pure and applied geophysics最新文献

Ambient noise vibrations are widely used in the field of geotechnical earthquake engineering for site response analysis. Though generally weak, these vibrations are available anywhere and anytime and are an ideal source of energy for conducting seismic surveys. The use of horizontal to vertical spectral ratio (H/V) from ambient noise records, to characterize local site effects is now an established technique. This technique can be applied to estimate predominant frequencies, amplitude ratios and seismic vulnerability index of an area. Ninety sites were selected in Durban (KwaZulu-Natal Province) for ambient vibration measurements and H/V analysis. The analysis of recorded waveforms resulted in the determination of predominant frequencies as well as the amplitude ratios that can be assumed to be equivalent to amplification factors, allowing the city to be divided into zones of differing site effects. The results showed predominant frequencies ranging between 0.78 and 36.8 Hz and peak amplitude ratios in the interval 1.3 to 30.0. The predominant frequency and amplitude ratio results were then used to estimate a seismic vulnerability index, which was observed to vary from 0.2 to 87.3. The city was then successfully divided into three zones according to the obtained vulnerability index values. High vulnerability index values were found to correlate with coastal areas, thus indicating areas that are likely to suffer significant amplification of earthquake ground motion. These coastal areas are associated with thick sediments and alluvial plains, conditions which have been shown in many studies to have a significant impact on ground motion resulting in amplification. Building structures with natural frequencies close to the site predominant frequencies in these areas will experience large resonance effects. This study can be considered as a first step towards the microzonation of the city of Durban.

Afghanistan’s complex tectonic setting within the Alpine-Himalayan belt results from the ongoing India-Eurasia collision. Through integrated analysis of seismic, geodetic, and gravity data, this study characterizes five distinct seismotectonic provinces. The Hindu Kush province exhibits extreme crustal thickness (65–80 km) and deep seismicity (≤ 300 km), with Bouguer anomalies reaching − 479 mGal, indicating active subduction processes. The Chaman fault system shows high left-lateral slip rates (5–35 mm/yr) with shallow seismicity. Northern Afghanistan exhibits moderate to shallow seismicity within thick sedimentary basins (up to 16 km), whereas central Afghanistan displays sparse seismicity despite its complex fault networks. The Helmand block remains stable with consistent crustal thickness (40–45 km). To understand Afghanistan’s seismotectonics within regional dynamics, the authors compared the findings with recent studies from the Himalayan and Northeast Indian regions. This study findings directly inform seismic hazard assessments, highlighting the urgent need for updated building codes in Kabul and Kandahar, as well as prioritized infrastructure reinforcement along the Chaman fault system. This provincial classification provides a framework for targeted risk mitigation strategies across Afghanistan.

This study examines the impact of deep convective activity in intense tropical cyclones (TCs) on cirrus clouds, specifically in terms of modulating cloud ice water content in the upper atmosphere. Cirrus clouds play a crucial role in Earth’s radiation budget. The study considers three major TCs (Fani, Tauktae, and Mocha) in the North Indian Ocean (NIO) basin to examine lightning activity within a 500 km radial distance from the TC eye, as a proxy for convective activity, and assess its contribution to Specific Cloud Ice Water Content (SCIWC). It reveals positive correlations between daily lightning strike counts and daily mean SCIWC, with correlation coefficients (r) reaching 0.76, 0.63, and 0.81 at 200 hPa for TCs Fani, Tauktae, and Mocha, respectively. It is found that the SCIWC consistently peaks near the 200 hPa level for all three TCs, highlighting this level as a key region for the accumulation of upper-tropospheric cloud ice. Lightning stroke counts and SCIWC at 200 hPa at the 500 km radial distance from the TC eye on the maximum lightning activity day possess strong positive correlations with correlation coefficients (r) = 0.64, 0.60, and 0.55 for Fani (with a 12-h latency), Tauktae, and Mocha (with a 12-h latency), respectively. Additionally, a multi-scale analysis across latitude–longitude grids further confirms that as lightning intensifies, cloud ice content increases significantly at high altitudes. Regression analysis shows that for every 100% increase in daily stroke counts within a 500 km radius from the TC eye, the daily SCIWC could rise by more than 60% in different TC cases, with a stronger response between 300 to 200 hPa. It highlights the role of deep convection in the formation of cirrus clouds. The study deduces that the lightning activity in TCs serves as a key indicator of convective strength, modulating cirrus cloud development through enhanced cloud ice water content.

