Inversion of earthquake H/V spectral ratios for shear wave velocity profiling in Elazığ using a dense dataset

Assessment of sleep quality in clinical practice remains constrained by tools that are either subjective or impractical for routine use. Self-report instruments, such as the Pittsburgh Sleep Quality Index (PSQI) and sleep diaries, are vulnerable to recall bias and demonstrate limited correspondence with objective neurophysiological measures. Polysomnography (PSG), while the gold standard, is costly, labor-intensive, and disruptive to natural sleep, rendering it unsuitable for longitudinal or outpatient monitoring. Recent advances in quantitative electroencephalography (qEEG), event-related potential (ERP) analysis, and source localization have created opportunities to bridge this gap. These methods enable scalable, objective evaluation of neurophysiological processes underpinning sleep architecture, cognitive function, and emotional regulation. This review synthesizes emerging evidence that EEG-derived biomarkers (e.g., spectral power ratios (delta/alpha, theta/ beta), cross-frequency coupling, and ERP dynamics) can serve as sensitive indices of sleep quality and neural health. We highlight the BrainView platform as an exemplar of this translational shift. BrainView is an FDA-cleared system combining qEEG, ERP, source localization, and normative comparison to quantify brain function non-invasively and longitudinally. By integrating neurophysiological data with actigraphy, self-report, and behavioral outcomes, EEG-based platforms such as BrainView may redefine how clinicians and researchers monitor sleep, treatment response, and neurocognitive resilience.

Western Java’s ongoing seismic hazard highlights the need for understanding earthquake precursor mechanisms. Recent studies have increasingly focused on ultra-low-frequency (ULF) signals that may carry information related to pre-seismic phases. However, a key difficulty persists: isolating faint, localized lithospheric signals from the stronger ionospheric activity. Most previous investigations in Western Java have relied on single-sensor measurements, a limitation that complicates the detection of true anomalies. This study addresses this limitation by examining daily ULF variations before the 2023 Banten earthquake sequence (M5.4 and M5.1), using a multi-point setup to distinguish lithospheric signals from stronger background ionospheric noise. Continuous three-component geomagnetic data from two primary stations near the epicenter, Serang (SRG) and Sukabumi (SKB), and a distant reference station (TRD) in East Kalimantan were analyzed. The Z/G spectral density ratio was calculated in the 0.01–0.09 Hz range, using only data from quiet nighttime intervals (15:00–21:00 UTC) and magnetic storm-free days (Dst > -50 nT). The results identified and filtered false positive anomalies by correlating them with signals at the TRD reference station. Two distinct, validated pre-seismic anomalies were identified, concentrated in the 0.04–0.08 Hz band: a multi-station anomaly at H-20 (at SRG and SKB) and a localized, broadband anomaly at H-15 (at SRG). Both emissions were absent at TRD, confirming their lithospheric origin. These results highlight the importance of a multi-station approach for reliably identifying lithospheric ULF anomalies. However, this study is limited to a specific event sequence. Future investigations should focus on integrating broader sensor networks and ionospheric models across multiple seismic events to validate these findings globally and enhance false positive rejection methods.

Antarctic ice shelves regulate ice-sheet discharge and global sea-level rise, yet their rapid retreat underscores the need for new, low-cost monitoring tools. We analyze ambient seismic noise recorded by seismometers on the Pine Island Glacier ice shelf to characterize wind-induced signals and detect persistent structural resonances. Power spectral analysis shows that wind sensitivity is strongly damped compared with bedrock sites: noise increases only 5–7 dB from 0 to 25 m s−1 winds, versus a 42 dB increase at an inland bedrock station, reflecting the contrasted coupling environments of floating and grounded substrates. The horizontal-to-vertical spectral ratio (HVSR) spectrograms reveal two temporally stable peaks at ~2.2 Hz and ~4.3 Hz that persist across stations and remain independent of environmental forcing. Forward modeling indicates that these peaks correspond to S-wave resonances within the ice shelf. The inferred ice-water interface depth (~440 m) agrees with the Bedmap2 thickness estimate (466 m). This work demonstrates that HVSR provides an effective passive, single-station method for measuring ice shelf thickness.

