Nonlinear Tomography Research Articles

A physics-informed neural network for non-linear laser absorption tomography

Hyperspectral absorption tomography has emerged as a promising technique for combustion diagnostics due to its rich spectral measurements. However, the non-linear and ill-posed nature of the inverse problem makes obtaining accurate results challenging. This paper proposes a novel application of a physics-informed neural network to address the non-linear inverse problem in hyperspectral absorption spectroscopy. This method utilizes physical laws and measurement data to guide the neural network in finding the optimal solution, without requiring training data. To demonstrate its capabilities, the physics-informed neural network is employed to retrieve temperature and CO2 mole fraction fields in axisymmetric laminar diffusion flames via 4.3μm TDLAS (tunable diode laser absorption spectroscopy). The developed neural network is applied to resolve the spatial distributions from the spectral dimensions, requiring fewer spatial measurements for directly retrieving temperature and CO2 mole fraction profiles. We investigate the minimum radial projections needed for accurate retrievals and evaluate the model’s robustness to random noise through the inversion of a simulated flame. The developed model is further applied to reconstruct the temperature and CO2 mole fraction fields for an experimentally measured flame. Our results demonstrate that the proposed model maintains high retrieval accuracy even with limited, noisy data. This work highlights the potential of the physics-informed neural network for robust solutions to non-linear laser absorption tomography problems in scientific and engineering applications.

Uncertainty quantification for goal-oriented inverse problems via variational encoder-decoder networks

In this work, we describe a new approach that uses variational encoder-decoder (VED) networks for efficient uncertainty quantification for goal-oriented inverse problems. Contrary to standard inverse problems, these approaches are goal-oriented in that the goal is to estimate some quantities of interest (QoI) that are functions of the solution of an inverse problem, rather than the solution itself. Moreover, we are interested in computing uncertainty metrics associated with the QoI, thus utilizing a Bayesian approach for inverse problems that incorporates the prediction operator and techniques for exploring the posterior. This may be particularly challenging, especially for nonlinear, possibly unknown, operators and nonstandard prior assumptions. We harness recent advances in machine learning, i.e. VED networks, to describe a data-driven approach to large-scale inverse problems. This enables a real-time uncertainty quantification for the QoI. One of the advantages of our approach is that we avoid the need to solve challenging inversion problems by training a network to approximate the mapping from observations to QoI. Another main benefit is that we enable uncertainty quantification for the QoI by leveraging probability distributions in the latent and target spaces. This allows us to efficiently generate QoI samples and circumvent complicated or even unknown forward models and prediction operators. Numerical results from medical tomography reconstruction and nonlinear hydraulic tomography demonstrate the potential and broad applicability of the approach.

Advanced Excitation Symmetry Analysis Method (ESAM) for improved automated ultrasonic testing of polished materials

One of the strategic plans of the international NDT community is to define standards for developing advanced and digitalized non-destructive testing for automated set-up in mass production [1,2]. Excitation Symmetry Analysis Method (ESAM) are new coded signal processing tools exploring symmetry properties [3]. ESAM method aims to extract the nonlinearity from an ultrasound output by studying the property of symmetry of the transfer function. More precisely, the extraction of nonlinearity is done using basis function associated to a group which is associated to an a priori transfer function. As soon as the linear tomography uses the invariant properties of amplitude dependence, nonlinear tomography should exploit the invariance properties (or symmetry properties) of the complex system. Consequently, the invariance of the stationary properties of a complex medium would be supposed to be associated to a signature of the degradation, that will be extracted with advanced signal processing tools. The idea remains the same: studying the symmetry and more precisely the time reversal one and the inversion. A signal processing is used to measure discrepancies in the autocorrelation function (output signal called chirp coda) for two inputs: a signal and its inversion (pulse inversion). The experiments were done on different sample with and without cracks. It has been shown that when the nonlinearities are extracted from the chirp coda amplitude are non- negligible in crack area and nonlinearities are out of phase. Then, mixing symmetries like time reversal and pulse inversion enable the detection of defect that are difficult to detect with conventional ultrasound method. Indeed, for such crack a destructive mechanical analysis called Jomini is done where the material is polished and examined with microscope. The sensitivity improvement of chirp-coded signal processing has been validated in various domains. Coded excitation techniques, used in communication systems such as radar and sonar provide improved SNR without increasing the amplitude of excitation. The typical test equipment consists of a preamplifier Juvitek TRA-02 (0.02 - 5 MHz) connected to a computer, an amplifier ENI model A150 (55 dB at 0.3-35 MHz), a shear wave transducer Technisonic (2.25 MHz), and a longitudinal wave transducer Panametrics V155 (5 MHz). The experimental device was tested with the V3 calibration block, improved, and specially scaled in order to access to a wide range of multivalued parameters: mechanical properties, ultrasonic parameters (celerity and attenuation) and local geometric data.

