Synthetic Field Research Articles

Wave-Function Tomography of Topological Dimer Chains with Long-Range Couplings.

The ability to tailor with a high accuracy the intersite connectivity in a lattice is a crucial tool for realizing novel topological phases of matter. Here, we report the experimental realization of photonic dimer chains with long-range hopping terms of arbitrary strength and phase, providing a rich generalization of the Su-Schrieffer-Heeger model which, in its conventional form, is limited to nearest-neighbor couplings only. Our experiment is based on a synthetic dimension scheme involving the frequency modes of an optical fiber loop platform. This setup provides direct access to both the band dispersion and the geometry of the Bloch wave functions throughout the entire Brillouin zone allowing us to extract the winding number for any possible configuration. Finally, we highlight a topological phase transition solely driven by a time-reversal-breaking synthetic gauge field associated with the phase of the long-range hopping, providing a route for engineering topological bands in photonic lattices belonging to the AIII symmetry class.

Testing a Hyperspectral, Bio‐Optical Approach to Identification of Phytoplankton Community Composition in the Chesapeake Bay Estuary

AbstractThe multi‐to hyperspectral evolution of satellite ocean color sensors is anticipated to enable satellite‐based identification of phytoplankton biodiversity, a key factor in aquatic ecosystem functioning and upper ocean biogeochemistry. In this work the bio‐optical Phytoplankton Detection with Optics (PHYDOTax) approach for deriving taxonomic (class‐level) phytoplankton community composition (PCC, e.g. diatoms, dinoflagellates) from hyperspectral information is evaluated in the Chesapeake Bay estuary on the U.S. East Coast. PHYDOTax is among relatively few optical‐based PCC differentiation approaches available for optically complex waters, but it has not yet been evaluated beyond the California coastal regime where it was developed. Study goals include: (a) testing the approach in a turbid estuary including novel incorporation of colored dissolved organic matter (CDOM) and non‐algal particles (NAP), and (b) performance assessment with both synthetic mixture and field data sets. Algorithm skill was robust on synthetic mixtures. Using field data, cryptophyte and/or cyanophyte phytoplankton groups were predicted, but diatom and dinoflagellate detection was not conclusive. For one field data set, small but significant improvements were observed in predicted PCC groups when tested with incorporation of CDOM and NAP into the algorithm, but not for the second field data set. Sensitivity to three hyperspectral‐relevant spectral resolutions (1, 5, 10 nm) was low for all field and synthetic data. PHYDOTax can identify some phytoplankton groups in the estuary using hyperspectral, field‐collected measurements, but validation‐quality data with broad temporospatial coverage are needed to determine whether the approach is robust enough for science applications.

Recycled tire rubber materials in the spotlight. Determination of hazardous and lethal substances

One way of recycling end-of-life tires is by shredding them to obtain crumb rubber, a microplastic material (<0.5 mm), used as infill in artificial turf sports fields or as playground flooring. There is emerging concern about the health and environmental consequences that this type of surfaces can cause. This research aims to develop an analytical methodology able to determine 11 compounds of environmental and health concern, including antiozonants such as N-1,3-dimethylbutyl-N′-phenyl-p-phenylenediamine (6PPD) or N, N´-diphenyl-1,4-phenylenediamine (DPPD), and vulcanization and crosslinking agents, such as N-cyclohexylbenzothiazole-2-sulfenamide (CBS), 1,3-di-o-tolylguanidine (DTG) or hexamethoxymethylmelamine (HMMM) from tire rubber. Ultrasound assisted extraction followed by liquid chromatography coupled to tandem mass spectrometry (UAE-LC-MS/MS) is validated demonstrating suitability. The methodology is applied to monitor the target compounds in forty real crumb rubber samples of different origin including, football pitches, outdoor and indoor playgrounds, urban pavements, commercial samples, and tires. Several alternative infill materials, such as sand, cork granulates, thermoplastic elastomers and coconut fibres, are also collected and analysed. All the target analytes are identified and quantified in the crumb rubber samples. The antiozonant 6PPD is present at the highest concentrations up to 0.2 % in new synthetic fields. The tire rubber-derived chemical 6PPD-quinone (2-((4-methylpentan-2-yl)amino)-5-(phenylamino)cyclohexa-2,5-diene-1,4-dione), recently linked to acute mortality in salmons, is found in all types of crumb rubber samples attaining concentrations up to 40 μg g−1 in football pitches. The crosslinking agent HMMM is detected in most of the playing surfaces, at concentrations up to 36 μg g−1. The tested infill alternatives are free of most of the target compounds. To the best of our knowledge, this study is the largest study considering the target compounds in tire rubber particles and the first to focus on these compounds in playgrounds including the analysis of HMMM, 6PPD-quinone and DTG in crumb rubber used as an infill material.

