Accurately measuring the elastic parameters of shale rock samples is highly significant for evaluating shale reservoirs. Resonant ultrasound spectroscopy (RUS) is a non-destructive measurement technique that is increasingly applied to anisotropic materials for elastic parameter estimation and iteratively determines the optimal elastic parameters by fitting the calculated resonance spectrum to the measured spectrum via optimization methods. Traditional measurement methods typically involve manually extracting resonance frequencies for mode matching and applying the Levenberg-Marquardt (LM) algorithm, which heavily relies on initial values for inversion. However, these methods often result in incorrect inversion, particularly when multiple elastic parameters are simultaneously inverted, as the increased complexity hinders the attainment of accurate results. A novel inversion framework, termed hybrid optimization RUS, has been proposed for optimizing the inversion process by integrating the particle swarm optimization algorithm with the limited-memory Broyden-Fletcher-Goldfarb-Shanno algorithm for inversion (RUS-HL). This approach circumvents mode matching and reduces the dependence on prior knowledge of sample elastic parameters. The RUS-HL misfit function uses resonance peak frequency and Q value information to fit a resonance spectrum with a Lorentzian function, defining the difference between the theoretical and calculated resonance spectra as the misfit function. Compared with conventional misfit functions such as the root mean square error, the proposed inversion calculation can be conducted without mode recognition and without relying on the initial values. The resonance spectra of isotropic and transversely isotropic samples were inverted separately to test the method, resulting in a more efficient and accurate determination of the elastic parameters.

Compared with traditional mechanical measurement methods, resonant ultrasound spectroscopy (RUS) is non-destructive and can recover multiple elastic moduli from a single specimen. However, for strongly anisotropic and low quality factor transversely isotropic rocks, practical measurements often suffer from missing resonance peaks and ambiguous mode identification, which severely degrades inversion stability. To address these challenges, we propose a dual-stage deep learning inversion framework termed Dual-stage Deep Learning Inversion for Resonant Ultrasound Spectroscopy (DDLI-RUS). Stage-1 aims to reconstruct a complete resonance frequency vector by inferring missing resonance modes: given a partially observed, sorted resonance sequence represented by three-channel features (frequency, local spacing, and log-scaled frequency), a convolutional neural network–bidirectional long short-term memory (CNN–BiLSTM) encoder extracts multi-scale sequential features and a slot-wise attention decoder predicts a fixed-length 55-mode resonance vector. Meanwhile, an auxiliary soft matcher outputs a probabilistic assignment between observed peaks and discrete modal slots. To avoid manual/discrete mode matching while preserving physical ordering, we introduce self-consistency objectives and monotonicity priors that enforce increasing expected slot indices along the measured peak order and penalize many-to-one slot collisions. Stage-2 maps the completed resonance vector to five independent elastic constants using a one-dimensional residual network (ResNet1D). Numerical simulations, benchmark comparisons, and experimental validations demonstrate that DDLI-RUS improves both accuracy and robustness under missing peaks and measurement perturbations. Once trained, the proposed framework performs inversion in approximately 3 ms per sample on a GPU, enabling efficient non-destructive characterization of elastic parameters in anisotropic materials.

Accurately solving high-fidelity acoustic fields is critical for advancing ultrasonic research. While conventional numerical solvers remain widely used, emerging approaches like Physics-Informed Neural Networks (PINNs) provide a promising alternative for modeling physical phenomena governed by partial differential equations. However, PINNs often struggle to resolve wavefields in large-scale, complex velocity models described by the Helmholtz equation, limiting their practical applications. To address these issues, we propose the Agent-Physics-Informed Neural Network (APINNs) architecture, which integrates the agent field concept and employs a multi-frequency band training strategy. Initially, APINNs are trained on single-frequency forward problems, with agent fields enhancing sensitivity to scattered waves. Subsequently, a step-by-step training methodology enables APINNs to directly predict scattered wavefields at arbitrary frequencies within a prescribed frequency band. By convolving the scattered fields with the source wavelet and applying an inverse Fourier transform, the time-evolving wave propagation in large-scale, heterogeneous models can also be reconstructed. Moreover, we extend APINNs to imaging-related inverse problems, such as velocity model reconstruction, within an ultrasound computed tomography framework. This extension only requires computational costs less than one order of magnitude higher than forward APINNs. Conversely, conventional FWI shows a higher cost ratio between inverse and forward problems. While this does not mean that inverse APINNs are currently more efficient than traditional FWI — since the ratio reflects only internal balance and forward APINN training remains expensive — training-driven APINNs are better positioned to benefit from advances in deep learning, potentially improving efficiency and scalability. Numerical experiments validate the effectiveness of APINNs in solving both forward and inverse problems based on the Helmholtz equation in complex scenarios.