Post-collisional lithospheric delamination in eastern Iran, revealed by non-linear teleseismic tomography and residual topography

The Eastern Iranian Mountain Ranges (EIR) emerged as a consequence of the Late Cretaceous collision between the Afghan and Lut blocks. However, the response of the uppermost mantle to this collision remains enigmatic. Additionally, although petrological evidence suggests that post-collisional delamination is possible, it has not been conclusively identified in prior regional seismic imagery. This observation leads us to further explore this possibility using a dense seismic network. To gain insight into the geodynamic implications for eastern Iran and address knowledge gaps, we extensively investigated the seismic structure of the uppermost mantle beneath the EIR using a dense seismic network of 34 temporary stations, complemented by data from nine additional local permanent stations. By meticulously analyzing 6589 relative arrival time residuals from teleseismic records with favorable signal-to-noise ratios, we applied a non-linear tomography method to map P-wave velocity perturbations in a relative sense. Our tomographic images unveiled distinct instances of rapid high-velocity anomalies beneath low-velocity regions in the shallow mantle, suggesting the potential occurrence of lithospheric dripping, followed by subsequent asthenospheric upwelling. This observation offers a plausible explanation for the observed post-collisional magmatism over the Lut Block. Furthermore, to maintain the approximately 1.5-km positive residual topography across the EIR, beyond the influence of crustal properties, additional support from the hot and buoyant asthenosphere becomes crucial, particularly in the absence of a substantial lithospheric mantle.

Measurement the 3D temperature distribution in the combustion zone using acoustic tomography and nolinear wavefont tracing

A non-invasive temperature measurement method in three dimensions is proposed to reconstruct the three-dimensional temperature. It relies on the use of nonlinear acoustic travel-time tomography, which incorporates acoustic path tracing, Gaussian kernel function, and modified Tikhonov regularization. Sound signals can be distorted due to large average temperature gradients and oscillatory release of heat in the combustion region. The effective acoustic signal is extracted based on spectral subtraction to evaluate the sound propagation time under distorted signals. The analysis of experimental errors indicates that the proposed acoustic pyrometry method has an average relative error of less than 15% compared to the time-averaged temperature measured by thermocouples. This result verifies the efficacy of the method when it comes to accurately reconstructing the temperature field in the combustion area. The reduction in error is attributed to the consideration of acoustic ray tracing within the proposed method.

Nonlinear Medical Ultrasound Tomography: 3D Modeling of Sound Wave Propagation in Human Tissues

The article aimed to show the fundamental possibility of constructing a computational digital twin of the acoustic tomograph within the framework of a unified physics–mathematical model based on the Navier–Stokes equations. The authors suggested that the size of the modeling area is quite small, sound waves are waves of “small” disturbance, and given that a person consists of more than 60% water, human organs can be modeled using a liquid model, taking into account their density. During numerical experiments, we obtained the pressure registered in the receivers that are located on the side walls of the tomograph. The differences in pressure values are shown depending on the configuration of inclusions in the mannequin imitating internal organs. The results show that the developed technology can be used to probe the human body in medical acoustic tomographs and determine the acoustic parameters of the human body to detect neoplasms.

