Important Applications Research Articles

Machine Learning-Based Traffic Flow Prediction and Intelligent Traffic Management

With the rapid development of information technology, multiple time series forecasting, which is typical of traffic flow forecasting, has become increasingly important in big data analysis. As the cornerstone of intelligent transportation system, traffic flow forecasting has important scientific research value and practical application value for urban traffic operation scheduling, quality and efficiency improvement of logistics transportation industry and public travel planning. Traffic flow prediction is always an important task of intelligent transportation system. Due to the complex temporal and spatial dependence of traffic flow sequence, it is very challenging to construct accurate traffic flow prediction in its ring neural network, graph network and Transformer model. Much of the existing work is based on very good models. Considering the advantages of convolutional networks, such as high computational efficiency and strong feature extraction ability, a traffic flow prediction model based on multi-view spatiotemporal convolution is proposed.

Study on influence coefficients of hydrodynamic force on dynamic response of deep-water pier under earthquake

The hydrodynamic force generated by the interaction between deep-water pier and water under earthquake exerts a significant influence on the dynamic response of the pier. At present, there is a lack of characterization methods to measure the influence degree of pier-water interaction on the dynamic response of the deep-water pier. Based on the structural dynamics theory and considering the coupling effect of pier-water interaction, the dynamic equilibrium equations for the deep-water pier under earthquake are established, and the solutions are deduced and validated by the finite element simulation results. Then the comprehensive analysis of influence mechanism is conducted. Further, the displacement influence coefficients and internal force influence coefficients are proposed to evaluate the influence degree of hydrodynamic force on dynamic response of deep-water piers. Validation shows that the proposed coefficients have high accuracy, indicating the important application of the proposed coefficients in seismic design of deep-water piers in real practice.

Highly efficient expression of Rasamsonia emersonii lipase in Pichia pastoris: characterization and gastrointestinal simulated digestion in vitro.

Acidic lipases with high catalytic activities under acidic conditions have important application values in the food, feed and pharmaceutical industries. However, the availability of acidic lipases is still the main obstacle to their industrial applications. Although a novel acidic lipase Rasamsonia emersonii (LIPR) was heterologously expressed in Escherichia coli, the expression level was unsatisfactory. To achieve the high-efficiency expression and secretion of LIPR in Pichia pastoris GS115, the combinatorial optimization strategy was adopted including gene codon preference, signal peptide, molecular chaperone co-expression and disruption of vacuolar sorting receptor VPS10. The activity of the combinatorial optimization engineered strain in a shake flask reached 1480 U mL-1, which was 8.13 times greater than the P. pastoris GS115 parental strain. After high-density fermentation in a 5-L bioreactor, the highest enzyme activity reached as high as 11 820 U mL-1. LIPR showed the highest activity at 40 °C and pH 4.0 in the presence of Ca2+ ion. LIPR exhibited strong tolerance to methanol, indicating its potential application in biodiesel biosynthesis. Moreover, the gastrointestinal digestion simulation results demonstrated that LIPR was tolerant to pepsin and trypsin, but its activity was inhibited by sodium taurodeoxycholate. This study provided an effective approach for the high expression of acidic lipase LIPR. LIPR was more appropriate for lipid digestion in the stomach than in intestine according to the gastrointestinal digestion simulation results. © 2024 Society of Chemical Industry.

Wheat Seed Detection and Counting Method Based on Improved YOLOv8 Model.

