Full Frequency Spectrum Research Articles

Resonant conversion of gravitational waves in neutron star magnetospheres

High-frequency gravitational waves are the subject of rapidly growing interest in the theoretical and experimental community. In this work we calculate the resonant conversion of gravitational waves into photons in the magnetospheres of neutron stars via the inverse Gertsenshtein mechanism. The resonance occurs in regions where the vacuum birefringence effects cancel the classical plasma contribution to the photon dispersion relation, leading to a massless photon in the medium which becomes kinematically matched to the graviton. We set limits on the amplitude of a possible stochastic background of gravitational waves using X-ray and IR flux measurements of neutron stars. Using Chandra (2–8 keV) and NuSTAR (3–79 keV) observations of RX J1856.6-3754, we set strain limits hclim≃10−26–10−24 in the frequency range 5×1017 Hz≲f≲2×1019 Hz. Our limits are many orders of magnitude stronger than existing constraints from individual neutron stars at the same frequencies. We also use recent JWST observations of the Magnetar 4U 0142+61 in the range 2.7×1013 Hz≲f≲5.9×1013 Hz, setting a limit hclim≃5×10−19. These constraints are in complementary frequency ranges to laboratory searches with CAST, OSQAR and ALPS II. We expect these limits to be improved both in reach and breadth with a more exhaustive use of telescope data across the full spectrum of frequencies and targets. Published by the American Physical Society 2024

Advanced Frequency Analysis of Signals with High-Frequency Resolution

In today’s era, it is important to analyze and utilize various signals in industrial or laboratory applications. Measured signals provide critical information about the controlled system, which can be contained precisely within a narrow frequency range. Many methods and algorithms exist to process such signals in both the time and frequency domains. In particular, signal processing in the frequency domain is primary in industrial practice because dominant components within a specific narrow frequency band are sought. The discrete Fourier transformation (DFT) algorithm is the tool used in practice to find these frequency components. The DFT algorithm provides the full frequency spectrum with a higher number of calculation steps, and its spectrum frequency resolution is low. Therefore, research has focused on finding a method to achieve high-frequency spectrum resolution. An important factor in selecting the technique was that such an algorithm should be implementable on a microprocessor-based system under harsh industrial conditions. Research results showed that the DFT ZOOM method meets these requirements. The frequency zoom has many advantages but requires some modification. It is implemented in high-performance analyzers, but a thorough and detailed description of the respective algorithm is lacking in technical articles and literature. This article mathematically and theoretically describes the modified frequency zoom algorithm in detail. The steps of the frequency zoom, from creating an analytical signal through frequency shifting and decimation to the frequency analysis of the signal, are realized. The algorithm allows for the analysis of a signal with high-frequency resolution in a limited frequency band. A significant modification of DFT ZOOM is that of using the Hilbert transform to create an analytic signal. This resolves the aliasing issue caused by the overlap between fundamental and sideband spectra. Results from processing deterministic and stochastic signals using the modified DFT ZOOM are presented. The presented experimental results contribute to a more detailed frequency analysis of the signal. As part of this scientific research, the issues of frequency zoom were thoroughly addressed, solving the partial problems of this algorithm, both in theory and in the context of signal theory.

Image restoration in frequency space using complex-valued CNNs.

Real-valued convolutional neural networks (RV-CNNs) in the spatial domain have outperformed classical approaches in many image restoration tasks such as image denoising and super-resolution. Fourier analysis of the results produced by these spatial domain models reveals the limitations of these models in properly processing the full frequency spectrum. This lack of complete spectral information can result in missing textural and structural elements. To address this limitation, we explore the potential of complex-valued convolutional neural networks (CV-CNNs) for image restoration tasks. CV-CNNs have shown remarkable performance in tasks such as image classification and segmentation. However, CV-CNNs for image restoration problems in the frequency domain have not been fully investigated to address the aforementioned issues. Here, we propose several novel CV-CNN-based models equipped with complex-valued attention gates for image denoising and super-resolution in the frequency domains. We also show that our CV-CNN-based models outperform their real-valued counterparts for denoising super-resolution structured illumination microscopy (SR-SIM) and conventional image datasets. Furthermore, the experimental results show that our proposed CV-CNN-based models preserve the frequency spectrum better than their real-valued counterparts in the denoising task. Based on these findings, we conclude that CV-CNN-based methods provide a plausible and beneficial deep learning approach for image restoration in the frequency domain.

