Reflection Imaging Research Articles

Ultrahigh-resolution 9C seismic survey in a landslide prone area in southwest of Sweden

SUMMARY We studied the benefits of a nine-component (9C) seismic survey over a landslide-prone area in southwest of Sweden to retrieve ultrahigh-resolution shear wave reflection images of the subsurface as well as crucial information on physical properties of the sediments. A complete, 1 m shot and receiver spacing, multicomponent 2-D seismic profile was acquired using three-component microelectromechanical-system-based landstreamer receivers, and a 5-kg sledgehammer strike in vertical and horizontal orientations as three-component seismic source. Given the rich number of shear wave reflections observed on all the 9C data, the processing work focused on their retrievals. It revealed three distinct reflections, two of which associated with coarse-grained materials and one with an extremely undulating bedrock surface. Given the extremely slow shear wave velocities on the order of 60–100 m s−1, we obtained ultrahigh-resolution shear wave sections avoiding temporal and spatial aliasing. Imaging results suggest vertical-source and horizontal-radial receiver (V–HR), and horizontal-transverse source–receiver orientations (HT–HT) provided the most optimum images of the subsurface. A non-hyperbolic algorithm was applied to the normal-moveout corrections justified by the traveltime differences of the bedrock reflection in different shear wave sections. The improved images by incorporating the anisotropy term suggest that the data set reveals moderate shear wave anisotropy along some portions of the profile. The Vp/Vs ratio obtained by using bedrock reflection in P- and S-wave sections suggests values ranging 10–16, which implies high water content. Areas with lower Vp/Vs coincides with greater anisotropic parameters and this can indicate disturbed clays or presence of sensitive clays.

Detection of water leakage defects in cable insulation sheath based on terahertz time-domain spectroscopy

Water leakage of the insulation sheath can lead to a decrease in the insulation performance of the power cable, which poses a hidden danger to the power cable. Accurate detection and positioning of the water leakage area are crucial for improving power safety and stability. In response to the water leakage defect of the cable insulation sheath, this article proposes a method based on reflective terahertz time-of-flight tomography technology to measure the water leakage area of the insulation sheath. A sample of insulation sheath water leakage defect was prepared by simulating real scenarios. By analysing the terahertz reflection time-domain optical spectrum and imaging results of the sample, the location and area of the insulation sheath water leakage defect can be accurately measured, verifying the application potential of reflective terahertz time-of-flight tomography technology in detecting water seepage defects in cable insulation sheaths.

Improved lithospheric seismic velocity and density model of the Korean Peninsula from ambient seismic noise data using machine learning

To accurately estimate physical properties and tectonic behavior in the near-surface, a reliable seismic property model is needed for realistic seismic modeling and earthquake location estimation. Recording ambient seismic noise (ASN) data and producing interferometric reflection images traditionally provides subsurface structure observation without an active source. However, due to low signal-to-noise ratio (SNR) and vertical resolution, interpreting upper mantle structures and inverting seismic models from noise data is difficult. To address this, machine learning (ML) techniques are applied to enhance vertical resolution and interpret geologically meaningful boundaries. Spectral enhancement with convolutional U-net generates higher resolution data by preserving temporal continuity. Unsupervised ML interprets lithospheric boundaries more robustly and objectively than manual horizon picking, and model-based seismic inversion integrates improved seismic data with prior full-waveform inversion (FWI) models. ML-based results improve inverted models, displaying more detailed geological structures and seismic property changes, surpassing seismic data limitations.

Seismic ambient noise imaging above an underground mine: An example from Boliden Tara Mines, Ireland

We use a passive seismic data set recorded from June 2017 to July 2018 by twenty-one stations deployed in a five by five km grid to the south-west of the Boliden Tara Mines orebody (Eastern Ireland) to image the shallower part of the Carboniferous rock units. We cross-correlate one year of ambient noise between all station pairs to generate cross-correlation functions that are rich in surface waves. Then we investigate the locations of the noise sources by applying f-k cross-correlation beamforming. We find two persistent noise sources associated with oceanic microseisms and a cluster of anthropogenic activities, respectively. We undertake dispersion analysis to extract group velocity measurements in the 1–9 Hz band that we invert using the fast marching method to create Rayleigh wave velocity maps. This is followed by a wavelet-based sparsity constrained tomographic method to produce a 3-D shear wave velocity model down to 800 metres. The results highlight low and high-velocity anomalies related to the structural and stratigraphic heterogeneity within the Upper Dark limestone formation. In addition, the velocity gradient of our model agrees with the sonic log velocity profile available adjacent to the study area, suggesting that this sparse network can be used to obtain reliable broad-scale information and could also guide the design phase of more expensive and involved experiments, such as active seismic reflection imaging.