Wheat seed detection has important applications in calculating thousand-grain weight and crop breeding. In order to solve the problems of seed accumulation, adhesion, and occlusion that can lead to low counting accuracy, while ensuring fast detection speed with high accuracy, a wheat seed counting method is proposed to provide technical support for the development of the embedded platform of the seed counter. This study proposes a lightweight real-time wheat seed detection model, YOLOv8-HD, based on YOLOv8. Firstly, we introduce the concept of shared convolutional layers to improve the YOLOv8 detection head, reducing the number of parameters and achieving a lightweight design to improve runtime speed. Secondly, we incorporate the Vision Transformer with a Deformable Attention mechanism into the C2f module of the backbone network to enhance the network's feature extraction capability and improve detection accuracy. The results show that in the stacked scenes with impurities (severe seed adhesion), the YOLOv8-HD model achieves an average detection accuracy (mAP) of 77.6%, which is 9.1% higher than YOLOv8. In all scenes, the YOLOv8-HD model achieves an average detection accuracy (mAP) of 99.3%, which is 16.8% higher than YOLOv8. The memory size of the YOLOv8-HD model is 6.35 MB, approximately 4/5 of YOLOv8. The GFLOPs of YOLOv8-HD decrease by 16%. The inference time of YOLOv8-HD is 2.86 ms (on GPU), which is lower than YOLOv8. Finally, we conducted numerous experiments and the results showed that YOLOv8-HD outperforms other mainstream networks in terms of mAP, speed, and model size. Therefore, our YOLOv8-HD can efficiently detect wheat seeds in various scenarios, providing technical support for the development of seed counting instruments.

Observation of non-Hermitian skin effect in thermal diffusion

The paradigm shift of Hermitian systems into the non-Hermitian regime profoundly modifies inherent property of the topological systems, leading to various unprecedented effects such as the non-Hermitian skin effect (NHSE). In the past decade, the NHSE has been demonstrated in quantum, optical and acoustic systems. Beside those wave systems, the NHSE in diffusive systems has not yet been observed, despite recent abundant advances in the study of topological thermal diffusion. In this work, we design a thermal diffusion lattice based on a modified Su-Schrieffer-Heeger model and demonstrate the diffusive NHSE. In the proposed model, the asymmetric temperature field coupling inside each unit cell can be judiciously realized by appropriate configurations of structural parameters. We find that the temperature fields trend to concentrate toward the target boundary which is robust against initial excitation conditions. We thus experimentally demonstrated the NHSE in thermal diffusion and verified its robustness against various defects. Our work provides a platform for exploration of non-Hermitian physics in the diffusive systems, which has important applications in efficient heat collection, highly sensitive thermal sensing and others.

Selectivity of lanthanum and ytterbium in binary and multi-component system by modified sericin/alginate/poly (vinyl alcohol) adsorbent beads

Rare earths are essential for important industrial applications and for producing high-tech materials. Its recovery from secondary sources is fundamental from an environmental and economic perspective. Biosorption is a promising method that can be used to remove and recover rare earth at low concentrations in an aqueous medium due to its low cost, simplicity, and efficiency. Recently, natural biomaterials based on sericin/alginate began to be investigated as a biosorbent for rare-earths uptake. Sericin is a natural protein of silkworm cocoons, usually discarded along with silk industry effluents, and alginate is a natural carbohydrate extracted from abundant sources of brown algae. The particles produced by the sericin/alginate blend were crosslinked with poly (vinyl alcohol) (PVA), which promotes better properties to this biomaterial. In this study, modified beads of a sericin/alginate/PVA blend were produced by ionic imprinting (Ca(NO3)2, Ca(NO3)2/La(NO3)3, or La(NO3)3) and leaching with HNO3 (0.1 mol/L). The developed particles were applied to remove ytterbium and lanthanum from aqueous solutions through adsorptive affinity and selectivity tests. The results indicated that the modified particles showed superior removal efficiency for lanthanum. For ytterbium, the highest removals were obtained for sericin/alginate/PVA-La(NO3)3 (94.33 ± 0.05%), sericin/alginate/PVA-Ca(NO3)2/La(NO3)3 (94.05 ± 3.12%), and sericin/alginate/PVA-Ca(NO3)2 (92.06 ± 2.01%). For lanthanum, higher removal values were obtained for sericin/alginate/PVA-La(NO3)3 (100.71 ± 0.77%), sericin/alginate/PVA-Ca(NO3)2/La(NO3)3 (99.21 ± 0.28%), and sericin/alginate/PVA-Ca(NO3)2 (98.45 ± 2.98%). In the binary system of rare earths, selectivity between the metals was not observed, however the modified beads showed great selectivity performance for lanthanum and ytterbium in multi-metal solution. Additionally, the modified beads with ionic imprinting were characterized by SEM, EDS, FTIR, XPS, and TGA/DTG. Thus, the results indicated that modified sericin/alginate/poly(vinyl alcohol) beads could be used as an alternative and promising biosorbent to remove and recover rare earths.