An updated 3D crustal velocity model in Cyprus by non-linear traveltime tomography

We have used >6000 regional earthquakes in the broader Cyprus island area, with 100,000+ P and S-phases recorded by 26 local seismological stations to obtain a 3D velocity model for the Cyprus area using a non-linear seismic tomography approach. Due to the limited aperture of the recording network, the tomographic model mainly recovers the dominant geophysical features of the upper crust of the Cyprus island. The obtained results reveal new information on the 3D structure of the Troodos ophiolithic complex, identifying a high velocity anomaly that coincides with the NW-SE trending ophiolitic units of the Troodos mountain. Furthermore, they clearly show that the anomaly associated with the ophiolite zone of Troodos is well preserved up to a depth of at least 10 Km, dipping towards the Northeast. The Mesaoria basin in central Cyprus is recognized as a large low-velocity feature, with the results suggesting a significant thickness of the basin, exceeding 5 km. A similar low-velocity pattern can be observed for the Circum Troodos formations (Pakhna and Lefkara) to the southwest of the Troodos mountain, while the Kyrenia range is not associated with a crustal velocity anomaly. To assess the reliability of the results we have conducted checkerboard resolution tests, which suggest that the uppermost crustal features are well reconstructed, while for larger depths (>10 km) we cannot recover mid-crustal anomalies due to the insufficient traveltime data coverage and quality (e.g., traveltime picking errors).

Domain independent post-processing with graph U-nets: applications to electrical impedance tomographic imaging⋆ ⋆SH and BH were supported by the National Institute Of Biomedical Imaging And Bioengineering of the National Institutes of Health under Award Number R21EB028064. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. AH

Objective. To extend the highly successful U-Net Convolutional Neural Network architecture, which is limited to rectangular pixel/voxel domains, to a graph-based equivalent that works flexibly on irregular meshes; and demonstrate the effectiveness on electrical impedance tomography (EIT). Approach. By interpreting the irregular mesh as a graph, we develop a graph U-Net with new cluster pooling and unpooling layers that mimic the classic neighborhood based max-pooling important for imaging applications. Main results. The proposed graph U-Net is shown to be flexible and effective for improving early iterate total variation (TV) reconstructions from EIT measurements, using as little as the first iteration. The performance is evaluated for simulated data, and on experimental data from three measurement devices with different measurement geometries and instrumentations. We successfully show that such networks can be trained with a simple two-dimensional simulated training set, and generalize to very different domains, including measurements from a three-dimensional device and subsequent 3D reconstructions. Significance. As many inverse problems are solved on irregular (e.g. finite element) meshes, the proposed graph U-Net and pooling layers provide the added flexibility to process directly on the computational mesh. Post-processing an early iterate reconstruction greatly reduces the computational cost which can become prohibitive in higher dimensions with dense meshes. As the graph structure is independent of ‘dimension’, the flexibility to extend networks trained on 2D domains to 3D domains offers a possibility to further reduce computational cost in training.

Simultaneous reconstruction of 3D non-uniform temperature and velocity fields in a furnace using a bidirectional acoustic path separation tracking method

Acoustic measurement techniques applied to furnace measurements are based on the coupling of multiple fields, such as sound, temperature, and flow. To better measure high-temperature fluids within furnaces using acoustic technology, this study establishes a physical model that separates the bidirectional propagation path of sound. Additionally, a novel nonlinear acoustic ray-tracing method is proposed that allows the simultaneous measurement of temperature and flow. The combustion process of high-temperature swirling flow in a furnace is simulated, the effective path of sound propagation is tracked, and the existence of a sound shadow zone in the low-temperature jet region of the burner is discovered. After reasonably arranging sensor positions, three-dimensional nonuniform temperature and complex flow fields are simultaneously reconstructed by combining ray tracing and nonlinear acoustic tomography. The feasibility of the method is analyzed via the error analysis of the computational fluid dynamics simulation data and the reconstructed data. The proposed method provides a solution for improving the existing acoustic measurement techniques in furnaces.

