Nonlinear Inversion Research Articles

Linear Disturbance Observer-Enhanced Continuous-Time Predictive Control for Straight-Line Path-Following Control of Small Unmanned Aerial Vehicles

This paper studies the straight-line path-following problem on the lateral plane for fixed-wing unmanned aerial vehicles (FWUAVs) which are susceptible to uncertainties. Firstly, based on the natural frame’s location on the prescribed reference paths, the command yaw angle (which is the basis for yaw angle control system design) is solved analytically by combining it with the errors of path following, attack angle, sideslip angle, attitude angles, and geometric parameters of the prescribed reference paths. Secondly, by considering complicated dynamic characteristics, a linear extended state observer is designed to estimate uncertainties such as nonlinearities, couplings, and unmodeled dynamics whose estimated values are incorporated into the continuous-time predictive controllers for feedback compensation. Finally, numerical simulations are conducted to demonstrate the advantages of the proposed method, including reduced tracking errors and enhanced robustness in the closed-loop system, as compared to the conventional nonlinear dynamic inversion and sliding mode control approaches.

Refined Coseismic Slip and Afterslip Distributions of the 2021 Mw 6.1 Yangbi Earthquake Based on GNSS and InSAR Observations

On 21 May 2021, an Mw 6.1 earthquake occurred in Yangbi County, Dali Bai Autonomous Prefecture, Yunnan Province, with the epicenter located in an unmapped blind fault approximately 7 km west of the Weixi-Qiaohou fault (WQF) on the southeastern margin of the Qinghai–Tibetan Plateau. While numerous studies have been conducted to map the coseismic slip distribution by using the Global Navigation Satellite System (GNSS), Interferometric Synthetic Aperture Radar (InSAR) and seismic data as well as their combinations, the understanding of deformation characteristics during the postseismic stage remains limited, mostly due to the long revisiting time interval and large uncertainty of most SAR satellites. In this study, we refined coseismic slip and afterslip distributions with nonlinear inversions for both fault geometry and relaxation time. First, we determined the fault geometry and coseismic slip distribution of this earthquake by joint inversion for coseismic offsets in the line-of-sight (LOS) direction of both Sentinel-1A/B ascending and descending track images and GNSS data. Then, the descending track time series of Sentinel-1 were further fitted using nonlinear least squares to extract the coseismic and postseismic deformations. Finally, we obtained the refined coseismic slip and afterslip distributions and investigated the spatiotemporal evolution of fault slip by comparing the afterslip with aftershocks. The refined coseismic moment magnitude, which was of Mw 6.05, was smaller than Mw 6.1 or larger, which was inferred from our joint inversion and previous studies, indicating a significant reduction in early postseismic deformation. In contrast, the afterslip following the mainshock lasted for about six months and was equivalent to a moment release of an Mw 5.8 earthquake. These findings not only offer a novel approach to extracting postseismic deformation from noisy InSAR time series but also provide valuable insights into fault slip mechanisms associated with the Yangbi earthquake, enhancing our understanding of seismic processes.

MR Elastography for Classification of Focal Liver Lesions Using Viscoelastic Parameters: A Pilot Study Based on Intrinsic and Extrinsic Activations.

