Prior Estimates Research Articles

Fluorescence microscopic image enhancement method based on multi-saliency guided filtering fusion

Abstract Aiming at addressing the issues of colour deviation and clarity reduction in digital pathological imaging systems caused by complex environmental interferences such as uneven slice thickness, sample jitter, and background noise, this paper proposes a multi-saliency-guided filtering fusion enhancement method that combines energy saliency and Gaussian saliency. Firstly, a series of Gamma correction and spatial linear saturation adjustments are applied to enhance the contrast and colour saturation of blurred images, resulting in a sequence of pseudo-exposure images with improved contrast and saturation. Then, the pseudo-exposure images are decomposed into base and detail layers, and the fusion rules of energy saliency and Gaussian saliency-guided filtering are applied to each layer separately. Finally, clear microscopic images are obtained through two-scale reconstruction. This method overcomes the limitations of conventional deblurring algorithms that rely on prior estimation of scene depth and complex depth mapping processes. Experimental results on a dataset of cytological bright-field microscopic imaging samples show that the clarity evaluation metrics, including peak signal-to-noise ratio, root mean square error, and structural similarity index, have significantly improved, indicating that the proposed method can effectively improve the colour fidelity of the samples while eliminating noise interference and enhancing the clarity of cellular texture structures.

Learning face super-resolution through identity features and distilling facial prior knowledge

Deep learning techniques in electronic surveillance have shown impressive performance for super-resolution (SR) of captured low-quality face images. Most of these techniques adopt facial priors to improve the feature details in the resultant super-resolved images. However, the estimation of facial priors from the captured low-quality images is often inaccurate in real-life situations because of their tiny, noisy, and blurry nature. Thus, the fusion of such priors badly affects the performance of these models. Therefore, this work presents a teacher–student-based face SR framework that efficiently preserves the personal facial structure information in the super-resolved faces. In the proposed framework, the teacher network exploits the facial heatmap-based ground-truth-prior to learn the facial structure that is utilized by the student network. The student network is trained with the identity feature loss for maintaining the identity and facial structure information in reconstructed high-resolution (HR) face images. The performance of the proposed framework is evaluated by conducting the experimental study on standard datasets namely CelebA-HQ and LFW face. The experimental results reveal that the proposed technique conquers the existing methods for the face SR task.

Small Area Estimation using Multilevel Regression and Poststratification to Estimate Cannabis Use in the State of Montana

Background: Small area substance use prevalence estimates at the county, city, or congressional district level are generally unavailable. In this study, we design a cannabis use survey for the state of Montana and use multilevel regression and poststratification (MRP) to generate county-level population prevalence estimates for past year cannabis use. Methods: We developed a survey that asks questions about cannabis perceptions and use patterns. We analyzed the survey data specifically for the outcome variable of past year cannabis use using MRP to generate population level prevalence estimates at the county level for the state of Montana. Results: We received 1,958 responses from our survey. We generated county level estimates by age group for cannabis use over the past year and found that MRP estimates were consistent with prior estimations of cannabis use at the state level and provided the ability to use additional data and validated assumptions to refine and downscale estimations of cannabis use, particularly in counties with low response rates. Conclusion: Multi-modal survey dissemination was cost effective, but future surveys should intend to recruit a larger and more representative sample to minimize selection bias and improve estimation for demographic sub-groups. Overall, MRP provided a promising methodology for generating small-area cannabis use prevalence estimates, adjusting as much as possible for non-representativeness and non-response.

Prediction of Redox Potentials for Ac, Th, and Pa in Aqueous Solution.

Density functional theory in conjunction with small core pseudopotentials and the associated basis sets was used to calculate potentials for multiple redox couples, covering a range of oxidation states for Ac (0 to III), Th (0 to IV), and Pa (0 to V) in aqueous solution. Solvation effects were incorporated using a supermolecule-continuum approach, with 30 water molecules representing two solvation shells, and the COSMO and SMD implicit solvation models. The calculated geometries for Ac(III), Th(IV), and Pa(V) were in reasonable agreement with the available experimental data. Using the COSMO model with the B3LYP functional, the calculated redox potentials were within ±0.2 V from experiment for most redox couples. Several pathways were explored for the Pa(V/IV) redox couple for different forms of Pa(V) and Pa(IV). Most Pa(V/IV) redox couples have very similar potentials, ranging from 0 to -0.4 V up to a pH of 1.4. At pH = 1.4, the potentials shift to values that are more negative than -0.7 V, reflecting the growing unfavorable nature of the redox process at higher pH levels. The calculated values for An(III/II) potentials were consistent with prior estimates and the available experimental data. The predicted redox potentials for An(II/I) were highly negative, as expected. For An(I/0) potentials, Th and Pa exhibited positive values, contrasting with the negative values calculated for Ac. The An+m/An(0) potentials agreed better with the experimental data when using the COSMO solvation model as compared to the SMD model.

