Inversion Of Data Research Articles

Application of electromagnetic method to seaward slope: Case study from Saemangeum Seawall in South Korea

As dams and seawalls around the world age, there has been increasing attention towards the safety of these structures and monitoring of their condition using various geophysical methods. However, assessing seaward slopes, which are directly impacted by waves and tides and a seawater-filled riprap stone deposit structure, has been challenging due to the highly conductive environment and the difficulty in installing contact equipment. This study examines the feasibility of using electromagnetic (EM) surveys to monitor seawall slopes by analyzing and interpreting multi-frequency EM data measured over 2-year period along the seawall slope of the Saemangeum Seawall in South Korea, which is recognized as the world’s longest seawall. The repeatability of data measured at the same survey line over the 2-years substantiates the reliability of EM survey data. The field data are inverted using a 2.5-dimensional regularized Gauss–Newton method. EM responses calculated from the inverted resistivity model closely mirrors the field data. The inversion results consistently indicate the presence of an isolated conductive anomaly with seawater resistivity below the survey line on the seaward slope. This suggests scouring erosion by seawater and affirms the applicability of the EM surveys to seaward slope monitoring. Additionally, the EM responses are affected significantly by sea level changes during the survey; these variations are quantified in this study. Thus, our results demonstrate the effectiveness of the EM method in monitoring the seaward slope to assess the safety of seawalls and provides a comprehensive overview of the entire data analysis and inversion processes, as a valuable reference for those using EM technology in their work.#xD;

ROM Inversion of Monostatic Data Lifted to Full MIMO

ROM Inversion of Monostatic Data Lifted to Full MIMO

Hybrid optimization for DC resistivity imaging via intelligible-in-time logic and the interior point method.

Geophysical DC resistivity imaging is crucial in subsurface exploration, environmental studies, and resource assessment. However, traditional inversion techniques face challenges in accurately resolving complex subsurface features because of the inherent nonlinearities in geophysical data. To overcome these challenges, we propose a hybrid optimization approach that combines incomprehensible but intelligible-in-time (IbI) logic with the interior point method (IPM). The IbI logic framework leverages complexity and temporal intelligibility, allowing for a dynamic interpretation of subsurface phenomena. By integrating IbI with IPM, our approach benefits from global exploration and local refinement, leading to improved convergence speed and solution quality. The objective function formulated for the inversion process includes data misfit and model regularization terms, which promote accurate and smooth solutions. Our methodology involves the use of the IbI logic algorithm (ILA) for initial global search, which identifies promising regions in the search space. This is followed by the application of IPM for local optimization. This synergy between the two algorithms ensures robustness and efficiency in handling large datasets and complex geological models. We conducted tests using synthetic and real DC resistivity data to validate our approach. The synthetic test demonstrated the accurate reconstruction of subsurface anomalies, whereas the real data test successfully identified fault zones, which is consistent with previous studies. The hybrid optimization algorithm significantly improves the resolution of subsurface structures and enhances geophysical data inversion practices. It balances the exploration and refinement phases effectively, optimizing the computation time and ensuring precise model delineation.

Gravity data inversion for parameters assessment over geologically faulted structures—A hybrid particle swarm optimization and gravitational search algorithm technique

AbstractInterpreting gravity anomalies caused by fault formations is associated with hydrocarbon systems, mineralized areas and hazardous zones and is the main goal of this research. To achieve an effective and robust model over the geologically faulted structures from gravity anomalies, we present a nature‐inspired hybrid algorithm, which synergizes the physics of the particle swarm optimization and gravitational search algorithm with variable inertia weights. The basic principle of developed particle swarm optimization and gravitational search algorithm method is to synergistically use the exploratory strengths of gravitational search algorithm with the exploitation capacity of particle swarm optimization in order to optimize and enhance the effectiveness by both algorithms. The technique has been tested on synthetic gravity data with varying settings of noises over geologically faulted structure before being applied to field data taken from Ahiri‐Cherla and Aswaraopet master fault present in Pranhita–Godavari valley, India. The optimization process is further refined through normalized Gaussian probability density functions, confidence intervals, histograms and correlation matrices to quantify uncertainty, stability, sensitivity and resolution. When dealing with field data, the true model is never known; in these circumstances, the quality of the outcome can only be inferred from the uncertainty in the mean model. The research utilizes a 68.27% confidence intervals to identify a location where the probability density function is more dominant. This region is then used to evaluate the mean model, which is expected to be more appropriate and closer to the genuine model. Correlation matrices further provide a clear demonstration of the strong connection between layer parameters. The results suggest that particle swarm optimization and gravitational search algorithm is less affected by model parameters and yields geologically more consistent outcomes with little uncertainty in the model, aligning well with the available results. The analysed results show that the method we came up with works well and is stable when it comes to solving the two‐dimensional gravity inverse problem. Future research may involve extending the approach to three‐dimensional inversion problems, with potential improvements in computational efficiency and search accuracy for global optimization methods.

