Recent researches show seismic polarization direction appears to be a promising data source for obtaining velocity gradient field directly. However, what remains unclear is the sensitivity of polarization direction to velocity gradient, which causes difficulty for correctly inverting polarization direction data. To clarify this problem, the sensitivity of velocity gradient parameters on polarization direction is discussed in this paper. It was found that the sensitivity of polarization direction is related to the spatial position of the model parameter. The further the parameter position is from the sensor, the lower the sensitivity is. Such nonuniform distribution of sensitivity may cause distortion of inversion results with incomplete projection data. Based on this analysis, adjustment factors are introducing to the polarization direction inversion algorithm for correctly inverting polarization direction data. Numerical tests are conducted to verify our theoretical analysis and inversion algorithm. Test results show that our theoretical analysis is accurate in both homogeneous velocity field and near velocity interfaces. The inversion method with the adjustment factor can more accurately recover the velocity gradient, offering a promising approach for geological boundary imaging. Full article

The Holy Cross Mountains represent an isolated outcrop of Palaeozoic rocks located in the Trans-European Suture Zone, which is the boundary between the Precambrian East European Craton and Phanerozoic mobile belts of South-Western Europe. Despite extensive structural history studies, high-resolution seismic profiling has not been applied to this region until now. This research introduces near-surface seismic imaging of the Holy Cross Fault, separating two tectonic units of different stratigraphic and deformation history. In our study, we utilize a carefully designed weight drop source survey with 5 m shot and receiver spacing and 4.5 Hz geophones. The imaging technique, combining seismic reflection profiling and travel time tomography, reveals detailed fault geometries down to 400 m. Precise data processing, including static corrections and noise attenuation, significantly enhanced signal-to-noise ratio and seismic resolution. Furthermore, the paper discusses various fault imaging techniques with their shortcomings. The data reveal a complex network of intersecting fault strands, confirming general thrust fault geometry of the fault system, that align with the region’s tectonic evolution. These findings enhance understanding of the Holy Cross Mountains’ structural framework and provide valuable reference data for future studies of similar tectonic environments. Full article

Non-destructive testing (NDT) enables the determination of internal defects and flaws in concrete structures without damaging them, making it a common application in current bridge concrete inspections. However, due to the complexity of the internal structure of this type of concrete, limitations regarding measurement point placement, and the extensive detection area, accurate defect detection cannot be guaranteed. This paper proposes a method that combines the Simultaneous Algebraic Reconstruction Technique with Group Sparsity Regularization (SART-GSR) to achieve tomographic imaging of bridge concrete under sparse measurement conditions. Firstly, a mathematical model is established based on the principles of the tomographic imaging of bridge concrete; secondly, the SART algorithm is used to solve for its velocity values; thirdly, on the basis of the SART results, GSR is applied for optimized solution processing; finally, simulation experiments are conducted to verify the reconstruction effects of the SART-GSR algorithm compared with those of the SART and ART algorithms. The results show that the SART-GSR algorithm reduced the relative error to 1.5% and the root mean square error to 89.76 m/s compared to the SART and ART algorithms. This improvement in accuracy makes it valuable for the tomographic imaging of bridge concrete and provides a reference for defect detection in bridge concrete. Full article

Recent advancements in seismic data analysis have enhanced our grasp of the seismic heterogeneities near the core–mantle boundary (CMB). Through seismic tomography, persistent lower-mantle structures like the large low shear velocity provinces (LLSVPs) beneath the Pacific and South Africa have been identified. However, variations in the finer-scale features across different models raise questions about their origins. This study utilizes differential travel-time measurements from the USArray, operational across the contiguous United States from 2007 to 2014, to examine the impact of upper-mantle heterogeneities on tomographic models. By averaging the P-wave travel times and calibrating them with diffracted P-waves at the same stations, we mitigate the effects of shallow heterogeneities. The findings confirm that this method accurately maps the seismic anomalies beneath the USArray, consistent with other North American studies. Calibrated Pdiff travel-time data indicate significant anomalies in the mid-Pacific and Bering Sea and lesser anomalies in the northern Pacific, aligning with the global tomographic images. Moreover, the study highlights sharp travel-time variations over short distances, such as those across the northern boundary of the mid-Pacific anomaly, suggesting a chemically heterogeneous Pacific LLSVP. Full article