Detection of moisture content and size of pumpkin seeds based on hyperspectral reflection and transmission imaging techniques

Moisture content and size can affect the vigor and weight of pumpkin seeds, where vigor is the key to evaluating seed quality, and weight is an important characteristic of the seeds. Therefore, detecting the moisture content and size of pumpkin seeds contributes to improving seed quality. In this study, hyperspectral reflection and transmission imaging techniques were used to detect the moisture and size of single pumpkin seeds. Linear PLSR and nonlinear LSSVM models were established to predict the moisture content of single pumpkin seeds using reflection and transmission spectral data. Five variable selection and data fusion methods, namely randomization test, variable selection based on C-value, uninformative variable elimination, Monte Carlo-uninformative variable elimination, and competitive adaptive reweighted sampling (CARS), were adopted to optimize the models. Compared with the models based on reflection and transmission spectra, low-level data fusion improved the model performance. The mid-level data fusion with models based on the CARS algorithm achieved the optimal performance, corresponding to R2P and RMSEP of 0.9219, 0.0279%, and 0.9231, 0.0278%, for the PLSR and LSSVM models, respectively. Finally, it was demonstrated that using hyperspectral reflection images to determine the size of pumpkin seeds is feasible, and the length and width of all samples were determined. The results show that hyperspectral imaging techniques and data fusion can effectively detect the moisture content of single pumpkin seeds, and measuring the size of pumpkin seeds is feasible using image processing algorithms.

A novel seismic full waveform inversion approach for assessing the internal structure of a medieval sea dike

AbstractCoastal protection in the form of dike constructions has a long history at the German North Frisian coast dating back to the High Middle Ages. As the vast majority of the dikes built prior to the devastating storm surges of the Middle Ages have been irretrievably destroyed, mostly sparse remains and only a few well preserved of these medieval dikes are found along the German North Frisian coast and within the Wadden Sea. Not all details of their construction and dimensions are yet understood. In the present case study, we investigate the historical Schardeich on the island of Pellworm in the German North Sea in a noninvasive way using shear waves (SH‐waves). For the data interpretation, we applied a combination of seismic full waveform inversion and classical seismic reflection imaging to determine the interior structure of the dike and its underlying layers at the highest possible resolution. The results obtained on land are compared with dike remains found in the tidal flats. These remains show up in marine seismic sections as characteristic reflections, which probably represent a compaction layer caused by the load of the former dike. For ground truthing, we compare the seismic results with internal dike structures found in nearby excavations. The comparison highlights that FWI is a reliable tool for near‐surface archaeological prospecting. We find that SH‐wave FWI provides decimetre‐scale velocity and density models that allow, together with the seismic reflection section, to determine distinct construction phases of the dike. The investigated dike further shows a depression at base level of about 0.75 m, which is of the same order as observed for the dike base reflections in the tidal flats. Transferring these findings to the dike remains mapped in the tidal flats, we derive a height of the former dike from 2.2 to 4.4 m.

