arXiv subjects

Hamzeh Mohammadigheymasi

Publications and source records attributed to Hamzeh Mohammadigheymasi.

11 recordsLinked to original sources

arXiv2026

The 2016 Mw 7.0 Kumamoto Earthquake Sequence, Japan revisited: Insights from Spatio-Temporal Analysis of Seismicity Parameters

Understanding how crustal faults accumulate strain, nucleate ruptures, and redistribute post-seismic stress is fundamental to seismic hazard assessment. The 16 April 2016 $M_w$ 7.0 Kumamoto earthquake, rupturing the Futagawa--Hinagu fault zone in the Beppu--Shimabara graben, central Kyushu, Japan, offers a premier dataset for tracking these processes across a full earthquake cycle. Using the JMA catalog (2014--2018) with dynamic completeness estimation ($M_c \approx 2.15$--$2.25$), we examine the Gutenberg--Richter $b$-value, the 3-D hypocentral fractal dimension ($D_c$), and the seismicity-rate anomaly ($Z$-value). During the one- to two-year preparatory phase, $b$ declined from a baseline of $1.35 \pm 0.10$ to a precursory minimum of $0.59$, while $D_c$ contracted from $0.85 \pm 0.15$ to $0.70$--$0.80$, recording stress concentration and microfracture coalescence onto a narrow nucleation zone. A coherent negative $Z$-value anomaly (-1.6 to -2.0) developed along the graben, strengthening with integration time---consistent with, though not proof of, progressive fault locking. Depth-sliced volumes show the low-$b$ locked core ($b \le 0.65$) was stratified at $10$--$12.5$~km depth and sharpened within the final four months before failure. Rupture reversed this within weeks: $b$ surged to $1.25$--$1.35$ and $D_c$ expanded to $2.04$--$2.11$, consistent with coseismic stress drop and aftershock activation, followed by recovery over 2.5 years. Cross-sections reveal sharp aftershock localization alongside two unrelaxed asperities ($b \approx 0.80$--$0.95$) near the Hinagu termination and Mount Aso, consistent with positive Coulomb stress loading. This framework resolves asperity locking, release, and healing better than any single metric; however, since anomalies were identified retrospectively, they should be read as evidence of coherent behavior rather than a validated forecast tool alone.

physics.geo-ph↗

arXiv2026

ANADEF: A Nested-Permutation Alarm for Dual-Parameter Earthquake Forecasting

Spatially resolved stress proxies and rate-based seismicity models are increasingly combined for regional earthquake forecasting, yet formally testing their non-redundancy remains largely unaddressed. We present the Nested-Permutation Alarm for Dual-Parameter Earthquake Forecasting (ANADEF) pipeline, integrating a stress-sensitive Gutenberg--Richter $b$-value field, estimated via a penalized 2D B-spline inversion, with a stationary background rate ($μ$) from space--time ETAS stochastic declustering. Applied to the Zagros Fold--Thrust Belt using an 18-year catalog ($n=40{,}731$, $M_{\mathrm{N}}\geq1.5$, 2006--2024) under a two-stage protocol with non-overlapping training (2006--2014) and target (2015--2024) windows, the model achieves, for $M_w\geq5.0$ ($N=55$), a retrospective Area Skill Score $S=0.69$ (95\% CI: 0.64--0.73), reducing alarmed area from $τ\approx0.38$ ($μ$-only) to $τ\approx0.28$ while retaining hit rate $ν=92.7\%$; since thresholds are calibrated on the evaluation catalog, these are in-sample, not out-of-sample, estimates. A nested permutation procedure re-optimizing thresholds within each null realization tests whether $b$-value adds information beyond $μ$, absorbing the optimization bias. Significance is null-model dependent: cell-wise randomization yields $p=0.012$, while a conservative spatial-structure-preserving null gives weaker, non-significant evidence ($p=0.057$)---incremental stress information is suggestive but not unambiguously established. Calibrated thresholds were frozen and applied to updated 2015--2024 fields, generating an unvalidated, forward-looking spatial alarm template for 2025--2029. These results establish a reproducible, statistically transparent methodology for testing, rather than assuming, complementarity between stress-sensitive and rate-based predictors, offering a candidate operational template pending prospective validation.

physics.geo-ph↗

arXiv2026

Joint Analysis of Shannon and Tsallis Entropy and GRACE-FO driven Equivalent Water Height Anomalies for Pre- and Post-Rupture Monitoring: An Example of the 2023 Mw = 7.8 Kahramanmaraş Earthquake, Türkiye