The Diffusion Tensor of Protons at 1 au: Comparing Simulation, Observation, and Theory

The natural variation in plasma parameters observed at 1 au can lead to a variation in transport parameters, such as diffusion and drift coefficients, for energetic charged particles of solar and galactic origin. Given the importance of these parameters to particle transport studies, this variation is investigated through test particle simulations over a range of energies in the presence of simulated turbulence with properties corresponding to an ensemble of observed turbulence conditions at Earth. The resulting transport coefficients are then compared with observational estimates from the literature, as well as the predictions of several scattering theories. Parallel and perpendicular mean free paths are shown to vary widely, for the former in agreement with prior observational estimates, but not for the latter. Furthermore, a large disparity between the predictions of theory and the simulation results is noted for the perpendicular mean free path. As such, these results indicate that particle transport studies, particularly predictive ones, need to take into account this natural variation in transport coefficients.

Real-Time LiDAR–Inertial Simultaneous Localization and Mesh Reconstruction

In this paper, a novel LiDAR–inertial-based Simultaneous Localization and Mesh Reconstruction (LI-SLAMesh) system is proposed, which can achieve fast and robust pose tracking and online mesh reconstruction in an outdoor environment. The LI-SLAMesh system consists of two components, including LiDAR–inertial odometry and a Truncated Signed Distance Field (TSDF) free online reconstruction module. Firstly, to reduce the odometry drift errors we use scan-to-map matching, and inter-frame inertial information is used to generate prior relative pose estimation for later LiDAR-dominated optimization. Then, based on the motivation that the unevenly distributed residual terms tend to degrade the nonlinear optimizer, a novel residual density-driven Gauss–Newton method is proposed to obtain the optimal pose estimation. Secondly, to achieve fast and accurate 3D reconstruction, compared with TSDF-based mapping mechanism, a more compact map representation is proposed, which only maintains the occupied voxels and computes the vertices’ SDF values of each occupied voxels using an iterative Implicit Moving Least Squares (IMLS) algorithm. Then, marching cube is performed on the voxels and a dense mesh map is generated online. Extensive experiments are conducted on public datasets. The experimental results demonstrate that our method can achieve significant localization and online reconstruction performance improvements. The source code will be made public for the benefit of the robotic community.

Enhancing sensitivity in fluorescence measurements across an ultra-wide concentration range through optimizing combined-segments strategy

Fluorescence measurement technology is crucial in analytical instrumentation. However, when applied in fields such as environmental science and medical research for quantification purposes, it requires prior estimation of sample concentration and sample dilution to ensure measurement precision. This requirement presents challenge for its broader application in online fluorescence sensing. This study addresses the challenge of achieving high-precision measurements across an ultra-wide concentration range, which is critical for applications ranging from environmental monitoring to clinical diagnostics. The binary segmentation method can initially achieve the goal of ultra-wide range fluorescence measurement, but it fails to maintain sensitivity and precision across the entire range, with relative errors exceeding ±15%. Therefore, we propose an optimizing combined-segments strategy, which examines the effects of adjusting the fluorescence reception position, range, and region interval parameters of the fluorescence reception probes on measurement sensitivity limits. This strategy aims to find the optimal measurement sensitivity limit (lmopt) and the best combined-segments configuration based on more than one quantitative relationship curves. Experimental results demonstrate that within a concentration range 20 times broader than the linear range, it can effectively maintain relative errors within ±5%. This enhances the applicability of fluorescence sensing techniques in various practical settings and advances online and direct fluorescence measurement technologies across an ultra-wide concentration range.