Borehole fibre-optic seismology inside the Northeast Greenland Ice Stream

SUMMARYIce streams are major contributors to ice sheet mass loss and sea level rise. Effects of their dynamic behaviour are imprinted into seismic properties, such as wave speeds and anisotropy. Here, we present results from a distributed acoustic sensing (DAS) experiment in a deep ice-core borehole in the onset region of the Northeast Greenland Ice Stream, with focus on phenomenological and methodological aspects. A series of active seismic surface sources produced clear recordings of the P and S wavefield, including internal reflections, along a 1500 m long fibre-optic cable that was placed into the borehole. The combination of nonlinear traveltime tomography with a firn model constrained by multimode surface wave data, allows us to invert for P and S wave speeds with depth-dependent uncertainties on the order of only 10 m s−1, and vertical resolution of 20–70 m. The wave speed model in conjunction with the regularly spaced DAS data enable a straightforward separation of internal upward reflections followed by a reverse-time migration that provides a detailed reflectivity image of the ice. While the differences between P and S wave speeds hint at anisotropy related to crystal orientation fabric, the reflectivity image seems to carry a pronounced climatic imprint caused by rapid variations in grain size. Further improvements in resolution do not seem to be limited by the DAS channel spacing. Instead, the maximum frequency of body waves below ∼200 Hz, low signal-to-noise ratio caused by poor coupling, and systematic errors produced by the ray approximation, appear to be the leading-order issues. Among these, only the latter has a simple existing solution in the form of full-waveform inversion. Improving signal bandwidth and quality, however, will likely require a significantly larger effort in terms of both sensing equipment and logistics.

3D reconstruction of nonuniform and unsteady flow velocity distribution using nonlinear acoustic ray tracing and tomography

The nonuniform and unstable gas flow distribution at the air inlets of the boiler combustion chamber can negatively affect the energy efficiency and safety of the boiler. Accurately reconstructing the flow velocity inside the furnace in three dimensions can help the boiler automatically adjust the air supply to reduce unwanted energy waste and gas emissions.In this study, we propose a novel method for three-dimensional velocity reconstruction based on nonlinear acoustic ray tracing and tomography. The model provides the equation of acoustic ray motion in the flow field, which includes spatial and temporal parameters. During the process of measuring flow velocity, higher precision reconstruction of the three-dimensional velocity distribution was achieved by combining acoustic refractive paths based on derivative-free optimization iteration updates with radial basis functions that use Gaussian kernels.The computational time cost and reconstruction accuracy of the proposed algorithm were analyzed numerically and through simulations. The results indicate that the method incorporating acoustic ray tracing greatly improves the reconstruction accuracy of large gradient nonuniform flow fields. Additionally, we analyzed the impact of different numbers of sound tracing paths on reconstruction quality and computation time, providing a reference for the rational placement of microphones and the selection of the most efficient paths in boilers.Experimental results show that this method has good reconstruction capabilities for nonuniform flow fields and practical applications.

A Positive Data Extraction Method for Electrical Impedance Tomography (EIT) Based on the Novel MSA-Net

The nonlinear and ill-posed Electrical Impedance Tomography (EIT) inverse problem leads to reconstructed images with distortion and low resolution. A positive data extraction method based on the deep learning is proposed in this study. This method realizes the accurate classification of the positive data and the general data in voltage measurements through a deep learning network called Multi Scale Attention Network (MSA-Net). The positive data is used for image reconstruction to improve the imaging quality. Multi scale feature extraction, residual learning and attention mechanism are introduced in the MSA-Net to improve classification accuracy. The Linear Back Projection (LBP) algorithm and the Tikhonov Regularization (TR) algorithm are used to verify the effectiveness of the proposed method. Both simulation and experiment are conducted around imaging of bubble flow. The simulation results indicate that there are fewer artifacts in the reconstructed images using the positive data of MSA-Net, and the shape and position of the bubbles can be better described. Moreover, the imaging quality of experimental samples can also be effectively improved. The existing test results preliminarily confirm that the distribution of the positive data and the general data in voltage measurements is related to the position of bubbles in the measurement domain.