The UV–vis‐NIR Broadband Ultrafast Flexible Sn‐Pb Perovskite Photodetector for Multispectral Imaging to Distinguish Substance and Foreign‐Body in Biological Tissues

AbstractMultispectral imaging plays an important role in many applications such as color recognition, substance distinction, and medical therapy. Although multicolor detection has been demonstrated in lead‐based perovskites, the limited near‐infrared responsivity and slow response impede their applications for identifying the substance. Herein, narrow bandgap highly stable Cs0.05MA0.45FA0.5Sn0.5Pb0.5I3 (Sn‐Pb) perovskite films with excellent crystallinity and selective growth orientation are realized by the synergy of a Sn‐Pb mixing method and electronegativity balance. The Sn‐Pb perovskite photodetector shows a broadband response from 350 to 1000 nm and high‐performance near‐infrared (NIR) detection with a fast response of 2.0 µs and excellent responsivity of 0.29 A W−1. Furthermore, flexible Sn‐Pb perovskite devices without any encapsulation in the air continuously exhibit fast response after 3000 bending cycles. In addition, the Sn‐Pb perovskite diffuse reflection imaging system is demonstrated for multispectral imaging and substance distinction. Meanwhile, foreign‐body identification in biological tissues by NIR imaging of Sn‐Pb perovskite photodetectors is further demonstrated, proving narrow bandgap mixed perovskite photodetectors an attractive candidate for biomedical imaging.

Effects of collar eccentricity on azimuthal response characteristics for acoustic reflection measurement while drilling

Acoustic reflection imaging has been successfully applied in postdrilling wireline logging, and the technique is potentially more valuable in acoustic logging while drilling (ALWD), particularly in geosteering while drilling. Because of the common occurrence of tool eccentering in drilling, the impact of drill collar eccentricity on the acoustic wavefield inside and outside the borehole must be considered. Therefore, we analyze the effect of an eccentric drill collar on acoustic reflection imaging while drilling using analytical solutions in a dual-cylindrical coordinate system. The virtual source analogy and the translational addition theorem for cylindrical Bessel functions are used to calculate the response of the fluid-filled borehole with an eccentric collar to the incident wavefield from a formation reflector. The analysis results are validated with those from 3D finite-difference simulations. We also have studied the azimuthal variation of the received wavefield and compared the results with their centralized counterparts, for which the effects of collar eccentricity, frequency of the source, and formation elastic property are analyzed. The results indicate that the asymmetric feature of the wavefield due to the eccentric collar allows for determining the azimuth of the reflector, providing a solution to the azimuth ambiguity problem of borehole acoustic reflection imaging. Our results, besides developing an effective method for eccentric collar ALWD modeling, can be used to provide a theoretical foundation for ALWD tool development and data interpretation.

Low-light image enhancement based onRetinex-Net with color restoration.

Low-light images often suffer from a variety of degradation problems such as loss of detail, color distortions, and prominent noise. In this paper, the Retinex-Net model and loss function with color restoration are proposed to reduce color distortion in low-light image enhancement. The model trains the decom-net and color recovery-net to achieve decomposition of low-light images and color restoration of reflected images, respectively. First, a convolutional neural network and the designed loss functions are used in the decom-net to decompose the low-light image pair into an optimal reflection image and illumination image as the input of the network, and the reflection image after normal light decomposition is taken as the label. Then, an end-to-end color recovery network with a simplified model and time complexity is learned and combined with the color recovery loss function to obtain the correction reflection map with higher perception quality, and gamma correction is applied to the decomposed illumination image. Finally, the corrected reflection image and the illumination image are synthesized to get the enhanced image. The experimental results show that the proposed network model has lower brightness-order-error (LOE) and natural image quality evaluator (NIQE) values, and the average LOE and NIQE values of the low-light dataset images can be reduced to 942 and 6.42, respectively, which significantly improves image quality compared with other low-light enhancement methods. Generally, our proposed method can effectively improve image illuminance and restore color information in the end-to-end learning process of low-light images.

Optical freeform reflective imaging system design method with manufacturing constraints.

With the development of space optics, optical freeform surfaces have gradually been utilized in reflective optical imaging systems in recent years. Freeform surfaces not only bring many benefits to the optical imaging system, but also present many challenges to their manufacture. Regardless of the machining method used, machining errors during the fabrication of freeform surfaces will exist, which limits the accuracy of freeform surface machining. In this paper, the deviation root mean square (RMS) of a freeform surface from the reference aspheric surface is proposed to evaluate the manufacturability of the freeform surface by using single-point diamond turning. Then the deviation RMS of freeform surfaces is added to the design process of the optical system as a manufacturing constraint. Subsequently, an off-axis three-mirror system and an off-axis two-mirror system with and without manufacturing constraints are designed, respectively. Then the imaging quality of these optical systems and the linear interpolation error RMS of freeform mirror are analyzed. It can be concluded that, on the basis of reaching the imaging quality requirements, the machining difficulty of a freeform mirror can be reduced when adding manufacturing constraints to the design process.