Neovascular age-related macular degeneration (nAMD) is one of the major causes of vision impairment that affect millions of people worldwide. Early detection of nAMD is crucial because, if untreated, it can lead to blindness. Software and algorithms that utilize artificial intelligence (AI) have become valuable tools for early detection, assisting doctors in diagnosing and facilitating differential diagnosis. AI is particularly important for remote or isolated communities, as it allows patients to endure tests and receive rapid initial diagnoses without the necessity of extensive travel and long wait times for medical consultations. Similarly, AI is notable also in big hubs because cutting-edge technologies and networking help and speed processes such as detection, diagnosis, and follow-up times. The automatic detection of retinal changes might be optimized by AI, allowing one to choose the most effective treatment for nAMD. The complex retinal tissue is well-suited for scanning and easily accessible by modern AI-assisted multi-imaging techniques. AI enables us to enhance patient management by effectively evaluating extensive data, facilitating timely diagnosis and long-term prognosis. Novel applications of AI to nAMD have focused on image analysis, specifically for the automated segmentation, extraction, and quantification of imaging-based features included within optical coherence tomography (OCT) pictures. To date, we cannot state that AI could accurately forecast the therapy that would be necessary for a single patient to achieve the best visual outcome. A small number of large datasets with high-quality OCT, lack of data about alternative treatment strategies, and absence of OCT standards are the challenges for the development of AI models for nAMD. Full article

This study is focused on one of the most active features of the Hellenic Volcanic Arc Southern Aegean Sea, the Santorini Island Volcanic Complex (SVC). The recent volcano-tectonic crisis in the intracalderic area has emerged the need for closer monitoring of the region. The 2011–2012 unrest has been attributed to the augmentation of fluid flow inside local mapped fracture zones. After March 2012, the seismic activity dropped significantly, raising questions about whether we would have a long period of quiescence or be on a break before the next period of unrest. In this research, a re-examination of the seismic outbreak of 2011–2012 was conducted by adding more travel-time data from 2013 while we further analyzed the waveform data from 2014 to May 2024 to explore the differences of the SVC body-wave velocity structure by performing seismic tomography in these two time windows. The new dataset serves to identify the state of the Santorini Volcanic Complex. The results show a significant reduction in Vp and Vs anomalies at shallow depths since the period of unrest. At the same time, the distribution of Vp/Vs ratio remains high (>1.87) in the area NNE of Kameni at a shallower depth (2 km). The areas of Christiana Islands and Columbo volcano are mainly characterized by negative body-wave anomalies and low Vp/Vs ratio (1.56–1.64) at shallow depths for the study period, while a possible explanation to results in the submarine volcano may be explained by dry steam/gas phases that may have resulted in the generation of the swarms that occurred in the region. Full article

This study examines the body-wave velocity structure of Attica, Greece. The region is located between two major rifts, the Gulf of Corinth and the Euboekos Gulf, and has experienced significant earthquakes throughout history. The distribution of seismic activity in the area necessitates a thorough investigation of geophysical properties, such as seismic velocities, to reveal the extent of significant fault zones or the presence of potential hidden faults. This case study utilized over 3000 revised events to conduct a local earthquake tomography (LET). P- and S-wave travel-time data were analyzed to explore small- to medium-scale (~10 km) anomalies that could be linked to local neotectonic structures. The study presents a detailed 3-D seismic velocity structure for Attica and its adjacent regions. The results of the study revealed strong lateral body-wave velocity anomalies in the upper crust were related to activated faults and that a significant portion of the observed seismicity is concentrated near the sites of the 1999 and 2019 events. Full article