Numerical and experimental investigation of many-objective optimization for efficient temperature and velocity fields reconstruction via acoustic tomography

Accurate and high-quality measurement of temperature and velocity fields is important in combustion and flow diagnosis, especially for the control of boiler operations and optimization of combustion processes. Nonlinear acoustic tomography (NAT) is an appealing approach to simultaneously monitor temperature and velocity fields by observing the changes in sound speed they induce. However, the inherent ill-posedness of the tomographic inverse problem and the lack of a priori information may lead to poor robustness, low precision, and the presence of artifacts. On the other hand, the inclusion of a priori information, such as smoothness and box constraints, may require the use of hyperparameters that hinder quasi real-time reconstruction and may degrade performance in actual applications. In this study, we aim to ensure quasi real-time reconstruction and alleviate the ill-posedness of the NAT problem by eliminating the regularization parameters and transforming the inverse problem into a many-objective optimization problem with four objectives. The knee point-driven evolutionary algorithm with improved environmental selection strategy (KEA-IES) is proposed to simultaneously reconstruct the arbitrary inhomogeneous temperature and aerodynamic fields in a furnace. Apart from the investigation of the performance of KEA-IES and the influence of various factors, including measurement noise, function evaluation number, population size, and functional norms, on the quality of the reconstruction, an experimental AT system with independent 16 T/R channels is developed to evaluate the proposed method. The results show that combustion flame temperature and velocity fields can be monitored simultaneously by using the proposed KEA-IES with more accuracy, less consumed time, and better noise immunity compared with the state-of-the-art algorithms. The proposed method can provide valuable guidance in the development of a non-intrusive real-time pyrometry and velocimetry system, particularly for applications with large velocity fields and temperature gradients. Furthermore, its capability in complex combustion and flow diagnosis will offer benefits in terms of energy conservation, emission reduction, boiler operations supervision, and combustion process optimization.

Acoustic reconstruction of the vortex field in the nonuniform temperature field of a simulated furnace

The high-temperature thermal vortex flows generated by the tangential combustion of large-scale coal-fired power plant boilers represent a complex medium that causes acoustic ray bending. In this paper, nonlinear acoustic tomography is combined with particle swarm optimization as a means of reconstructing the two-dimensional thermal vortex field. The nonuniform and large-gradient temperature distribution is the main cause of acoustic ray bending. We establish curved paths of acoustic propagation that are closely related to the temperature distribution. Based on these curved paths, a set of radial basis functions combined with Tikhonov regularization is derived to achieve more accurate reconstruction of the temperature field. The nonuniform temperature field affects the reconstruction of the flow field information. Our model considers a variation in the angle between the direction vector of the acoustic curved paths and the horizontal and vertical components of the flow velocity, and incorporates a priori information and a six-parameter hypersurface into the flow field reconstruction scheme. Particle swarm optimization is applied to minimize the objective function and obtain the vortex velocity field. Numerical experiments show that the proposed mathematical model of acoustic ray bending improves the reconstruction accuracy of the temperature distribution and vortex flow field. The acoustic time-of-flight is experimentally measured through the generalized cross-correlation with different window functions. The temperature and vortex fields reconstructed based on the actual time-of-flight are compared with thermocouple and anemometer measurements to demonstrate the validity and robustness of the proposed reconstruction model.