Modelling Low-Frequency Vibration and Defect Detection in Homogeneous Plate-Like Solids

The inspection of thick-section sandwich structures with skins around core materials such as honeycomb, balsa, and foam relies on low-frequency vibration techniques to identify defects through changes in amplitude or phase response. However, current industrial methods are often limited to detecting specific types of defects, potentially overlooking others. Moreover, these methods do not gather detailed information about the defect type or depth, as they only analyse a small portion of the available data instead of the full relevant response spectrum. This paper explores the scientific basis of using low-frequency vibration in the pitch-catch variant for defect detection in homogeneous solids, through analysis of the full relevant frequency spectrum (5–50 kHz). Defects in structures lead to reduced local stiffness and mass in the affected area, causing resonance in the layer above, resulting in amplified vibrations known as local defect resonance (LDR). In this work, an aluminium plate with a 40 mm diameter circular flat-bottomed hole (FBH) at a depth of 1 mm (representing a skin defect) is excited with a chirp signal of 5–50 kHz, and the response is monitored 17 mm away from the excitation point. Finite-element analysis (FEA) is used for the numerical model, addressing challenges in creating an accurate model. The process to optimise the numerical model and the reduce model-experiment error is outlined, including challenges such as the lack of knowledge of material damping. The study emphasizes the importance of modelling the probe’s stiffness and damping effects for achieving agreement between the model and experiment. After incorporating these effects, the maximum LDR frequency error decreased from approximately 3 kHz to less than 1 kHz. In addition, this study presents a method with the potential for defect classification through comparison to modelled responses. The minimum difference error was used to quantify the resonance frequencies’ error between the model and the experiment. Since the resonant frequencies are a function of the defect’s shape, size, and depth, a relatively low root mean squared (RMS) error across the resonance frequency error spectrum indicates the defect’s characteristics. Finally, defect detection and sizing using the pitch-catch probe are explored with a wide-band excitation signal and a line scan through the mid-plane of the defect. A method for defect sizing using a pitch-catch probe is presented and experimentally validated. Accurate defect sizing is achieved with the pitch-catch probe when the defect width is at least ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge $$\\end{document} twice the 17 mm pin-spacing of the probe.

Neuronal Coupling Modes Show Differential Development in the Early Cortical Activity Networks of Human Newborns.

The third trimester is a critical period for the development of functional networks that support the lifelong neurocognitive performance, yet the emergence of neuronal coupling in these networks is poorly understood. Here, we used longitudinal high-density electroencephalographic recordings from preterm infants during the period from 33 to 45 weeks of conceptional age (CA) to characterize early spatiotemporal patterns in the development of local cortical function and the intrinsic coupling modes [ICMs; phase-phase (PPCs), amplitude-amplitude (AACs), and phase-amplitude correlations (PACs)]. Absolute local power showed a robust increase with CA across the full frequency spectrum, while local PACs showed sleep state-specific, biphasic development that peaked a few weeks before normal birth. AACs and distant PACs decreased globally at nearly all frequencies. In contrast, the PPCs showed frequency- and region-selective development, with an increase of coupling strength with CA between frontal, central, and occipital regions at low-delta and alpha frequencies together with a wider-spread decrease at other frequencies. Our findings together present the spectrally and spatially differential development of the distinct ICMs during the neonatal period and provide their developmental templates for future basic and clinical research.

Robustness in population-structure and demographic-inference results derived from the Aedes aegypti genotyping chip and whole-genome sequencing data.