Robust inversion of 1D magnetotelluric data using the Huber loss function

In this study, a robust 1D inversion was performed on magnetotelluric (MT) data by utilizing the Huber loss function to avoid misleading interpretation of results, which is caused by the presence of outliers in the data. The MT method utilizes the ratio between the electric field (E) and the magnetic field (H), which are perpendicular to each other, to obtain impedance values (Z). These values are used to extract subsurface information in the form of electrical resistivity variations with depth. In the study, forward modeling of the 1D MT responses was conducted by recursively calculating Z values. Robust inversion was performed using the Huber loss function as an objective function to minimize the difference between the calculated and observed data. The Huber loss function combines the squared loss and the absolute loss to anticipate the presence of outliers in MT observation data. The robust inversion was performed on synthetic data with additional noise and field data. The inversion process utilized 50 layers with a thickness of 500 m, a hyperparameter δ=5×10-2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\varvec{\\delta }= 5\ imes {10}^{-2}$$\\end{document}, λ=0.01\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\lambda =0.01$$\\end{document}, and λt=100\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\lambda }_{t}=100$$\\end{document}. The number of iterations used was 500 for inversions that used synthetic data and 3000 for inversions that used field data. The robust inversion scheme using the Huber loss function successfully overcame the presence of outliers and accurately estimated the actual model parameters, both for synthetic data and field data that contained outliers. The misfit was relatively small and close to zero, indicating that the inversion code works well.

Pseudo-spin switches and Aharonov-Bohm effect for topological boundary modes.

Topological boundary modes in electronic and classical-wave systems exhibit fascinating properties. In photonics, topological nature of boundary modes can make them robust and endows them with an additional internal structure-pseudo-spins. Here, we introduce heterogeneous boundary modes, which are based on mixing two of the most widely used topological photonics platforms-the pseudo-spin-Hall-like and valley-Hall photonic topological insulators. We predict and confirm experimentally that transformation between the two, realized by altering the lattice geometry, enables a continuum of boundary states carrying both pseudo-spin and valley degrees of freedom (DoFs). When applied adiabatically, this leads to conversion between pseudo-spin and valley polarization. We show that such evolution gives rise to a geometrical phase associated with the synthetic gauge fields, which is confirmed via an Aharonov-Bohm type experiment on a silicon chip. Our results unveil a versatile approach to manipulating properties of topological photonic states and envision topological photonics as a powerful platform for devices based on synthetic DoFs.

Seismic data AVO analysis in time frequency domain using synchroextracting transform

Time-frequency analysis is a crucial tool for processing and interpreting seismic data in hydrocarbon exploration. Accurately determining the temporal and spectral characteristics is essential for applications such as reservoir characterization and fluid identification. High-resolution representation of seismic data time-frequency properties improves reliability in these applications. Recent advancements in post-processing techniques aim to enhance conventional methods. The synchroextracting transform (SET) is an innovative approach that combines a post-processing procedure with the short-time Fourier transform. It retains time-frequency information associated with spectral components while eliminating excessive energy dispersed in the time-frequency domain. This study evaluates the effectiveness of SET for seismic data time-frequency analysis, comparing it with contemporary post-processing techniques using synthetic signals and field seismic data. Results show that SET provides higher resolution for distinct signal components and effectively reduces energy dispersion. SET exhibits advantages such as robustness against random noise, reasonable computational cost, and offering more interpretable time-frequency information regarding attenuated amplitudes. Building on synthetic experiments, we demonstrate the capability of SET to generate high-resolution single frequency sections of seismic data, facilitating accurate analysis of amplitude variation with offset (AVO) in the time-frequency domain.