Although shear horizontal waves have advantages over longitudinal waves, including a higher resolution, less wave mode conversion, and much better reflection coefficients at void and crack interfaces in nondestructive detection, they require good contact surface flatness and efficient coupling agents. In this paper, we analyze and design the basic components of the dry-coupled ultrasonic shear wave probe through theoretical analyses and numerical simulations. The admittance characteristics, resonant frequency, and electromechanical coupling coefficients of the double-laminated vibrator under different size parameters in both 2D and 3D models are simulated, and the probe structures are optimized based on the simulation results and operational requirements. The simulation results of the wave field excited by the double-laminated vibrator show the effectiveness of the optimized probe models. Additionally, the dry coupling method of the probe is simulated to study the acoustic energy distribution under various dry-coupled structures. Finally, we compare the measured admittance with the simulated values, and they are in good agreement.

Atmospheric radiation is widely used in the field of space-based detection of spacecraft and atmospheric remote sensing. With the increase of altitude, the air density decreases and the intermolecular collision becomes weaker. As a result, the energy distribution of gas molecules deviates from the equilibrium state, and the radiation transmittance and source function need to be calculated on the basis of the specific non-LTE energy level population. Energy level population is a key and difficult problem in the calculation of non-equilibrium radiation. In this paper, the physical process of molecular energy level transition and radiation transfer is studied, including the relationship between molecular transition, radiation spectrum, radiation transfer and energy level distribution. The numerical algorithm for energy level population calculation is established. The non-equilibrium energy level distribution and limb infrared radiation characteristics of CO2, an important infrared radiation component in the atmosphere, were studied by using the program. The non-equilibrium radiation mechanism of CO2 4.3μm band under different day and night conditions was analyzed. Finally, the model calculation of CO2 4.3μm band under different conditions is compared with the limb observation results, and the reliability of the radiation model, calculation method and program are verified.

This study introduces a tie-rod inter-module connection (IMC) method for reinforced concrete modular structures to advance their application and development. The tie-rod IMC method achieves vertical connections through tie rods and horizontal connections via steel plates. This innovative approach eliminates the traditional reliance on on-site concrete casting processes, ensuring that interior and exterior finishing remain unaffected during module assembly. Additionally, this method significantly reduces on-site construction time, simplifies the installation process, and enhances manufacturing precision. To verify the feasibility of this connection in practical engineering applications, quasi-static cyclic loading tests were conducted on three full-scale end joints of a modular concrete frame structure. The seismic properties of the grouted joints were thoroughly evaluated, including initial stiffness, bearing capacity, ductility, and energy dissipation capacity. The results demonstrate that the tie-rod IMC joints meet the seismic design requirements outlined in the Chinese code and exhibit good ductility and energy dissipation capabilities. Greater axial compression loads enhanced the rigidity and load-bearing capabilities of the tie-rod IMC joints by increasing inter-module friction, reducing sliding deformation, and facilitating force transfer between adjoining modules. Comprehensive consideration of inter-module sliding and axial compression effects is essential for the design and application of modular concrete structures utilizing tie-rod inter-module connections.