The Multi‐Segment Complexity of the 2024 MW ${M}_{W}$ 7.5 Noto Peninsula Earthquake Governs Tsunami Generation

AbstractThe 1 January 2024, moment magnitude 7.5 Noto Peninsula earthquake ruptured in complex ways, challenging analysis of its tsunami generation. We present tsunami models informed by a 6‐subevent centroid moment tensor (CMT) model obtained through Bayesian inversion of teleseismic and strong motion data. We identify two distinct bilateral rupture episodes. Initial, onshore rupture toward the southwest is followed by delayed re‐nucleation at the hypocenter, likely aided by fault weakening, causing significant seafloor uplift to the northeast. We construct a complex multi‐fault uplift model, validated against geodetic observations, that aligns with known fault system geometries and is critical in modeling the observed tsunami. The simulations can explain tsunami wave amplitude, timing, and polarity of the leading wave, which are crucial for tsunami early warning. Upon comparison with alternative source models and analysis of 2000 multi‐CMT ensemble solutions, we highlight the importance of incorporating complex source effects for realistic tsunami simulations.

Estimation of Terrestrial Water Storage Changes in Brazil From the Joint Inversion of GRACE‐Based Geopotential Difference and GNSS Vertical Displacement Data

AbstractGravity Recovery and Climate Experiment (GRACE) satellite gravimetry and Global Navigation Satellite System (GNSS) surface displacement measurements offer complementary advantages for monitoring terrestrial water storage (TWS) changes. We propose a new joint inversion model based on the combination of GRACE‐based geopotential difference (GPD) observations using the mascon method and GNSS vertical displacements through the Green's function method to obtain reliable TWS changes in Brazil. The performance of the jointly inverted TWS changes is assessed through closed‐loop simulations and comparisons with hydrometeorological data (precipitation‐P, evapotranspiration‐ET, and runoff‐R) using water budget closure (P‐ET‐R) and river water level observations from satellite altimetry. The simulation results indicate that the joint inversion results exhibit higher accuracy and reliability than GRACE GPD‐based mascon (GPD‐mascon) and GNSS solutions, and the standard deviations (STDs) of joint results decrease by ∼6.98 and ∼37.5 mm compared to those from GPD‐mascon and GNSS solutions. The joint inversion of real GRACE and GNSS data demonstrates notably lower uncertainty than that of GPD‐mascon solutions and exhibits significant improvement than GNSS‐only solutions. The STDs and correlation coefficients between monthly R time series derived from three inversion methods (joint inversion, GPD‐mascon and GNSS solutions, combined with P and ET through water budget closure) and in situ observations are 19.607 mm and 0.912, 20.879 mm and 0.904, and 31.370 mm and 0.778, respectively. The joint estimates also yield better correlation with river water level observations compared to GPD‐mascon and GNSS solutions. Furthermore, at the weekly time scale, the joint inversion results show better consistency with P‐ET‐R data than those from GPD‐mascon and GNSS solutions.