Stochastic representation for solutions of a system of coupled HJB-Isaacs equations with integral–differential operators

In this paper, we focus on the stochastic representation of a system of coupled Hamilton–Jacobi–Bellman–Isaacs (HJB–Isaacs (HJBI), for short) equations which is in fact a system of coupled Isaacs’ type integral-partial differential equation. For this, we introduce an associated zero-sum stochastic differential game, where the state process is described by a classical stochastic differential equation (SDE, for short) with jumps, and the cost functional of recursive type is defined by a new type of backward stochastic differential equation (BSDE, for short) with two Poisson random measures, whose wellposedness and a prior estimate as well as the comparison theorem are investigated for the first time. One of the Poisson random measures μ appearing in the SDE and the BSDE stems from the integral term of the HJBI equations; the other random measure in BSDE is introduced to link the coupling factor of the HJBI equations. We show through an extension of the dynamic programming principle that the lower value function of this game problem is the viscosity solution of the system of our coupled HJBI equations. The uniqueness of the viscosity solution is also obtained in a space of continuous functions satisfying certain growth condition. In addition, also the upper value function of the game is shown to be the solution of the associated system of coupled Isaacs’ type of integral-partial differential equations. As a byproduct, we obtain the existence of the value for the game problem under the well-known Isaacs’ condition.

Porous material characterization with Bayesian and machine learning method

Two main classes of characterization methods are usually employed for acoustic parameters of porous media, namely the analytical inversion method or the numerical fitting procedure. While analytical inverse methods suffer from a high sensivity to the operator, the numerical fitting methods can lead to non physical sets of parameters. The fitting procedure can be bounded by the analytical inversion. The Bayesian method, which is presented in this work, is the appropriate tool to couple these methods. It enables to carry out a numerical fitting procedure using the analytical inversion as prior estimation while increasing the confidence in the resulting uncertainties. In a second part, machine learning method and data mining are used as an alternative characterization method which could be a good prior estimation for the Bayesian method. Results and performances will be shown for various porous media.

China’s methane emissions derived from the inversion of GOSAT observations with a CMAQ and EnKS-based regional data assimilation system

At present, inversions at higher temporal and spatial resolution tend to be in increasing demand. The resolution and precision of methane (CH4) inversions depend on quality of observations, transport model, and inversion scheme. Currently, most inversion-based estimates of CH4 in China use a global atmospheric transport model to relate emissions to observations, or a Lagrangian model to quantify the receptor-to-source sensitivity. Taking advantage of the ability that regional transport models have in mesoscale simulation, a regional inversion system was developed to infer China’s CH4 emissions. The CMAQ (Community Multi-scale Air Quality) model was configured for forward simulation of CH4, including processes of emission, transport, diffusion, and chemical transformation. Furthermore, the Ensemble Kalman Smoother was extended to assimilate satellite observations with joint optimization scheme of concentrations and emissions to reduce the impact of initial uncertainty. We found that the posterior annual estimated emissions (53.30 Tg a−1) in 2020 were closer to the official reported figure (53.57 Tg a−1) than to the prior (63.80 Tg a−1), with a downward correction of 16.45% in prior estimates based on extrapolation of the bottom-up inventory, which likely led to overestimation. Moreover, under current observational coverage, monthly posterior estimates reflected region-dependent responses to local conditions. Generally, the regional assimilation system estimated annual and monthly CH4 emissions well, attributable to reliable CMAQ simulation, joint assimilation scheme, and careful selection of satellite retrievals. In addition, evaluation of the posterior estimates indicates that inversion delivers reasonable improvements, but amelioration of uncertainties in prior information and observations is still needed.

Impact on Prognosis of Stage I Non-Small Cell Lung Cancer Secondary to Delays in Diagnostic Workup.

Background Diagnostic workup of small pulmonary nodules often requires follow-up CT scans to confirm nodule growth before invasive diagnostics or treatment. Purpose To confirm prior results from the International Early Lung Cancer Action Program (I-ELCAP) on quantifying decreases in lung cancer prognosis by using two large databases, the National Lung Screening Trial (NLST) and International Association for the Study of Lung Cancer (IASLC). Materials and Methods In this retrospective study, a model was developed to predict cure rates based on size of solid nodules using the NLST (August 2002 to summer 2007) and IASLC (January 2011 to December 2019) databases, focusing on stage I non-small cell lung cancer (NSCLC). Kaplan-Meier methods were used to calculate 10-year lung cancer-specific survival and 5-year overall survival rates for different tumor sizes. Tumor diameter increases after 90-, 180-, and 365-day delays were estimated using volume doubling times (VDTs) of 60, 120, and 240 days corresponding to fast, moderate, and slow tumor growth. Initial and delayed lung cancer cure rates were assessed across nine scenarios of time delays and tumor growth rates and compared with the previous results of the I-ELCAP database. Results Using regression models based on 166 NLST and 22 590 IASLC patients with NSCLC, 10-year lung cancer-specific survival and 5-year overall survival, respectively, for tumors 4.0-20.0 mm in diameter were estimated. For a 20.0-mm tumor with a 60-day VDT in the NLST database, the lung cancer-specific survival decreased from 83.4% to 76.5%, 66.8%, and 32.3% after 90, 180, and 365 days, respectively. The IASLC database showed similar decreases in 5-year overall survival, from 81.2% to 73.4%, 62.4%, and 23.3% after 90, 180, and 365 days, respectively. Comparison across NLST, IASLC, and I-ELCAP databases revealed minor variations in lung cancer cure rates between 79.9% and 83.4%, with reductions of 6.9%-8.3% after a 180-day delay with a 120-day VDT. Conclusion The NLST and IASLC databases confirmed prior estimates from the I-ELCAP database for the decrease in lung cancer prognosis due to diagnostic delays. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Park and Lee in this issue.

