Inverse Approach Research Articles

Spheroid models to elaborate the broken symmetry and equivalent volume of molecules in crystalline phase.

Dense packing of particles has provided powerful models to elaborate the important structural features of matter in various systems such as liquid, glassy, and crystalline phases. The simplest sphere packing models can represent and capture salient properties of the building blocks for covalent, metallic, and ionic crystals; it, however, becomes insufficient to reflect the broken symmetry of the commonly anisotropic molecules in molecular crystals. Here, we develop spheroid models with a minimal degree of anisotropy, which serve as a simple geometrical representation for a rich spectrum of molecules-including both isotropic and anisotropic, convex and concave ones-in crystalline phases. Our models are determined via an inverse packing approach: Given a molecular crystal, an optimal spheroid model is constructed using a contact diagram, which depicts the packing relationship between neighboring molecules within the crystal. The spheroid models are capable of accurately capturing the broken symmetry and characterizing the equivalent volume of molecules in the crystalline phases. Moreover, our model retrieves such molecular information from low-quality x-ray diffraction data with poorly resolved structures, and by using soft spheroids, it can also describe the packing behavior in cocrystals.

Application of a Bayesian-Based Integrated Approach for Groundwater Contamination Sources Parameter Identification Considering Observation Error

Groundwater contamination source (GCS) parameter identification can help with controlling groundwater contamination. It is proverbial that groundwater contamination concentration observation errors have a significant impact on identification results, but few studies have adequately quantified the specific impact of the errors in contamination concentration observations on identification results. For this reason, this study developed a Bayesian-based integrated approach, which integrated Markov chain Monte Carlo (MCMC), relative entropy (RE), Multi-Layer Perceptron (MLP), and the surrogate model, to identify the unknown GCS parameters while quantifying the specific impact of the observation errors on identification results. Firstly, different contamination concentration observation error situations were set for subsequent research. Then, the Bayesian inversion approach based on MCMC was used for GCS parameter identification for different error situations. Finally, RE was applied to quantify the differences in the identification results of each GCS parameter under different error situations. Meanwhile, MLP was utilized to build a surrogate model to replace the original groundwater numerical simulation model in the GCS parameter identification processes of these error situations, which was to reduce the computational time and load. The developed approach was applied to two hypothetical numerical case studies involving homogeneous and heterogeneous cases. The results showed that RE could effectively quantify the differences caused by contamination concentration observation errors, and the changing trends of the RE values for GCS parameters were directly related to their sensitivity. The established MLP surrogate model could significantly reduce the computational load and time for GCS parameter identification. Overall, this study highlights that the developed approach represents a promising solution for GCS parameter identification considering observation errors.

Joint inversion of induced polarization and hydraulic tomography data for hydraulic conductivity imaging

SUMMARY For accurate modelling of groundwater flow and transport processes within an aquifer, precise knowledge about hydraulic conductivity K and its small-scale heterogeneities is fundamental. Methods based on pumping tests, such as hydraulic tomography (HT), allow for retrieving reliable K-estimates, but are limited in their ability to image structural features with high resolution, since the data from time-consuming hydraulic tests are commonly sparse. In contrast, geophysical methods like induced polarization (IP) can potentially yield structural images of much higher resolution, but depend on empirical petrophysical laws that may introduce significant uncertainties to the K-estimation. Therefore, this paper presents a joint inversion procedure for both HT and IP data, which allows for combining the complementary abilities of both methods. Within this approach, a traveltime inversion is applied to the HT data, while the IP inversion is based on a full-decay time-domain forward response, as well as a reparametrization of the Cole–Cole model to invert for K directly. The joint inversion is tested on a synthetic model mimicking horizontally layered sediments, and the results are compared with the individual HT and IP inversions. It is shown that jointly inverting both data sets consistently improves the results by combining the complementary sensitivities of the two methods, and that the inversion is more robust against changes in the experimental setups. Furthermore, we illustrate how a joint inversion approach can correct biases within the petrophysical laws by including reliable K-information from hydraulic tests and still preserving the high-resolution structural information from IP. The different inversion results are compared based on the structural similarity index (SSIM), which underlines the robustness of the joint inversion compared to using the data individually. Hence, the combined application of HT and IP within field surveys and a subsequent joint inversion of both data sets may improve our understanding of hydraulically relevant subsurface structures, and thus the reliability of groundwater modelling results.