Review of physics-informed machine-learning inversion of geophysical data

We review five types of physics-informed machine-learning (PIML) algorithms for inversion and modeling of geophysical data. Such algorithms use the combination of a data-driven machine-learning (ML) method and the equations of physics to model or invert geophysical data (or both). By incorporating the constraints of physics, PIML algorithms can effectively reduce the size of the solution space for ML models, enabling them to be trained on smaller data sets. This is especially advantageous in scenarios in which data availability may be limited or expensive to obtain. In this review, we restrict the physics to be that from the governing wave equation, either as a constraint that must be satisfied or by using numerical solutions of the wave equation for modeling and inversion. This approach ensures that the resulting models adhere to physical principles while leveraging the power of ML to analyze and interpret complex geophysical data. There are several potential benefits of PIML compared to standard numerical modeling or inversion of seismic data computed by, for example, finite-difference solutions to the wave equation. 1) Empirical tests suggest that PIML algorithms constrained by the physics of wave propagation can sometimes resist getting stuck in a local minima compared with standard full-waveform inversion (FWI). 2) After the weights of the neural network are found by training, then the forward and inverse operations by PIML can be more than several orders of magnitude more efficient than FWI. However, the computational cost for general training can be enormous. 3) If the ML inversion operator [Formula: see text] is locally trained on a small portion of the recorded data [Formula: see text], then there is sometimes no need for millions of training examples that aim for global generalization of [Formula: see text]. The benefit is that the locally trained [Formula: see text] can be used to economically invert the remaining test data [Formula: see text] for the true velocity [Formula: see text], where [Formula: see text] can comprise more than 90% of the recorded data.

WUS324: Multiscale Full Waveform Inversion Approaching Convergence Improves Waveform Fits While Imaging Seismic Structure of the Western United States

AbstractWe report a new model of radially anisotropic crustal and upper mantle structure of the western United States (WUS324) obtained from full waveform inversion of earthquake data. We ran three multiscale inversion stages beyond model WUS256 (Rodgers et al., 2022, https://doi.org/10.1029/2022jb024549) allowing them to approach convergence to fit a larger data set to a shorter minimum period of 16 s. WUS324 is based on 324 total iterations from its starting model, significantly more (16 times) than previous studies. Waveform misfit reductions are 66%–70% for both the inversion data and an independent validation data set providing confidence in the predictive power of the model. WUS324 provides much better fits and reveals shear wavespeed, vS, structure of this large region with more detail than previous waveform tomography models. We show representative images demonstrating the resolution of diverse seismic structure across this highly heterogeneous region including oceanic lithosphere, subducting slabs and continental magmatism.

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.

Distance-regularized level set inversion of magnetic data

Inversion is one of the key and difficult issues in potential field data processing and interpretation, and high-precision inversion has long been a popular research topic. We develop the distance-regularized level set inversion of the magnetic data (DRLSI-M) method. A double-well function is introduced into the objective function of the level set inversion, and we take advantage of its mathematical properties to minimize the gradient of the level set function so that it reaches a minimum value at points 0 and 1. As a result, the distance-regularized term not only replaces the costly reinitialization process so that the level set function maintains a signed distance property but also stabilizes the evolution of the level set function. When solving for the minimum value of the objective function, we transform the optimization problem into an initial-boundary value problem and solve it with the finite-difference method. The distance-regularized level set inversion method and the reinitialization level set inversion method are tested using three models: a single-level-set double-inclined plate model, a double-level-set double-inclined plate model, and a multiple-level-set three-cuboid model. Through model tests, the soundness of our method is verified and compared with the reinitialization level set inversion method. Our method avoids the limitations such as low efficiency and instability caused by the reinitialization of the level set inversion method. Furthermore, we apply our method for the inversion of aeromagnetic data from the Jinchuan copper-nickel mine. The results are consistent with the known geologic information, thus validating the practicality and effectiveness of DRLSI-M. The distance-regularized level set inversion method provides a theoretical basis for deep mine exploration.

2D Transdimensional Joint Inversion of Radio Magnetotelluric and Electrical Resistivity Tomography Data

Summary The joint inversion of radio magnetotelluric and electrical resistivity tomography data has the potential to reduce the uncertainties in the subsurface conductivity model. This is particularly beneficial when the datasets offer complementary information about the subsurface. However, the traditional gradient-based inversion methods pose challenges in quantifying uncertainty, as they yield a single model with limited appraisal of parameter uncertainty. The Bayesian inversion approach stands out for its capacity to provide quantitative assessments of uncertainty in the inverted model parameters. This is accomplished by generating an ensemble of models, leading to a posterior distribution that encapsulates both prior information concerning model parameters and the dataset information. We have implemented a transdimensional Markov chain Monte Carlo algorithm to perform the joint inversion of radio magnetotelluric and electrical resistivity tomography data. Through synthetic data studies, we illustrate how the inclusion of two complementary datasets can effectively reduce uncertainties in model parameters and how the model parameter uncertainties can be quantified. Subsequently, the developed algorithm is tested using exemplary field data from a waste site near Roorkee, India. Intensive prior geoelectric and transient electromagnetic as well as radio magnetotelluric studies investigated possible waste water seepage with a potential to contaminate the shallow aquifers. The derived subsurface structure from our transdimensional Bayesian results compare well with the deterministic results for the exemplary profile, but in addition provide comprehensive uncertainty estimates.