Incidence of Clinically Diagnosed Psoriatic Arthritis in Sweden.

Prior incidence estimates of psoriatic arthritis (PsA) vary considerably. We aimed to assess the annual incidence of clinically diagnosed PsA among adults in Sweden in 2014-2016, overall and stratified by age/sex/education/geography, and to investigate potential time trends in incidence in 2006-2018. Use of disease-modifying antirheumatic drugs (DMARDs) during the 2 years after diagnosis was also examined. Patients (aged ≥ 18 years) with incident clinically diagnosed PsA in Sweden were identified from the National Patient Register (NPR) and/or the Swedish Rheumatology Quality Register (SRQ). Population statistics, stratification variables, and DMARD information were retrieved from other nationwide registers. Incidence was estimated according to a base case (BC) definition (ie, ≥ 1 main International Classification of Diseases, 10th revision, diagnosis of PsA [L40.5/M07.0-M07.3] from rheumatology/internal medicine in NPR, or a PsA diagnosis in SRQ during the relevant year, and no prior such diagnoses) and 4 different sensitivity analysis case definitions. The mean annual incidence of clinically diagnosed PsA among adults in Sweden in 2014-2016 was estimated at 21.77 per 100,000 person-years (PYs) at risk, according to the BC definition; 17.41 per 100,000 PYs at risk after accounting for diagnostic misclassification; and 15.78 to 28.83 per 100,000 PYs at risk across all sensitivity analyses. Incidence was slightly higher in female individuals, was lower in those with higher education (aged > 12 years), and peaked during the ages of 50 to 59 years. No apparent increasing or decreasing time trend was observed in 2006-2018. Within 2 years of diagnosis, 71.03% of patients had received DMARD therapy (22.37% biologic or targeted synthetic DMARDs). From 2014 to 2016, the annual incidence of clinically diagnosed PsA in the adult Swedish population was approximately 20 per 100,000 PYs at risk. Two years after diagnosis, almost three-quarters of patients had received DMARD therapy.

Uncharted territory in linking population-based laboratory data for the epidemiology of celiac disease in Canada

IntroductionWe previously investigated the frequency of screening for celiac disease (CD) based on tissue transglutaminase antibody testing (tTG-IgA) and developed the first incident cohort of celiac autoimmunity in Canada. MethodsAdministrative data sources used in this study were population-based and covered the entire province of Alberta (~4.3M residents during study period). Various approaches to querying data were employed within a diverse team of health information managers, data analysts, clinical scientists, and gastroenterologists. This process involved a broad search for CD screening tests, thorough data inspection/cleaning, and numerous discussions to determine potential explanations for the findings. ResultsApproximately ~950,000 records for tTG-IgA were first identified. Records were then excluded due to missing/invalid patient identifiers or test results (0.8%), non-Alberta residency (0.4%), and duplicate records (1.1%). A final dataset included ~920,000 tTG-IgA tests on ~680,000 unique patients, which was also validated through a separate query performed by an analyst external to the study team. A conservative approach to excluding as many potential prevalent cases of CD was applied given a robust algorithm for CD has not yet been established in Canada. The final rate of celiac autoimmunity (34 per 100,000) offered further face validity based on prior estimates of diagnosed CD in Alberta and other countries reporting on celiac autoimmunity. ConclusionWhen developing a novel case definition or investigating unfamiliar outcomes using routinely collected data, collaboration across several disciplines is highly recommended. Certain stages of the project may require additional scrutiny and discussion to ensure findings are valid and reliable.