Combining simulation and experimental data via surrogate modelling of continuum dislocation dynamics simulations

Several computational models have been introduced in recent years to yield comprehensive insights into microstructural evolution analyses. However, the identification of the correct input parameters to a simulation that corresponds to a certain experimental result is a major challenge on this length scale. To complement simulation results with experimental data (and vice versa) is not trivial since, e.g. simulation model parameters might lack a physical understanding or uncertainties in the experimental data are neglected. Computational costs are another challenge mesoscale models always have to face, so comprehensive parameter studies can be costly. In this paper, we introduce a surrogate model to circumvent continuum dislocation dynamics simulation by a data-driven linkage between well-defined input parameters and output data and vice versa. We present meaningful results for a forward surrogate formulation that predicts simulation output based on the input parameter space, as well as for the inverse approach that derives the input parameter space based on simulation as well as experimental output quantities. This enables, e.g. a direct derivation of the input parameter space of a continuum dislocation dynamics simulation based on experimentally provided stress–strain data.

Wave impedance inversion and interpretation of Ground Penetrating Radar (GPR) data for Amery Ice Shelf in East Antarctica

The polar ice sheet is vital for the global climate system, influencing the Antarctic ice sheet's mass balance, sea level rise, and Earth's surface energy. Accurate measurement of its thickness and internal structure is imperative for comprehending glacier evolution and climate change. Ground Penetrating Radar (GPR) is a high-resolution and non-invasive exploration instrument that is extensively used for glacier detection, but only limited geometric information can be identified in the GPR profile, making it difficult to capture the quantitative distribution of physical parameters. To image the fine dielectric parameters of GPR data acquired from the Amery Ice Shelf in East Antarctica in 2003, we propose a multi-trace GPR impedance inversion approach. This method utilizes the limited-memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS) algorithm for GPR data, coupled with weighted L2-norm total-variation multiplicative regularization (MR) to quantitatively estimate the dielectric parameters in the interior of the ice sheet. This scheme adaptively adapts regularization parameters, enhances vertical resolution, and suppresses noise. Additionally, considering lateral correlation, we integrate directional differences to enhance lateral continuity. After validating with complex test data, we apply the method to Amery Ice Shelf GPR data, quantitatively imaging the ice sheet's geometric and dielectric parameters. The inversion results reveal ice thickness, internal features, and the interface between freshwater ice and refrozen sea ice. This allowed us to determine the ablation and refreezing zone boundary at the bottom of ice shelf, providing insights into melting/freezing mechanisms, ice shelf stability, and simulating ice shelf interior dynamics.

Free-standing microscale photonic lantern spatial mode (De-)multiplexer fabricated using 3D nanoprinting

Photonic lantern (PL) spatial multiplexers show great promise for a range of applications, such as future high-capacity mode division multiplexing (MDM) optical communication networks and free-space optical communication. They enable efficient conversion between multiple single-mode (SM) sources and a multimode (MM) waveguide of the same dimension. PL multiplexers operate by facilitating adiabatic transitions between the SM arrayed space and the single MM space. However, current fabrication methods are forcing the size of these devices to multi-millimeters, making integration with micro-scale photonic systems quite challenging. The advent of 3D micro and nano printing techniques enables the fabrication of freestanding photonic structures with a high refractive index contrast (photopolymer-air). In this work we present the design, fabrication, and characterization of a 6-mode mixing, 375 µm long PL that enables the conversion between six single-mode inputs and a single six-mode waveguide. The PL was designed using a genetic algorithm based inverse design approach and fabricated directly on a 7-core fiber using a commercial two-photon polymerization-based 3D printer and a photopolymer. Although the waveguides exhibit high index contrast, low insertion loss (−2.6 dB), polarization dependent (−0.2 dB) and mode dependent loss (−4.4 dB) were measured.