The Sempu Area, located in Cowek Village, Purwodadi Subdistrict, Pasuruan Regency, has a high potential for ground movement due to its lithological conditions, which consist of loose volcanic deposits and weathered sedimentary rocks, thereby increasing the risk of seismic wave amplification. This study aims to analyze the dynamics and soil vulnerability to ground movement phenomena using the Horizontal-to-Vertical Spectral Ratio (HVSR) method based on microtremor data. Data collection was conducted at 15 measurement points using a three-component seismograph, with a recording duration of 20 minutes per point. The data were analyzed using SeismoWin for signal filtering, Geopsy for extracting the fundamental frequency (f₀) and amplification values, and Surfer and ArcGIS for spatial visualization in the form of dominant frequency maps, amplification maps, and soil vulnerability index (Kg) distribution. The results showed that the dominant frequency values ranged from 2.75 to 5.92 Hz, with a maximum amplification value of 6.18. The most vulnerable zones were identified in the central part of the hamlet, specifically at points 10 and 14, which exhibited the highest Kg value of 14.12. These findings indicate the presence of significant local resonance zones arising from unconsolidated lithology, thereby increasing the risk of damage from seismic shaking. The implications of this study support land-use planning based on seismic microzonation and the development of more precise disaster mitigation strategies in areas prone to ground movement.

ABSTRACT In this study, a horizontal-to-vertical spectral ratio (HVSR)–based degree of nonlinearity (DNL) analysis was applied to investigate nonlinear soil behavior during the 2024 Hualien earthquake sequence in eastern Taiwan, including the 3 April mainshock (Mw 7.3) and two significant aftershocks (Mw 6.6 on 3 April and Mw 6.0 on 23 April). By comparing HVSRs from weak and strong motions, DNL values were computed for 18 stations and regressed against peak ground acceleration (PGA), peak ground velocity (PGV), and a strain proxy (PGV/VS30). All three regressions showed clear piecewise linear trends with identified nonlinear thresholds: 111 cm/s2 (PGA), 9.77 cm/s (PGV), and 0.0283% (strain proxy), with the last one showing the strongest correlation with DNL (correlation coefficient = 0.81). DNL–strain relationships from the 2018 Mw 6.4 Hualien earthquake generally fall within the regression model derived from the 2024 Hualien earthquake sequence, indicating similar nonlinear site behavior across events through a period of six years. In contrast, sites on the ChiaNan Plain, which are underlain by fine-grained sediments, exhibited more gradual nonlinear trends without a clear threshold, highlighting the influence of sediment composition. Variations in the vertical-to-horizontal ratios of the PGA and PGV further underscore the role of site conditions. These results offer empirical constraints that can improve the modeling of nonlinear site responses and contribute to applications in seismic microzonation, hazard assessment, and ground-motion prediction.

The dynamic characterization of historical buildings located in a complex geological and seismological context is essential to assess seismic vulnerability and to guide conservation strategies. This study presents a non-invasive, ambient vibration-based, investigation of the Norman Castle of Aci Castello (Sicily, Italy), applying Horizontal to Vertical Spectral Ratio (HVSR), Horizontal to Horizontal Spectral Ratio (HHSR), and Random Decrement Method (RDM) to evaluate the structure’s dynamic behavior and potential Soil–Structure Interaction (SSI) effects. The fundamental site frequency, estimated within a broad plateau in the range 2.05–2.70 Hz, does not overlap with the structural frequencies of the castle, which range approximately from 6.30 Hz to 9.00 Hz in the N–S structural direction and from 3.50 Hz to 8.50 Hz in the E–W direction, indicating absence of global SSI resonance. However, the structure exhibits a complex multimodal response, with direction-dependent behavior evident both in spectral peaks and in damping ratios, ranging from 2.10–7.73% along N–S and 0.90–5.84% along E–W. These behaviors can be interpreted as possibly linked to structural complexity and the interaction with the fractured volcanic substrate, characterized by shallow cavities, as well as to the material degradation of the masonry. In particular, the localized presence of subsurface voids may induce a perturbation of the low-frequency ambient vibration wavefield (e.g., microseisms), producing a localized increase in spectral amplitude observed at Level I. The analysis indicates the absence of global SSI resonance due to the lack of overlap between site and structural fundamental frequencies, while significant local SSI effects, mainly related to cavity-induced wavefield perturbation, are observed and may represent a potential vulnerability factor. These findings highlight the relevance of vibration-based diagnostics for heritage vulnerability assessment and conservation strategies.

