pure and applied geophysics最新文献

We investigate the influence of a fine-scale (FS) layered structure in the atmosphere on the propagation of infrasound signals generated by fragmenting meteoroids. Using a pseudo-differential parabolic equation (PPE) approach, we model broadband acoustic signals from point sources at altitudes of 35–100 km. The presence of FS fluctuations in the stratosphere (37–45 km) and the lower thermosphere (100–120 km) modifies ray trajectories, causing multiple arrivals and prolonged signal durations at ground stations. In particular, meteoroids fragmenting at 80–100 km can produce two distinct thermospheric arrivals beyond 150 km range, while meteoroids descending to 50 km or below yield weak, long-lived arrivals within the acoustic shadow zone via antiguiding propagation and diffraction. Comparison with observed infrasound data confirms that FS-layered inhomogeneities can account for multi-arrival “N-waves,” broadening potential interpretations of meteoroid signals. The results also apply to other atmospheric-entry objects, such as sample return capsules, emphasizing how FS structure impacts shock wave propagation. Our findings advance understanding of wavefield evolution in a layered atmosphere and have broad relevance for global infrasound monitoring of diverse phenomena (e.g., re-entry capsules, rocket launches, and large-scale explosions).

Random adiabatic pressure fluctuations in a turbulent medium are related to vortex fluctuations described by the classical theory of incompressible turbulence. These two types of pressure fluctuations have the same order of amplitude but different dispersion relations and different frequency spectra. Based on measurements in the atmospheric boundary layer and known results from measurements in wind tunnels, the equilibrium form of the spectrum of adiabatic noise in a turbulent medium is proposed. Calculations confirming the relationship between its energy and spectral width and the intensity of turbulent mixing in the ABL are performed. A hypothesis is proposed that allows us to represent Kolmogorov spectra of velocity and temperature fluctuations in the form of series of spectral densities that have no features in the low and high frequency regions of the spectrum.

Boeotia, located in Central Greece, experiences frequent seismic activity, mainly due to its proximity to the Gulf of Corinth. Significant earthquakes have occurred in the broader study area, such as the ones of Atalanti (M_w = 6.8, 6.9) in 1894, as well as the Alkyonides sequence in 1981 that included three M_w > 6.0 events. In late 2020, a M_w = 4.6 mainshock took place near Thiva, a populated town in Boeotia, followed by the 2021–2022 seismic sequence with three M_w > 4.0 earthquakes. The objective of this study is to perform a Probabilistic Seismic Hazard Assessment (PSHA) for Boeotia through the computation of the Peak Ground Acceleration (PGA) and Peak Ground Velocity (PGV) using two truncation levels (ε = 0 and 3). Moreover, Uniform Hazard Spectra (UHS) are constructed in terms of Spectral acceleration (S_a) for Thiva and Livadia, the capital of Boeotia. To achieve this, three seismotectonic models in the form of area sources are employed in the computational framework. Ground Motion Prediction Equations (GMPEs), using data of the area of Greece, are utilized to estimate PGA and PGV. For each area source, the percentages of normal and non-normal (reverse or strike-slip) fault plane solutions are computed in order to generate minor branches for each GMPE that takes into account the focal mechanism type. This approach introduces variability and reduces uncertainties in PSHA. Additionally, a sensitivity analysis was performed by keeping constant one logic tree, first the GMPE, then the source-model tree, while varying the other, to assess the consistency of individual GMPEs and source models. The findings reveal that western and eastern Boeotia have higher seismic hazard, attributed to the seismotectonics of the study area. Additionally, the seismic hazard level in Thiva is higher compared to Livadia.

Alluvial gold mining has a long-standing tradition in many regions of the world and is typically conducted through sediment deposition and riverbed modification. While numerous studies have explored the application of machine learning (ML) techniques for mineral prospectivity mapping in various mining contexts, their use in alluvial gold mining environments remains limited. This study presents three ML-based approaches for prospectivity analysis in alluvial mining settings. The first is based on a natural language processing (NLP) methodology originally introduced in Australia. The second is a hybrid approach that combines a convolutional neural network with a transfer learning enhanced position encoder. The third is a linear regression model used as a baseline for comparative analysis. These models are evaluated using data from the Cauca River basin in Colombia, a region with significant alluvial gold activity. Validation results show that the hybrid neural network approaches consistently outperform both the NLP-based method and linear interpolation in this context. The proposed approach provides a data-driven, automated, and scalable methodology for resource prospecting that shows potential for application in alluvial gold mining and broader mining exploration in regions where geological mapping and manual exploration are limited or cost-prohibitive. By integrating field data with ML and spatial analysis mining companies could prioritize drill targets, reduce exploration costs, and improve the sustainability of their operations through informed decision-making.