Acoustically enhanced porous media enables dramatic improvements in filtration performance

Indoor air pollution affects millions of people worldwide. Conventional air purification techniques struggle to balance between safety, power consumption, efficacy, and cost. Acoustics have previously been leveraged to improve air purification with fiber filters, but little is known about how the arrangement of the fibers influences the acoustic effects, particularly for non-agglomerative purification performance relevant to fine and ultrafine pollutants. Here, we explore performance enhancements to fiber filters by applying external standing acoustic waves. We investigate the effects of the porous media arrangement in ordered (aligned and staggered) and disordered fiber media by building a multiphysics model, incorporating thermos-viscous acoustics, laminar flow, and particle tracing. Particles are modeled as point forces to isolate acoustic radiation forces and streaming. Attractive acoustic radiation forces dominate in the porous domain, leading to increased particle/boundary interactions. We find that the average acoustic radiation forces scale linearly with the average scattered acoustic pressure by the fiber medium. The scattered pressure varies depending on the fiber arrangement, indicating that there is an optimal design to increase the performance of acoustic-induced effects. We show a dramatic increase in performance efficiency, up to two orders of magnitude (123 times) for a staggered domain. This work has important applications to further advance acoustically enhanced filtration as a compelling solution for micro/nanoparticle separation processes.

EHR-HGCN: An Enhanced Hybrid Approach for Text Classification Using Heterogeneous Graph Convolutional Networks in Electronic Health Records.

Text classification is a central part of natural language processing, with important applications in understanding the knowledge behind biomedical texts including electronic health records (EHR). In this article, we propose a novel heterogeneous graph convolutional network method for classifying EHR texts. Our method, called EHR-HGCN, is able to combine context-sensitive word and sentence embeddings with structural sentence-level and word-level relation information to perform text classification. EHR-HGCN reframes EHR text classification as a graph classification task to better capture structural information about the document using a heterogeneous graph. To mine contextual information from a document, EHR-HGCN first applies a bidirectional recurrent neural network (BiRNN) on word embeddings obtained via Global Vectors for word representation (GloVe) to obtain context-sensitive word-level and sentence-level embeddings. To mine structural relationships from the document, EHR-HGCN then constructs a heterogeneous graph over the word and sentence embeddings, where sentence-word and word-word relationships are represented by graph edges. Finally, a heterogeneous graph convolutional neural network is used to classify documents by their graph representation. We evaluate EHR-HGCN on a variety of standard text classification benchmarks and find that EHR-HGCN has higher accuracy and F1-score than other representative machine learning and deep learning methods. We also apply EHR-HGCN to the MedLit benchmark and find it performs with high accuracy and F1-score on the task of section classification in EHR texts. Our ablation experiments show that the heterogeneous graph construction and heterogeneous graph convolutional network are critical to the performance of EHR-HGCN.

Pulsed Optical Vortex Array Generation in a Self-Q-Switched Tm:YALO3 Laser.