Intrinsic activation MR elastography (iMRE) uses cardiovascular pulsations to assess tissue viscoelastic properties. Applying it to focal liver lesions extends its capabilities. To assess the viscoelastic parameters of focal liver lesions measured by iMRE and compare its diagnostic performance with extrinsic MRE (eMRE) for differentiating malignant and benign lesions. Prospective. A total of 55 participants underwent MRI with research MRE sequences; 32 participants with 17 malignant and 15 benign lesions underwent both iMRE and eMRE. FIELD STRENGTH/SEQUENCE: iMRE at ~1 Hz heart rate used a 3 T scanner with a modified four-dimensional (4D)-quantitative flow gradient-echo phase contrast and low-velocity encoding cardiac-triggered technique. eMRE employed a gradient-echo sequence at 30, 40, and 60 Hz. Liver displacements were measured using 4D-phase contrast and reconstructed via a nonlinear inversion algorithm to determine shear stiffness (SS) and damping ratio (DR). iMRE parameters were normalized to the corresponding values from the spleen. Lesions were manually segmented, and image quality was reviewed. Kruskal-Wallis, Mann-Whitney, Dunn's test, and areas under receiver operating characteristic curves (AUC) were assessed. SS was significantly higher in malignant than benign lesions with iMRE at 1 Hz (3.69 ± 1.31 vs. 1.63 ± 0.45) and eMRE at 30 Hz (3.76 ± 1.12 vs. 2.60 ± 1.26 kPa), 40 Hz (3.76 ± 1.12 vs. 2.60 ± 1.26 kPa), and 60 Hz (7.32 ± 2.87 vs. 2.48 ± 1.12 kPa). DR was also significantly higher in malignant than benign lesions at 40 Hz (0.36 ± 0.11 vs. 0.21 ± 0.01) and 60 Hz (0.89 ± 0.86 vs. 0.22 ± 0.09). The AUC were 0.86 for iMRE SS, 0.87-0.98 for eMRE SS, 0.47 for iMRE DR, and 0.62-0.86 for eMRE DR. Cardiac-activated iMRE can characterize liver lesions and differentiate malignant from benign lesions through normalized SS maps. 2 TECHNICAL EFFICACY: Stage 2.

A nonlinear inversion method for Young’s modulus and shear modulus based on the exact Zoeppritz equations

IntroductionYoung’s modulus and shear modulus are essential mechanical parameters for evaluating subsurface rocks, playing a pivotal role in the exploration and development of unconventional resources. Young’s modulus indicates the brittleness of the reservoir, while shear modulus determines the ease of fracturing rock layers.MethodsTraditional methods estimate these moduli through indirect calculations and approximate expressions, which are prone to cumulative errors and rely on multiple assumptions, reducing inversion accuracy. This paper presents a direct inversion method for acquiring of Young’s modulus and shear modulus using the exact Zoeppritz equations, integrated within a Bayesian framework for pre-stack inversion. The quantum particle swarm optimization (QPSO) algorithm is introduced to achieve a nonlinear solution to the objective function.ResultsTests on synthetic and actual field data demonstrate the feasibility and effectiveness of the proposed method, yielding more accurate inversion results compared to traditional methods.DiscussionThese findings provide valuable insights for predicting reservoir brittleness and characterizing reservoirs in unconventional shale gas exploration and development.

Markov Chain Monte Carlo Solution of the Implicit Nonlinear Inverse Problem with Application to Curve Fitting and Filter Estimation

Markov Chain Monte Carlo Solution of the Implicit Nonlinear Inverse Problem with Application to Curve Fitting and Filter Estimation

2D nonlinear inversion of DC resistivity measurements, a case study; southeastern part of Ras El Dabaa, Northwestern coast, Egypt

The southern Mediterranean coast suffers from limited water resources as a result of exploitation of water supply, population growth, and climate change. Spatial lineaments and Seawater Intrusion (SWI) were detected at the southeast portion of Ras El Dabaa, on Egypt’s northwest coast, using the direct current resistivity (DCR) method. The Vertical Electrical Sounding (VES) data were acquired using Schlumberger array along four profiles and inverted both independently and jointly, aiming to obtain Two Dimensional (2D) geoelectrical images. The results of the one Dimensional (1D) inversion of VES data at each profile were stitched to form pseudo-2D sections on which the resistivity values and aquifer thickness in the southwest of the region appeared to be generally increasing, indicating a potential improvement in water quality.However, the results did not fully image the lateral variation but focused on the horizontal boundaries of the subsurface. On the contrary, the results of 2D inversion of the same data sets successfully managed to provide images that depicted resistivity distribution in both lateral and vertical directions. The detected sets of lineaments and fractured zones within the oolitic limestone and fossiliferous limestone units control the occurrence of groundwater in the region. The 2D inversion scheme revealed a low resistivity zone that indicated the presence of SWI and/or the dissolution of marine salts from the marine limestone bedrock of these aquifers in the northern portions of the studied area. Additionally, analysis of the 2D apparent porosity section shows how aquifers are connected by secondary porosity, which is defined by structures that resemble channels. The current approach offers valuable structural information for future planning and development of such complex geological coastal locations, taking into consideration the vulnerability of the groundwater system.