Quantifying the variability of hydro-meteorological variables and various land-surface processes in response to the diverse sources of uncertainties arising from the structure of hydrological models, model parameters, calibration procedures, and schemes related to the various physical process representations is a significant scientific endeavour with important implications in hydrology. Sensitivity analysis is the only plausible way to provide valuable insights into how different parameters influence model outputs and helps evaluate the rationality of each model parameter. Hence, the present study employs sensitivity analysis for comprehensive evaluation of the impact of an enhanced hydrological parameterized and newly developed WRF-Hydro model towards the simulation of hydro-meteorological variables, in terms of model parameter uncertainty and structural variability. The study is carried out in the vicinity of the Krishna River basin in India with a sub grid-scale of 200 m. We evaluate the model's fidelity to accurately simulate streamflow and key hydrometeorological variables, including Evapotranspiration, soil moisture, and land heat and moisture fluxes, through a series of sensitivity experiments. These experiments quantify the impact of uncertainties related to land surface model parameterization, thresholds of model parameters, and structural configurations of the WRF-Hydro framework. The characteristics of the simulations thus generated have been validated against in-situ gauge data and remote sensing-based multi-platform observations, revealing a significant improvement in the simulation accuracy, with correlation coefficients exceeding 0.9 and 0.7 for soil moisture and soil temperature, respectively. Our findings show marked enhancement in the model’s predictive accuracy. By identifying the configurations with superior performance, this study provides practical insight for selecting optimal model parameterizations, thereby contributing to improving the hydro-meteorological forecasting frameworks and supporting better water management practices in regions with limited data availability.

Droughts are complex and costly natural hazards with significant impacts on water resources, agriculture, and ecosystems. This study investigates drought patterns in Southeast Türkiye by clustering regional drought behaviour using four widely applied indices: the Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), Palmer Drought Severity Index (PDSI), and self-calibrated PDSI (scPDSI). Hierarchical clustering based on Dynamic Time Warping (DTW) and Euclidean Distance (ED), optimized using the Silhouette and Elbow methods, identified two primary clusters across 29 stations for all indices. Cluster validation using the Davies–Bouldin (DBI) and Calinski–Harabasz (CHI) indices shows that DTW generally outperforms ED for indices incorporating precipitation and evapotranspiration dynamics. DTW yields lower mean DBI values for SPEI-12 (1.04 vs. 1.08) and scPDSI (1.20 vs. 1.35) and higher CHI scores in five of eight clusters. Stronger separation is observed for scPDSI (CHI ≈ 400 vs. 327–372 under ED) and SPI-12 Cluster 2 (397 vs. 268), while ED performs better for PDSI (mean DBI = 0.99 vs. 1.21). Sen’s slope and Mann–Kendall analyses reveal significant drying trends across all indices and clusters. Cluster 2, largely representing high-altitude, snow-covered regions, shows steeper drying for SPI-12, SPEI-12, and scPDSI. These areas are increasingly vulnerable due to declining snowpack and a shift in the spring season from March–May to April–June, disrupting water availability and agricultural activities. Overall, the findings highlight the importance of region-specific drought management strategies, particularly for Southeastern Anatolia, where effective water management is vital for sustainability and climate adaptation.

Forecasting earthquake sequences remains a central challenge in seismology, particularly under non-stationary conditions. While deep learning models have shown promise, their ability to generalize across time remains poorly understood. We evaluate neural and hybrid (NN + Markov) models for short-term earthquake forecasting on a regional catalog using temporally stratified cross-validation. Models are trained on earlier portions of the catalog and evaluated on future unseen events, enabling realistic assessment of temporal generalization. We find that while these models outperform a purely Markovian model on validation data, their test performance degrades substantially in the most recent quintile. A detailed attribution analysis reveals a shift in feature relevance over time, with later data exhibiting simpler, more Markov-consistent behavior. To support interpretability, we apply Integrated Gradients, a type of explainable AI (XAI) to analyze how models rely on different input features. These results highlight the risks of overfitting to early patterns in seismicity and underscore the importance of temporally realistic benchmarks. We conclude that forecasting skill is inherently time-dependent and benefits from combining physical priors with data-driven methods.

