Inverse Parameter Research Articles

Stochastic inversion of soil dynamic parameters based on non-intrusive data

The prediction and evaluation of environmental vibrations generated by traffic loads have been playing more and more important role during the planning and designing of high-quality traffic system. From the traffic source to sensitive building, the subsoil is the one of key propagation media, so the determination of soil dynamic parameters is very crucial in studying the transmitting and attenuation of environmental vibration. Due to the spatial variability of natural soil layers, the soil dynamic parameters are actually not certain but probabilistic with stochastic characteristics. However, most current inversion methods of soil parameters cannot consider the uncertainty. In this paper, an efficient inversion method is proposed to obtain the random distribution of soil dynamic parameters based on the combination of field experiment, forward soil modelling, and Bayesian theory. Take the shear modulus and damping ratio of subsoil as the typical parameters, the experimental dispersion curve and spatial attenuation curve can be obtained by field experiment, while the corresponding theoretical ones can be calculated by the thin-layered soil model with perfectly matched layer. The combining of experimental and theoretical curves generates the likelihood function. A prior probability distribution model is firstly established, and subsequently the posterior probability distribution model is deduced by genetic algorithm, Monte Carlo-Markov chain algorithm, and Bayesian theory. In order to verify the proposed inversion method, an on-site experiment based on Multichannel Analysis of Surface Waves(MASW) was conducted in an open field in Beijing. The results show the mean values of inversed soil parameters were close to the determined parameters provided by geological exploration. Compared to the traditional determined inversion, the proposed stochastic inversion method can consider the variety of soil parameters within small depth by providing probability distribution, mean and root-mean-square error, which has greater applicability in the further probabilistic prediction of environmental vibration.

Bottom Multi-Parameter Bayesian Inversion Based on an Acoustic Backscattering Model

The geoacoustic and physical properties of the bottom, as well as spatial distribution, are crucial factors in analyzing the underwater acoustic field structure and establishing a geoacoustic model. Acoustic inversion has been widely used as an economical and effective method to obtain multi-parameters of the bottom. Compared with traditional inversion methods based on acoustic propagation models, acoustic backscattering models are more suitable for multi-parameter inversion, because they contain more bottom information. In this study, a Bayesian inversion method based on an acoustic backscattering model is proposed to obtain bottom multi-parameters, including geoacoustic parameters (the sound speed and loss parameter), partial physical parameters of the sediment, and statistical parameters of the seafloor roughness and sediment heterogeneity. The bottom was viewed as a kind of fluid medium. A high-frequency backscattering model based on fluid theory was adopted as the forward model to fit the scattering strength between the model prediction and the measured data. The Bayesian inversion method was used to obtain the posterior probability density (PPD) of the inversion parameters. Parameter estimation, uncertainty, and correlation were acquired by calculating the maximum a posterior (MAP), the mean values, the one-dimensional marginal distributions of the PPD, and the covariance matrix. Finally, the high-frequency bottom backscattering strength from the Quinault Range site was employed for inversion tests. The estimated values and uncertainties of various bottom parameters are presented and compared with the directly measured bottom parameters. The comparison results demonstrate that the method proposed herein can be used to estimate the sediment/water sound speed ratio, the sediment/water density ratio, and the spectral exponent of the roughness spectrum effectively and reliably.

Petrophysical parameter estimation using Biot-poroelastic full-waveform inversion

SUMMARY The increasing need for petrophysical parameter inversion has spurred the advancement of full-waveform inversion (FWI). This study introduces a new inversion method that can directly estimate petrophysical parameters from seismic waveform data. Specifically, we modified the regular elastic wave equation by including Biot poroelastic parameters and designed an inversion workflow based on the framework of elastic FWI. The newly introduced inversion algorithms have demonstrated their effectiveness and accuracy using the Marmousi model and a synthetic model related to the gas hydrate deposits based on the Shenhu area of the South China Sea. Our work demonstrates the feasibility of directly inverting petrophysical parameters and assessing if a deposit contains gas hydrate.