Cascaded Incremental Nonlinear Dynamic Inversion Trajectory Following for a Stratospheric Airship in Wind

This paper presents a guidance and control system for controlling the trajectory of a stratospheric unmanned airship using a cascaded Incremental Nonlinear Dynamic Inversion (INDI) method. The control system uses a two-loop cascaded INDI controller with virtual efforts and moments formulation to control both the airship attitude and velocity, while providing perturbation rejection properties. An active-set solver allocates the virtual controls to the airship propellers and aerodynamic surfaces while respecting the actuator bounds and rates saturation. The guidance system uses a Nonlinear Dynamic Inversion controller, where the position error dynamics formulation explicitly considers the lateral velocity of the airship, which enables it to counteract the wind influence. Simulation results demonstrate that the reference trajectory is precisely followed, even in the presence of unknown wind perturbations and both aerodynamic coefficients and mass and inertia parametric uncertainties through Monte-Carlo simulations.

Characterizing brain mechanics through 7 tesla magnetic resonance elastography

Magnetic resonance elastography (MRE) is a non-invasive method for determining the mechanical response of tissues using applied harmonic deformation and motion-sensitive MRI. MRE studies of the human brain are typically performed at conventional field strengths, with a few attempts at the ultra-high field strength, 7T, reporting increased spatial resolution with partial brain coverage. Achieving high-resolution human brain scans using 7T MRE presents unique challenges of decreased octahedral shear strain-based signal-to-noise ratio (OSS-SNR) and lower shear wave motion sensitivity. In this study, we establish high resolution MRE at 7T with a custom 2D multi-slice single-shot spin-echo echo-planar imaging sequence, using the Gadgetron advanced image reconstruction framework, applying Marchenko-Pastur Principal component analysis denoising, and using nonlinear viscoelastic inversion. These techniques allowed us to calculate the viscoelastic properties of the whole human brain at 1.1 mm isotropic imaging resolution with high OSS-SNR and repeatability. Using phantom models and 7T MRE data of eighteen healthy volunteers, we demonstrate the robustness and accuracy of our method at high-resolution while quantifying the feasible tradeoff between resolution, OSS-SNR, and scan time. Using these post-processing techniques, we significantly increased OSS-SNR at 1.1 mm resolution with whole-brain coverage by approximately 4-fold and generated elastograms with high anatomical detail. Performing high-resolution MRE at 7T on the human brain can provide information on different substructures within brain tissue based on their mechanical properties, which can then be used to diagnose pathologies (e.g. Alzheimer's disease), indicate disease progression, or better investigate neurodegeneration effects or other relevant brain disorders,in vivo.

Optical flow-based control for micro air vehicles: an efficient data-driven incremental nonlinear dynamic inversion approach

Optical flow-based control for micro air vehicles: an efficient data-driven incremental nonlinear dynamic inversion approach

Accelerated Cartesian cardiac T2 mapping based on a calibrationless locally low-rank tensor constraint.