The southern Korean Peninsula faces complex seismic challenges due to the concentration of critical infrastructure and the region’s unique intraplate tectonic environment. In this study, over 300 strong-motion records from 10 moderate-magnitude earthquakes were analyzed using data from 10 representative seismic stations. Acceleration response spectra, normalized by peak ground acceleration, were generated and systematically compared with international and domestic seismic design standards, including USNRC Regulatory Guide 1.60 and KBC 2016. The observed spectra frequently exceeded existing code requirements in the mid-to-high-frequency range critical for local infrastructure, indicating potential vulnerabilities in applying generic global standards to Korean conditions. Analysis of vertical-to-horizontal spectral ratios further revealed pronounced frequency dependence and amplification effects, especially in sedimentary basin sites. These findings underscore the importance of accounting for site-specific geological and seismic characteristics in the seismic design of critical infrastructure in Korea. The results advocate for the development of regionally calibrated, risk-informed seismic design frameworks and provide essential empirical data to support safer, more resilient infrastructure amid moderate but potentially hazardous earthquake activity.

ProblemsTomato Spotted Wilt Virus (TSWV) severely affects tobacco yield and quality, creating an urgent need for accurate, rapid, non-destructive monitoring to support disease management. While existing TSWV detection methods perform well at the leaf scale, their field-scale application remains challenging. Due to complex crop canopy structures, spectral characteristics at the field level differ significantly from leaf-level observations, and TSWV-sensitive spectral features are still unclear. This study therefore aims to develop a field-scale TSWV identification model using UAV-based hyperspectral imaging to enable targeted disease control.MethodologyA UAV-mounted hyperspectral camera (400–1000 nm) was deployed to capture imagery of tobacco plants at the rosette stage, enabling comparative spectral analysis between healthy and infected specimens. To identify sensitive features associated with tobacco plants infected with TSWV, six distinct feature extraction methodologies encompassing traditional statistical approaches (spectral ratio, correlation analysis, and principal component analysis [PCA]), machine learning-based techniques (relevant features [Relief], successive projections algorithm) and vegetation indices were utilized. Subsequently, we conducted a systematic evaluation of 18 classification models developed using three machine learning algorithms—support vector machine (SVM), k-nearest neighbors, and extreme gradient boosting —with the derived feature variables.ResultsThis study demonstrates that while all integrated models combining Relief- and Correlation- selected feature bands with three machine learning algorithms delivered excellent performance, the SVM-Relief model achieved the most outstanding results (OA = 97.3%, AUC = 0.994, Kappa=0.947). Based on the SVM-Relief combination, a proposed method called RPR —which integrates PCA with recursive feature elimination— was further employed to reduce the number of feature indicators from 15 to 4 (775.6/772.9/781.1/756.4 nm). The resulting SVM-RPR combination model achieved performance (OA = 97.3%, AUC = 0.990, Kappa=0.947) comparable to that of the SVM-Relief model.ContributionThis indicated that red-edge bands were of significant value in distinguishing healthy and TSWV-infected tobacco plants. Our study indicates the significant potential of integrating UAV-based hyperspectral imaging with machine learning techniques for rapid, non-destructive detection of tobacco TSWV at the field scale. The proposed approach offers a novel and efficient pathway for remote sensing-based monitoring of viral diseases in crops, with implications for precision agriculture and plant disease management.

Three-dimensional (3D) modeling of soil texture components serves as an essential tool for the sustainable management of soil and agricultural resources. In this study, a 3D modeling framework was applied in Jiangsu Province, China, integrating the equal-area spline function with the Quantile Regression Forest (QRF) algorithm. Soil profile measurements of sand, silt, and clay contents were smoothed using the spline function across five standard depth intervals: 0-25, 25-50, 50-75, 75-100, and 100-125cm. These processed data, together with 16 environmental covariates, were employed as inputs to the QRF model to generate depth-specific predictive maps of soil texture components. The model exhibited strong predictive performance in the surface layer (0-25cm), with coefficients of determination (R2) of 0.78 for clay, 0.76 for sand, and 0.77 for silt. However, accuracy declined with depth, reaching R2 values of 0.55, 0.54, and 0.52 for clay, sand, and silt, respectively, at 100-125cm. Model error, expressed as the root mean square error (RMSE), increased with depth-from 8.1% for surface clay to 13.4% in the deepest layer. Variable importance analysis identified piezometric elevation, precipitation, the Grain Size Index (GSI), the spectral band ratio Band3/Band7, and topographic indices such as the Multiresolution Ridge Top Flatness (MrRTF) as the most influential predictors of soil texture. Furthermore, uncertainty evaluation using the Prediction Interval Coverage Probability (PICP) index revealed decreasing prediction reliability with greater depth. Based on these findings, future research should prioritize the use of higher-resolution elevation datasets, increase sampling density, and incorporate geological and land-use variables to enhance model accuracy and generalizability. This study presents a robust methodology for generating accurate, spatially consistent estimates of soil texture components across defined profile depths and provides a scientific foundation for advancing regional-scale 3D soil mapping.