Residual subsidence time series model in mountain area caused by underground mining based on GNSS online monitoring

The residual subsidence caused by underground mining in mountain area has a long subsidence duration time and great potential harm, which seriously threatens the safety of people's production and life in the mining area. Therefore, it is necessary to use appropriate monitoring methods and mathematical models to effectively monitor and predict the residual subsidence caused by underground mining. Compared with traditional level survey and InSAR (Interferometric Synthetic Aperture Radar) technology, GNSS (Global Navigation Satellite System) online monitoring technology has the advantages of long-term monitoring, high precision and more flexible monitoring methods. The empirical equation method of residual subsidence in mining subsidence is effectively combined with the rock creep equation, which can not only describe the residual subsidence process from the mechanism, but also predict the residual subsidence. Therefore, based on GNSS online monitoring technology, combined with the mining subsidence model of mountain area and adding the correlation coefficient of the compaction degree of caving broken rock and the Kelvin model of rock mechanics, this paper constructs the residual subsidence time series model of arbitrary point on the ground in mountain area. Through the example, the predicted results of the model in the inversion parameter phase and the dynamic prediction phase are compared with the measured data sequence. The results show that the model can carry out effective numerical calculation according to the GNSS monitoring data of any point on the ground, and the model prediction effect is good, which provides a new method for the prediction of residual subsidence in mountain mining.

A Feature Selection Method Based on Relief Feature Ranking with Recursive Feature Elimination for the Inversion of Urban River Water Quality Parameters Using Multispectral Imagery from an Unmanned Aerial Vehicle

The timely monitoring of urban water bodies using unmanned aerial vehicle (UAV)-mounted remote sensing technology is crucial for urban water resource protection and management. Addressing the limitations of the use of satellite data in inferring the water quality parameters of small-scale water bodies due to their spatial resolution constraints and limited input features, this study focuses on the Zao River in Xi’an City. Leveraging UAV multispectral imagery, a feature selection method based on Relief Feature Ranking with Recursive Feature Elimination (Relief F-RFE) is proposed to determine the quality parameters of the typical urban pollution in water (dissolved oxygen (DO), total nitrogen (TN), turbidity, and chemical oxygen demand (COD). By constructing a potential feature set and utilizing optimal feature combinations, inversion models are developed for the four water quality parameters using three machine learning (ML) algorithms (Random Forest (RF), Support Vector Regression (SVR), Light Gradient Boosting Machine (LightGBM). The inversion accuracies of the different models are compared, and the spatial distribution of the four water quality parameters is analyzed. The results show that the models constructed based on UAV-based multispectral remote sensing imagery perform well in inferring the water quality parameters of the Zao River. The SVR algorithm, based on Relief F-RFE feature selection, achieves a higher accuracy, with RMSE values of 7.19 mg/L, 1.14 mg/L, 3.15 NTU, and 4.28 mg/L, respectively. The methods and conclusions of this study serve as a reference for research on the inversion of water quality parameters in urban rivers.

A Novel Inversion Method for Permeability Coefficients of Concrete Face Rockfill Dam Based on Sobol-IDBO-SVR Fusion Surrogate Model

The accurate and efficient inversion of permeability coefficients is significant for the scientific assessment of seepage safety in concrete face rockfill dams. In addressing the optimization challenge of permeability coefficients with few samples, multiple parameters, and strong nonlinearity, this paper proposes a novel intelligent inversion method based on the Sobol-IDBO-SVR fusion surrogate model. Firstly, the Sobol sequence sampling method is introduced to extract high-quality combined samples of permeability coefficients, and the equivalent continuum seepage model is utilized for the forward simulation to obtain the theoretical hydraulic heads at the seepage monitoring points. Subsequently, the support vector regression surrogate model is used to establish the complex mapping relationship between the permeability coefficients and hydraulic heads, and the convergence performance of the dung beetle optimization algorithm is effectively enhanced by fusing multiple strategies. On this basis, we successfully achieve the precise inversion of permeability coefficients driven by multi-intelligence technologies. The engineering application results show that the permeability coefficients determined based on the inversion of the Sobol-IDBO-SVR model can reasonably reflect the seepage characteristics of the concrete face rockfill dam. The maximum relative error between the measured and the inversion values of the hydraulic heads at each monitoring point is only 0.63%, indicating that the inversion accuracy meets the engineering requirements. The method proposed in this study may also provide a beneficial reference for similar parameter inversion problems in engineering projects such as bridges, embankments, and pumping stations.