Cardiac T2 mapping is a valuable tool for diagnosing myocardial edema, inflammation, and infiltration, yet its spatial resolution is limited by the single-shot balanced steady-state free precession acquisition and duration of the cardiac quiescent period, which may reduce sensitivity in detecting focal lesions in the myocardium. To improve spatial resolution without extending the acquisition window, this study examined a novel accelerated Cartesian cardiac T2 mapping technique. We introduce a novel improved-resolution cardiac T2 mapping approach leveraging a calibrationless space-contrast-coil locally low-rank tensor (SCC-LLRT)-constrained reconstruction algorithm in conjunction with Cartesian undersampling trajectory. The method was validated with phantom imaging and in vivo imaging that involved 13 healthy participants and 20 patients. The SCC-LLRT algorithm was compared with a conventional locally low-rank (LLR)-constrained algorithm and a nonlinear inversion (NLINV) reconstruction algorithm. The improved-resolution T2 mapping (1.4 mm × 1.4 mm) was compared globally and regionally with the regular-resolution T2 mapping (2.3 mm × 1.9 mm) according to the 16-segment model of the American Heart Association. The agreement between the improved-resolution and regular-resolution T2 mappings was evaluated by linear regression and Bland-Altman analyses. Image quality was scored by two experienced reviewers on a five-point scale (1, worst; 5, best). In healthy participants, SCC-LLRT significantly reduced artifacts (4.50±0.39) compared with LLR (2.31±0.60; P<0.001) and NLINV (3.65±0.56; P<0.01), suppressed noise (4.12±0.35) compared with NLINV (2.65±0.50; P<0.001), and improved the overall image quality (4.38±0.40) compared with LLR (2.54±0.41; P<0.001) and NLINV (3.04±0.50; P<0.001). Compared with the regular-resolution T2 mapping, the proposed method significantly improved the sharpness of myocardial boundaries (4.46±0.60 vs. 3.04±0.50; P<0.001) and the conspicuity of papillary muscles and fine structures (4.46±0.63 vs. 2.65±0.30; P<0.001). Myocardial T2 values obtained with the proposed method correlated significantly with those from regular-resolution T2 mapping in both healthy participants (r=0.79; P<0.01) and patients (r=0.94; P<0.001). The proposed SCC-LLRT-constrained reconstruction algorithm in conjunction with Cartesian undersampling pattern achieved improved-resolution cardiac T2 mapping of comparable accuracy, precision, and scan-rescan reproducibility compared with the regular-resolution T2 mapping. The higher resolution improved the sharpness of myocardial borders and the conspicuity of image fine details, which may increase diagnostic confidence in cardiac T2 mapping for detecting small lesions.

Active Maneuver Load Alleviation for a Pitching Wing via Spanwise-Distributed Camber Morphing

This paper presents the design and verification of a nonlinear model inversion (NMI) controller for the maneuver load alleviation of a pitching oscillating wing based on spanwise-distributed active camber morphing. Recurrent neural networks (RNNs) are used to predict nonlinear and unsteady aerodynamic forces due to wing's large amplitude pitching maneuver, and a fully connected neural network is introduced to build the dynamic inversion of the aeroelastic system for control law design. The inversed system is concatenated with a PI controller to assemble a nonlinear active controller. The controller is first utilized in an offline environment for a 1DoF pitching finite-span wing with spanwise-distributed active camber morphing and then verified in CFD-based fluid-structure-control coupling simulation. The results show that the offline controller could eliminate the maneuver load. In the online CFD-based fluid-structure-control simulation, the bending moment can be alleviated by 38%.

The Influence of Mantle Structure on Dynamic Topography in Southern Africa

AbstractDue to relatively high terrain and negligible active tectonics, the southern Africa region boasts over 30 independent estimates of dynamic topography. These published estimates display a wide variance due to both the variety of methods used in computation and a lack of constraints on the regional mantle structure. Here we show that a focus on regional mantle structure is important to generate models of lithospheric and mantle dynamics. Global average mantle properties are not representative of a particular region, and it is necessary to generate viscosity profiles specific to a region of interest. We develop a Bayesian inversion using dynamic geoid kernels, existing seismic tomography models, and Slepian functions to invert for a localized radial viscosity profile that best explains the geoid in southern Africa. With an understanding of viscosity uncertainty, we constrain dynamic topography in southern Africa to lie between 1,000 and 2,000 m. Additionally, we model vertical displacements from 112 Global Navigation Satellite System stations across our region to examine the long‐term, long wavelength pattern of present‐day vertical motion, suggesting that a mean of 1.5 mm/yr (1: 0.8–2.0 mm/yr) of vertical motion may be related to ongoing dynamic topography. Our study demonstrates the utility of dynamic geoid kernels in local nonlinear inversions of non‐unique geophysical data. Furthermore, we present evidence that the mantle beneath southern Africa is generating significant dynamic support for and vertical displacement of the lithosphere in this region.