Reinforcement-Learning-Based Visual Servoing of Underwater Vehicle Dual-Manipulator System

As a substitute for human arms, underwater vehicle dual-manipulator systems (UVDMSs) have attracted the interest of global researchers. Visual servoing is an important tool for the positioning and tracking control of UVDMSs. In this paper, a reinforcement-learning-based adaptive control strategy for the UVDMS visual servo, considering the model uncertainties, is proposed. Initially, the kinematic control is designed by developing a hybrid visual servo approach using the information from multi-cameras. The command velocity of the whole system is produced through a task priority method. Then, the reinforcement-learning-based velocity tracking control is developed with a dynamic inversion approach. The hybrid visual servoing uses sensors equipped with UVDMSs while requiring fewer image features. Model uncertainties of the coupled nonlinear system are compensated by the actor–critic neural network for better control performances. Moreover, the stability analysis using the Lyapunov theory proves that the system error is ultimately uniformly bounded (UUB). At last, the simulation shows that the proposed control strategy performs well in the task of dynamical positioning.

Estimation of sound absorption coefficient at modal frequencies using decay time measurements and eigenvalue model in a small room

Measuring the sound absorption coefficient in a small reverberation room below the Schroeder frequency is a challenging task and the results are subject to a high degree of uncertainty. Recent research shows that the low-frequency acoustic properties of the enclosure and the test sample should be characterized by quantities that describe the modal sound field. The aim of this paper is to present a method for estimating the absorption coefficient of a test sample at modal frequencies in the low frequency range and to demonstrate its applicability in a small room. The hybrid inverse approach is based on eigenvalue measurement data coupled with a finite element model to estimate the sound-absorbing properties of a small room and the test sample. The method was tested experimentally and the results were consistent with those of the long impedance tube method. The applicability of the proposed method was further demonstrated by the in-situ characterization of different acoustic samples in the low frequency range.

Quadruplet therapy for transplant-eligible newly diagnosed multiple myeloma (TENDMM): A systematic review and meta-analysis.

e19534 Background: Immunomodulatory drugs, proteasome inhibitors plus dexamethasone are the standard triplet induction regimen for TENDMM. We analyzed the efficacy of adding either anti-CD38 monoclonal antibodies (mAbs) or anti-SLAMF7 antibody to the triplet therapy. Methods: Ovid MEDLINE, EMBASE and published abstracts were systematically searched from each database’s inception through 01-31-2024 to identify II/III trials assessing quadruplet therapy versus triplet therapy in TENDMM patients. Hazard ratios (HR) for progression-free survival (PFS) and overall survival (OS), odds ratios (OR) for overall response rates (ORR), measurable residual disease (MRD) negativity rate (with sensitivity of 10 -5) and grade 3 or higher treatment-related adverse events (G≥3 TRAE) were summarized using random effects meta-analysis via inverse variance approach. Additional subgroup analyses based on the cytogenetic risk were performed. P-value of interaction (Pint ) < 0.1 was pre-specified as statistically significant for the difference between the two groups. Results: 3806 studies were screened and seven trials were included with a total of 3546 patients. Four trials (2303 patients) assessed the addition of anti-CD-38 mAbs and three (1243 patients) assessed anti-SLAMF7 mAb, elotuzumab to the triplet regimen. Quadruplet therapy significantly improved PFS as compared to triplet (HR: 0.63; 95% CI: 0.43-0.92), and the PFS benefit was also observed in standard risk group (HR 0.5; 95% CI 0.2-0.9); however, results were insignificant for high-risk group (HR 0.8; 95% CI 0.62-1.93). No significant OS benefit was observed (HR: 0.79; 95% CI 0.49–1.26); however, data for three trials (CASSIOPEIA, DSMM XVII, PERSEUS) is immature, with longer-term follow-up ongoing. Quadruplet therapy showed significant results for very good partial response or better (OR: 1.7; 95% CI 1.2-2.36), stringent complete response (OR: 1.74; 95% CI 1.27-2.07) and complete response or better (OR: 1.91; 95% CI 1.19-3.06). MRD rate shows benefit with quadruplet therapy (OR: 2.59; 95% CI 2.22-3.01). Improved MRD rate was also observed for high-risk (OR 2.13; 95% CI 1.4-3.2) and standard-risk group (OR 2.58; 95% CI 1.68-3.94). Secondary analysis for efficacy of anti-CD38 mAbs showed benefit for PFS (HR: 0.44; 95% CI: 0.35-0.56), ORR (OR: 1.77;95% CI: 1.02-3.06), and MRD (OR: 2.98; 95% CI 1.8-4.8). G ≥3 TRAE did not significantly differ between the groups (OR: 1.05; 95% CI 0.97-1.12). However, quadruplet therapy showed significant risk of infection (OR: 1.2; 95% 1.05-1.35) and thrombocytopenia (OR: 1.41; 95% CI 1.24-1.59). Conclusions: Quadruplet therapy improved PFS, ORR, and MRD rates compared to triplet regimen and has demonstrated promising benefits to be considered the new standard of care for TENDMM patients.