Light-emitting diode or LED lighting often faces challenges with color rendering due to its unique spectral characteristics compared to natural light. While efforts to enhance the color rendering index (CRI) have typically focused on improving light source elements, there has been less attention on optimizing the software aspects, such as the combination of light sources and spectral composition. Notably, there has been no efficient method proposed specifically for enhancing R9 and R12, which are critical for improving overall color rendering in LED lighting. This paper presents an optimization control method based on the spectral ratios of LED lighting to achieve color rendering similar to natural light. By analyzing the wavelength characteristics of both natural and artificial light, a high CRI light was realized through reinforcement of deficient wavelength bands. Experimental results showed an average CRI of 97, with R9 and R12 values around 93 and 98, respectively, demonstrating that LED technology can achieve color renderings comparable to natural light.

Ambient noise data from 45 stations of the BRASIS network were analyzed, and the spectral ratio between horizontal and vertical amplitudes as a function of frequency was calculated, allowing site effects to be analyzed. The H/V curves were obtained using the Nakamura technique, which enables the evaluation of soil characteristics. This study allows us to improve the resolution of shallow structures in the region's models, showing a better spatial correlation with known tectonic features. By analyzing these curves, information was obtained on the contact between the basement and the underlying sedimentary basin, and for some stations, a shallower contrast was observed. In general, for stations located on the basins, lower fundamental frequency values were obtained, while for stations located at the basin edges, the frequencies showed high values indicative of firm rock. In the stations located in the Chaco-Paraná basin, the fundamental frequency was determined, representing the response to the contact between the basin and the basement, and a second contrast was observed, possibly linked to a volcanic stratum from the Upper Jurassic-Middle Cretaceous at a depth of 550 meters. In the Pantanal basin, the fundamental frequency presented a similar value. Additionally, two impedance contrasts were observed, the most superficial linked to the interface between an Aeolian deposit from the Pleistocene-Holocene, and an older unit associated with lateral migration channels. These results represent a progress in the seismological knowledge of the Chaco-Paraná, Paraná, and Pantanal basins.

Abstract Earthquakes in stable continental regions occur less frequently than those at plate boundaries, and their seismic hazards remain poorly understood. In this study, we analyze the source complexity of the Mw 4.8 Zhongdian earthquake in southeastern Tibet, which occurred in an area where no major faults were previously mapped. Notably, the focal mechanism solutions differ significantly between the different approaches: whereas the centroid moment tensor (CMT) indicates oblique-normal faulting, the first-motion (FM) solution suggests purely dip-slip faulting. This discrepancy points to a complex rupture process involving multiple slip modes. Using multi-point-source inversion, we identify two near-simultaneous subevents. The first aligns with the FM dip-slip mechanism, and the second is dominated by strike-slip motion; together, they are consistent with the overall CMT solution. However, source spectral ratio analysis and displacement records show no distinct temporal separation between the subevents. On the other hand, structural complexities are revealed by relocated aftershocks and spatially diverse focal mechanisms, which enable the observed rupture behavior. We further compare the stress fields across multiple scales and find no significant differences in principal stress orientations between the Zhongdian source region and the broader western Chuandian block. Nevertheless, the two subevent areas display contrasting faulting styles, highlighting the influence of localized strain or deformation heterogeneity. These findings suggest that the moderate-size Zhongdian earthquake was governed by both complex fault geometry and heterogeneous faulting behavior.

Unimodal and bimodal classification methods for breast carcinomas based on laser-induced autofluorescence spectroscopy.

Abstract The Corbeyrier and Yvorne mass movement events that happened in the year 1584 have mainly been studied at the surface level such that the shallow subsurface structure remains largely unknown. Geophysical measurements allow for insights into the physical properties of the subsurface including the geometry of deposits after such events. We applied three methods with the specific interest of obtaining information about the spatial variation of thickness and lithology of the mass movement deposits: the horizontal to vertical spectral ratio of ambient seismic noise (54 measurements), electrical resistivity tomography (2 profiles) and ground-penetrating radar (7 profiles). These measurements were then complemented with a physical sample obtained from drilling a 10.6 m long core in the Luan forest. We found that the mass movement deposits contain angular clasts of gravel and cobbles in a clay matrix (~ 30%). We also saw a thinning of the deposits downslope such that they reached a thickness of 3.5 m in the Luan forest and a thickness greater than 10 m (estimated between 50 and 100 m) near the source region at Plan Falcon. We identified two areas which call for further investigation in terms of the possibility of sediment mobility of the deposits in the event of an earthquake: Plan Falcon and the mostly bare scree slope just below.