In order to understand the variations in fault systems throughout the seismogenic cycle, mechanical states and the complexities of seismic interactions must be considered. In this study, we present a data integration framework combining a 25-year seismic catalog with Equivalent Water Height (EWH) datasets from the GRACE-FO mission and two information-theoretic complexity measures (Shannon and Tsallis entropy) to examine spatiotemporal changes in the East Anatolian Fault System associated with the 2023 Kahramanmaraş earthquake doublet. The pre-rupture period exhibits a systematic increase in the entropy measures alongside a gradual decrease in EWH, suggesting a transition towards fault network criticality driven by segment fragmentation, long-range correlations, poroelastic contraction, fluid migration, and progressive stress accumulation. During the co-seismic phase, we observe an abrupt increase in entropy with a corresponding negative shift in EWH. In the post-seismic period, the persistence of elevated entropy and EWH anomalies indicates that the fault system remains in a non-equilibrium state dominated by aftershock clustering, fault zone damage, permeability changes, and viscoelastic relaxation. Additionally, structured computational workflows detailing these joint methodologies are provided via the Seismic Entropy Analysis (Algorithm 1) and the Relationship Between Tsallis q and Gutenberg-Richter b-value (Algorithm 2) pseudo-codes, facilitating the direct reproduction and regeneration of all results.

physics.geo-ph↗

arXiv2026

Crustal Structure Imaging of Ghana from Single-Station Ambient Noise Autocorrelations and Earthquake Arrival Time Inversion

The crustal architecture of southern Ghana remains inadequately resolved despite its tectonic significance and resource potential. Existing geological and geophysical studies provide only broad constraints on crustal composition, lacking the resolution to accurately define sediment-basement interfaces or intra-crustal stratigraphy. To address these limitations, we employ single-station ambient noise autocorrelation (SSANA) on continuous waveform data from the Ghana Digital Seismic Network (GHDSN). We extract P-wave reflectivity responses using a processing sequence that involves data pre-processing, Phase Cross-Correlation (PCC) for robust noise correlation, and phase-weighted stacking (PWS) of the derived autocorrelograms. This procedure yields a two-way travel-time (TWT) function representing the zero-offset P-wave reflection response beneath each station, enabling high-resolution imaging of the stratified crustal column. To facilitate depth conversion, we develop an enhanced one-dimensional crustal velocity model for the region. Using a compiled dataset of local earthquake P- and S-wave arrival times from the GHDSN and an additional station in Cote d'Ivoire, we perform a joint inversion via a grid-search algorithm to derive a regional 1D velocity structure. Our results provide new constraints on the depth and configuration of the Paleozoic basement beneath the Voltaian Basin, demonstrating the efficacy of ambient noise autocorrelation for crustal imaging in sparsely instrumented regions. We also present an updated seismicity catalog, relocated using the new velocity model, and analyze the spatial clustering of seismicity in southern Ghana. This study highlights the utility of passive seismic methods for elucidating crustal structure and evaluating resources in intraplate West Africa and analogous Precambrian terrains.

physics.geo-ph↗

arXiv2026

Cross-Sectional and Spatio-Temporal Analysis of Seismicity Parameters in the Zagros Orogenic Belt: Insights into Crustal Stress Distribution and Seismic Hazard

The Zagros Orogenic Belt, formed by the Arabian-Eurasia collision, is a highly active collision zone hosting a large portion of Iran's seismicity. In this study, the IRSC catalog (2006-2024) was used to construct a homogeneous seismic dataset for the Zagros Belt. We analyzed the spatial and depth distribution of seismicity together with key parameters: b-value, fractal dimension (Dc-value), and differential stress (sigma1 - sigma3) to evaluate stress variations, fault clustering, and seismic hazard. The overall b-value of 0.81 +/- 0.01 indicates an elevated stress state, with persistently low b-value anomalies (0.4-0.7) systematically aligned along major fault zones (Mountain Front Fault, High Zagros Fault, and Main Zagros Reverse Fault). Cross-sectional analyses show that these low b-values (<0.6) are concentrated within the upper ~10 km of the crust, pointing to the shallow brittle layer as the primary zone of stress concentration. The correlation fractal dimension (Dc) ranges from 1.0 to 2.05, with high values (>=1.5) spatially coinciding with low b-value zones, reflecting intense deformation partitioning and structural complexity. This spatial complexity decreases with depth, where lower Dc-values show seismicity localizing onto simpler, discrete planes. Differential stress varies between 100 and 520 MPa (predominantly >=520 MPa) and is strongly anti-correlated with b-value, confirming that low b-values trace critically loaded fault segments. The spatial and depth convergence of these independent parameters confirms that seismic hazard is localized along shallow, highly stressed, and structurally complex fault segments capable of generating future moderate-to-large events. These findings highlight the need for targeted monitoring and hazard mitigation across the region.