Efficacy of first line (1L) poly ADP-ribose polymerase inhibitors (PARPi) in combination with androgen pathway inhibitors (API) by homologous recombination repair (HRR) genes in metastatic castration-resistant prostate cancer (mCRPC): A living meta-analysis.

e17059 Background: It is unclear if patients with different HRR gene-mutated mCRPC derive consistent benefit from treatment with PARPi and API combination. Approval labels for PARPi+API are inconsistent across regulatory bodies in the US (Food and Drug Administration, FDA) and Europe (European Medical Agency, EMA), adding to confusion among practicing clinicians. Therefore, we aimed to assess the differential efficacy of PARP+API therapy by different genes using up-to-date trial evidence. Methods: We maintain a living meta-analysis on mCRPC treatments that is updated when new data becomes available. Here, we report data from phase III randomized control trials (RCTs) assessing PARPi+API in 1L mCRPC. The outcomes of interest included radiographic progression-free survival (rPFS) and overall survival (OS). Hazard ratios (HR) for rPFS and OS by different genes were summarized using random effects meta-analysis via inverse variance approach. Results: We have screened 27376 references since the inception of this living review. Here, we report data from three trials (15 references) with a total of 1618 patients. Frequently reported HRR gene mutations in the included trials were BRCA2 (35%), ATM (21%), CDK12 (12.2%), CHEK2 (12.2%), BRCA1 (4.6%), and PALB2 (3.4%). In terms of rPFS, statistical benefit was observed in BRCA (0.28; 95% CI: 0.13-0.59) and CDK12 (0.58; 95% CI: 0.35-0.95) subgroups, whereas no statistically significant benefit was seen in PALB2 (0.53; 95% CI: 0.21-1.32), ATM (0.93; 95% CI, 0.57-1.53), and CHEK2 (0.92; 95% CI: 0.53-1.61) subgroups. For OS, benefit was observed in BRCA (0.47; 95%CI: 0.31-0.71) subgroup. While not statistically significant, a signal of meaningful OS benefit was observed in PALB2 (0.33; 95%CI: 0.10-1.16) but no OS benefit was observed in ATM (0.97; 95% CI: 0.57-1.67), CDK12 (0.80; 95% CI: 0.36-1.78), and CHEK2 (0.81; 95% CI: 0.37-1.75) subgroups. Conclusions: Current results show that mCRPC patients with BRCA and CDK12 alterations had delayed disease progression with PARPi+API as 1L therapy. BRCA mutations are also associated with improved OS, while patients with CHEK2 and ATM derived no significant benefit with PARPi+API combination. [Table: see text]

Novel approaches to uncertainty estimation in seismic subsurface characterization