Global impact of unproductive splicing on human gene expression

Alternative splicing (AS) in human genes is widely viewed as a mechanism for enhancing proteomic diversity. AS can also impact gene expression levels without increasing protein diversity by producing ‘unproductive’ transcripts that are targeted for rapid degradation by nonsense-mediated decay (NMD). However, the relative importance of this regulatory mechanism remains underexplored. To better understand the impact of AS–NMD relative to other regulatory mechanisms, we analyzed population-scale genomic data across eight molecular assays, covering various stages from transcription to cytoplasmic decay. We report threefold more unproductive splicing compared with prior estimates using steady-state RNA. This unproductive splicing compounds across multi-intronic genes, resulting in 15% of transcript molecules from protein-coding genes being unproductive. Leveraging genetic variation across cell lines, we find that GWAS trait-associated loci explained by AS are as often associated with NMD-induced expression level differences as with differences in protein isoform usage. Our findings suggest that much of the impact of AS is mediated by NMD-induced changes in gene expression rather than diversification of the proteome.

Consistent and accurate TDOA localization in dynamic NLOS environments via prior information independent estimation

Consistent and accurate TDOA localization in dynamic NLOS environments via prior information independent estimation

Class-Imbalanced-Aware Distantly Supervised Named Entity Recognition.

Distantly supervised named entity recognition (NER), which automatically learns NER models without manually labeling data, has gained much attention recently. In distantly supervised NER, positive unlabeled (PU) learning methods have achieved notable success. However, existing PU learning-based NER methods are unable to automatically handle the class imbalance and further depend on the estimation of the unknown class prior; thus, the class imbalance and imperfect estimation of the class prior degenerate the NER performance. To address these issues, this article proposes a novel PU learning method for distantly supervised NER. The proposed method can automatically handle the class imbalance and does not need to engage in class prior estimation, which enables the proposed methods to achieve the state-of-the-art performance. Extensive experiments support our theoretical analysis and validate the superiority of our method.

Accessibility, neighborhood socioeconomic disadvantage and expenditures on electronic gambling machines: a spatial analysis based on player account data

BackgroundGambling and its harmful effects on human health and well-being represent a significant public health concern in many countries, with electronic gambling machines (EGMs) recognized as one of the most detrimental forms of gambling. Previous research has established an association between EGM accessibility, expenditure, gambling harm, and the socioeconomic status (SES) of neighborhoods. However, there is limited understanding of the direct impact of SES and EGM accessibility on individual player expenditures. Prior estimations of expenditure often rely on self-reported data or venue-level revenue statistics. This study uses high spatial resolution socioeconomic data together with individual-level account-based location and expenditure (point of sales) data (71,669 players, 745 EGM venues) to explore the association between EGM accessibility and neighborhood SES and to examine whether the EGM expenditure of neighborhood residents is associated with EGM accessibility and neighborhood SES.Data and methodsPlayer account data include information on the home location and expenditure of the entire EGM gambling population across every EGM venue located in the Helsinki region, Finland. High-resolution (250 × 250 m) grid-level data on socioeconomic variables were used to obtain the local socioeconomic conditions of the players. EGM accessibility was estimated for every grid cell using a calibrated gravity model derived from the player account data. Statistical analyses included correlation analysis, spatial autocorrelation analysis, and regression models.ResultsFirst, significantly higher levels of EGM accessibility were found in areas with lower local SES. Second, regression analysis revealed that both higher EGM accessibility and lower local SES were associated with higher annual losses per adult. These results, in combination with visual and spatial autocorrelation analyses, revealed that accessibility to EGM gambling is highly concentrated, especially in lower socioeconomic neighborhoods with higher levels of EGM expenditure.ConclusionsThe results lay the groundwork for future spatial research on gambling harm, expenditure, accessibility, and SES utilizing detailed account data on the interaction between players and venues. The results underscore the importance of spatial restrictions when regulating EGM accessibility, particularly in areas with vulnerable populations, as a crucial measure for public health and harm prevention. The results also enable targeted gambling harm prevention actions at the local level.