Cascaded robust fixed-time terminal sliding mode control for uncertain cartpole systems with incremental nonlinear dynamic inversion

This paper proposes a cascaded fixed-time terminal sliding mode controller (TSMC) for uncertain underactuated cartpole dynamics using incremental nonlinear dynamic inversion (INDI). Leveraging partial linearization and prioritizing pole dynamics for internal tracking, the proposed controller achieves efficient stabilization of the cart upon convergence of the pole. Stability analysis is carried out using Lyapunov stability theorem, proving that the proposed controller stabilizes the state variables to an arbitrarily small neighborhood of the equilibrium in fixed-time, along with the suboptimality (steady-state error), existence and uniqueness of the solutions. The INDI is also integrated into TSMC to further improve the robustness while suppressing the conservativeness of conventional TSMC. The stability of INDI is rigorously proved using sampling-based Lyapunov function under sampling-based control realm. The simulation results illustrate the superiority of the proposed method with comparison and ablation studies.

A new automated procedure to obtain reliable moment tensor solutions of small to moderate earthquakes (3.0 ≤ M ≤ 5.5) in the Bayesian framework

SUMMARY The complete catalogue of moment tensor (MT) solutions is essential for a wide range of research in solid earth science. However, the number of reliable MT solutions for small to moderate earthquakes (3.0 ≤ M ≤ 5.5) is limited due to uncertainties arising from data and theoretical errors. In this study, we develop a new procedure to enhance the resolvability of MT solutions and provide more reliable uncertainty estimates for these smaller to moderate earthquakes. This procedure is fully automatic and efficiently accounts for both data and theoretical errors through two sets of hybrid linear–non-linear Bayesian inversions. In the inversion process, the covariance matrix is estimated using an empirical approach: the data covariance matrix is derived from the pre-event noise and the theoretical covariance matrix is derived from the residuals of the initial solution. We conducted tests using synthetic data generated from the 3-D velocity model and interference from background seismic noise. The tests found that using a combination of the non-Toeplitz data covariance matrix and the Toeplitz theoretical covariance matrix improves the solution and its uncertainties. Test results also suggest that including a theoretical covariance matrix when analysing MT in complex tectonic regions is essential, even if we have the best 1D velocity model. The application to earthquakes in the northern region of the Banda Arc resulted in the first published Regional Moment Tensor (RMT) catalogue, containing more than three times the number of trusted solutions compared to the Global Centroid Moment Tensor (GCMT) and the Indonesian Agency for Meteorology Climatology and Geophysics Moment Tensor (BMKG-MT) catalogue. The comparison shows that the trusted solutions align well with the focal mechanism of the GCMT and BMKG-MT, as well as with the maximum horizontal stress of the World Stress Map, and tectonic conditions in the study area. The newly obtained focal mechanisms provide several key findings: (i) they confirm that the deformation in the northern and eastern parts of Seram Island is influenced by oblique intraplate convergence rather than by the subduction process; (ii) they validate the newly identified Amahai Fault with a greater number of focal mechanisms and (iii) they reveal an earthquake Mw 4.7 with the same location and source mechanism 6 yr before the 2019 Ambon-Kairatu earthquake (Mw 6.5) which occurred on a previously unidentified fault.