physics.geo-ph↗

arXiv2026

Integrating b-Value and Background Seismicity Rate for Spatial Earthquake Forecasting in the Alborz Region, Northern Iran

In this study, we evaluate the spatial forecasting skill of the $b$-value and background seismicity rate $μ$ across the Alborz region using a homogenized catalog of 23,961 earthquakes ($M \geq 1.5$) recorded by the Iranian Seismological Center between 2006 and 2024. Forecast performance for $M \geq 4.0$ and $M \geq 4.5$ is assessed using Molchan error diagrams, probability gain, probability difference, and the modified area skill score. The results show that $μ$ provides a consistently strong spatial signal, with Molchan curves well below the random baseline and probability gains of 5--6 at low alarm rates, reflecting the persistent clustering of seismicity along major Alborz faults. The $b$-value exhibits limited skill at lower magnitudes but improves steadily with increasing magnitude; its skill score becomes positive above $M \approx 5.3$, indicating that $b$-value anomalies begin to capture meaningful stress concentrations only for larger events. Spatial patterns reveal low $b$ zones along active reverse and strike-slip structures and high $μ$ zones following long-term seismicity clusters, underscoring their complementary physical roles. Retrospective testing confirms this complementarity: the combined $b$--$μ$ forecast achieves detection rates of 0.81--0.83 at spatial alarm rates of 0.43 and 0.36 for $M \geq 4.0$ and $M \geq 4.5$, respectively, representing the most efficient forecast configuration among all tested models. These findings demonstrate that integrating stress-state and tectonic-loading indicators yields a more efficient and physically grounded framework for operational earthquake forecasting in the Alborz region.

physics.geo-ph↗

arXiv2026

Seismic Depth Imaging of the 2024 Noto Earthquake (M7.6) Rupture Area

On January 1, 2024, a moment magnitude (Mw) 7.6 earthquake struck the Noto Peninsula, Japan, causing intense ground shaking and triggering a tsunami along the Japan Sea coast. Preliminary analysis by the Japan Meteorological Agency (JMA) identified a reverse-fault rupture consistent with a northwest-southeast compressional stress regime. Aftershock distribution analysis (JMA, 2024) revealed that the causative fault extended approximately 150 km from the western Noto Peninsula to the northeastern offshore area, aligning with the inferred tsunami source region. While the rupture mechanism and impacts have been studied, high-resolution seismic imaging of the shallow crustal structure within the rupture zone remains limited. To address this gap, the Atmosphere and Ocean Research Institute (AORI) at the University of Tokyo conducted a multichannel seismic (MCS) reflection survey aboard the R/V Hakuho-Maru in March 2024, collecting high-quality MCS data along 14 profiles (approx. 45 km each). The data were processed using an advanced depth imaging workflow incorporating grid-based tomography refined by automated continuity attributes to enhance reflection coherency. Structural attributes (dip and continuity) were extracted from migrated sections and used for automated horizon picking via seismic pencil construction. The P-wave velocity model was iteratively refined using grid-based tomography to optimize horizon alignment and minimize residual moveout (RMO) in migrated common image gathers. The resulting 2D seismic sections and 3D visualizations provide the first high-resolution images of the shallow rupture zone associated with the 2024 Noto earthquake. This dataset offers a critical foundation for ongoing research into fault geometry, rupture dynamics, and the broader seismotectonic framework of the region.

physics.geo-ph↗

arXiv2025

Earthquake body wave extraction using sparsity-promoting polarization filtering in the time-frequency domain

Seismic waves generated by earthquakes consist of multiple phases that carry critical information about Earth's internal structure as they propagate through heterogeneous media. These phases provide constraints from different regions of the Earth, such as the crust, mantle, and even the cores. The choice of phase depends on the study target and scientific objective: surface waves are suited for imaging shallow structures, whereas body waves yield higher-resolution information at depth. A key challenge in body-wave studies is that the low-amplitude P and S arrivals are often masked by surface waves overlapping in both time and frequency. Although body waves typically contain higher-frequency content, their spectral overlap with surface waves limits the effectiveness of conventional filtering approaches. Addressing this issue requires advanced signal-processing techniques. One such method, Sparsity-Promoting Time-Frequency Filtering (SP-TFF, Mohammadigheymasi et al., 2022), exploits high-resolution polarization information in the time-frequency domain. SP-TFF integrates amplitude, directivity, and rectilinearity to enhance phase discrimination. Here, we further develop SP-TFF by designing a filter set tailored to isolate body-wave arrivals otherwise masked by high-amplitude surface waves. The directivity filters are constructed from predicted seismic ray incidence angles, enabling focused extraction of body-wave energy and suppression of interfering phases. We evaluate the method on both synthetic tests and waveform data from the Mw 7.0 Guerrero, Mexico, earthquake of September 8, 2021, recorded by the United States National Seismic Network (USNSN). Our results show that SP-TFF provides a robust computational framework for automated body-wave extraction, integrating polarization-informed filtering into seismological data-processing pipelines.