An air- and moisture-stable ruthenium precatalyst for diverse reactivity

Versatile, efficient and robust (pre)catalysts are pivotal in accelerating the discovery and optimization of chemical reactions, shaping diverse synthetic fields such as cross-coupling, C–H functionalization and polymer chemistry. Yet, their scarcity in certain domains has hindered the advancement and adoption of new applications. Here we present a highly reactive air- and moisture-stable ruthenium precatalyst [(tBuCN)5Ru(H2O)](BF4)2, featuring a key exchangeable water ligand. This versatile precatalyst drives an array of transformations, including late-stage C(sp2)–H arylation, primary/secondary alkylation, methylation, hydrogen/deuterium exchange, C(sp3)–H oxidation, alkene isomerization and oxidative cleavage, consistently outperforming conventionally used ruthenium (pre)catalysts. The generality and applicability of this precatalyst is exemplified through the potential for rapid screening and optimization of photocatalytic reactions with a suite of in situ generated ruthenium photocatalysts containing hitherto unknown complexes, and through the rapid discovery of reactivities previously unreported for ruthenium. The diverse applicability observed is suggestive of a generic platform for reaction simplification and accelerated synthetic discovery that will enable broader applicability and accessibility to state-of-the-art ruthenium catalysis.

Generative Ensemble Deep Learning Severe Weather Prediction from a Deterministic Convection-Allowing Model

Abstract An ensemble postprocessing method is developed for the probabilistic prediction of severe weather (tornadoes, hail, and wind gusts) over the conterminous United States (CONUS). The method combines conditional generative adversarial networks (CGANs), a type of deep generative model, with a convolutional neural network (CNN) to postprocess convection-allowing model (CAM) forecasts. The CGANs are designed to create synthetic ensemble members from deterministic CAM forecasts, and their outputs are processed by the CNN to estimate the probability of severe weather. The method is tested using High-Resolution Rapid Refresh (HRRR) 1–24-h forecasts as inputs and Storm Prediction Center (SPC) severe weather reports as targets. The method produced skillful predictions with up to 20% Brier skill score (BSS) increases compared to other neural-network-based reference methods using a testing dataset of HRRR forecasts in 2021. For the evaluation of uncertainty quantification, the method is overconfident but produces meaningful ensemble spreads that can distinguish good and bad forecasts. The quality of CGAN outputs is also evaluated. Results show that the CGAN outputs behave similarly to a numerical ensemble; they preserved the intervariable correlations and the contribution of influential predictors as in the original HRRR forecasts. This work provides a novel approach to postprocess CAM output using neural networks that can be applied to severe weather prediction. Significance Statement We use a new machine learning (ML) technique to generate probabilistic forecasts of convective weather hazards, such as tornadoes and hailstorms, with the output from high-resolution numerical weather model forecasts. The new ML system generates an ensemble of synthetic forecast fields from a single forecast, which are then used to train ML models for convective hazard prediction. Using this ML-generated ensemble for training leads to improvements of 10%–20% in severe weather forecast skills compared to using other ML algorithms that use only output from the single forecast. This work is unique in that it explores the use of ML methods for producing synthetic forecasts of convective storm events and using these to train ML systems for high-impact convective weather prediction.

Seismic noise attenuation method based on low‐rank adaptive symplectic geometry decomposition

AbstractThe basic assumption of low‐rank methods is that noise‐free seismic data can be represented as a low‐rank matrix. Effective noise reduction can be achieved through the low‐rank approximation of Hankel matrices composed of the data. However, selecting the appropriate rank parameter and avoiding expensive singular value decomposition are two challenges that have limited the practical application of this method. In this paper, we first propose symplectic geometric decomposition that avoids singular value decomposition. The symplectic similarity transformation preserves the essence of the original time sequence as well as the signal's basic characteristics and maintains the approximation of the Hankel matrix. To select an appropriate rank, we construct the symplectic geometric entropy according to the distribution of eigenvalues and search for high‐contributing eigenvalues to determine the needed rank parameter. Therefore, we provide an adaptive approach to selecting the rank parameter by the symplectic geometric entropy method. The synthetic examples and field data results show that our method significantly improves the computational efficiency while adaptively retaining more effective signals in complex structures. Therefore, this method has practical application value.

Model-based evaluation of cloud geometry and droplet size retrievals from two-dimensional polarized measurements of specMACS