A multistage filtering strategy was proposed to target the periodic noise present in the cavity sonar signal of salt cavern gas storage. First, the relevant signal's frequency band range is selected, and the parameters of the signal's time-frequency domain are collected using the Short-Time Fourier Transform (STFT). Second, the adaptive Wiener filter is adjusted with windows of variable lengths, completing the first stage of filtering. Lastly, the second stage involves utilizing the wavelet transform to enhance the capacity for filtering periodic noise. The Signal-to-Noise Ratio (SNR) and correlation coefficient are thoroughly estimated to assess the sonar signal after the second stage of filtering, and the Minimum Mean Squared Error (MMSE) is employed to evaluate the impact of the first filtering stage, confirming the effectiveness of the proposed filtering technique. According to various experiments, the method presented in this work effectively suppresses multiple types of noise, improves the accuracy of echo extraction, and enhances the SNR by approximately 10 dB, all while preserving the characteristics of the original signal.

In this paper we present the current status of the enhanced X-ray Timing and Polarimetry mission, which has been fully approved for launch in 2030. eXTP is a space science mission designed to study fundamental physics under extreme conditions of matter density, gravity, and magnetism. The mission aims at determining the equation of state of matter at supra-nuclear density, measuring the effects of quantum electro-dynamics, and understanding the dynamics of matter in strong-field gravity. In addition to investigating fundamental physics, the eXTP mission is poised to become a leading observatory for time-domain and multi-messenger astronomy in the 2030's, as well as providing observations of unprecedented quality on a variety of galactic and extragalactic objects. After briefly introducing the history and a summary of the scientific objectives of the eXTP mission, this paper presents a comprehensive overview of: 1) the cutting-edge technology, technical specifications, and anticipated performance of the mission's scientific instruments; 2) the full mission profile, encompassing spacecraft design, operational capabilities, and ground segment infrastructure.

The distribution of water droplets in clouds determines solar radiation and microphysical properties, thereby influencing the global energy balance and water cycle processes. Accurate retrieval of cloud water droplet distribution is of significant scientific value for improving the accuracy of weather forecasts, understanding feedback mechanisms in the climate system, and optimizing climate model predictions. This study employs multiangle data from the second-generation Directional Polarization Camera on board the Chinese Gaofen-5 02 satellite (GF-5B DPC). By performing a detailed analysis of the polarization characteristics of water clouds and integrating the UNL-VRTM vector radiative transfer model, a lookup table for retrieving the water cloud droplet distribution. Using this approach, the effective particle radius and effective particle variance of water clouds over three surface types: forest, desert/semi-desert, and ocean, were successfully retrieved from the lookup table and polarization reflectance data. The retrieval results under various surface conditions were compared with the MOD06 cloud products. The correlation coefficient between the two sets of water cloud droplet distribution products ranged from 0.74 to 0.82, indicating strong consistency and validating the effectiveness and accuracy of the proposed retrieval algorithm. By utilizing GF-5B-DPC multi-angle polarization data in conjunction with the UNL-VRTM model, we analyzed the spatial distribution of water cloud droplets. The results demonstrate that the polarization measurements, particularly from GF-5B-DPC, exhibit high sensitivity to small particles, providing valuable insights into cloud microphysical properties.

Key detection performance metrics, particularly resolution, are largely determined by the design parameters of ultrasonic arrays. The structural design of the transducer strongly influences critical indicators, including side lobe levels, beam directivity, and focal spot size. To improve parameter selection, this study proposes a multi-objective optimization strategy specifically tailored for cylindrical surface ultrasonic transducers. The geometric parameters of the array and the variables influencing resolution performance are mapped in a nonlinear manner. The NSGA-II algorithm is employed to perform extremum seeking optimization on a trained BPNN, generating a Pareto-optimal solution set by specifying main-lobe width, side-lobe intensity, and sound-pressure uniformity as optimization objectives. For validation, the geometric configurations derived from this solution set are applied in acoustic field simulations. Simulation results demonstrate that the dynamic aperture exhibits clear regularity when the array settings meet millimeter-level resolution requirements. These findings support real-world engineering applications and provide valuable insights for enhancing the geometric design of cylindrical ultrasonic arrays.