The mosquito Aedes aegypti is the primary vector of many human arboviruses such as dengue, yellow fever, chikungunya, and Zika, which affect millions of people worldwide. Population genetic studies on this mosquito have been important in understanding its invasion pathways and success as a vector of human disease. The Axiom aegypti1 SNP chip was developed from a sample of geographically diverse A. aegypti populations to facilitate genomic studies on this species. We evaluate the utility of the Axiom aegypti1 SNP chip for population genetics and compare it with a low-depth shotgun sequencing approach using mosquitoes from the native (Africa) and invasive ranges (outside Africa). These analyses indicate that results from the SNP chip are highly reproducible and have a higher sensitivity to capture alternative alleles than a low-coverage whole-genome sequencing approach. Although the SNP chip suffers from ascertainment bias, results from population structure, ancestry, demographic, and phylogenetic analyses using the SNP chip were congruent with those derived from low-coverage whole-genome sequencing, and consistent with previous reports on Africa and outside Africa populations using microsatellites. More importantly, we identified a subset of SNPs that can be reliably used to generate merged databases, opening the door to combined analyses. We conclude that the Axiom aegypti1 SNP chip is a convenient, more accurate, low-cost alternative to low-depth whole-genome sequencing for population genetic studies of A. aegypti that do not rely on full allelic frequency spectra. Whole-genome sequencing and SNP chip data can be easily merged, extending the usefulness of both approaches.

A Fully Integrated, Domino-Like-Buffered LDO Regulator With High Power-Supply Rejection Across the Full Frequency Spectrum

A Fully Integrated, Domino-Like-Buffered LDO Regulator With High Power-Supply Rejection Across the Full Frequency Spectrum

Long-term measurement study of urban environmental low frequency noise.

Environmental low frequency noise (LFN < 125 Hz), ubiquitous in urban areas, is an understudied area of exposure science and an overlooked threat to population health. Environmental noise has historically been measured and regulated by A-weighted decibel (dBA) metrics, which more heavily weight frequencies between 2000 and 5000 Hz. Limited research has been conducted to measure and characterize the LFN components of urban environmental noise. We characterized LFN noise at two urban sites in Greater Boston, Massachusetts (USA) using dBA and full spectrum noise measurements with aims to (1.) analyze spatio-temporal differences in the two datasets; (2.) compare and contrast LFN metrics with dBA noise metrics in the two sites; and (3.) assess meteorological covariate contributions to LFN in the dataset. We measured A- and C-weighted, and flat, unweighted noise levels and 1/3-octave band continuously for 5 months using sound level meters sampling at f = 1 Hz and we recorded sound samples at 44.1 kHz. Our measurement sites were located in two urban, densely populated communities, burdened by close proximity to bus, rail, and aircraft routes. We found that (1.) LFN does not follow the same seasonal trends as A-weighted dBA loudness; there are spatial differences in LFN and its very low frequency noise components (VLFN) between two urban sites; (2.)VLFN and LFN are statistically significant drivers of LCeq (nearly independent of frequency) minus LAeq, (LCeq-LAeq) >10 dB, an accepted LFN metric; and (3.)LFN was minimally affected by high wind speeds at either Site. Environmental low-frequency noise (LFN < 125 Hz), ubiquitous in urban areas, is an understudied area of exposure science and an overlooked risk to population health. We measured environmental noise across the full spectrum of frequencies continuously for five months at two urban sites located in Environmental Justice communities. We found that LFN did not follow the same seasonal trends as A-weighted (dBA) loudness, and we observed spatial differences in LFN and very low frequency noise (VLFN < 20 Hz) at the two sites. Not characterizing LFN and basing noise regulations only on A-weightings, a poor predictor of LFN, may expose populations to LFN levels of concern.