Effect of spatial-domain pulse width on the resolution of scattering images in ultrasound computed tomography

Full-aperture tomography (FAT) is the major image reconstruction method for a circular ring array (CRA)-based ultrasound computed tomography (USCT) system. The FAT technique requires transferring the reconstruction process from the temporal domain to the spatial domain, during which the imaging resolution of the USCT is degraded by the spatial-domain pulse width (SDPW) of backprojection areas. To tackle this challenge, this study investigates the characteristics of the SDPW and how it degrades the image resolution. We show that the SDPW depends on the frequency of the ultrasound and the position of the transmitting elements, receiving elements and the imaging point. To quantify the deterioration of image resolution associated with the position of the transmitting and receiving elements, a SDPW broadening factor (SDPWBF) is introduced. The results of numerical simulation show a smaller SDPWBF provides a better reflection image resolution, and the distribution of SDPWBF shows that a shorter distance between the receiving element and the transmitting element yields a smaller SDPWBF. The SDPWBF is therefore able to be an indicator of selecting the signals acquired from the transmitting and receiving elements to perform optimal image resolution. Single-scatterer phantom and in vivo experiments demonstrate how the SDPWBF affects the USCT image spatial resolution and signal-to-noise ratio (SNR), and the results agree well with the theoretical predictions.

Airborne hydrophilic microplastics in cloud water at high altitudes and their role in cloud formation

Microplastic pollution is occurring in most ecosystem, yet their presence in high altitude clouds and their influence on cloud formation and climate change are poorly known. Here we analyzed microplastics in cloud water sampled at the summits of Japan mountains at 1300–3776 m altitude by attenuated total reflection imaging and micro-Fourier transform infrared spectroscopy. We observed nine microplastics including polyethylene, polypropylene, polyethylene terephthalate, polymethyl methacrylate, polyamide 6, polycarbonate, ethylene–propylene copolymer or polyethylene–polypropylene alloy, polyurethane, and epoxy resin. Microplastic were fragmented, with mean concentrations ranging from 6.7 to 13.9 pieces per liter, and with Feret diameters ranging from 7.1 to 94.6 μm. Microplastics bearing hydrophilic groups such as carbonyl and/or hydroxyl groups were abundant, suggesting that they might have acted as condensation nuclei of cloud ice and water. Overall, our finding suggest that high-altitude microplastics cloud influence cloud formation and, in turn, might modify the climate.

Characterizing hydraulic fracture growth using distributed acoustic sensing-recorded microseismic reflections

Hydraulic fracturing is crucial for enhancing hydrocarbon production from unconventional reservoirs. The characterization of fracture geometry and propagation has significant value in understanding reservoir response and designing more efficient completions. Distributed acoustic sensing (DAS) is a rapidly developing technology that can be used for this purpose because it provides wide-aperture observations of microseismic wavefields that contain direct P and S arrivals as well as converted and reflected waves. In addition to traditional approaches for microseismic event location and source mechanism analysis, the high spatial resolution of DAS microseismic recordings allows the imaging of induced fractures with reflected waves. Reflections are generated by waves radiated from microseismic events that impinge on hydraulic fractures created during prior treatment stages. We use a straightforward method based on f- k filtering and ray tracing to map reflected S waves from the time domain to reflectivity in the space domain. A case study of fracture imaging indicates that inferred fracture development, based on reflection imaging, is consistent with fracture-driven interactions observed using low-frequency DAS (LF-DAS) data. In addition, this study reveals reflection images of apparent distal fractures that do not reach the fiber and thus are not directly observed by LF-DAS. Fracture images obtained from several microseismic events during the same stage provide the opportunity to observe snapshots of dynamic fracture evolution processes.