Abstract. Cloud radiative properties play a significant role in radiation and energy budgets and are influenced by both the cloud top height and the particle size distribution. Both cloud top heights and particle size distributions can be derived from 2-D intensity and polarization measurements by the airborne spectrometer of the Munich Aerosol Cloud Scanner (specMACS). The cloud top heights are determined using a stereographic method (Kölling et al., 2019), and the particle size distributions are derived in terms of the cloud effective radius and the effective variance from multidirectional polarized measurements of the cloudbow (Pörtge et al., 2023). In this study, the accuracy of the two methods is evaluated using realistic 3-D radiative transfer simulations of specMACS measurements of a synthetic field of shallow cumulus clouds, and possible error sources are determined. The simulations are performed with the 3-D Monte Carlo radiative transport model MYSTIC (Mayer, 2009) using cloud data from highly resolved large-eddy simulations (LESs). Both retrieval methods are applied to the simulated data and compared to the respective properties of the underlying cloud field from the LESs. Moreover, the influence of the cloud development on both methods is evaluated by applying the algorithms to idealized simulated data where the clouds did not change during the simulated overflight of 1 min over the cloud field. For the cloud top height retrieval, an absolute mean difference of less than 70 m with a standard deviation of about 130 m compared to the expected heights from the model is found. The elimination of the cloud development as a possible error source results in mean differences of (46±140) m. For the effective radius, an absolute average difference of about (-0.2±1.30) µm from the expected effective radius from the LES model input is derived for the realistic simulation and (-0.03±1.28) µm for the simulation without cloud development. The difference between the effective variance derived from the cloudbow retrieval and the expected effective variance is (0.02±0.05) for both simulations. Additional studies concerning the correlations between larger errors in the effective radius or variance and the optical thickness of the observed clouds have revealed that low values in the optical thickness do not have an impact on the accuracy of the retrieval.

Tomofast-x 2.0: an open-source parallel code for inversion of potential field data with topography using wavelet compression

Abstract. We present a major release of the Tomofast-x open-source gravity and magnetic inversion code that incorporates several functionalities enhancing its performance and applicability for both industrial and academic studies. The code has been re-designed with a focus on real-world mineral exploration scenarios, while offering flexibility for applications at regional scale or for crustal studies. This new version includes several major improvements: magnetisation vector inversion, inversion of multi-component magnetic data, wavelet compression, improved handling of topography with support for non-uniform grids, a new and efficient parallelisation scheme, a flexible parameter file, and optimised input–output operations. Extensive testing has been conducted on a large synthetic dataset and field data from a prospective area of the Eastern Goldfields (Western Australia) to explore new functionalities with a focus on inversion for magnetisation vectors and magnetic susceptibility, respectively. Results demonstrate the effectiveness of Tomofast-x 2.0 in real-world studies in terms of both the recovery of subsurface features and performances on shared and distributed memory machines. Overall, with its updated features, improved capabilities, and performances, the new version of Tomofast-x provides a free open-source, validated advanced and versatile tool for constrained gravity and magnetic inversion.

Distributed wide-field electromagnetic method for coal mining goaf detection in a complex urban environment: A case study in Jinan, China

Urban expansion has resulted in many proposed projects located over coal mining zones, which has highlighted the importance of detecting the spatial scope and water abundance of goafs before the commencement of construction work. Although electromagnetic (EM) methods have proven effective for goaf detection, their applications in intensely noisy and urbanized environments remain limited. To address this challenge, we evaluate an investigation of a coal mining goaf in Jinan, China, using the distributed wide-field EM (DWFEM) method. A third-order [Formula: see text] sequence pseudorandom signal with 39 survey frequencies is transmitted to achieve long-time data acquisition at each station. Unlike the controlled-source audio-frequency magnetotellurics method, the DWFEM records only the electric field [Formula: see text] component. The synthetic model tests and field data demonstrate the consistency of the [Formula: see text] apparent resistivity and the Cagniard resistivity in the far field. The long-time acquisition and 1C recording greatly improve data quality and exploration efficiency. We also use an all-angle resistivity calculation formula and an electrode layout method parallel to the wire source to obtain electrical connections in different directions. The DWFEM inversion results are obtained using the 1D Gauss-Newton iterative method under a plane-wave assumption. By interpolating the data from different measurement stations, we image resistivity depth profiles and obtain 3D subsurface electrical data for the subsurface from 0 to 1000 m. We interpret the obtained profiles with geologic and mining information, revealing two significant water-enriched goaf areas. Validation is performed using seismic data and drill cores. The results significantly enhance our understanding of the characteristics of the coal mine under our project and highlight the applicability of the DWFEM for detecting goafs in complex urban environments.