Background:The detection of colorectal liver metastases (CRLMs) in complex hepatic backgrounds due to chemotherapy-associated liver injury (CALI) and prior local CRLM treatment is challenging and requires advanced imaging modalities capable of providing both precise lesion detection and CALI assessments. This study aimed to evaluate the diagnostic performance of 5 Tesla (T) multimodal magnetic resonance imaging (MRI) for detecting CRLMs while accurately assessing the hepatic background. Methods:A total of 35 post-chemotherapy patients with suspected CRLMs and a combined total of 118 MRI-identified lesions were prospectively enrolled. Participants underwent 5T liver multimodal MRI with acquisition of: T1-weighted (T1W) in/out-of-phase imaging and proton density fat fraction (PDFF), susceptibility-weighted imaging with fast technique (uSWIFT), and standard hepatic gadoxetic acid-enhanced MRI (EOB-MRI) with three-dimensional (3D) isotropic T1W fast spoiled gradient-recalled echo (FSPGR) hepatobiliary phase imaging and artificial intelligence (AI)-assisted compressed sensing (ACS-HBP) (acquisition voxel size 1.2 mm3, reconstructed voxel size 0.6 mm3, 300 slices). Image analysis was performed by two readers, blinded and randomized, who assessed CALI and CRLM diagnostic performance. Statistical analyses included inter-rater agreement (Cohen's kappa), diagnostic metrics-sensitivity, positive predictive value (PPV), and area under the receiver operating characteristic (ROC) curve (AUC)-and generalized estimating equations (GEEs) for identifying factors associated with CRLM detection and for comparing diagnostic performance. Results:The combination of ACS-HBP and diffusion-weighted imaging (DWI) demonstrated excellent diagnostic performance in detecting CRLMs, yielding the highest sensitivity (97.2%) and PPV (94.6-95.5%), whereas for small lesions (≤10 mm), the combination yielded a sensitivity of 95.7%, outperforming DWI alone (66.0%). Neither CALI nor a prior history of local CRLM treatment had a significant impact on diagnostic performance (all P>0.05). On ACS-HBP imaging, 86.1% (93/108) of all lesions and 71.7% (33/46) of lesions ≤10 mm presented with a target sign (central hyperintensity with a hypointense rim), or reverse target sign (central hypointensity with a hyperintense rim). Conclusions:The combination of ACS and 5T EOB-MRI demonstrates excellent diagnostic performance for CRLMs, including for ≤10 mm lesions, with high sensitivity and PPV across diverse hepatic backgrounds. On HBP imaging, CRLMs characteristically display target and reverse-target signs, especially in lesions ≤10 mm, facilitating differentiation from hepatic cysts.

Abstract Wideband dipole signal is the basis for dipole dispersion correction and near-borehole imaging. Segment linear frequency modulation (SLFM) sound source signal is an effective method to broaden the dipole frequency band. In the actual logging process, the formation environment changes in real time, and the SLFM sound source with fixed parameters cannot compensate the bending wave excitation intensity curve at any time, which affects the quality of logging. This paper presents a method for adaptive adjustment of the SLFM sound source signal. Adaptive parameter adjustment can be made according to the change of downhole environment, so that the signal can be adapted to the changing downhole geological environment.