Coupled simulations and parameter inversion for neural system and electrophysiological muscle models

AbstractThe functioning of the neuromuscular system is an important factor for quality of life. With the aim of restoring neuromuscular function after limb amputation, novel clinical techniques such as the agonist‐antagonist myoneural interface (AMI) are being developed. In this technique, the residual muscles of an agonist‐antagonist pair are (re‐)connected via a tendon in order to restore their mechanical and neural interaction. Due to the complexity of the system, the AMI can substantially profit from in silico analysis, in particular to determine the prestretch of the residual muscles that is applied during the procedure and determines the range of motion of the residual muscle pair. We present our computational approach to facilitate this. We extend a detailed multi‐X model for single muscles to the AMI setup, that is, a two‐muscle‐one‐tendon system. The model considers subcellular processes as well as 3D muscle and tendon mechanics and is prepared for neural process simulation. It is solved on high performance computing systems. We present simulation results that show (i) the performance of our numerical coupling between muscles and tendon and (ii) a qualitatively correct dependence of the range of motion of muscles on their prestretch. Simultaneously, we pursue a Bayesian parameter inference approach to invert for parameters of interest. Our approach is independent of the underlying muscle model and represents a first step toward parameter optimization, for instance, finding the prestretch, to be applied during surgery, that maximizes the resulting range of motion. Since our multi‐X fine‐grained model is computationally expensive, we present inversion results for reduced Hill‐type models. Our numerical results for cases with known ground truth show the convergence and robustness of our approach.

Measurement and Analysis of Loran-C Sky Waves throughout the Day

In this study, Loran-C signals were collected throughout the day, and the characteristics of the received signals at different propagation distances were analyzed. Because the signal amplitude is small and difficult to recognize at a long distance and there is mutual interference between stations, a linear average method is used to process the received signal. At locations closer to the receiver, clear observations of the time delay and amplitude variation in the one-hop sky wave can be made by using the ground wave as a reference, which can be applied to studying the characteristics and parameter inversion of the lower ionosphere. When the distance is further, the significant enhancement of the sky-wave signal during the night may lead to decreased accuracy in timing and positioning. When the distance is much larger than the propagation range of the ground wave, only the sky-wave signal can be received, and the signal is more stable than when the distance is closer. During the night, multiple amplitude-comparable multi-path signals may appear in the sky wave, making the identification of the one-hop sky wave more difficult.

Prestack sparse envelope seismic inversion method adopting the L0−L2-norm regularization

The high drilling costs limit the possibility of obtaining reliable low-frequency constrained models in marine environments or areas with sparse well-log data, making it difficult to constrain lateral continuity and deep data. To enhance stability and accuracy in prestack seismic inversion in these areas, an elastic parameter estimation approach using sparse envelope inversion with [Formula: see text]-norm regularization is proposed. The method combines signal sparse representation and modulation theories to derive a new formula for sparse envelope extraction at lower frequencies. By applying [Formula: see text]-norm regularization to the sparse envelope, we obtain parameter inversion results with smoothed trends, augmenting low-wavenumber information for improved model constraints. In addition, taking the envelope inversion results obtained by the [Formula: see text] norm as the model constraint, the sparsest inversion results with obvious block-like characteristics are obtained by regularizing the inversion equation with the [Formula: see text] norm. Notably, the new method effectively suppresses wavelet side lobes, resulting in stable and accurate inversions without the need for initial low-frequency models. A synthetic example is used to illustrate the feasibility and stability of proposed approach and further demonstrate its practicality in reservoir parameter estimation through a field case study of gas exploration.