Active fault detection and characterization by ultrashallow seismic imaging: A case study from the 2016 Mw 6.5 central Italy earthquake

We present the first high-resolution ultrashallow seismic image of a normal fault segment that ruptured the surface during the Mw 6.5 2016 Norcia earthquake (central Italy). This is the only fault, in the entire activated 25 km-long system, cutting a thick succession of Quaternary deposits, with an associated 3-m-high cumulative scarp. A 190-m-long profile crossing the fault was acquired and analyzed combining reflection seismic, non-linear multiscale refraction P-wave tomography and multi-channel analysis of surface waves. The joint interpretation of the seismic reflection, P- and S-wave velocity images unravels a 100-m-thick sequence of sandy-gravel alluvial fans, disrupted by a main normal fault zone, named as Valle delle Fonti fault (Vf1), which branches upward into three splays. The eastern splay of Vf1 matches with the 2016 coseismic surface rupture. Near-surface truncated reflections and growth strata in the hanging wall of the western and intermediate splays attest to their activity in Late Pleistocene-Holocene times. We also detect an additional normal fault in the footwall of Vf1, probably inactive since the Late Pleistocene.Comparing the seismic images with the Poisson's coefficient model and with the results of a previous electrical resistivity tomography, we constrain the lithology and the hydraulic behavior of the uppermost 50 m of the fault. A steep, W-dipping zone with high-Vp, very high Poisson's coefficient and low resistivity correlates with the eastern splay of Valle delle Fonti fault and unravels a water-saturated region. These results suggest that the fault zone may act as a partial barrier for horizontal fluid flow. Our findings indicate that the active fault zone detected by seismic imaging is much wider than what previously estimated from surface geological analyses. In terms of surface faulting hazard, this study confirms the effectiveness of high-resolution seismic surveys in defining the geometry and physical properties of active fault zones.

Deep-plug-and-play proximal Gauss-Newton method with applications to nonlinear, ill-posed inverse problems

In this paper we propose a proximal Gauss-Newton method for the penalized nonlinear least squares optimization problem arising from regularization of ill-posed nonlinear inverse problems. By exploiting the modular structure that characterizes the proximal-type methods, we plug in a pre-trained graph neural net denoiser in place of the standard proximal map. This allows to mould the prior on the data. An encoder-decoder Graph U-Net architecture is proposed as denoiser, which works on unstructured data; its mathematical formulation is derived to analyse the Liptschitz condition. With the intent of showing the benefits of applying deep Plug-and-Play reconstructions, we consider as an exemplar application, the nonlinear Electrical Impedance Tomography, a promising non-invasive imaging technique mathematically formulated as a highly nonlinear ill-posed inverse problem.

Open structure magnetic particle imaging by nonlinear back projection tomography reconstruction.

In open structure MPI systems, the nonlinear variation of the field free lines in the large region of interest scanning process distorts the x-space image reconstruction. In this study, we propose a nonlinear field free line projection reconstruction algorithm to solve the edge distortion problem of open structure MPI imaging. First, we calculate the curvature change law of the field free line in the scanning process. Then, we design a nonlinear back projection reconstruction algorithm according to the nonlinear characteristics of the field free line in the scanning process. Finally, the nonlinear back projection reconstruction algorithm is used to complete the tomography of blood vessels. The numerical calculation and simulation results show that the open structure MPI combined with a nonlinear back projection reconstruction algorithm can accomplish vascular fault reconstruction. The reconstruction algorithm proposed in this paper suppresses the edge distortion of the image and improves the positioning accuracy of the image. The size of the region of interest where distortions are low is increased 16 times by allowing 10.9% degradation in the gradient. We provide a non-linear inverse projection reconstruction algorithm to reduce the structural artefacts caused by FFL distortion. It provides a reconstruction scheme for a large region of interest fine imaging of open structure FFL-MPI.