Quality Control of Composite parts by robot guided Terahertz imaging

In this work a new approach for in-line quality inspection of drilling holes in glass reinforced fibre composites (GFRP) is presented. The method relies on Terahertz radiation and its large penetration depth in non-conductive materials. By applying THz reflection imaging, 3D information such as interfaces or defects within the sample can be obtained. As a starting point, a laboratory setup based on an industrial THz time-domain spectrometer (TDS) in combination with linear stages for the sample manipulation is built. A software for measurement automatization and data acquisition is developed. Based on measurements performed on drilled GFRP plates, an algorithm highlighting possible defects within the measurement data is developed. The results correlate very well with x-ray tomography (XCT) measurements. Finally, the THz-TDS system is transferred onto an industrial robot and the THz signal synchronized with the position of the robot’s head, expanding the flexibility of the method regarding the applicability to a wide variety of specimen geometries.

Innovative land seismic investigations for CO2 geologic storage in Denmark

The increasing global interest in geologic carbon storage as a feasible way of reducing CO2 atmospheric levels requires extensive onshore high-resolution seismic investigations to characterize suitable storage sites, for example, close to major CO2 emitters. To partly address this challenge and to acquire quality data at shallower and greater depths in a cost- and time-effective approach, a tailored acquisition scheme is adopted and tested at a candidate site in Stenlille, Denmark. The survey aims to understand whether an anticline reservoir structure known to exist at a 1000 m depth can serve for long-term CO2 storage and presents the structural integrity for this purpose. The data are recorded using a combination of nodal arrays, spaced at 10 m in a fixed geometry, and a set of more closely (2 m) spaced digital recorders, mounted on a landstreamer, which was moved at each shot location. Two 12 t mini vibrators are used as seismic sources. The nodal and landstreamer data sets are compared and combined into a unique data set for reflection imaging purposes along five profiles with a total length of approximately 12 km. The seismic sections obtained using this tailored combination of different recorders provide images of the entire shallow and deeper structures with an unprecedented resolution at the different depth levels necessary to assess the full potential of the suggested CO2 storage reservoir. The results significantly increase the existing knowledge of the extent and structural closure of the reservoir as well as a possible fault, all of which are critical for future risk analysis and planning of the storage.

Segmentation of borehole acoustic reflection image using feature pyramid network and transfer learning

Borehole acoustic reflection imaging technology is widely utilized for high-resolution detection of subsurface geological structures. However, the presence of significant noise in migration images poses a challenge for accurate identification of useful reflectors. In this paper, we propose a deep learning-based image segmentation method to address this challenge. Our approach employs a Feature Pyramid Network with an EfficientNet-b4 encoder, which is trained on a large simulation dataset. To handle the class imbalance issue caused by the small proportion of fractures, we employ the Dice loss function. Additionally, the Lovász-hinge loss function is utilized to enhance network training and achieve higher IoU metric values. To extract high-level semantic features, the trained network is fine-tuned on a hand-labeled field dataset using transfer learning. The results obtained from processing field migration images demonstrate the effectiveness and robustness of our proposed methods.

Understanding Spatial Context in Convolutional Neural Networks Using Explainable Methods: Application to Interpretable GREMLIN

Abstract Convolutional neural networks (CNNs) are opening new possibilities in the realm of satellite remote sensing. CNNs are especially useful for capturing the information in spatial patterns that is evident to the human eye but has eluded classical pixelwise retrieval algorithms. However, the black-box nature of CNN predictions makes them difficult to interpret, hindering their trustworthiness. This paper explores a new way to simplify CNNs that allows them to be implemented in a fully transparent and interpretable framework. This clarity is accomplished by moving the inner workings of the CNN out into a feature engineering step and replacing the CNN with a regression model. The specific example of the GOES Radar Estimation via Machine Learning to Inform NWP (GREMLIN) is used to demonstrate that such simplifications are possible and to show the benefits of the interpretable approach. GREMLIN translates images of GOES radiances and lightning into images of radar reflectivity, and previous research used explainable artificial intelligence (XAI) approaches to explain some aspects of how GREMLIN makes predictions. However, the Interpretable GREMLIN model shows that XAI missed several strategies, and XAI does not provide guarantees on how the model will respond when confronted with new scenarios. In contrast, the interpretable model establishes well-defined relationships between inputs and outputs, offering a clear mapping of the spatial context utilized by the CNN to make accurate predictions, and providing guarantees on how the model will respond to new inputs. The significance of this work is that it provides a new approach for developing trustworthy artificial intelligence models. Significance Statement Convolutional neural networks (CNNs) are very powerful tools for interpreting and processing satellite imagery. However, the black-box nature of their predictions makes them difficult to interpret, compromising their trustworthiness when applied in the context of high-stakes decision-making. This paper develops an interpretable version of a CNN model, showing that it has similar performance as the original CNN. The interpretable model is analyzed to obtain clear relationships between inputs and outputs, which elucidates the nature of spatial context utilized by CNNs to make accurate predictions. The interpretable model has a well-defined response to inputs, providing guarantees for how it will respond to novel inputs. The significance of this work is that it provides an approach to developing trustworthy artificial intelligence models.