Quantitative Cell Classification Based on Calibrated Impedance Spectroscopy and Metrological Uncertainty

AbstractElectrochemical impedance spectroscopy (EIS) is widely used for battery cell testing in industrial production and R&amp;D labs. This work addresses the use of EIS calibration and uncertainty analysis in cell classification. Five scenarios are investigated to discuss qualitatively the impact of calibration and uncertainty on classification. For an experimental demonstration, a cylindrical cell was measured with two mechanical fixtures of different qualities and characterized regarding random errors and calibrated impedances. The impact of uncertainty and impedance calibration on the cell classification is shown, and based on the uncertainty and corresponding error bounds, a confidence level was established for the classification results. Quantitative uncertainty bounds are presented for the full EIS frequency spectrum ranging from 150 mHz to 5 kHz.

Joint Sampling of the Full Spectrum of Spatial Frequencies and Orientations During Face Recognition

Joint Sampling of the Full Spectrum of Spatial Frequencies and Orientations During Face Recognition

A Stable EEG Epilepsy Network Spans From Infraslow to Ripple and From Interictal to Ictus.

To characterize the epilepsy network as reflected in intracranial electroencephalography (iEEG) across the full spectrum of iEEG frequencies and different phases of epilepsy, using a single, conceptually straightforward mathematical measure. The authors applied the spectral Granger causality techniques to intracranial electroencephalography recordings and computed contact-by-contact inward, outward, and total causal flow across frequencies and seizure phases in a selected group of three patients with well-defined, nonlesional seizure foci and prolonged responses to invasive procedures. One seizure and one interictal sample were analyzed per subject. A prominent intracranial electroencephalography network was identified by Granger causality at both high and low frequencies. This network persists during the preictal and interictal phases of epilepsy and closely matches the visible seizure onset. The causal inflow network corresponded to seizure onset electrode contacts in 8 of 12 conditions, including ripple, infraslow, preictal, and interictal phases of epilepsy. Its most striking feature is the consistent dominance of causal inflow rather than outflow in the vicinity of the seizure onset zone. Findings of this study indicate that a stable intracranial electroencephalography epilepsy network persists, and it can be characterized by a single Granger causality measure from infraslow to ripple frequencies and from the interictal to the immediate preictal phases of epilepsy.

Internal damage localization for large-scale hollow cylinders based on helical sensor network

The hollow cylinder with a large diameter is the typical shell structure in major engineering applications such as aviation, aerospace, ocean, and so on. Unlike the geometric properties of transportation pipe, the shell structure has a large size and short length. It is relatively difficult to detect damage globally. According to the abovementioned characteristics, a damage localization method based on helical sensor network is proposed in the current research work to locate the internal damage in large-scale hollow cylinder in which the sensors are arranged on the outer surface of the structure. The wavefront theory about flexural modes is fully explored to determine helical sensor network which is suitable for the full frequency spectrum and avoids the approximate estimation to the wave in plate. The flexible sensing paths and single mode excitation in helical sensor network also provide more possibilities for the damage localization in hollow cylinder. The verification method of circumferential orders based on the Fourier transform is developed by the normal mode expansion approach. To apply the helical sensor network of flexural mode in damage localization, the effective loading length of flexural modes is investigated using simulation. And the simulation and experiment both are conducted to locate the internal damage in hollow cylinders and verify the credibility of proposed localization method. The scale parameter and sensing path number are further investigated to determine the effect on location accuracy.