Double-shield Tunnel Boring Machines (TBMs) have been extensively used in urban tunneling. Advanced prospecting of unfavorable geological conditions ahead of the tunnel face is crucial for ensuring construction safety. To address the challenges of limited observation aperture and low signal-to-noise ratio (SNR) in data acquisition during double-shield TBM tunneling, this study develops a seismic ahead-prospecting system and equipment. The proposed method enables the data acquisition of high SNR reflected seismic data within the small space of the tunnel. Numerical simulations demonstrate that acquiring seismic data from the exposed rock effectively minimizes waveform distortion and noise interference, thereby enhancing data quality. Laboratory experiments confirm the instrument’s stability and its capability for long-term, continuous data acquisition in double-shield TBM tunnel. Field tests further verify that this method effectively can eliminate the influence of segmental linings, obtain high SNR seismic data, and identify geological anomalies (fault, fractured zone, etc.). These results provide a reliable technical approach for detecting and locating adverse geologies ahead of the tunnel face.

Climate change-induced variability in the water budget leads to significant drought events. In this study, severity, duration, and magnitude parameters obtained from 1-month and 3-month series of the Standardized Precipitation Evapotranspiration Index (SPEI) were used to investigate short-term drought from different perspectives. The lower part of the Euphrates-Tigris Basin, which is a transboundary basin, was preferred as the study area within the borders of Türkiye. In this study, monthly total precipitation and monthly mean temperature data for the period 1963–2021 were used. Both graphical (Şen Innovative Trend Analysis) and statistical (Mann–Kendall, Spearman’s Rho, Sen’s Slope Estimator) trend analysis methods were applied to detect, categorize, and analyze the variability of drought. The Combination of Wilcoxon Test and Scatter Diagram (CWTSD) method, which offers both graphical and statistical trend analysis capabilities, was preferred to enhance consistency and facilitate comparison of results. According to the findings, more severe droughts are observed in the eastern part of the basin compared to the western part. Across the basin, severity and magnitude are decreasing, while duration is increasing. The increase in duration indicates that droughts are shifting from short-term to long-term periods. This situation is of great importance in terms of planning agricultural strategies.

This study diagnoses monthly trends in streamflow magnitude and regime for the Susurluk Basin (Türkiye), which spans semi-arid and mountainous sub-climates. Monthly records from 1990 to 2019 were analyzed at 14 stream gauges and nine precipitation stations. Trends are assessed with the Mann–Kendall (MK) test alongside recent graphical approaches, Improved Visualization for Innovative Trend Analyses (IV-ITA) to resolve value class-based (low/high) behavior, and Innovative Polygon Trend Analysis (IPTA) with the Star Concept to quantify intra-annual transitions. Before the trend analyses, the stations were tested to determine whether their values were homogeneous, and any inhomogeneous stations were excluded from the study. The consistency between the trends of nine precipitation stations and the streamflow data in the basin was analyzed. Across the basin, the average streamflow increased by approximately 40–60% between January and March at many measurement points. However, it then decreased by around 80% in April, albeit at a more moderate rate in December. This suggests significant rebalancing occurred during the December–April period, when most of the annual streamflow occurs. IV-ITA exhibits broadly similar trends for both low and high streamflow classes, with notable exceptions in January–February. Precipitation–streamflow trend directions are largely consistent in the high-flow season (December–April), supporting the notion that climatic control influences the detected shifts. To contextualize these signals, basin-wide land-use/land-cover shifts (1990–2018), notably urban growth, forest expansion, and cropland reconfiguration, provide process context, indicating that a larger fraction of rainfall is routed as fast surface runoff while infiltration and base streamflow recharge decline, alongside seasonally modified water demand. Collectively, the MK+IV-ITA+IPTA framework reveals class-specific and intra-annual dynamics that are obscured by monolithic tests alone and provides decision-relevant evidence for allocation, drought–flood risk, and operations in an intensively managed basin.