Biomarkers of endothelial glycocalyx damage are associated with microvascular dysfunction in resuscitated septic shock patients

BackgroundMicrovascular dysfunction plays a central role in organ dysfunction during septic shock. Endothelial glycocalyx (eGC) damage could contribute to impaired microcirculation. The aim was to assess whether several eGC-damaged biomarkers are associated with microvascular dysfunction in resuscitated septic shock patients. MethodsThis cross-sectional study included resuscitated septic shock patients (N = 31), and a group of healthy individuals (N = 20). The eGC damage biomarkers measured were syndecan-1 (SDC-1), soluble CD44 (CD44s), hyaluronic acid (HYAL) in blood sample; sulfated glycosaminoglycans (GAGs) in urine sample; and thrombomodulin (TBML) in blood sample as biomarker of endothelial cell damage. Microcirculation was assessed through sublingual videocapillaroscopy using the GlycoCheck™, which estimated the perfused vascular density (PVD); the perfused boundary region (PBR), an inverse parameter of the eGC thickness; and the microvascular health score (MVHS). We defined a low MVHS (<50th percentile in septic patients) as a surrogate for more impaired microvascular function. ResultsThe SDC-1, CD44s, TBML and GAGs levels were correlated with impaired microvascular parameters (PVD of vessels with diameter < 10 μm, MVHS and flow-adjusted PBR); p < 0.05 for all comparisons, except for GAGs and flow-adjusted PBR. The SDC-1 [78 ng/mL (interquartile range (IQR) 45–336) vs. 48 ng/mL (IQR 9–85); p = 0.052], CD44s [796ρg/mL (IQR 512–1995) vs. 526ρg/mL (IQR 287–750); p = 0.036], TBML [734ρg/mL (IQR 237–2396) vs. 95ρg/mL (IQR 63–475); p = 0.012] and GAGs levels [0.42 ρg/mg (IQR 0.04–1.40) vs. 0.07 ρg/mg (IQR 0.02–0.20); p = 0.024]; were higher in septic patients with more impaired sublingual microvascular function (low MVHS vs. high MVHS). ConclusionSDC-1, CD44s, TBML and GAGs levels were associated with impaired microvascular function in resuscitated septic shock patients.

Seismic amplitude inversion based on a new PP-wave reflection coefficient approximation equation for vertical transversely isotropic media

We develop a method for the simultaneous amplitude-variation-with-offset or -angle inversion of anisotropic parameters for transversely isotropic media with vertical axis of symmetry (VTI media). First, we introduce a nonlinear PP-wave reflection coefficient approximation equation in terms of only P- and S-wave impedances for isotropic elastic media. Then, by replacing the isotropic part of Rüger’s equation with this equation, we obtain a new PP-wave reflection coefficient approximation equation called the ASI Rüger equation for VTI media. To invert the parameters for VTI media based on the ASI Rüger equation, we adopt the Bayesian generalized linear inversion (GLI) method, a combination of GLI and Bayesian linear inversion, in which the noise and model perturbation are assumed to conform to the zero-mean Gaussian distribution. Compared with Rüger’s equation, the ASI Rüger equation decreases the trade-off between the parameters and reduces the ill-posedness of the inverse problem. The synthetic and field data tests demonstrate the feasibility of our method for inverting VTI media parameters (the vertical P-wave impedance, the vertical S-wave impedance, and Thomsen’s parameters [Formula: see text] and [Formula: see text]).

Forest Aboveground Biomass Estimation Using Multisource Remote Sensing Data and Deep Learning Algorithms: A Case Study over Hangzhou Area in China

The accurate estimation of forest aboveground biomass is of great significance for forest management and carbon balance monitoring. Remote sensing instruments have been widely applied in forest parameters inversion with wide coverage and high spatiotemporal resolution. In this paper, the capability of different remote-sensed imagery was investigated, including multispectral images (GaoFen-6, Sentinel-2 and Landsat-8) and various SAR (Synthetic Aperture Radar) data (GaoFen-3, Sentinel-1, ALOS-2), in aboveground forest biomass estimation. In particular, based on the forest inventory data of Hangzhou in China, the Random Forest (RF), Convolutional Neural Network (CNN) and Convolutional Neural Networks Long Short-Term Memory Networks (CNN-LSTM) algorithms were deployed to construct the forest biomass estimation models, respectively. The estimate accuracies were evaluated under the different configurations of images and methods. The results show that for the SAR data, ALOS-2 has a higher biomass estimation accuracy than the GaoFen-3 and Sentinel-1. Moreover, the GaoFen-6 data is slightly worse than Sentinel-2 and Landsat-8 optical data in biomass estimation. In contrast with the single source, integrating multisource data can effectively enhance accuracy, with improvements ranging from 5% to 10%. The CNN-LSTM generally performs better than CNN and RF, regardless of the data used. The combination of CNN-LSTM and multisource data provided the best results in this case and can achieve the maximum R2 value of up to 0.74. It was found that the majority of the biomass values in the study area in 2018 ranged from 60 to 90 Mg/ha, with an average value of 64.20 Mg/ha.