Optimal Implementation Parameters of a Nonlinear Electrical Impedance Tomography Method Using the Complete Electrode Model

This study discusses a nonlinear electrical impedance tomography (EIT) technique under different analysis conditions to propose its optimal implementation parameters. The forward problem for calculating electric potential is defined by the complete electrode model. The inverse problem for reconstructing the target electrical conductivity profile is presented based on a partial-differential-equation-constrained optimization approach. The electrical conductivity profile is iteratively updated by solving the Karush–Kuhn–Tucker optimality conditions and using the conjugate gradient method with an inexact line search. Various analysis conditions such as regularization scheme, number of electrodes, current input patterns, and electrode arrangement were set differently, and the corresponding results were compared. It was found from this study that the proposed EIT method yielded appropriate inversion results with various parameter settings, and the optimal implementation parameters of the EIT method are presented. This study is expected to expand the utility and applicability of EIT for the non-destructive evaluation of structures.

Analysis and modeling of laser-driven ion-beam trace probe diagnostics of poloidal magnetic fields in field-reversed configurations

The field-reversed configuration (FRC) is a promising magnetic confinement fusion concept [M. Tuszewski, Nucl. Fusion 28, 2033 (1988)] and is often chosen as the target plasma for magneto inertial fusion [S. A. Slutz and M. R. Gomez, Phys. Plasmas 28, 042707 (2021)]. In FRCs, the toroidal magnetic field is essentially zero, and the poloidal magnetic field (Bp) pressure is comparable with the plasma pressure. Applying the traditional Bp diagnostics to FRCs is a major challenge because Bp is small, and reversal occurs across the core region of FRCs. The laser-driven ion-beam trace probe (LITP) is a newly developing diagnostic method to measure Bp and the radial electric field (Er) in tokamak. Here, the principles of using LITP to diagnose Bp in FRCs are proposed, verified, and numerically implemented using an iterative method to reconstruct the Bp profile. Least square tomography employing a dissipative term is used to solve the nonlinear tomography problem, which arises when applying LITP to the unique FRC magnetic topology. Numerical modeling results show that the relative errors of the reconstruction are mostly below 10%, verifying the feasibility of LITP diagnostics for FRC internal magnetic field measurements. Ion beam orbits and detector arrangements are optimized to meet the experimental requirements of FRCs. LITP can still be applied to diagnose Bp in FRCs when there is 5% measurement errors.

Crustal and upper mantle structure of northern Algeria inferred from a 3-D inversion of teleseismic tomography

The northern Algeria forms the southern edge of the Western Mediterranean Sea that formed within the zone of convergence between the African and Eurasian plates. It represents a key area in the Mediterranean formation where controversial kinematic and geodynamical models are proposed. The current structure of northern Algeria is the result of the geodynamic evolution that occurred in the western Mediterranean. Several tomographic studies revealed the presence of velocity anomalies beneath the south of the western Mediterranean region. Namely, these anomalies suggest the existence of abnormally cold material in the asthenosphere, interpreted as the trace of subducted plunging panels (slab). Unfortunately, the lack of suitable data remains the main limit confronted by most tomographic studies in the northern Algeria, which remains to date one of the poorly studied regions in the western Mediterranean. During the recent decades, the seismological network in northern Algeria has significantly developed, giving rise to a significant increase in the amount of information given by the network database. This development motivated us to investigate the crust and the upper mantle structure beneath the northern Algeria using a non-linear teleseismic tomography method. An overall of 29 stations distributed along the northern part of Algeria have been used to collect 11018 P-waves from 2847 earthquakes recorded over ten years. The proposed model suggests the presence of high-velocity anomalies along of the northern Algeria. We interpreted these abnormal anomalies as a slab teared into two panels. A central segment bending along the Atlasic massif of Hodna and plunging under the Lesser Kabylia. The second segment at the North-East, shifted to the north plunging towards the NE. A series of Tellian high-velocity anomalies axes mark the limit between the African and the Eurasian plate. The south eastern region covered by our model (Aurès massif) is characterized by a low-velocity anomaly potentially linked to the remnant of the Mesozoic rifting.