Recent and active faulting along the exposed front of the Northern Apennines (Italy): New insights from field and geochronological constraints

Establishing genetic links between active shallow faulting and deep seismogenic sources is challenging, especially in areas where seismogenic faults lack clear and readily interpretable geological evidence at the surface. The architecture of the Pedeapenninic margin of the Northern Apennines (Italy) reflects a regional-scale and complex NE-verging blind thrust system, which is dissected by transpressive/transtensive faults resulting from active NE-SW orogenic compression. The local geological framework is defined by allochthonous ocean-derived units resting atop Pliocene-to-present successions exposed along the Northern Apennines margin and to the NE of it, while the innermost chain sector mostly contains Adria-related units. We present results from field structural-geological investigations to identify and characterize potential active faults along the margin. There, the Pliocene-to-present units are faulted and folded, indicating that tectonic activity is still on-going, thus contributing to the local seismic hazard. Top-to-the NE and SW normal faults are common in the area and deform the Pliocene-to-present succession together with NE-SW strike-slip and transpressional/transtensional faults. Based on field evidence, we define four potentially active thrust segments affecting Middle Pleistocene to Holocene deposits exposed along the margin. Calcite U-Th dating on samples from faults extend the most recent datable tectonic activity back to the Middle Pleistocene. Paleostress analysis from inversion of fault-slip data from the most recent identified striated fault planes constrains a NE-SW shortening direction parallel to the Apennines regional migration direction. A distinct but coaxial extensional stress regime, recorded by structures measured within Plio-Pleistocene formations, was also identified. Our results offer a sound starting point for future investigations aimed at improving our understanding of active and seismogenic faulting in the area, so as to create robust Probabilistic Seismic and Fault Displacement Hazard Assessment (PSHA and PFDHA) models that can implement refined seismic hazard maps benefitting from structural-geological deterministic inputs in addition to the classic seismological constraints.

Time‐lapse inversion of self‐potential data through particle filtering

AbstractIn environmental sciences, comprehending the movement of subsurface contaminants is crucial for formulating effective remediation measures. The self‐potential (SP) method has become a common tool for delineating landfill contamination plumes. Contaminant diffusion or migration represents dynamic processes, with corresponding SP responses evolving over time. However, conventional SP interpretation approaches have predominantly relied on static single‐frame inversion, overlooking the temporal correlation in time‐series SP data and resulting in cumulative errors. To tackle this challenge, we introduce a novel method for time‐lapse inversion of SP data leveraging particle filtering. This approach recursively refines the priori state model through posteriori observations to achieve precise estimations of dynamic models. Specifically, a spherical polarization model is deployed to establish the state equations of underground contaminant diffusion and transport models, whereas the observation model is derived through forward modeling. The proposed method is validated using two synthetic examples and one lab‐measured dataset. The findings demonstrate the efficacy of the time‐lapse inversion algorithm in precisely estimating dynamic models, outperforming static single‐frame inversion based on the particle swarm optimization algorithm. The posteriori distribution of particles approximates a bell‐shaped distribution, with the true state closely positioned near the peak probability. Therefore, we affirm that conducting time‐lapse inversion of time‐series SP data through particle filtering is an effective and dependable approach for accurately estimating dynamic model states.

Use of three-dimensional crossgradient joint inversion of marine controlled-source electromagnetic (CSEM) and seismic refraction data for overburden geohazards mapping.