The establishment of standardized tsunami magnitude scales is a challenging issue. Five main types of scales have been proposed since the 1940s but no comparative studies have been carried out so far. Based on data of local and remote tsunamis reaching Japan from the historical times up to 2011 we established for the first-time empirical relationships between the Imamura-Iida (m), Soloviev (S), Hatori (m′), Abe (M_t) and Murty-Loomis (ML) scales, as well as among these magnitudes and the moment magnitude, M_w, of the parent earthquakes. M_t and ML do not represent real tsunami magnitude scales but alternative earthquake magnitude estimates since both calibrates for M_w. The best correlation was found for the pair M_w/M_t. However, no good correlation was found for the pairs M_w/ML and M_t/ML. This is due to difficulties in calculating accurately ML from the potential energy at the source which is the physical basis of ML. Between the various pairs composed by m, S, m′ and M_w (or M_t), the magnitude m′, ranging from − 1 to 4, showed the best correlation performance very likely since it calibrates wave height, h, for epicentral distance, Δ. On the contrary, m and S do not calibrate h for Δ. Utilizing the seismological tradition, we developed tsunami frequency-magnitude distributions (FMDs) based on the Gutenberg-Richter law established for earthquakes. Tsunami hazard descriptors in Japan have been calculated from empirical relationships and FMDs. For example, the mean repeat time of m′ ≥ 3.0 (heavily damaging wave) is about 38 yrs, while the maximum m′ expected in 10 and 100 years is ~ 2.4 and ~ 3.4, respectively. Earthquakes of M_w (or M_t) ≥ 8.2, regardless local or remote, that generate tsunamis reaching Japan repeat about every 21 years with corresponding m′ of ~ 2.4. We tested successfully the applicability of the relations developed with data of recent tsunamis generated by the 1 Jan. 2024 Noto Peninsula, Japan, earthquake (M_w = 7.5; we found m′ = 0.18, M_t = 7.48) and the 30 Oct. 2020 Samos, east Aegean Sea, Greece, earthquake (M_w = 7.0; we found m′ = 0, M_t ~ 6.9). Systematic magnitude calculation of past tsunamis will allow the development of regional FMDs and tsunami hazard descriptors in tsunami prone regions of the world with possible contribution in the long-term tsunami risk mitigation planning. However, the scales m, S, m′ are characterized by coarse resolution and do not allow for accurate hazard assessments.

The Zagros is known as one of the most seismically active mountain belts on Earth. In this study, we apply the parametric generalized inversion technique to estimate the source (i.e., moment magnitude, corner frequency, and stress parameter), path (i.e., quality factor and geometric attenuation slope), and site parameters for the entire Zagros mountains. We rely on strong ground motion records from 264 earthquakes (3.0 ≤ M_w ≤ 6.5) between 1976 and 2020, recorded by 241 three-component accelerometers. We find stress-parameter values ranging from 0.07 MPa to 32.9 MPa, with an average of 4.47 MPa, showing no clear dependence on magnitude. The estimated geometric attenuation and corresponding quality factor are, (gamma =1.012pm 0.009), and (Q=left(159right){f}^{left(0.49right)}), respectively. The mean value of near-surface attenuation, from regression of high-frequency empirical site transfer functions, is κ₀ = 0.0351 ± 0.0299 s. Furthermore, we observe a weak correlation between κ₀ and V_S30. Our results present updated values for ground motion parameters (i.e., stress parameter, near-surface attenuation, quality factor and geometric attenuation slope) in the Zagros.