Bias and inconsistency in the estimation of tumour mutation burden

BackgroundTumour mutation burden (TMB), defined as the number of somatic mutations per megabase within the sequenced region in the tumour sample, has been used as a biomarker for predicting response to immune therapy. Several studies have been conducted to assess the utility of TMB for various cancer types; however, methods to measure TMB have not been adequately evaluated. In this study, we identified two sources of bias in current methods to calculate TMB.MethodsWe used simulated data to quantify the two sources of bias and their effect on TMB calculation, we down-sampled sequencing reads from exome sequencing datasets from TCGA to evaluate the consistency in TMB estimation across different sequencing depths. We analyzed data from ten cancer cohorts to investigate the relationship between inferred TMB and sequencing depth.ResultsWe found that TMB, estimated by counting the number of somatic mutations above a threshold frequency (typically 0.05), is not robust to sequencing depth. Furthermore, we show that, because only mutations with an observed frequency greater than the threshold are considered, the observed mutant allele frequency provides a biased estimate of the true frequency. This can result in substantial over-estimation of the TMB, when the cancer sample includes a large number of somatic mutations at low frequencies, and exacerbates the lack of robustness of TMB to variation in sequencing depth and tumour purity.ConclusionOur results demonstrate that care needs to be taken in the estimation of TMB to ensure that results are unbiased and consistent across studies and we suggest that accurate and robust estimation of TMB could be achieved using statistical models that estimate the full mutant allele frequency spectrum.

Data Driven Seal Wear Classifications using Acoustic Emissions and Artificial Neural Networks

The work presented in this paper is built on a series of experiments aiming to develop a data-driven and automated method for seal diagnostics using Acoustic Emission (AE) features. Seals in machineries operate in harsh conditions, and seal wear in hydraulic cylinders results in fluid leakage, and instability of the piston rod movement. Therefore, regular inspection of seals is required using automated approaches to improve productivity and to reduce unscheduled maintenance. In this study, we implemented a data-driven diagnostics approach which utilizes AE measurements along with light weight Artificial Neural Networks (ANN) as a classifier to investigate the performance and resources (hardware &amp; software) required for implementing a real-time soft sensor unit for monitoring seal wear condition. We used a feedforward multilayer perceptron ANN (Scaled Conjugate Gradient- SCG algorithm) that is trained with the back propagation algorithm, which is a popular network architecture for a multitude of applications (automotive, oil and gas, electronics). We benchmark the developed method against previous work conducted based on Support Vector Machine (SVM), and we compare ANN performance in classifying the running condition of seals in hydraulic cylinders by applying it to both raw (full frequency spectrum) and down sampled frequency measurements. The experiments were performed at varying pressure conditions on a hydraulic test rig that can simulate fluid leakage conditions like that of hydraulic cylinders. The test cases were generated with seals of three different conditions (unworn, semi-worn, worn). From the AE spectrum, the frequency bands were identified with peak power and by heterodyning the signal. This technique results in 10X down sampling without losing the information of interest. Further, the signal was divided into smaller “snapshots” to facilitate rapid diagnosis. In these tests, the diagnosis was made on short-time windows, as low as 0.3 seconds in length. A general set of 16 time and frequency domain features were designed. Then a training set was developed using relevant set of features (4, 5, and 16 features). The data was used to train the ANN (70% training – 30% test &amp; validation) and SVM (60 % training - 40% test and validation). Classification of down sampled measurements, both ANN and SVM were able to accurately classify the status irrespective of the pressure conditions, with an accuracy of ~99% with execution time less than seconds. Therefore, the proposed approach can be applied as part of an automated seal wear classification technique based on AE and ANN/SVM and can be used for real-time monitoring of seal wear in hydraulic cylinders.

Spatial Frequency Bias in Convolutional Generative Adversarial Networks

Understanding the capability of Generative Adversarial Networks (GANs) in learning the full spectrum of spatial frequencies, that is, beyond the low-frequency dominant spectrum of natural images, is critical for assessing the reliability of GAN-generated data in any detail-sensitive application. In this work, we show that the ability of convolutional GANs to learn an image distribution depends on the spatial frequency of the underlying carrier signal, that is, they have a bias against learning high spatial frequencies. Our findings are consistent with the recent observations of high-frequency artifacts in GAN-generated images, but further suggest that such artifacts are the consequence of an underlying bias. We also provide a theoretical explanation for this bias as the manifestation of linear dependencies present in the spectrum of filters of a typical generative Convolutional Neural Network (CNN). Finally, by proposing a proof-of-concept method that can effectively manipulate this bias towards other spatial frequencies, we show that the bias is not fixed and can be exploited to explicitly direct computational resources towards any specific spatial frequency of interest in a dataset, with minimal computational overhead.