The ongoing global energy shift from fossil fuels to renewable sources highlights the importance of underground hydrogen storage (UHS) as a sustainable mechanism to counterbalance the seasonal inconsistencies of renewable energy. Comparable to geological CO2 sequestration, UHS necessitates precise subsurface imaging and a thorough understanding of hydrogen behavior within geological formations, including its location and migration patterns. Since direct subsurface measurement is unattainable, there is a pronounced need for robust subsurface characterization and monitoring techniques. This research focuses on UHS feasibility in enduring storage scenarios, employing seismic monitoring strategies adapted from CO2 storage practices. Specifically, we integrate Continuous Active Source Seismic Monitoring (CASSM) and Time-Lapse Full Waveform Inversion (TLFWI) to scrutinize UHS. The study employs synthetic crosshole surveys that initially model the coexistence of hydrogen and water within fractured reservoirs, aiming to pinpoint hydrogen-related velocity alterations in acoustic waveforms. Subsequently, we utilize a time-lapse synthetic model of hydrogen injection to emulate CASSM observations. Utilizing TLFWI in conjunction with White's model, we successfully process the CASSM data to discern and quantify temporal variations in velocity and saturation, which effectively maps the spatial and temporal distribution of mobile hydrogen. The results affirm the capabilities of TLFWI and CASSM as proficient monitoring systems for UHS. This study aspires to offer a dependable methodology for the administration of large-scale and long-term geological hydrogen storage.

Abstract Oil and gas from deep reservoirs are mostly enriched in the fracture-cave reservoirs. For this reason, fine evaluation of large-scale fracture-cave and effective detection of small scale fracture-cave are the key issues to be solved in the exploration of fracture-cave type carbonate reservoirs. The acoustic remote detection logging technology is based on acoustic reflection method to continuously image the formation around the well. The cross dipole array sonic data can be processed to image geological anomalies within a range of about 20 meters outside the well. Dipole S wave remote detection has the remarkable advantages of low emission frequency and larger investigation depth, but it has 180 degrees’ azimuthal ambiguity. This study is based on previously developed far field dipole S wave remote detection tool, finite difference is performed on formation models with and without are obtained for representative fracture-cave formations and remote detection processing and interpretation software is updated. These are applied on a well in the northwestern area. Through the comprehensive analysis of remote logging, conventional logging, electrical imaging logging and seismic data of the fracture-cave geological anomalies, a fine picture of the morphology and development of the geological anomalies from the near wellbore to the far end is formed

Qingshankou shale (Gulong area, China) exhibits strong acoustic anisotropy characteristics, posing significant challenges to its exploration and development. In this study, the five full elastic constants and multipole response law of the Qingshankou shale were studied using experimental measurements. Analyses show that the anisotropy parameters ϵ and γ in the study region are greater than 0.4, whereas the anisotropy parameter δ is smaller, generally 0.1. Numerical simulations show that the longitudinal and transverse wave velocities of these strong anisotropic rocks vary significantly with inclination angle, and significant differences in group velocity and phase velocity are also present. Acoustic logging measures the group velocity in dipped boreholes; this differs from the phase velocity to some extent. As the dip angle increases, the longitudinal and SH wave velocities increase accordingly, while the qSV-wave velocity initially increases and then decreases, reaching its maximum value at a dip of approximately 40°. These results provide an effective guide for the correction and modeling of acoustic logging time differences in the region.

This study aimed to address the challenges encountered in traditional bulk wave delamination detection methods characterized by low detection efficiency. Additionally, the limitations of guided wave delamination detection methods were addressed, particularly those utilizing reflected waves, which are susceptible to edge reflections, thus complicating effective defect extraction. Leveraging the full waveform inversion algorithm, an innovative approach was established for detecting delamination defects in multi-layered structures using ultrasonic guided wave arrays. First, finite element modeling was employed to simulate guided wave data acquisition by a circular array within an aluminum-epoxy bilayer structure with embedded delamination defects. Subsequently, the full waveform inversion algorithm was applied to reconstruct both regular and irregular delamination defects. Analysis results indicated the efficacy of the proposed approach in accurately identifying delamination defects of varying shapes. Furthermore, an experimental platform for guided wave delamination defect detection was established, and experiments were conducted on a steel-cement bilayer structure containing an irregular delamination defect. The experimental results validated the exceptional imaging precision of our proposed technique for identifying delamination defects in multi-layered boards. In summary, the proposed method can accurately determine both the positions and sizes of defects with higher detection efficiency than traditional pulse-echo delamination detection methods.