Uncertainty estimation in subsurface characterization workflows is an important input to decision-making in earth science-related problems. We present three methods to characterize seismic-related uncertainty, each of which includes a real data case study. Two of these methods are designed to characterize depth uncertainty in positioning of migrated reflectors. Such estimates may be used in derisking well depth prognoses, analysis of well misties, and for estimating ranges on resource volume. The first method derives rapid and robust estimates of depth uncertainty around an existing velocity model using traditional velocity analysis to constrain the solution space while limiting a-priori constraints, consistent with a frequentist approach to uncertainty characterization. The second method characterizes depth uncertainty more rigorously in respect to the physics and prior information available by performing full-waveform inversion in a Bayesian framework. This method produces similar uncertainty estimates to the first in the case of simple velocity models but is more accurate where the overburden is complex; however, it requires significantly greater computational expense, thus limiting current practical applications. The third method is an amplitude variation with angle (AVA) inversion for reservoir properties designed to output uncertainty products for interpreters, utilizing the Bayesian framework and Zoeppritz equations to define the forward physics. Application to an offshore Egypt field demonstrates that it can generate reliable estimates of reservoir properties (e.g., lithofluid type or shale volume) including uncertainty, useful in various parts of subsurface characterization. These results also show that the method could provide improved point estimates of reservoir properties compared to conventional deterministic AVA inversion approaches. There is usually a trade-off between increasing the accuracy of subsurface characterization versus creating faster, less expensive, and more readily understood workflows for practitioners. We discuss how the relative importance of these competing factors should be considered within the context of how the outputs will be used.

Nondestructively identifying the mechanical behavior of soft tissues using surface deformation with an explicit inverse approach

Identifying the spatial variation of stiffness properties in soft tissues nondestructively, with limited surface measurements, poses significant challenges. In this paper, we present a novel explicit inverse approach designed to characterize the nonhomogeneous elastic property distribution of soft tissues using only surface displacement datasets. In contrast to the prevalent implicit inverse approach, which focuses on optimizing the elastic properties of individual pixels, our proposed method optimizes the geometric parameters of deformable and movable components, as well as shear moduli of each component. As a result, the proposed approach requires far fewer optimization variables, streamlining the process. Numerical tests conducted in this study demonstrate the superiority of the explicit inverse method over the implicit inverse method, providing much-improved reconstructed results. In particular for a ring structure, while the average relative error of reconstruction using the implicit inverse method can exceed 40 %, the explicit inverse method achieves a remarkable average relative error of only 5 %. Given that surface displacements are easily measurable, the integration of our proposed explicit inverse method with low-cost imaging techniques shows great potential in accurately mapping the elastic property distribution of biological tissues.

Identification of Elastoplastic Constitutive Model of GaN Thin Films Using Instrumented Nanoindentation and Machine Learning Technique

This study presents a novel inverse identification approach to determine the elastoplastic parameters of a 2 µm thick GaN semiconductor thin film deposited on a sapphire substrate. This approach combines instrumented nanoindentation with finite element (FE) simulations and an artificial neural network (ANN) model. Experimental load–depth curves were obtained using a Berkovich indenter. To generate a comprehensive database for the inverse analysis, FE models were constructed to simulate load–depth responses across a wide range of GaN thin film properties. The accuracy of both 2D and 3D simulations was compared to select the optimal model for database generation. The Box–Behnken design-based data sampling method was used to define the number of simulations and input variables for the FE models. The ANN technique was then employed to establish the complex mapping between the simulated load–depth curves (input) and the corresponding stress–strain curve (output). The generated database was used to train and test the ANN model. Then, the learned ANN model was used to achieve high accuracy in identifying the stress–strain curve of the GaN thin film from the experimental load–depth data. This work demonstrates the successful application of an inverse analysis framework, combining experimental nanoindentation tests, FE modeling, and an ANN model, for the characterization of the elastoplastic behavior of GaN thin films.

Shape Programming of Porous Bilayer Hydrogel Structures

Abstract Shape-programmable materials have garnered significant attention for their ability to morph into complex three-dimensional (3D) configurations under external stimuli, with critical applications in the fields of biomedical engineering, soft robotics, and sensing technologies. A current challenge lies in determining the geometric parameters of the initial two-dimensional (2D) structure and the intensity of the external stimulus required to achieve a target 3D shape. In this work, we introduce a novel inverse design strategy based on hole pattern engineering. Utilizing a temperature-sensitive bilayer hydrogel with differing coefficients of thermal expansion in each layer, we achieve controlled bending deformations by varying the porosity distribution in one of the layers. Drawing on the Timoshenko theory on bimetallic beam, we establish a quantitative relationship between porosity and curvature, allowing for the hole distribution of the initial structure to be tailored to the desired curvature. We demonstrate the efficacy of our inverse design approach with several prototypical 3D structures, including variable-curvature strip and ellipsoidal surface, validated through finite element simulations and experimental trials. This strategy paves the way for advanced fabrication techniques in developing smart materials and devices with programmable shapes.