How Do You Treat Trigeminal Neuralgia in Your Practice?

How Do You Treat Trigeminal Neuralgia in Your Practice?

Building Blocks for the Development of a Self-Consistent Electromagnetic Field Theory of Consciousness.

The goal of this work is to compile the basic components for the construction of an electromagnetic field theory of consciousness that meets the standards of a fundamental theory. An essential cornerstone of the conceptual framework is the vacuum state of quantum electrodynamics which, contrary to the classical notion of the vacuum, can be viewed as a vibrant ocean of energy, termed zero-point field (ZPF). Being the fundamental substrate mediating the electromagnetic force, the ubiquitous ZPF constitutes the ultimate bedrock of all electromagnetic phenomena. In particular, resonant interaction with the ZPF is critical for understanding rapidly forming, long-range coherent activity patterns that are characteristic of brain dynamics. Assuming that the entire phenomenal color palette is rooted in the vibrational spectrum of the ZPF and that each normal mode of the ZPF is associated with an elementary shade of consciousness, it stands to reason that conscious states are caused by the coupling of the brain to a particular set of normal modes selectively filtered from the full frequency spectrum of the ZPF. From this perspective, the brain is postulated to function as a resonant oscillator that couples to a specific range of ZPF modes, using these modes as a keyboard for the composition of an enormous variety of phenomenal states. Theoretical considerations suggest that the brain-ZPF interface is controlled by altering the concentrations of neurotransmitters, placing the detailed study of the neurotransmitter-ZPF interaction at the center of future research activities.

Further improvements to iterative off-axis digital holography.

In order to break through the limitation of off-axis holography in the field of measuring rough or strong scattering objects, a new iterative algorithm based on the concept of wavefront-coding was proposed. The reference wave is regarded as a wave modulator and it starts with random guess freed from the result of traditional off-axis holography. The full frequency spectrum could be retrieved iteratively after taking full advantage of the space-bandwidth production of the detector. As one form of coherent diffractive imaging, the theoretical resolution is diffraction limitation. According to the simulations and experiments with random phase plate, when the object fails to be reconstructed by traditional off-axis holography and other iterative off-axis holography algorithm due to the frequency spectrum of object is too wide, the proposed algorithm works well. It could be a general algorithm to prominently improve the capability of off-axis holography to measure rough or strong scattering objects.

Overburden analysis and seal integrity study for CO2 sequestration in the North Sea (OASIS)