Optical vortex arrays are characterized by specific orbital angular momentums, and they have important applications in optical trapping and manipulation, optical communications, secure communications, and high-security information processing. Despite widespread research on optical vortex arrays, the 2 μm wavelength range remains underexplored. Pulsed lasers at 2 μm are vital in laser medicine, sensing, communications, and nonlinear optic applications. The need for 2 μm-pulsed structured optical vortices, combining the advantages of this wavelength range and optical vortex arrays, is evident. Therefore, using just three elements in the cavity, we demonstrate a compact self-Q-switched Tm:YALO3 vortex laser by utilizing the self-modulation effect of a laser crystal and a defect spot mirror. By tuning the position of the defect spot and the output coupler, the resonator delivers optical vortex arrays with phase singularities ranging from 1 to 4. The narrowest pulse widths of the TEM00 LG0,-1, two-, three-, and four-vortex arrays are 543, 1266, 1281, 2379, and 1615 ns, respectively. All the vortex arrays in our study have relatively high-power outputs, slope efficiencies, and single-pulse energies. This work paves the way for a 2 μm-pulsed structured light source that has potential applications in optical trapping and manipulation, free-space optical communications, and laser medicine.

Radio-astronomical image reconstruction with a conditional denoising diffusion model

Context. Reconstructing sky models from dirty radio images for accurate source extraction, including source localization and flux estimation, is a complex yet critical task, and has important applications in galaxy evolution studies at high redshift, particularly in deep extragalactic fields using for example the Atacama Large Millimetre Array (ALMA). With the development of large-scale projects, such as the Square Kilometre Array (SKA), we anticipate the need for more advanced source-extraction methods. Existing techniques, such as CLEAN and PyBDSF, currently struggle to effectively extract faint sources, highlighting the necessity for the development of more precise and robust methods. Aims. The success of the source-extraction process critically depends on the quality and accuracy of image reconstruction. As the imaging process represents an “information-lossy” operator, the reconstruction is characterized by uncertainty. The current study proposes the application of stochastic neural networks for the direct reconstruction of sky models from “dirty” images. This approach allows us to localize radio sources and to determine their fluxes with corresponding uncertainties, providing a potential advancement in the field of radio-source characterization. Methods. We used a dataset of 10 164 images simulated with the CASA tool based on the ALMA Cycle 5.3 antenna configuration. We applied conditional denoising diffusion probabilistic models (DDPMs) to directly reconstruct sky models from these dirty images, and then processed these models using Photutils to extract the coordinates and fluxes of the sources. To test the robustness of the proposed model, which was trained on a fixed water vapor value, we examined its performance under varying levels of water vapor. Results. We demonstrate that the proposed approach is state of the art in terms of source localisation, achieving over 90% completeness at a signal-to-noise ratio (S/N) of as low as 2. Additionally, the described method offers an inherent measure of prediction reliability thanks to the stochastic nature of the chosen model. In terms of flux estimation, the proposed model surpasses PyBDSF in terms of performance, accurately extracting fluxes for 96% of the sources in the test set, a notable improvement over the 57% achieved by CLEAN+ PyBDSF. Conclusions. Conditional DDPMs are a powerful tool for image-to-image translation, yielding accurate and robust characterization of radio sources, and outperforming existing methodologies. While this study underscores the significant potential of DDPMs for applications in radio astronomy, we also acknowledge certain limitations that accompany their use, and suggest directions for further refinement and research.

Novel solitonic structure, Hamiltonian dynamics and lie symmetry algebra of biofilm

In this study, the Lie point symmetries and optimal system have been established. We discuss the biofilm model’s soliton solutions. To examine a nonlinear dynamical biofilm system, which is simply a bistable Allen–Cahn equation with quartic potential, to determine solitary wave profiles using the considered equation, a new auxiliary equation technique is used. A suitable variable transformation is used to convert the governing equation into a nonlinear ordinary differential equation. The unified approach is utilized to evaluate periodic solutions, solitary and soliton solutions as well as several newly discovered exact solitary wave solutions, it can be accomplished via Mathematica (https://www.wolfram.com/mathematica/online/). The new auxiliary equation approach is an efficient method for creating unique wave profiles based on a variety of soliton families. Also, the results are graphically visualized, by using the appropriate parametric settings. The outcomes are shown graphically in two dimensions, three dimensions, and contour form. The Hamiltonian conditions are satisfied by the planer dynamical framework of equations to ensure as the system that was generated is a conservative Hamiltonian dynamical system which includes all traveling wave structures. A sensitivity evaluation is used to explore the governing model’s thoroughly dynamical properties. It is demonstrated that the model becomes more dependent on the beginning conditions than the variables. The strategy could be used to look for exact solutions to various nonlinear evolution equations. We believe that this study will have important applications in different areas of science.