A research on inversion of water quality parameters in the mulan river based on GF-1B\C\D remote sensing images

Remote sensing inversion technology can be used for water quality parameter inversion to realize water quality monitoring in large scale space. The current research on water quality parameter inversion is only for a single satellite. In order to make full use of satellite image resources, the remote sensing images of GF-1B\C\D satellite group are taken as the research object. The Mulan River is taken as the research area. The linear regression method is used to construct the regression equations of total phosphorus and ammonia nitrogen, and the inversion model of total phosphorus and ammonia nitrogen is determined according the evaluation parameters. The MSE of the total phosphorus inversion model is 0.049, and the correlation between the inversion value and the measured value is 0.701. The MSE of the ammonia nitrogen inversion model is 0.063, and the correlation between the inversion value and the measured value is 0.813. These data show that the inversion effect is good. The inversion models are applied to the GF-1D satellite remote sensing image on March 15, 2021 to obtain the large-scale spatial distribution maps of total phosphorus concentration and ammonia nitrogen concentration. The water quality classification maps of the the Mulan River in Putian urban area are obtained too, which are convenient for further analysis and evaluation of the water quality.

Neural network and thermodynamic optimization of magnetized hybrid nanofluid dissipative radiative convective flow with energy activation

This article, motivated by hybrid magnetic coating manufacturing developments, utilizes a neural network-based computational program to study the dynamics of hybrid magnetic nanofluids with entropy generation. A new physico-chemo-mathematical model has been presented to simulate the hybrid magnetic nano-coating flow along a stretching surface to a porous medium with viscous heating. A Rosseland flux model is used for radiation heat transfer and Darcy’s model for the isotropic porous medium. The stretching sheet is porous, and wall suction or injection are possible. A robust neural network has been deployed to optimize the physical parameters controlling the transport characteristics of hybrid nanofluids. Specifically, two hybrid nanoparticle combinations are addressed, namely graphite oxide (GO)-molybdenum disulfide ( Mo S 2 ) and copper (Cu)-silicon dioxide ( Si O 2 ), both with engine oil as the base fluid. The dimensional boundary layer model is transformed via suitable scaling variables from a partial differential system into a dimensionless non-linear coupled ordinary differential system. The transformed boundary value problem is solved numerically with the BVP4C subroutine in the symbolic software MATLAB, which achieves exceptional accuracy. Validation with previous simpler studies is conducted and a good correlation is obtained. The neural network optimization analysis incorporates Bayesian regularization as the training algorithm. The Bejan entropy generation minimization (EGM) analysis shows that with increasing radiation parameter R d , both entropy generation rate and Bejan number are increased. Furthermore, an elevation in Brinkman number Br leads to an upsurge in entropy generation rate and a downtrend in the Bejan number. The numerical solution of the boundary value problem reveals that with an increment in nanoparticle solid volume fraction φ 2 , magnetic parameter M , inverse permeability parameter ϵ , surface injection parameter ( s < 0 ) , Eckert number Ec and radiation parameter R d and with a decrement in suction parameter ( s > 0 ) and Prandtl number Pr , there is a strong enhancement in temperature magnitude and thermal boundary layer thickness. With greater nanoparticle solid volume fraction φ 2 , magnetic parameter M , inverse permeability parameter ϵ , suction parameter s and a reduction in thermal buoyancy parameter λ , strong flow deceleration is induced, and momentum boundary layer thickness is increased. The skin friction coefficient is substantially boosted with lower values of magnetic parameter M , inverse permeability parameter ϵ , suction parameter s and higher values of thermal buoyancy parameter λ . There is a significant decrement also computed in Nusselt number with a greater radiation parameter R d . The simulations provide a good benchmark for future extensions that may consider non-Newtonian behavior.