Evaluation of the horizontal-to-vertical spectral ratio method for marine subsurface assessment under ocean-bottom currents

Using logarithmic multi-channel seismoelectric spectral ratios to estimate porosity and permeability

Abstract Given the critical role of the Madden–Julian Oscillation (MJO) in modulating global climate variability and subseasonal-to-seasonal predictability, this study evaluates its simulation in the Korea Meteorological Administration’s Global Seasonal Forecasting System version 6 (GloSea6) and compares it with version 5 (GloSea5), focusing on prediction skill and key physical processes over the Maritime Continent (MC). Both models exhibit systematic biases, including weaker amplitudes and a tendency for the MJO to stall over the MC. Nevertheless, GloSea6 shows enhanced propagation across the MC, consistent with improved thermodynamic processes. The eastward-to-westward spectral power ratio increases from 1.52 in GloSea5 to 1.93 in GloSea6, closer to the observed 2.79, reflecting a more realistic dominance of eastward propagation. Process-based diagnostics reveal region-dependent improvements: more pronounced over the MC but limited over the Indian Ocean. MC improvements are linked to better simulation of lower-level moisture convergence, equivalent potential temperature, and available potential energy, supported by reduced SST biases and a steeper meridional moisture gradient. These background-state changes strengthen moistening processes that precondition convection and sustain eastward propagation over the MC. These findings highlight that GloSea6’s advancements are process- and region-dependent, emphasizing the role of mean-state biases in shaping MJO prediction skill and providing guidance for targeted improvements in subseasonal-to-seasonal prediction systems. However, improvements in spatial pattern similarity did not always translate into propagation skill gains, particularly over the IO, underscoring the complexity of dynamical responses.

ABSTRACT Accurate evaluation of the scaling and variability of earthquake source spectra is essential for reliable ground-motion prediction. This study presents a comprehensive analysis of the broadband source spectral characteristics of crustal, interplate, and intraslab earthquakes in Japan (2004–2024, Mw≤7.5), aiming to improve the predictability of the scaling and variability of ground motions. A multistage spectral ratio method was applied to estimate source spectra and source parameters, such as the apparent stress and the stress parameter, which represents a form of spectral stress drop that characterizes the high-frequency level. This method enables the estimation of double-corner-frequency (DCF) spectra in contrast to the standard approach that assumes single-corner-frequency (SCF) spectra. A systematic transition from SCF to DCF spectra with increasing magnitude was observed across all earthquake types, suggesting a fundamental difference in rupture characteristics between small and large earthquakes. The variability of the stress parameter is consistent with the between-event variability of peak ground acceleration (PGA), suggesting that the long-standing variability discrepancy between spectral stress drop and PGA has been resolved for all earthquake types. Moreover, PGAs for crustal earthquakes were successfully predicted from the estimated spectral parameters, as indicated by their strong correlation with observations. These findings highlight the effectiveness of the DCF model for improving ground-motion predictions. The study also offers broader implications for source parameter estimation, scaling relations, rupture dynamics, and ground-motion prediction. In conclusion, greater attention to the potential of DCF spectra may yield deeper insights into earthquake rupture physics and contribute to more accurate and robust ground-motion prediction.

The Wasatch Fault Corridor in northern Utah (USA) faces increasing seismic risks due to rising population density. Vs30 is a vital parameter for understanding how a site will respond to earthquake shaking; however, obtaining Vs30 can be costly or impractical because of infrastructure or access challenges. The horizontal-to-vertical spectral ratio (HVSR) enables rapid assessment, provided a relationship between Vs30 and the resonant frequency (f0) of the shallow subsurface can be established. Previously surveyed Vs30 sites in the Provo segment of the Wasatch Fault Zone were measured with a three-component seismometer to obtain f0. These sites are located on the hanging wall of the fault zone, within alluvial and lacustrine Quaternary sediments. For each of the 20 sites, ambient noise was recorded for 30 minutes and amplitude-frequency spectra computed for each component. A rubric was applied to select site results most suitable for analysis and forward modelling, based on uncertainty of f0, uncertainty of H/V response, and peak quality. The H/V response was then derived for 15 selected sites. The strongest low-frequency peak identified the f0, which ranged from 0.28 to 1.38 Hz. Experimenting with linear regression helps guide understanding of the potential for estimating Vs30 from HVSR in this region.