Azimuthally extreme-ultraviolet focal splitter by modified spiral photon sieves

Extreme Ultraviolet (EUV) radiation is a short-wavelength light source that has important applications in many fields, such as optical communication, particle manipulation, and ultrahigh resolution imaging. However, the highly absorptive nature of EUV light makes it challenging to design suitable focusing optics, such as focal splitters, to properly manipulate the energetic light. Here, we propose modified spiral photon sieves to transform EUV laser light into azimuthally splitting focusing. A genetic algorithm was used to design and optimize the azimuthally focal splitters. A capillary discharge EUV laser at 46.9 nm was used to verify the effectiveness of our proposed method, and PMMA targets were used to record the focused laser spot. The profile of the recorded patterns measured by atomic force microscopy shows that the focal spots in the experiment are diffraction-limited and agreed with the theoretical analysis. The proposed technique provides a new way for manipulating EUV light and further extends the applications ranging from EUV to soft x rays.

Doping Induced Optical Band Gap Modification of Potassium Dihydrogen Phosphate

Aim: Potassium dihydrogen phosphate (KDP) is an excellent nonlinear optical material that has many potential applications, such as laser frequency conversion, Qswitches, Pockels cells, 3-D optical data storage devices, etc. Doping enhances various optical parameters of KDP. Doping changes in the absorbance and dopants such as aspartic acid, succinic acid, glycine, and L-alanine improved the optical band gap of the sample. Background: KDP is a non-linear optic material that has many important applications. Many researchers all over the world are trying to improve its optical properties. Objective: To synthesise pure KDP and KDP doped with aspartic acid, succinic acid, glycine, and L-alanine. Also, to study the temperature stability of the grown samples using TGA, DTA, and DSC analysis, Method: Growth from solution at low temperatures is used for the growth of crystals. TGA, DTA, and DSC are used to analyse the stability of the samples against temperature variations. Results: KDP crystals of good quality were grown using the slow cooling solution growth technique. Determined their band gap using the Tauc Plot. Significant variations in the optical bandgap are noticed. Also, the TGA, DTA, and DSC characterizations were done. Conclusion: KDP solution and grown crystals of good quality were synthesized. The variation in the bandgap on doping is a significant result as the modifications in bandgap enable the sample to be used in a more enhanced applications. Also, the doped samples were stable for variations in temperature, like the pure KDP.

Techno-Economic Analysis of Hemp Production, Logistics and Processing in the U.S

Industrial hemp is a versatile crop, and its products have important applications in the food, cosmetic, pharmaceutical, textile, paper and composite industries. Since its legalization in the U.S. in 2018, interest in growing and using hemp has been increasing. This study evaluated the techno-economics of hemp grain and fiber production, harvest and post-harvest logistics, the drying and storage of hemp grain, and the decortication of fiber stalks. The analysis was performed using a process modeling approach with data obtained from the literature considering a farm size of 162 ha (average U.S. farm size). The input parameters were used as distributed functions and the results obtained are reported as interquartile ranges after 10,000 Monte Carlo simulations. The total cost of producing and processing hemp grain and fiber was estimated to be in the interquartile range of USD 2911–3566 Mg−1 and USD 1155–1505 Mg−1, respectively. The costs of seed and fertilizer along with grain and fiber yields were found to be the major factors influencing field production costs, while costs associated with facilities and labor were the main costs in fiber processing. Despite the high resource requirements and processing costs, high-value applications of hemp grain and fiber show great potential to produce net incomes of USD 426–3701 Mg−1 and USD 1570–2016 Mg−1, respectively.