A Comparative Study of Subsurface Profile Using Bore Log Data and Geophysical Method at Mandideep Region, India

Before beginning construction on any civil structure, it is imperative to conduct a soil investigation to determine the soil’s parameters and to learn about the subsoil’s behavior. A thorough analysis must be performed, taking into account the foundation’s cost-effectiveness and any potential overdesign. In the early stages of a soil investigation, geophysical testing is used to find out about the subsurface. This is because geophysical tests are fast, easy to do, do not cause damage, and are cost-effective. In this study, subsurface profiling is performed using the inverse slope approach after resistivity tests are performed at numerous sites on varying terrain types. We generate a subsurface profile using inverse slope electrical resistivity testing and compare it with bore log data to identify any discrepancies. The results of the inverse slope method and the bore log data are comparable at different depths; further, the range of agreement of both results is determined by Bland–Altman analysis.

Next-generation membranes for verapamil removal: Graphene oxide quantum dot-modified polyethersulfone membranes

The presence of verapamil in water sources has gained attention due to its potential risks to ecosystems and human health. Because of its great separation performance and simplicity of integration into existing treatment processes, membrane filtration could be a viable alternative. Polyethersulfone (PES) membrane is usually employed for filtration processes due to its good mechanical and thermal stability. However, pristine PES membrane exhibits low flux and lower fouling properties due to its poor antibacterial properties. Thus, the phase inversion approach was used in the current study to develop graphene oxide quantum dots (GOQDs)-modified PES ultrafiltration (UF) membranes with varying quantities of GOQDs loading (0.01–0.07 wt%). With 0.03 wt% GOQDs, the PES composite membrane exhibited a water flux of 75.23 ±10.43 L/m2 h with a verapamil retention capability of 80.36%. All the modified PES membranes containing GOQDs exhibited the formation of inhibition zones, with PES-0.07 membranes (21.00 mm) showing more pronounced zones. The incorporation of GOQDs proved to be a promising and sustainable approach for modifying PES membranes, enhancing their flux, rejection performance, and antibacterial properties, which could be beneficial in removing pharmaceutical compounds from aqueous solution.

Accurate in situ rock density measurement with cosmic ray muon radiography

Muon radiography, which relies on measuring the absorption and attenuation of muons as they pass through matters, offers a new imaging technique capable of revealing the internal structure of large objects. Recent technological advancement allows for the application or testing of muon radiography in various fields, including mining, civil engineering, security check, etc. This study investigates the factors that influence muon radiography, which is used in density inversion, through simulations and experiments. The materials considered for density inversion include water, standard rock, and iron. Our simulation studies show that the number of events detected and selected has an impact on the reconstruction results, and several factors, such as multiple Coulomb scattering processes, recording time, and spatial resolution, which influence the number of muons, must be taken into account when measuring the rock density. We design and conduct a laboratory scale experiment based on the simulation results. We filter the 220 h of recording signals through time coincidence and straight-line fitting to obtain the selected events. Our results reveal that the statistical error of muons survival ratio in recording time significantly impacts the inversion result and decreases the error can improve accuracy greatly. In the experiment, the deviation between the inversion mean value and the expected value can be reduced to 2.4%–2.9% for iron, 7% for water, and 1.5% for standard rock. This density inversion approach provides insight into future density detection of underground structures.