Deblending and interpolation of subsampled blended seismic data based on damped randomized singular spectrum analysis

AbstractWhen compared to traditional seismic data acquisition, irregular blended acquisition significantly promotes the acquisition efficiency. Yet, the blending noise of subsampled blended data introduces new obstacles for the subsequent processing of seismic data. Due to the predictability of linear events in the frequency–space domain, the constructed Hankel matrices exhibit low‐rank characteristics. However, the blending noise of subsampled blended data increases the rank, so deblending and interpolation can be implemented via rank‐reduction algorithms such as the singular spectrum analysis. The significant computing cost of the singular value decomposition, however, makes the traditional singular spectrum analysis inefficient. An alternative algorithm, known as the randomized singular spectrum analysis, employs the randomized singular value decomposition instead of the traditional singular value decomposition for rank‐reduction. The randomized singular spectrum analysis significantly enhances the efficiency of the decomposition process, particularly when dealing with large Hankel matrices. There still remains some random noise when using the singular spectrum analysis or randomized singular spectrum analysis for subsampled blended data, because the noise subspace and signal subspace are coupled together. Thus, we incorporate a damping operator into the randomized singular value decomposition and propose a novel damped randomized singular spectrum analysis method. The damped randomized singular spectrum analysis combines the advantages of the randomized singular value decomposition and the damping operator to enhance the computational efficiency and suppress the remaining noise. Moreover, an iterative projected gradient descent strategy is introduced to achieve deblended and interpolated seismic data for subsequent processing. Examples from synthetic data and field data are used to demonstrate the effectiveness and superiority of the proposed damped randomized singular spectrum analysis method, which enhances the accuracy and efficiency during simultaneous deblending and interpolation.

Magnetic signatures of lunar impact craters

The Moon almost certainly had an internally generated magnetic field in its past, but the duration of the dynamo, its temporal stability, and the surface field intensity are poorly known. Impact cratering events heat portions of the crust above the Curie temperature, allowing the ambient magnetic field strength to be recorded as the crater cools. We systematically analyzed the magnetic signatures of lunar impact craters with diameters greater than 90 km using recent magnetic field models and crater databases. Craters were classified as having evidence for impact-related central magnetization or demagnetization, and synthetic magnetic field models were used to estimate the number of incorrect identifications. In total, about 15% of craters were found to have impact-related magnetized or demagnetized signatures. The proportion of pre-Nectarian and Nectarian aged craters in the magnetized class is about 2%–3%, and there is little evidence for magnetized craters in the Imbrian and younger periods. The percentage of craters in the demagnetized class is about 0.3%–3% in the pre-Nectarian and Nectarian periods and abruptly increases to 16% in the Imbrian period. Our observations are consistent with the presence of strong dynamo fields during portions of the older pre-Nectarian and Nectarian periods, with a weakening (or cessation) of the dynamo at the beginning of the younger Imbrian period. These results differ from paleomagnetic analyses of lunar samples that imply the existence of strong dynamo field strengths from the pre-Nectarian up until at least the first half of the Imbrian period.

A Wavelet Extraction Method of Attenuation Media for Direct Acoustic Impedance Inversion in Depth Domain

The seismic image produced by pre-stack depth migration is more accurate and has clearer geological significance than the time image. However, the waveform of the depth-domain seismic image is affected not only by depth-dependent velocity variation but also by media attenuation, resulting in strong spectral variation of depth-domain seismic data. Therefore, depth-domain seismic inversion is still challenging. We propose a wavelet extraction method of attenuation media based on the generalized seismic wavelet, to address this issue. Then, the estimated depth-domain wavelets were applied to the direct acoustic impedance inversion. First, we investigated the effect of attenuation media on depth-domain source wavelets and derived an analytical formula for the depth-domain wavelets of attenuation media. Next, the time-domain generalized seismic wavelet was extended to the depth domain, which was utilized to study the feasibility of using the generalized seismic wavelet to characterize the seismic wavelet of the depth-domain attenuation media. Based on the orthogonal matching pursuit, we propose a method to extract the depth-domain generalized seismic wavelet directly from depth-domain seismic data. Finally, we applied this method to the depth-domain direct acoustic impedance inversion of a 3D field data example. Tests on the synthetic and 3D field datasets show that the proposed method can correctly extract the depth-domain seismic wavelet of attenuation media and attain direct inversion of the depth-domain acoustic impedance with high accuracy. Therefore, our method is effective and has robust potential in reservoir characterization, fluid prediction, and attribute extraction in the depth domain.