Assessing radiometric calibration methods for multispectral UAV imagery and the influence of illumination, flight altitude and flight time on reflectance, vegetation index and inversion of winter wheat AGB and LAI

Radiometric correction is essential to unmanned aerial vehicle (UAV) precision agriculture applications, especially for crop multi-period analysis and quantitative inversion of phenotypic and physiological parameters. In this study, we investigated the performance of four radiometric correction methods: “Camera only” (A), “Camera and sun irradiance” (B) and “Camera, sun irradiance and sun angle” (C) methods provided by Pix4d Mapper and an irradiance sensor-based method (D) implemented with the R software. UAV campaigns were conducted at flight altitudes of 30 m, 60 m and 90 m under clear and cloudy skies at different times over an experimental field of winter wheat. The influences of illumination, flight altitude and flight time on reflectance and vegetation indexes derived from different radiometric calibration methods were analyzed. Moreover, we further assessed the impact of reflectance and vegetation index variations on the above-ground biomass (AGB) and leaf area index (LAI) estimations of winter wheat. Results showed that method D is a promising approach for radiometric calibration, which produced consistent reflectance with method A at noon (e.g., 10:44:41 AM March 17 and 11:39:37 AM April 7) and mid-afternoon (e.g., 15:30:02 PM May 1) and more reliable reflectance in the morning (e.g., 7:15:44 AM March 17). Methods B and C seriously underestimated the reflectance at 10:44:41 AM March 17 and 11:39:37 AM April 7. This was caused by the incorrect attitude angles of the irradiance sensor and further miscalculation of the solar irradiance by Pix4d Mapper. Interestingly, these errors were eliminated in the calculation of the ratio type of vegetation indexes. Methods B and C yielded vegetation indexes that were almost the same as those of methods A and D. Under cloudy sky, when direct sunlight is not blocked by clouds, method D was effective; with cloud occlusion, results on April 24 indicated that the method failed to estimate the total sunlight and direct sunlight ratio. Reflectance and vegetation indexes obtained from different radiometric correction methods were all obviously affected by flight altitude. Furthermore, the variations of NDVI with flight altitude increasing were different with reflectance. Changes in reflectance and vegetation indexes with flight altitude had little effect on the correlation coefficients yielded with AGB, but showed a significant influence on those of LAI. Through analyzing the variation of observed solar radiation at different time intervals, caution is recommended when using the assumption that solar radiation is stable during the UAV flight. The longer the flight, the greater the variation in solar radiation, especially at sunrise and sunset. Even at noon, when the flight duration is 30 min, the change in solar radiation may exceed 10 % (e.g., 16.5 % on March 20).

Anisotropic media hybrid grid tomographic inversion method based on angle domain imaging gathers

The grid tomography velocity inversion method prevails as the most efficacious technique for acquiring background velocity requisite for migration. Originating from imaging gathers, this method not only inverses velocity but also subsequently ascertains the anisotropic parameters field. Traditional approaches utilize a parameter field discretized in a rectangular grid for both ray tracing and parameter inversion. However, the rectangular grids may not provide optimal accuracy for structures with pronounced lateral parameter variations. In contrast, triangular grid discretization allows for grid division along steep slopes, offering sparsity and flexibility when juxtaposed with conventional rectangular grid methods. This paper introduces an optimized triangular grid travel time parameter tomography inversion method, formulated on the parameter sensitivity to travel time and employing angle gathers to derive travel time residuals. Analytical modeling and real data processing substantiate that the triangular grid ray tracing method ameliorates the ill-conditioned nature of the tomography equation and enhances the accuracy of the inversion parameter field effectively.

Dynamic Industrial Optimization: A Framework Integrates Online Machine Learning for Processing Parameters Design

We develop the online process parameter design (OPPD) framework for efficiently handling streaming data collected from industrial automation equipment. This framework integrates online machine learning, concept drift detection and Bayesian optimization techniques. Initially, concept drift detection mitigates the impact of anomalous data on model updates. Data without concept drift are used for online model training and updating, enabling accurate predictions for the next processing cycle. Bayesian optimization is then employed for inverse optimization and process parameter design. Within OPPD, we introduce the online accelerated support vector regression (OASVR) algorithm for enhanced computational efficiency and model accuracy. OASVR simplifies support vector regression, boosting both speed and durability. Furthermore, we incorporate a dynamic window mechanism to regulate the training data volume for adapting to real-time demands posed by diverse online scenarios. Concept drift detection uses the EI-kMeans algorithm, and the Bayesian inverse design employs an upper confidence bound approach with an adaptive learning rate. Applied to single-crystal fabrication, the OPPD framework outperforms other models, with an RMSE of 0.12, meeting precision demands in production.