Offshore energy-transition activities typically require the construction of new subsurface facilities where overburden heterogeneity and deformation pattern may not be fully understood. Anisotropic marine controlled-source electromagnetic (CSEM) and seismic refraction imaging can be adapted to geohazards assessment of the overburden to mitigate safety and costs risks in such areas. This is investigated using an area in deepwater NW Borneo as the type-example. For this proof-of-concept study, a well-established Multiphysics joint inversion code was extended to handle marine seismic refraction data from conventional towed streamers. Realistic field-scale synthetic models with anomalous resistivities and velocities in the shallow section (∼50-200 m below mudline) were built and their CSEM and seismic refraction responses were first inverted separately and then jointly in 3D using the crossgradient method. The joint inversion recovered the anomalous bodies better than the separate inversions. The velocity model from the joint inversion showed improvement from the separate inversion but required a good starting model. As a further test of the methodology, first-break travel times were picked from legacy 3D seismic data and inverted with the available sparse CSEM data. For model verification, resistivity and velocity profiles from separate and joint inversion models were extracted at a well location in the survey area and compared with actual well logs. The joint inversion models matched the well logs better than the separate inversion models. Moreover, known geohazards (complex fault-sets and lateral-inflows) from independent geomatic study were imaged in the joint inversion models. Also, the resistivity anisotropy ratio tracked anomalous zones that correspond to zones of recent complex faulting and potential lateral inflows in the overburden mass transport systems. Thus, joint inversion of CSEM and seismic refraction data has potential to improve the mapping of overburden hazards offshore, with the associated resistivity anisotropy serving as a proxy for active ground deformation.

Joint Inversion of DC Resistivity and Gravity Data with Undulating Terrain Based on Deformed Hexahedral Mesh

In the field of mineral resource exploration, accurate imaging of subsurface structures is key to discovering and assessing potential mineral deposits. Traditional single geophysical methods, limited by terrain variations and their own constraints, can lead to divergent solutions and structural inconsistencies, affecting the reliability of exploration outcomes. To address these challenges, this paper presents a joint inversion method for three-dimensional direct current (DC) resistivity and gravity data based on a deformed hexahedral mesh. The article begins by outlining the current state of development of the method under study and proposes a research plan, followed by a detailed explanation of the theoretical basis and algorithmic implementation of the proposed method. Model tests confirm the advantages of the deformed hexahedral mesh in reducing terrain impacts and enhancing model resolution, demonstrating the optimization and complementarity of the resolution between the two methods after joint inversion. Finally, applying this method to actual data from the Huaniu Mountain area shows that joint inversion not only improves the consistency of the ore belt structure but also provides a more precise analysis for the quantitative interpretation of the distribution of underground mineral resources. This confirms the method’s effectiveness and potential in practical geological exploration.

Simultaneous seismic interpolation and statics estimation of land data

Imaging and inversion of land seismic data affected by complex weathering layers near the surface are challenging. When the data are additionally subsampled for economical reasons such as monitoring of sequestrated carbon dioxide and hydrogen, the problem is further exacerbated due to the combined influence of subsampling and weathering layers. On the one hand, interpolation performs poorly since the weathering layers reduce the data's coherency. On the other hand, near-surface corrections require knowledge of the subsurface model, separation between primaries and multiples as well as subsurface velocity estimation, which are difficult to perform from subsampled data. To overcome these hurdles, we combine seismic interpolation and statics estimation into a joint single rank-reduction-based algorithm. To our knowledge, this is the first time that this has been done. The proposed method simultaneously accounts for the weathering and subsampling effects, which both contribute to the low-rank structure destruction typically associated with statics-free densely-sampled data, to provide accurate reconstruction. Since a low-rank approximation is used for statics estimation, we also use it in rank-minimization interpolation as a cost-free initial solution to the optimization problem. As both statics estimation and interpolation operate in the midpoint-offset domain, we avoid the cost of transformations back and forth from the source-receiver to midpoint-offset transform domain. Consequently, the proposed reconstruction, which shows its potential on synthetic and field data is also computationally efficient.

Modelling uncertainty in P-wave arrival-times retrieved from DAS data: case-studies from 15 fibre optic cables

SUMMARY Distributed acoustic sensing (DAS) technology enables the detection of waves generated by seismic events, generally as uniaxial strain/strain rate time-series observed for dense, subsequent, portions of a Fibre Optic Cable (FOC). Despite the advantages in measurement density, data quality is often affected by uniaxial signal polarization, site effects and cable coupling, beyond the physical energy decay with distance. To better understand the relative importance of these factors for data inversion, we attempt a first modelling of noise patterns affecting DAS arrival times for a set of seismic events. The focus is on assessing the impact of noise statistics, together with the geometry of the problem, on epicentral location uncertainties. For this goal, we consider 15 ‘real-world’ cases of DAS arrays with different geometry, each associated with a seismic event of known location. We compute synthetic P-wave arrival times and contaminate them with four statistical distributions of the noise. We also estimate P-wave arrival times on real waveforms using a standard seismological picker. Eventually, these five data sets are inverted using a Markov chain Monte Carlo method, which offers the evaluation of the relative event location differences in terms of posterior probability density (PPD). Results highlight how cable geometry influences the shape, extent and directionality of the PPDs. However, synthetic tests demonstrate how noise assumptions on arrival times often have important effects on location uncertainties. Moreover, for half of the analysed case studies, the observed and synthetic locations are more similar when considering noise sources that are independent of the geometrical characteristics of the arrays. Thus, the results indicate that axial polarization, site conditions and cable coupling, beyond other intrinsic features (e.g. optical noise), are likely responsible for the complex distribution of DAS arrival times. Overall, the noise sensitivity of DAS suggests caution when applying geometry-only-based approaches for the a priori evaluation of novel monitoring systems.