Stereo X-Ray Tomography

X-ray tomography is a powerful volumetric imaging technique, but detailed three dimensional (3D) imaging requires the acquisition of a large number of individual X-ray images, which is time consuming. For applications where spatial information needs to be collected quickly, for example, when studying dynamic processes, standard X-ray tomography is therefore not applicable. Inspired by stereo vision, in this paper, we develop X-ray imaging methods that work with two X-ray projection images. In this setting, without the use of additional strong prior information, we no longer have enough information to fully recover the 3D tomographic images. However, up to a point, we are nevertheless able to extract spatial locations of point and line features. From stereo vision, it is well known that, for a known imaging geometry, once the same point is identified in two images taken from different directions, then the point’s location in 3D space is exactly specified. The challenge is the matching of points between images. As X-ray transmission images are fundamentally different from the surface reflection images used in standard computer vision, we here develop a different feature identification and matching approach. In fact, once point like features are identified, if there are limited points in the image, then they can often be matched exactly. In fact, by utilising a third observation from an appropriate direction, matching becomes unique. Once matched, point locations in 3D space are easily computed using geometric considerations. Linear features, with clear end points, can be located using a similar approach.

Noise suppression in the reconstructed image of digital holography based on the BEMDV method using improved particle swarm optimization.

In digital holography, the speckle noise caused by the coherent nature of the light source and the light scattering generated by the light path system degrade the quality of the reconstructed image seriously. Therefore, in this paper, we propose what we believe to be is a novel noise reduction method combining bidimensional empirical mode decomposition (BEMD) with the variational method, termed BEMDV. The reconstructed image is first decomposed into a series of bidimensional intrinsic mode function (BIMF) components with different frequencies using the BEMD method, and then a certain number of BIMF components are selected for noise reduction by the variational method. An improved particle swarm optimization algorithm is adopted to optimize the key parameters of the proposed method, so as to further improve its noise reduction performance. A reflective off-axis digital holographic imaging system is used to collect the holograms of the coin and optical resolution plate, and the experimental research on noise reduction is carried out. The results with qualitative and quantitative analyses show that the proposed method achieves a better performance on noise reduction and detail preservation than other general methods, enormously enhancing the image quality of holographic reconstruction.

Catadioptric sensor concept with interlaced beam paths for imaging and pinpoint spectroscopy.

This paper presents the concept, optical design, and implementation of a catadioptric sensor for simultaneous imaging of a scene and pinpoint spectroscopy of a selected position, with object distances ranging from tens of centimeters to infinity and from narrow to wide adjustable viewing angles. The use of reflective imaging elements allows the implementation of folded and interlaced beam paths for spectroscopy and image acquisition, which enables a compact setup with a footprint of approximately 90m m×80m m. Although the wavelength range addressed extends far beyond the visible spectrum and reaches into the near infrared (∼400n m to 1000 nm), only three spherical surfaces are needed to project the intermediate image onto the image detector. The anamorphic imaging introduced by the folded beam path with different magnification factors in the horizontal and vertical directions as well as distortion can be compensated by software-based image processing. The area of the scene to be spectrally analyzed is imaged onto the input of an integrated miniature spectrometer. The imaging properties and spectroscopic characteristics are demonstrated in scenarios close to potential applications such as product sorting and fruit quality control.