Data-driven PSO-CatBoost machine learning model to predict the compressive strength of CFRP- confined circular concrete specimens

This work articulates the development of a sophisticated machine-learning model for the prediction of compressive strength in Carbon Fiber-Reinforced Polymer Confined-Concrete (CFRP-CC) specimens. Despite extensive empirical studies conducted over the last three decades, prevailing predictive models predominantly rooted in linear or nonlinear regression analyses are constrained by their dependency on limited data scopes. Addressing this deficiency, our research delineates the formulation of an innovative Particle Swarm Optimization- Categorical Boosting (PSO-CatBoost) algorithm, underpinned by an expansive database encompassing 916 experimental outcomes from 105 scholarly articles, spanning the period from 1991 to mid-2023. This innovative approach effectively combines the strengths of Particle Swarm Optimization and the CatBoost algorithm. It carefully evaluates various vital factors that affect the compressive strength of CFRP-CC. The uniqueness of our approach is further accentuated through the application of SHapley Additive exPlanations (SHAP) and Permutation Feature Importance (PFI) methodologies, thereby elucidating the relative importance of each contributory feature. In an unprecedented comparative analysis, the PSO-CatBoost model is rigorously benchmarked against six contemporary machine learning paradigms: CatBoost, XgBoost, AdaBoost, GBoost, Extra Trees, and Random Forest. Furthermore, this model is assessed against six empirical models for further comparison. The model exhibits superior predictive efficacy, evidenced by an exemplary coefficient of determination R-squared of 0.9847, surpassing the methodologies. This research introduces a new predictive model for CFRP-CC and represents a significant shift in concrete research, moving towards a more sophisticated, data-driven, and machine learning-focused methodology. This work thus establishes a new benchmark in the predictive modeling realm for CFRP-CC compressive strength, offering a robust and comprehensive analytical tool for both researchers and practitioners in the field. Lastly, a graphical user interface was designed for modeling the compressive strength of CFRP-CC to facilitate practical use.

Enthalpy, mean energy, entropy, and Gibbs free energy of lithium dimer under magnetic field

It is well known that the lithium diatomic molecule has important potential application in engineering, and industry. For this purpose, in this research, the thermal properties of the lithium diatomic molecule are theoretically calculated taking into account both an external magnetic field and Aharonov-Bohm (AB) flux. To this end, the interaction in the diatomic molecule is proposed as an improved Scarf ΙΙ potential model (ISPM). The Schrödinger equation is solved using an appropriate Pekeris-like approximation form for the centrifugal barrier and the energy spectrum and wave functions are obtained. Then, the partition function is obtained to study the thermal properties of the lithium dimer. The calculated thermal properties of 7Li2 (2 3Πg) in this work are mean energy, entropy, specific heat, enthalpy, and Gibbs free energy. Our theoretical results are also compared with the experimental data. The findings show that the obtained results are in good agreement with experimental data. Without considering the magnetic field and AB flux, the deviation of specific heat, Gibbs free energy, and enthalpy are 3.73%, 4.96%, and 5.31%, respectively. By considering the magnetic field and AB flux, we find that the deviation of the aforementioned properties are 1.91%, 2.04%, and 2.32%, respectively. Therefore, it is found that the external magnetic field and AB flux have great influences on the thermal properties of lithium.

Pressure-induced emission enhancement: A review

The luminescent properties of some materials undergo significant changes under compression. High pressure generated by a diamond anvil cell (DAC) is often used as an external stimulus to explore the relationship between the structures and luminescent properties of materials, provide traceable color and structural changes, and quantify the environment in which the materials are located. Under high pressure, the luminous intensity or color of materials changes, which has important potential applications in fields such as safety detection, information storage, optoelectronic devices, and mechanical sensing. Recently, many phenomena of pressure-induced luminescence enhancement have been discovered in DAC, commonly referred to as pressure-induced emission enhancement. In this review, recent pressure-induced emission enhancement phenomena have been collected, and the role of pressure in promoting the luminescent enhancement of materials in DAC was revealed and discussed, which helps to design some materials with specific emission characteristics and provides a perspective for in-depth research on the photophysical behavior of materials.