Nonlinear dynamic inversion based full envelope robust flight control for coaxial compound helicopter

The flight control system of coaxial compound helicopters (CCHs) is a complex multi-variable system characterized by redundant control effectors, strong nonlinear characteristics, and multiple flight modes. This paper presents a unified robust control framework tailored for CCH full-envelope flight, utilizing nonlinear dynamic inversion and extended state observer techniques. The framework is specifically designed to tackle issues arising from a multi-mode nature, parametric uncertainties, and external disturbances. Within the unified framework, the study addresses inherent complexities such as nonlinearities and cross-coupling effects in CCH by leveraging nonlinear dynamic inversion to formulate flight control laws for both inner and outer-loops. To mitigate model uncertainties and external disturbances, the extended state observer is employed to estimate lumped disturbance effectively. Control allocation for both inner and outer-loops is devised to adapt to changes in control effector authorities and flight modes. Furthermore, an exponential control allocation strategy is proposed for the outer-loop to ensure a smooth pitch angle during transition flight. The effectiveness of the proposed control laws and control allocation strategies in achieving precise attitude tracking, transition flight and mission task elements (MTEs), even in the presence of notable model uncertainties and external disturbances, is demonstrated through numerical simulations.

A probabilistic machine learning framework for stiffness tensor estimation of carbon composite laminate

The potential uses of carbon composite material are vast, particularly in the civil, mechanical and aero-structures. Nonetheless, the practical utilization of carbon composite faces challenges due to complex experimental methods and imprecise algorithms for inverting material properties. Characterization of full set of stiffness tensor for an anisotropic material in particular orthotropic material can be considered as a complex non-linear inversion problem. The inversion process requires a robust optimization algorithm and forward models. The complexity of this inversion process impedes the practical utility in large-scale automation and in real-time structural health monitoring (SHM) application. To cater this, in this work an advanced probabilistic machine learning framework based on multi-output Gaussian process regression (moGPR) is proposed as an inversion algorithm. The inversion algorithm proposed is based on probabilistic framework that utilises the dispersion of Lamb waves as an input for optimal estimation of stiffness tensor. Experimental measurements of full wavefield data of propagating waves are conducted by scanning laser Doppler vibrometer. Further, an optimal training dataset is generated by employing a forward model based on stiffness matrix method (SMM). In the presence of measurement uncertainties, the effectiveness of the optimal prediction algorithm is showcased through a comparison between the estimated parameter and its ground truth. This comparison reveals that the proposed inversion algorithm is both efficient and robust, delivering satisfactory performance even in the presence of substantial measurement uncertainties.

Nonlinear Inversion for a Multilayer Seismic S‐Wave Attenuation Model Using Radiative Transfer Theory

AbstractWe numerically solve the acoustic radiative transfer equation for seismic S‐waves via Monte Carlo simulation. By assuming a von Kármán‐type random medium with anisotropic scattering, we are able to simulate a realistic medium and determine its attenuation properties. In this study, we present an improved method, called QEST, to determine the frequency‐dependent intrinsic and scattering attenuation by nonlinear envelope inversion for a 1‐D multilayer model. Additionally, the spectral source energy of earthquakes and the energy site amplification of stations are determined. The code was applied to real data from the northern and southern Leipzig‐Regensburg fault zone (LRZ), Germany, as well as fluid‐induced earthquakes at the Insheim geothermal reservoir, Germany. The attenuation was analyzed in several frequency bands between 4.2 and 33.9 Hz and between 6.0 and 67.9 Hz, respectively. The inversion results reveal that the crystalline crustal subsurface along the LRZ shows little to no depth dependence, but there are differences in attenuation between the north and south. At Insheim, the near‐surface sedimentary basin exhibits significantly greater absorption and scattering than the crystalline basement. The inversion also shows that isotropic scattering can be an oversimplification and thus underestimate attenuation.

An asymmetrically variable wingtip anhedral angles morphing aircraft based on incremental sliding mode control: Improving lateral maneuver capability