Enhancement of filtration performance and antifouling properties of polyethersulfone membranes using Fe3O4@walnut shell-derived activated carbon nanocomposite for heavy metal ions removal

Effective water treatment methods are required owing to heavy metal ion contamination. The phase inversion approach was employed in this work to prepare antifouling polyethersulfone (PES)/Fe3O4@activated walnut shell carbon (AWSC) nanofiltration membranes to reject heavy metal ions. Characterization techniques for Fe3O4@AWSC nanoparticle (NP) included FT-IR, DLS-ZP, XRD, SEM, EDS mapping, BET, and VSM analyses. Also, SEM, EDS mapping, XRD, AFM, Zeta potential, and water contact angle investigations were utilized to characterize the synthesized membranes. The modified membranes' hydrophilicity and porosity were observed to have improved. To affirm the presence of mesoporous Fe3O4@AWSC in the membrane matrix, EDS mapping, SEM, and AFM studies were used. It was also confirmed how the membrane's characteristics and performance were influenced by the existence of mesoporous Fe3O4@AWSC. The mixed matrix membrane made of a 0.5 wt% mesoporous Fe3O4@AWSC demonstrated a high pure water flow of 72.19 L/m2 h, a flux recovery ratio of 71.66%, and a desired rejection of over 98% against Cu2+, Pb2+, and Zn2+. The 0.5 wt% NP membrane had a noticeably improved surface hydrophilicity, which promoted successful antifouling performance. The results demonstrate that a mixed-matrix antifouling Fe3O4@AWSC membrane is highly effective in eliminating heavy metal ions.

Optimising groundwater recharge to counteract seawater intrusion through an inverse modelling approach and identifying efficient recharge structure locations

In semi-arid coastal urban regions, groundwater faces challenges from seawater intrusion due to reduced recharge and increased extraction. This study employs advanced numerical modeling to comprehensively assess seawater intrusion along the South Chennai coast from 2018 to 2022. The inverse modelling approach utilizes observed data, such as groundwater heads or concentrations, to estimate aquifer properties, like hydraulic conductivity, by minimizing the disparity between model predictions and actual measurements to identify the best-fit parameter values. This inverse method forecasts the necessary recharge rate to address seawater intrusion by increasing groundwater head and decreasing chloride concentrations. The estimation of recharge incorporates variables such as rainfall, soil infiltration capacity, land use, and the vital groundwater draft calculated based on per capita consumption and population. The study uses FEFLOW software to develop a robust conceptual model, simulating groundwater flow and chloride transport. The inverse modeling approach resulted in critical recharge adjustments, enhancing rates by 40%–60% in specific regions. The predictive scenarios, integrating proposed recharge structures, show promising results, notably with a 50% reduction in chloride concentration during the post-monsoon season compared to the pre-monsoon period in wells 8 and 3. A notable decrease in chloride concentration of over 90% was observed during May 2025 in well no: 15 due to its proximity to the Adayar River. These fluctuations are influenced by seasonal variations resulting from climatic changes in South India, with intense rainfall accompanying northeast monsoon winds playing a significant role. Increased recharge rates and strategically placed structures along the Buckingham Canal provide significant relief in the most impacted northern coastal area, acting as effective barriers. These interventions successfully reduce chloride concentration, providing a sustainable and effective solution to mitigate the persistent challenge of seawater intrusion.

The lithospheric structure of Greenland from a stepwise forward and inverse modelling approach

Summary Greenland’s tectonic history is complex, and the resulting lithospheric structure is, although extensively researched, not well constrained. In this study, we model the lithospheric structure of Greenland in a consistent, integrated framework with three steps. First, we build a lithospheric background model by forward modelling, adjusted to gravity gradient data and shear wave velocities from a regional tomography model. Subsequently, we jointly invert for the upper crustal density and susceptibility structure by minimizing the gravity residuals and magnetic total field anomaly misfit. The last modelling step searches for upper crustal thermal parameters to fit our model to the most recent geothermal heat flow predictions for Greenland. Finally, we present 3D models of the density, temperature and velocity structure for the lithosphere as well as thermal parameters and susceptibilities for the upper crust. Our model also includes the depth of the Moho and LAB in Greenland. A comparison between inverted crustal parameters and surface geology shows a clear correlation. The novelty of our model is that all these results are consistent with each other and simultaneously explain a wide range of observed data.