Tunnel resistivity deep learning inversion method based on physics‐driven and signal interpretability

AbstractData‐driven deep learning technology has a strong non‐linear mapping ability and has good development potential in geophysical inversion problems. Traditional inversion techniques offer broad generality, but they can remain trapped in local minima, particularly for three‐dimensional tunnelling resistivity inversion. In this work, we present an inversion methodology that combines traditional physics‐driven and deep learning data‐driven inversion approaches. To further support deep neural networks' dependability on unseen data, the interpretability of their working mechanism is explored. We execute migration learning based on small sample data after identifying the critical parameters that restrict the effectiveness of inversion by analysing the feature maps of various model data. We demonstrate, using both synthetic examples and field data, that the proposed method can improve the accuracy in detecting water‐bearing anomalies (caves and faults), which are typically encountered during tunnel excavation.

Environmental risks of breakdown nanoplastics from synthetic football fields

The widespread use of synthetic turf in sports has raised health concerns due to potential risks from nanoplastic inhalation or ingestion. Our research focused on detecting nanoplastics in drainage water from a synthetic football field and evaluating the toxicity of these materials after mechanical fragmentation. We collected and analysed drainage water samples for polymer content and subjected high-density polyethylene (HDPE) straws and ethylene propylene diene monomer (EPDM) granules used on synthetic football fields, to mechanical breakdown to create nanoplastics. The results indicated the presence of trace amounts of EPDM in the water samples. Furthermore, the toxicological assessment revealed that the broken-down nanoplastics and leachate from the surface of EPDM rubber granules exhibited high toxicity to Daphnia magna, while nanoplastics from the inner material exhibited no significant toxicity. The findings highlight the urgent need for future research to identify these specific toxic agents from the surface of EPDM granules.

Magnetic Field Analysis and Development of Disk Axial–Radial Hybrid Excitation Generator for Range Extenders in Extended-Range Electric Vehicles

Magnetic Field Analysis and Development of Disk Axial–Radial Hybrid Excitation Generator for Range Extenders in Extended-Range Electric Vehicles

Probing the Anisotropy and Non-Gaussianity in the Redshift Space through the Conditional Moments of the First Derivative

Focusing on the redshift space observations with plane-parallel approximation and relying on the rotational dependency of the general definition of excursion sets, we introduce the so-called conditional moments of the first derivative (cmd) measures for the smoothed matter density field in three dimensions. We derive the perturbative expansion of cmd for the real space and redshift space where peculiar velocity disturbs the galaxies’ observed locations. Our criteria can successfully recognize the contribution of linear Kaiser and Finger-of-God effects. Our results demonstrate that the cmd measure has significant sensitivity for pristine constraining the redshift space distortion parameter β = f/b and interestingly, the associated normalized quantity in the Gaussian linear Kaiser limit has only β dependency. Implementation of the synthetic anisotropic Gaussian field approves the consistency between the theoretical and numerical results. Including the first-order contribution of non-Gaussianity perturbatively in the cmd criterion implies that the N-body simulations for the Quijote suite in the redshift space have been mildly skewed with a higher value for the threshold greater than zero. The non-Gaussianity for the perpendicular direction to the line of sight in the redshift space for smoothing scales R ≳ 20 Mpc h −1 is almost the same as in the real space. In contrast, the non-Gaussianity along the line-of-sight direction in the redshift space is magnified. The Fisher forecasts indicate a significant enhancement in constraining the cosmological parameters Ω m , σ 8, and n s when using cmd + cr jointly.

An inversion method for imaging near-wellbore thin beds slowness based on array acoustic logging data

As a practical approach to reflecting the properties of the formation, the slowness of near-wellbore formation is of great significance to geophysical exploration, which can be used to evaluate rock brittleness, wellbore stability, fracturing effect, and invasion depth. Although traditional slowness imaging methods perform well in areas where the thickness of the heterogeneous formation is greater than the length of the receiver array of the logging instrument, they may fail when encountering thin beds. The thin beds’ axial thickness, radial invasion depth, and radial slowness are challenging to identify, resulting in obtaining an average slowness value without longitudinal resolution. This paper proposes a thin beds slowness imaging inversion method that can effectively invert the axial thickness, radial invasion thicknesses, and radial slowness variations of thin beds with higher axial resolution compared to traditional methods. The new method adaptively extracts slowness sequences with different radial depths by combining receivers with different source distances. It obtains their corresponding radial thicknesses through ray theory, which does not depend on the arrival times of the first wave. This method is sensitive to thin beds, and the axis thickness of thin beds can be estimated by the change of radial slowness sequence and the combined source distance length. Combining the results at different depths allows a slowness image of the thin beds near-wellbore to be directly obtained. The effectiveness and accuracy of the proposed method are verified by synthetic data and field data.