The Norwegian government recently launched a full-scale CCS project (Longship) in the North Sea that will store CO2 captured from Norway and other European countries. One of the proposed CO2 storage sites is “Smeaheia,” located east of the Troll oil and gas field, northern North Sea. Another neighboring prospective storage site, “Aurora” is located south of the Troll field. With a history of oil and gas exploration and production, Norway possesses all the necessary skills and technology to establish and run sub-surface CO2 storage. The geologic evaluation of an area for CO2 storage warrants detailed characterization of the reservoir, seal and overburden rocks. In the case of a hydrocarbon trap, the oil/gas accumulation manifests the caprock and the overburden integrity. However, in geological CO2 storage, a careful investigation of the reservoir, seal, and overburden rocks can avoid leakage risk. The Smeaheia area is bounded by a fault array separating the Troll field in the west and the Basement Complex in the east. The upper Jurassic Sognefjord Formation sandstone is the target CO2 storage reservoir in the Smeaheia area capped by Heather and Draupne Formations shales. In the Aurora prospect, the Johansen and Cook Formation sandstones of Early Jurassic age around the Troll field are potential storage units. The reservoir is enveloped by Amundsen shale, whereas in the southeast, where the Amundsen shale pinches out, the Drake Formation shale directly overlies the Johansen and Cook Formations. The OASIS project deals with various aspects of the seal and overburden rocks above a potential CO2 reservoir. This study aims to evaluate the viability of the seal and overburden rocks using seismic, well-log, and geological data to make sure the injected CO2 remains safely in the underground forever. The work investigated the seals overlying the potential CO2 reservoirs in the area using various techniques. This information will be used at the later stage of the OASIS project to generate and calibrate a field-scale 3D geomechanical model. To assess the integrity of seal rocks, the brittleness and ductility of shales are essential. If a rock is brittle, there is a risk of developing fractures once the pressure increases while injecting CO2. Conversely, a ductile rock preserves its integrity under high pressure. In the Aurora area, the Amundsen and Drake Formation shales’ brittleness relatively increases with depth and temperature; however, our analysis showed a low possibility of fracturing. The Johansen Formation reservoir sandstone quality is improving towards the south at shallower depths within the Aurora area. According to the rock physics analysis, some calcium-rich, thin baffles are found. In the Johansen Formation reservoir sandstone, at deeper levels, the material binding the sand grains is quartz cement, which decreases where the clay/shale percentage is relatively high. The presence of cement might reduce the void spaces between the sand grains, resulting in a reduction of space for CO2 storage. Using a 3D seismic GN1101 in the Smeaheia area, we decomposed the full frequency spectrum to 20 Hz, 40Hz, and 60 Hz frequency cubes. Draping horizons from the Lower Jurassic Drake Formation up to the seafloor on the RGB blend of the three frequencies enabled identifying various sedimentary features within the window below the respective surface. We also generated a dip-steered “Similarity” cube to identify faults intersecting a formation surface and detect associated fractured areas. Based on the seismic data, formations below Quaternary dip towards the west to south-west with several faults associated with the north-south running Vette fault system close to the western edge of the area. The attributes indicate a sedimentary environment ranging from coastal, shallow marine to open marine within the analyzed interval. Weak fault traces are identifiable on the base Quaternary surface; furthermore, we identified several pockmarks on the Draupne Formation sealing layer and the seafloor. The discontinuities/ faults do not extend from the reservoir to the seafloor. However, one linear impression was observed on the seafloor on the low-frequency spectrum, indicating possible palaeo-leakage of hydrocarbon gas from the underlying strata. The risk of CO2 leakage through the pockmarks and the said fault needs further investigation, though these features are not present above the potential storage structures. The Amplitude Variation with Angle (AVA) technique yields valuable information about the reservoir fluids. The AVA models confirmed that the changes in CO2 saturation and conversion to gaseous state within the reservoir are possible to monitor remotely using a time-lapse seismic or 4D survey. We also extracted mechanical properties from the Smeaheia 3D seismic using pre-stack Inversion. These property cubes will be used for making a geomechanical model that will be calibrated using the information obtained by the methods mentioned above.

Complex Rayleigh Waves in Nonhomogeneous Magneto-Electro-Elastic Half-Spaces.

The complex Rayleigh waves play an important role in the energy conversion efficiency of magneto-electro-elastic devices, so it is necessary to explore the wave propagation characteristics for the better applications in engineering. This paper modifies the Laguerre orthogonal polynomial to investigate the complex Rayleigh waves propagating in nonhomogeneous magneto-electro-elastic half-spaces. The improved method simplifies the calculation process by incorporating boundary conditions into the constitutive relations, shortens the solving time by transforming the solution of wave equation to an eigenvalue problem, and obtains all wave modes, including real and imaginary and complex wavenumbers. The three-dimensional curves of full frequency spectrum and phase velocities are presented for the better description of the conversion from complex Rayleigh wave modes to real wave ones; besides, the displacement distributions, electric and magnetic potential curves are obtained in thickness and propagation directions, respectively. Numerical results are analyzed and discussed elaborately in three cases: variation of nonhomogeneous coefficients, absence of magnetism, and absence of electricity. The results can be used to optimize and fabricate the acoustic surface wave devices of the nonhomogeneous magneto-electro-elastic materials.