In the realm of groundwater science, characterization of heterogeneous aquifers is pivotal for resolving diverse groundwater resource and engineering-related problems that require the detailed spatial distribution of hydraulic parameters. As research progresses, one hydraulic tomographical method, which is based on hydraulic travel time inversion, emerges as a promising and rapid method due to its robust and efficient calculation. In the field, the acquisition of hydraulic excitation and head observation data required for inversion is less time-consuming. Data collection from a single hydraulic test (such as a pumping test) typically takes only a few minutes or even a few tens of seconds. However, the field application of this method faces challenges. Hydraulic travel time is typically generated in the early stages of hydrogeological tests (e.g., early drawdown of a pumping test), yet accurate data may not be readily available because of the noise signals from test equipment, which can contaminate travel time signals, leading to inaccurate inversion results. A potential solution lies in utilizing the smooth head observation during the recovery period after the pump is turned off, which yields more accurate travel times for inversion calculations. In this paper, the mathematical development suggests that the travel time of the recovery phase aligns with that of the pumping phase when pumping reaches a steady or quasi-steady state. Subsequently, by employing Monte-Carlo simulations, 1200 realizations of two-dimensional heterogeneous confined aquifer models were generated for simulating pumping tests with different pumping durations. The calculated head data were then utilized to compute the travel time derived from drawdown data (t) and recovery data (t′), respectively. Comparisons showed that t is equal to t′ when drawdown reaches a steady or quasi-steady state. Conversely, when the pump is turned off before reaching a quasi-steady state, t differs from t′. However, results also indicate the fact that a decent hydraulic travel time diagnosis can be obtained, especially for the cases when travel times are smaller than 15 s. Given the statistical results of Monte-Carlo simulations, as well as experience during pumping tests in the field with different scenarios, using the recovery data from 60 s of pumping duration, or extended pumping durations of 100 s or 200 s as a more conservative alternative, can replace the aquifer characterization based on drawdown data. The new inversion strategy not only has less data uncertainty and equivalent inversion accuracy, but also can greatly enhance the repeatability of field tests and reduce the environmental impact of long-term pumping tests. Full article

Concrete-filled steel tube (CFST) members have been widely used in civil engineering due to their advanced mechanical properties. However, internal defects such as the concrete core voids and interface debonding in CFST structures are likely to weaken their load-carrying capacity and stiffness, which affects the safety and serviceability. Visualizing the inner defects of the concrete cores in CFST members is a critical requirement and a challenging task due to the obvious difference in the material mechanical parameters of the concrete core and steel tube in CFST members. In this study, a curved ray theory-based travel time tomography (TTT) with a least square iterative linear inversion algorithm is first introduced to quantitatively identify and visualize the sizes and positions of the concrete core voids in CFST members. Secondly, a numerical investigation of the influence of different parameters on the inversion algorithm for the defect imaging of CFST members, including the effects of the model weighting matrix, weighting factor and grid size on the void’s imaging quality and accuracy, is carried out. Finally, an experimental study on six CFST specimens with mimicked concrete core void defects is performed in a laboratory and the mimicked defects are visualized. The results demonstrate that TTT can identify the sizes and positions of the concrete core void defects in CFST members efficiently with the use of optimal parameters. Full article

Seismic characterization of landslides offers the potential for developing high-resolution models on subsurface shear-wave velocity profile. However, seismic methods based on reflection processing are challenging to apply in such scenarios as a consequence of the disturbance to the often well-defined structural and stratigraphic layering by the landslide process itself. We evaluate the use of alternative seismic characterization methods based on elastic full waveform inversion (E-FWI) to probe the subsurface of a landslide complex in Majes, southern Peru, where recent agricultural development and irrigation activities have altered the hydrology and groundwater table and are thought to have contributed to increased regional landslide activities that present continuing sustainability community development challenges. We apply E-FWI to a 2D near-surface seismic data set for the purpose of better understanding the subsurface in the vicinity of a recent landslide location. We use seismic first-arrival travel-time tomography to generate the inputs required for E-FWI to generate the final high-resolution 2D compressional- and shear-wave (P- and S-wave) velocity models. At distances greater than 140 m from the cliff, the inverted models show a predominantly vertically stratified velocity structure with a low-velocity near-surface layer between 5–15 m depth. At distances closer than 140 m from the cliff, though, the models exhibit significantly reduced shear-wave velocities, stronger heterogeneity, and localized shorter wavelength structure in the top 20 m. These observations are consistent with those expected for a recent landslide complex; however, follow-on geotechnical analysis is required to confirm these assertions. Overall, the E-FWI seismic approach may be helpful for future landslide characterization projects and, when augmented with additional geophysical and geotechnical analyses, may allow for improved understanding of the hydrogeophysical properties associated with suspected ground-water-driven landslide activity. Full article