Highly sensitive and specific detection of Ni2+ using a novel fluorometric probe in the DMSO-H2O system.

The rapid detection of Ni ions has important research and application value. This paper presents a novel specific turn-off fluorescence probe PCTMP-FS for detecting Ni2+ ions. The carbazole-based compound PCTMP is first synthesized via a two-step reaction. PCTMP-FS comprises PCTMP dispersed into a DMSO-H2O (fw=30% v/v) mixed solvent. The probe demonstrates prominent selectivity and anti-interference abilities for detecting Ni2+ with a limit of detection (LOD) of 0.233μM. The probe exhibits good applicability over a wide range of acidities. The detecting mechanism of the probe is due to the complex formed by PCTMP and Ni2+ (2:1), which destroys intramolecular charge transfer in the compound. The probe has good repeatability and demonstrates excellent stability and sensitivity for the detection of Ni2+ in real water samples.

NuSC: An Effective Local Search Algorithm for Solving the Set Covering Problem.

The set covering problem (SCP) is a fundamental NP-hard problem in computer science and has a broad range of important real-world applications. In practice, SCP instances transformed from real-world applications would be of large scale, so it is of significant importance to design effective heuristic algorithms, especially local search ones. However, there exist only few research works on developing local search algorithms for solving SCP. In this article, we propose a new local search algorithm for solving SCP, dubbed NuSC. In particular, NuSC introduces a new combined scoring function for subset selection, which combines different subset properties in an effective way and helps NuSC find more optimized solutions. Besides, NuSC incorporates a dynamic weighting scheme for elements, a tabu search strategy, and a novelty selection mechanism to further enhance its practical performance. In order to study the effectiveness and robustness of our proposed NuSC algorithm, we conduct extensive experiments to compare NuSC against many state-of-the-art competitors on various types of SCP instances. Our experimental results demonstrate that NuSC significantly outperforms its competitors on the majority of instances, indicating the superiority of NuSC. Also, our empirical evaluations confirm the effectiveness of each algorithmic technique underlying NuSC.

Imaging faults and fractures with the difference of fast and slow shear-wave splitting reflectivity, Δγ(S): 3D/9C survey in Midland Basin, West Texas, and 3D/3C survey in Washakie Basin, Wyoming

One of the most important applications of shear-wave (S-wave) seismic exploration has been in reservoir fracture characterization. While many advancements have been made over the past 30 years to compute and correct for the long-wavelength kinematics of S-wave splitting (SWS) (fast S-wave polarization directions and slow S-wave time delays), practically no progress has been made in imaging the short-wavelength reflectivity of fractures directly with Δγ(S). This property is the contrast in the SWS anisotropy parameter, γ(S), and represents the reflection amplitude at vertical incidence for changes in fracture density and orientation across an interface. In this article, we examine the Lupin nine-component survey in Midland Basin, Texas, for the technical reliability of imaging fractures in depth with converted P to S waves (PS waves) guided by pure-mode horizontal shear waves and vertical shear waves. Final Δγ(S) amplitude maps for each mode show sensitivity to fractures, faults, and the maximum horizontal stress direction. These maps are computed from the difference between fast and slow S-wave stacks (after SWS analysis and correction) and P-wave amplitude variation with offset gradient stacks. The S-wave difference maps identify an east–west lineament, possibly a strike-slip fault or fracture corridor, that is not observed by P-wave depth slices. Pure-mode S waves and PS waves are orders of magnitude more sensitive to Δγ(S) than P waves. We also review the development of Δγ(S) and find that it has been relatively unexploited by the exploration industry. In addition, we demonstrate that Δγ(S) can be obtained directly from the objective function of the transverse energy to correct for SWS, and show a four-component Alford rotation example from a previous PS-wave survey in the Washakie Basin, Wyoming.