Sensitivity analysis of inverted model parameters from transient electromagnetic measurements affected by induced polarization effects

We investigate the application of the distance-based global sensitivity analysis (DGSA) to evaluate the sensitivity of electrical model parameters obtained from transient electromagnetic (TEM) data including induced polarization (IP) effects. We propose novel open-source forward modeling and inversion routines for single-loop TEM data including IP effects with the maximum phase angle model to model the frequency dependence of the complex resistivity. In a first step, we evaluate the accuracy of our forward modeling and inversion routines using numerical studies, where the actual variations of layer thicknesses and resistivities, as well as the frequency dependence of the complex resistivity is known. In a second step, we extend our investigation to field data and apply our approach to three distinct case studies in layered media: 1) a confined aquifer corresponding to conductive non-polarizable media, 2) a graphite deposit corresponding to highly conductive and polarizable anomalies in a resistive host rock and 3) an ice glacier corresponding to highly resistive polarizable media. Our DGSA results reveal that standard depth of investigation (DOI) approaches may overestimate the true sensitivity of the model obtained from the inversion. TEM data collected in conductive layered media without IP effects show reduced sensitivity above the predicted DOI. The case studies in polarizable media demonstrate that the maximum phase angle is more influential on the TEM model response than the relaxation time and dispersion coefficient. Our DGSA results for polarizable media reveal that TEM field data collected at the graphite deposit and at the ice glacier are sensitive to the geometry of the polarizable layer.

A method to estimate the rectangular orthotropic plate elastic constants using least-squares and Chladni patterns

A method to retrieve the elastic constants of rectangular wooden plates is presented, relying on the measurement of a set of eigenfrequencies and the identification of the corresponding mode shapes, and belonging to the more general category of non-destructive inverse parameter estimation methods. Compared to previous work, the current method is effective with any choice of boundary conditions. Furthermore, the error function is linear in the elastic constants, which may be computed easily via a matrix inversion. This framework lends itself naturally to a physical interpretation of the results in terms of linear combinations of eigenmodes, yielding new sets of modes and associated combined mode shapes in which the elastic constants are completely uncoupled. Several numerical benchmark tests and experimental cases are treated in detail, highlighting the reliability of the proposed methodology in cases of interest in acoustics and musical acoustics.

A simple weighting method for inverting earthquake source parameters using geodetic multisource data under Bayesian algorithm

SUMMARY More accurate inversion of source fault geometry and slip parameters under the constraint of the Bayesian algorithm has become a research hotspot in the field of geodetic inversion in recent years. In nonlinear inversion, the determination of the weight ratio of the joint inversion of multisource data is more complicated. In this context, this paper proposes a simple and easily generalized weighting method for inversion of source fault parameters by joint geodetic multisource data under the Bayesian framework. This method determines the relative weight ratio of multisource data by root mean square error (RMSE) value and can be extended to other nonlinear search algorithms. To verify the validity of the method in this paper, this paper first sets up four sets of simulated seismic experiment schemes. The inversion results show that the joint inversion weighting method proposed in this paper has a significant decrease in the large residual value compared with the equal weight joint inversion and the single data source joint inversion method. The east–west deformation RMSE is 0.1458 mm, the north–south deformation RMSE is 0.2119 mm and the vertical deformation RMSE is 0.2756 mm. The RMSEs of the three directions are lower than those of other schemes, indicating that the proposed method is suitable for the joint inversion of source parameters under Bayesian algorithm. To further verify the applicability of the proposed method in complex earthquakes, the source parameters of the Maduo earthquake were inverted using the method of this paper. The focal depth of the inversion results in this paper is closer to the focal depth released by the GCMT agency. In terms of strike angle and dip angle, the joint inversion in this paper is also more inclined to the GCMT results. The joint inversion results generally conform to the characteristics of left-lateral strike-slip, which shows the adaptability of this method in complex earthquakes.