physics.geo-ph↗

arXiv2025

Enhancement of Quantum Semi-Supervised Learning via Improved Laplacian and Poisson Methods

This paper develops a hybrid quantum approach for graph-based semi-supervised learning to enhance performance in scenarios where labeled data is scarce. We introduce two enhanced quantum models, the Improved Laplacian Quantum Semi-Supervised Learning (ILQSSL) and the Improved Poisson Quantum Semi-Supervised Learning (IPQSSL), that incorporate advanced label propagation strategies within variational quantum circuits. These models utilize QR decomposition to embed graph structure directly into quantum states, thereby enabling more effective learning in low-label settings. We validate our methods across four benchmark datasets like Iris, Wine, Heart Disease, and German Credit Card -- and show that both ILQSSL and IPQSSL consistently outperform leading classical semi-supervised learning algorithms, particularly under limited supervision. Beyond standard performance metrics, we examine the effect of circuit depth and qubit count on learning quality by analyzing entanglement entropy and Randomized Benchmarking (RB). Our results suggest that while some level of entanglement improves the model's ability to generalize, increased circuit complexity may introduce noise that undermines performance on current quantum hardware. Overall, the study highlights the potential of quantum-enhanced models for semi-supervised learning, offering practical insights into how quantum circuits can be designed to balance expressivity and stability. These findings support the role of quantum machine learning in advancing data-efficient classification, especially in applications constrained by label availability and hardware limitations.

quant-ph↗

arXiv2024

A Laplacian-based Quantum Graph Neural Network for Semi-Supervised Learning

Laplacian learning method is a well-established technique in classical graph-based semi-supervised learning, but its potential in the quantum domain remains largely unexplored. This study investigates the performance of the Laplacian-based Quantum Semi-Supervised Learning (QSSL) method across four benchmark datasets -- Iris, Wine, Breast Cancer Wisconsin, and Heart Disease. Further analysis explores the impact of increasing Qubit counts, revealing that adding more Qubits to a quantum system doesn't always improve performance. The effectiveness of additional Qubits depends on the quantum algorithm and how well it matches the dataset. Additionally, we examine the effects of varying entangling layers on entanglement entropy and test accuracy. The performance of Laplacian learning is highly dependent on the number of entangling layers, with optimal configurations varying across different datasets. Typically, moderate levels of entanglement offer the best balance between model complexity and generalization capabilities. These observations highlight the crucial need for precise hyperparameter tuning tailored to each dataset to achieve optimal performance in Laplacian learning methods.

cs.LG↗

arXiv2023

A Cluster-Based Opposition Differential Evolution Algorithm Boosted by a Local Search for ECG Signal Classification

Electrocardiogram (ECG) signals, which capture the heart's electrical activity, are used to diagnose and monitor cardiac problems. The accurate classification of ECG signals, particularly for distinguishing among various types of arrhythmias and myocardial infarctions, is crucial for the early detection and treatment of heart-related diseases. This paper proposes a novel approach based on an improved differential evolution (DE) algorithm for ECG signal classification for enhancing the performance. In the initial stages of our approach, the preprocessing step is followed by the extraction of several significant features from the ECG signals. These extracted features are then provided as inputs to an enhanced multi-layer perceptron (MLP). While MLPs are still widely used for ECG signal classification, using gradient-based training methods, the most widely used algorithm for the training process, has significant disadvantages, such as the possibility of being stuck in local optimums. This paper employs an enhanced differential evolution (DE) algorithm for the training process as one of the most effective population-based algorithms. To this end, we improved DE based on a clustering-based strategy, opposition-based learning, and a local search. Clustering-based strategies can act as crossover operators, while the goal of the opposition operator is to improve the exploration of the DE algorithm. The weights and biases found by the improved DE algorithm are then fed into six gradient-based local search algorithms. In other words, the weights found by the DE are employed as an initialization point. Therefore, we introduced six different algorithms for the training process (in terms of different local search algorithms). In an extensive set of experiments, we showed that our proposed training algorithm could provide better results than the conventional training algorithms.

cs.NE↗