Expert-based prior uncertainty analysis of gridded water balance components: Application to the irrigated Hindon River Basin, India

Study regionHindon River Basin, North India. Study focusAccurate estimation of water balance components is crucial for water management applications yet challenging due to errors in monthly gridded water balance data products. Error and uncertainty quantification is especially important in the absence of extensive in-situ data. This paper presents a prior uncertainty analysis for such situations consisting of two components: (i) quantification of prior uncertainties using metrics that quantify errors in individual products and variability and consistency between products, and (ii) reduction of prior uncertainties by eliminating unrealistic water balance estimates. New hydrological insights for the regionGrid-scale inter-product uncertainty or variability, computed as the coefficient of variation (CV, %) at various temporal scales, reveals discrepancies between water balance products due to a combination of factors, including methodological differences, inherent spatial variability, data sources, and resolution disparities. At the mean annual scale, P fluxes display a lower grid-scale inter-product uncertainty (5–9 %) than ET (20–55 %), while the ∆TWS from GRACE solutions show a moderate mean annual grid-scale inter-product uncertainty (15–19 %). Grid-scale inter-product uncertainties of ∆SMS for July – representing the onset of the monsoon season – are high (CV = 54–122 %), indicating that the uncertainty in estimates of this component may have a large impact on water balance analyses. P fluxes exhibited fewer spatio-temporal uncertainties (R2 above 0.8) than ET fluxes (R2 less than 0.75). The exclusion of the unreliable data sets resulted in (a) reducing uncertainties in input water balance components with triple collocation range shifting from 15–38 to 17–23 mm/month for ET and from 16–52 to 11–23 mm/month for P, (b) obtaining updated prior estimates of seasonal water balance. The updated priors of water balance variables per season suggest a net basin outflow (from −318 to −57 mm/season) during the monsoon (rainy) season and net basin inflow (from −38 to 330 mm/season) during the non-monsoon (dry) season, the latter related to surface-water imports from outside the basin. All GRACE data sets exhibit a regional long-term decreasing trend in total water storage (ranging from −31 to −61 mm/year), qualitatively confirming previously documented unsustainable groundwater depletion in the basin. Prior ranges and uncertainties for all water balance variables reported here can be used as input into a posterior analysis that uses in-situ data for locally calibrating (bias-correcting, noise-filtering) and further updating the prior estimates.

Rapid Biphasic Decay of Intact and Defective HIV DNA Reservoir During Acute Treated HIV Disease.

Despite antiretroviral therapy (ART), HIV persists in latently-infected cells ("the reservoir") which decay slowly over time. Here, leveraging >500 longitudinal samples from 67 people with HIV (PWH) treated during acute infection, we developed a novel mathematical model to predict reservoir decay from peripheral CD4+ T cells. Nonlinear generalized additive models demonstrated rapid biphasic decay of intact DNA (week 0-5: t1/2~2.83 weeks; week 5-24: t1/2~15.4 weeks) that extended out to 1 year. These estimates were ~5-fold faster than prior decay estimates among chronic treated PWH. Defective DNA had a similar biphasic pattern, but data were more variable. Predicted intact and defective decay rates were faster for PWH with earlier timing of ART initiation, higher initial CD4+ T cell count, and lower pre-ART viral load. These data add to our limited understanding of HIV reservoir decay at the time of ART initiation, informing future curative strategies targeting this critical time.

Porosity estimation based on the shear modulus inversion of seismic shear wave

Reliable estimation of subsurface porosity is necessary for hydrocarbon reservoir characterization and fluid identification. Porosity estimations from seismic data can provide the lateral distribution of subsurface porosity, but the results may be highly nonunique because subsurface elastic properties (such as velocity and density) can be affected by porosity and pore fluids. Because shear modulus is insensitive to pore fluid content, it can be effectively used to estimate porosity. We develop a novel porosity estimation method using the reflected SH wave (SH-SH wave), whose propagation characteristics depend mainly on shear modulus and S-wave velocity. We derive a new analytical expression for the SH-SH-wave reflection coefficient, which acts as a function of shear modulus and S-wave velocity in its natural logarithm form. This new approximation has high accuracy, and both coefficients corresponding to shear modulus and S-wave velocity are “model-parameter independent”; thus, there is no need for prior estimation of any model parameter during inversion. Numerical analysis indicates that shear modulus inverted from the SH-SH wave has lower uncertainty, the problem is better conditioned, and the method requires data with fewer incidence angles than algorithms that invert for the shear modulus from the PP wave. Furthermore, the highly correlated rock-physics relationships between porosity and shear modulus facilitate accurate porosity estimation. A field data application indicates that high-resolution porosity of fine structures can be reliably recovered using our method.