In petroleum oil wells, the heterogeneity of near-borehole formations is frequently encountered when the rock properties are altered by mechanical damages from drilling or mud invasion, resulting in radial changes in shear-wave velocities. The altered zone must be accurately described for proper processing and interpretation of acoustic logging data. Inversion of the radial shear velocity from the acoustic logging data is essential for the quantitative evaluation of rock properties, e.g., brittleness and permeability, and can provide optimized strategies for reservoir production. We propose an inversion method that utilizes a layered model to assess radial variations in shear-wave velocity by exploiting the dispersion of borehole flexural waves. A convolutional neural network long short-term memory (CNN-LSTM) deep learning model is used to establish a nonlinear mapping between dispersion data and velocity profiles. Through sensitivity analysis, we investigate the influencing factors of flexural-wave dispersion and provide a method for constructing a synthetic training dataset based on formation alteration characteristics. To eliminate the influence of borehole size variation on the dispersion data, we propose a correction approach by scaling the borehole model while maintaining a constant frequency-thickness product, effectively normalizing the borehole diameter to a fixed value. The means are validated by the inversion of a multilayered structure from synthetic waveform arrays. Good agreement is observed between the inverted and input models. Finally, we apply our proposed method to a real field dataset. The shear-velocity inversion results demonstrate the potential of the proposed inversion method for near-borehole elastic imaging.

Full waveform inversion (FWI) is recognized as a leading data-fitting methodology, leveraging the detailed information contained in physical waveform data to construct accurate, high-resolution velocity models essential for crosshole surveys. Despite its effectiveness, FWI is often challenged by its sensitivity to data quality and inherent nonlinearity, which can lead to instability and the inadvertent incorporation of noise and extraneous data into inversion models. To address these challenges, we introduce the scale-aware edge-preserving FWI (SAEP-FWI) technique, which integrates a cutting-edge nonlinear anisotropic hybrid diffusion (NAHD) filter within the gradient computation process. This innovative filter effectively reduces noise while simultaneously enhancing critical small-scale structures and edges, significantly improving the fidelity and convergence of the FWI inversion results. The application of SAEP-FWI across a variety of experimental and authentic crosshole datasets clearly demonstrates its effectiveness in suppressing noise and preserving key scale-aware and edge-delineating features, ultimately leading to clear inversion outcomes. Comparative analyses with other FWI methods highlight the performance of our technique, showcasing its ability to produce images of notably higher quality. This improvement offers a robust solution that enhances the accuracy of subsurface imaging.

In order to improve the detection range and imaging resolution of dipole remote detection logging, an optimal nonlinear frequency modulation (ONLFM) excitation method is proposed in this paper. In this method, the optimal waveform model of the sound source is designed with objective functions of SNR and resolution to obtain the highest resolution under the condition of the required SNR. This optimal model is a multi-constraint optimization problem, and the simulated annealing method has been used to solve it. Solving the optimal model can obtain the optimal spectrum, and the ONLFM waveform can be designed by using the stationary phase principle. Compared with the electric pulse sound source used in traditional technology, this ONLFM sound source can improve the energy and extend the frequency band range of the reflected wave, which can provide the higher resolution and SNR. The simulation results show that the ONLFM sound source can effectively improve the SNR and resolution of the reflected wave, and the detection range and imaging resolution of dipole shear wave remote detection will be improved.

Carbon reduction in the industrial sector is an important basis for cities to achieve carbon peak and carbon neutrality, especially for such super industrial cities as Suzhou. In order to study the main impact factors of terminal industrial carbon emissions, this paper proposes an extended Kaya equation that takes into account such impact factors as scale growth, industrial structure, energy consumption intensity, and energy structure, decomposes the impact factors based on LMDI method, and then performs an empirical study based on the actual data of Suzhou from 2015 to 2019. The research shows that scale growth is the main driving factor for the growth of industrial carbon emissions; the improvement of the industrial structure significantly inhibits industrial carbon emissions; energy efficiency improvement in key industries is an important measure to reduce industrial carbon emissions; considering the clean production of electricity and heat, the improvement of energy structure will help conspicuously reduce industrial carbon emissions.