This paper presents the design of an asymmetrically variable wingtip anhedral angles morphing aircraft, inspired by biomimetic mechanisms, to enhance lateral maneuver capability. Firstly, we establish a lateral dynamic model considering additional forces and moments resulting during the morphing process, and convert it into a Multiple Input Multiple Output (MIMO) virtual control system by importing virtual inputs. Secondly, a classical dynamics inversion controller is designed for the outer-loop system. A new Global Fast Terminal Incremental Sliding Mode Controller (NDO-GFTISMC) is proposed for the inner-loop system, in which an adaptive law is implemented to weaken control surface chattering, and a Nonlinear Disturbance Observer (NDO) is integrated to compensate for unknown disturbances. The whole control system is proven semi-globally uniformly ultimately bounded based on the multi-Lyapunov function method. Furthermore, we consider tracking errors and self-characteristics of actuators, a quadratic programming-based dynamic control allocation law is designed, which allocates virtual control inputs to the asymmetrically deformed wingtip and rudder. Actuator dynamic models are incorporated to ensure physical realizability of designed allocation law. Finally, comparative experimental results validate the effectiveness of the designed control system and control allocation law. The NDO-GFTISMC features faster convergence, stronger robustness, and 81.25% and 75.0% reduction in maximum state tracking error under uncertainty compared to the Incremental Nonlinear Dynamic Inversion Controller based on NDO (NDO-INDI) and Incremental Sliding Mode Controller based on NDO (NDO-ISMC), respectively. The design of the morphing aircraft significantly enhances lateral maneuver capability, maintaining a substantial control margin during lateral maneuvering, reducing the burden of the rudder surface, and effectively solving the actuator saturation problem of traditional aircraft during lateral maneuvering.

Self-supervised learning for improved calibrationless radial MRI with NLINV-Net.

To develop a neural network architecture for improved calibrationless reconstruction of radial data when no ground truth is available for training. NLINV-Net is a model-based neural network architecture that directly estimates images and coil sensitivities from (radial) k-space data via nonlinear inversion (NLINV). Combined with a training strategy using self-supervision via data undersampling (SSDU), it can be used for imaging problems where no ground truth reconstructions are available. We validated the method for (1) real-time cardiac imaging and (2) single-shot subspace-based quantitative T1 mapping. Furthermore, region-optimized virtual (ROVir) coils were used to suppress artifacts stemming from outside the field of view and to focus the k-space-based SSDU loss on the region of interest. NLINV-Net-based reconstructions were compared with conventional NLINV and PI-CS (parallel imaging + compressed sensing) reconstruction and the effect of the region-optimized virtual coils and the type of training loss was evaluated qualitatively. NLINV-Net-based reconstructions contain significantly less noise than the NLINV-based counterpart. ROVir coils effectively suppress streakings which are not suppressed by the neural networks while the ROVir-based focused loss leads to visually sharper time series for the movement of the myocardial wall in cardiac real-time imaging. For quantitative imaging, T1-maps reconstructed using NLINV-Net show similar quality as PI-CS reconstructions, but NLINV-Net does not require slice-specific tuning of the regularization parameter. NLINV-Net is a versatile tool for calibrationless imaging which can be used in challenging imaging scenarios where a ground truth is not available.

Application of the high-resolution layer-stripping constrained seismic inversion method in thin reservoirs: a case study of Triassic reservoir prediction in Lunnan, Tarim Basin, NW China

The Lunnan Oilfield’s Triassic reservoir is a braided river delta deposition, exhibiting notable characteristics such as rapid lateral sand body changes, thin reservoir thickness, and deep reservoir burial. However, the conventional seismic inversion technique used in this geological context is hindered by the low accuracy of the low-frequency model, resulting in inversion outcomes with limited vertical resolution. Consequently, accurately characterizing the reservoir distribution becomes challenging, posing a significant obstacle for the subsequent exploration and development. In this study, we introduce an enhanced seismic inversion approach using high-resolution layer-stripping constraints. The process begins with the creation of a fine-grained layer sequence grid on wells through Integrated Prediction Error Filter Analysis (INPEFA) technology. Subsequently, this grid aids in interpreting high-resolution sequence layers on the seismic volume, obtained through a frequency division RGB (Red, Green, and Blue) fusion technique. Finally, a novel nonlinear stochastic inversion method is formulated, utilizing the high-resolution sequence to refine both the initial model and inverted results. This innovative seismic inversion technique significantly enhances accuracy, particularly in predicting thin reservoirs, approximately 3 meters thick. The application of this method in a case study on Triassic reservoir prediction in Lunnan, Tarim Basin, NW China, demonstrates its promising future applications.