Characterizing Egyptian National Seismic Network station sites using genetic optimization for microtremor data inversion

Characterizing Egyptian National Seismic Network station sites using genetic optimization for microtremor data inversion

CSEM Optimization Using the Correspondence Principle

Traditionally, 3D modeling of marine controlled-source electromagnetic (CSEM) data (in the frequency domain) involves high-memory demand, requiring solving a large linear system for each frequency. To address this problem, we propose to solve Maxwell’s equations in a fictitious dielectric medium with time-domain finite-difference methods, with the support of the correspondence principle. As an advantage of this approach, we highlight the possibility of its implementation for execution with GPU accelerators, in addition to multi-frequency data modeling with a single simulation. Furthermore, we explore using the correspondence principle to the inversion of CSEM data by calculating the gradient of the least-squares objective function employing the adjoint-state method to establish the relationship between adjoint fields in a conductive medium and their counterparts in the fictitious dielectric medium, similar to the approach used in forward modeling. We validate this method through 2D inversions of three synthetic CSEM datasets, computed for a simple model consisting of two resistors in a conductive medium, a model adapted from a CSEM modeling and inversion package, and the last one based on a reference model of turbidite reservoirs on the Brazilian continental margin. We also evaluate the differences between the results of inversions using the steepest descent method and our proposed momentum method, comparing them with the limited-memory BFGS (Broyden–Fletcher–Goldfarb–Shanno) algorithm (L-BFGS-B). In all experiments, we use smoothing by model reparameterization as a strategy for regularizing and stabilizing the iterations throughout the inversions. The results indicate that, although it requires more iterations, our modified momentum method produces the best models, which are consistent with results from the L-BFGS-B algorithm and require less storage per iteration.

Source Geometry and Rupture Characteristics of the 20 February 2023 Mw 6.4 Hatay (Türkiye) Earthquake at Southwest Edge of the East Anatolian Fault

AbstractFollowing the catastrophic 6 February 2023 Mw 7.8 and Mw 7.6 Kahramanmaraş earthquakes in the East Anatolian Fault Zone (EAFZ; southeast Türkiye), numerous aftershocks occurred along the major branches of this left‐lateral shear zone. The spatio‐temporal distribution of the earthquakes implied the stress‐triggering effects of co‐seismic ruptures on closely connected fault segments over large distances. On the 20 February 2023 two earthquakes with Mw 6.4 and Mw 5.2 struck Hatay (Türkiye) located near the Samandağ‐Antakya segment of the EAFZ. To understand the rupture evolution of these earthquakes, we first re‐located the aftershock sequence that occurred over a 3‐month period in the Hatay‐Syria region. A normal faulting mechanism with a significant amount of left‐lateral strike‐slip component at a shallow focal depth of 12 km was estimated for the 2023 Mw 6.4 earthquake from the inversion of seismological data. Our slip models describe a relatively simple and unilateral rupture propagation along about 36 km‐long active segments of the EAFZ. The co‐seismic horizontal displacements inferred from the Global Navigation Satellite System data are compatible with the oblique slip kinematics. Furthermore, we suggest that this earthquake did not produce notable tsunami waves on the adjacent coasts since the rupture plane did not extend to the seafloor of the Eastern Mediterranean with substantial amount of vertical displacement. We reckon that a future large earthquake (Mw ≥ 7.0) in the Hatay‐Syria region where increased stress was transferred to the fault segments of the EAFZ and the Dead Sea Fault Zone (DSFZ) after the 2023 earthquakes will be a probable source of tsunami at the coastal plains of the Eastern Mediterranean Sea region.