In this study, the monthly maximum and minimum surface temperature (T_max and T_min) of all India (AI), and 7 temperature homogeneous regions from India are decomposed into several orthogonal components namely Intrinsic Mode Functions (IMFs) using the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) method. Further, the intrinsic modes obtained are transformed analytically using the Normalized Hilbert Transform coupled with Direct Quadrature (NHT-DQ) to understand the time–frequency characteristics of 16 temperature time series pertaining to eight different regions in India. The non-linear and non-stationary nature of all the time series is shown and the dynamic behavior of dominant time scale in different regions of India is highlighted. The spectral analysis of IMFs of each time series depicted the evolution of temperature over the data length along with the modulation of frequencies. Then, the trends of instantaneous amplitudes are estimated to understand the dominant IMFs resulting in temperature changes in India. The results indicated that the higher order IMFs with inter-decadal periodicity displayed a clearly increasing trend in amplitudes since 1970, supporting the signatures of climate change in India. It is also found that a statistically significant change in amplitudes is observed for all oscillatory modes in North East (NE) region for the minimum temperature time series. It is further noticed that instantaneous amplitudes from oscillatory mode 2 (IMF2) of annual periodicity shows a significant trend for both T_max and T_min time series for most of the regions. The trend of instantaneous amplitudes for annual scale oscillatory mode and inter-decadal periodicity of 30 years of West Coast (WC) regions is contrasting character when compared with that in other regions, which depict a distinct response of temperature regime of WC region. Moreover, four climate indices and indicators such as Pacific Decadal Oscillation (PDO), Sunspot Number (SN), Total Solar Irradiance (TSI) and CO₂ concentration time series data are decomposed using CEEMDAN. The comparison of these components with the modes of extreme (T_max, T_min) and mean (T_mean) annual temperature datasets of AI is performed in the time domain. The correlation analysis established the link between these climatic indicators and different temperature time series from India. It is further noticed that such inter-relationships between different indicators and temperature is mainly deciphered in the low frequency modes.

The study is focused on the spatial–temporal details of rainfall characteristics of the Mayurakshi basin from 1991 to 2020. The main goal of the present study is to examine the spatio temporal rainfall variability of the study area based on the long-term trend along with checking how the rainfall variability has influenced the rainfall trend and forecasting of the study area. For the same, rainfall data was downloaded from IMD Puna, and the same is predicted for the ensuing ten years. The study examines the long-term trend, variability, and seasonal character of rainfall, including the changing nature with dry and wet conditions dominating in the basin. This could provide some insight into the basin's characteristics. The long-term rainfall trend was evaluated by modified Mann–Kendall (mMK) statistics, and the magnitude of the trend was estimated using Sen’s slope. The result showed a variable trend of rainfall, while the magnitude of the trend was found to be negative. The change point in rainfall was evaluated by applying the Pettitt test, the Standard Normal Homogeneity Test (SNHT), and Buishand's U test. In general, for the study area, the change point statistics showed that the computed p-value is lower than the significant level ∝  = 0.05, which indicated a significant change point of rainfall in 2008. The rainfall seasonality index (RSI) of all rainfall stations was evaluated to understand the seasonal nature of rainfall. RSI was found to be equable in indefinite weather, i.e., monsoonal rainfall. The drought and wet conditions of the basin were evaluated by the rainfall anomaly index (RAI). The RAI revealed that the basin experienced 50% of the years of drought during the 30-year period. Rainfall forecasting was done using the empirical approach of the Autoregressive Integrated Moving Average (ARIMA). The fitted model ARIMA (0,0,0) (0,1,1) [12] revealed a decrease in the annual rainfall (1342.69 mm) over the period of the next 10 years. The study area being an agrarian region, the result of this study will be helpful for future sustainable planning that can be beneficial for sustainable agriculture through proper crop management and water use based on the chaging environmental conditions, thereby enabling inching forward towards SDG 2.

This paper focuses on the study of the temporal evolution of seismicity and the role of fluids during major earthquake sequences that occurred in the Central Apennines and Southern Walker Lane belt-Eastern California Shear Zone over the last two decades: the 1997 Colfiorito, the 2009 L’Aquila, the 2016 Amatrice-Norcia, and the 2019 Ridgecrest sequences. The availability of high-quality earthquakes catalogs offers the opportunity to evaluate in detail the temporal evolution of the earthquake's size distribution (or b-value) and propose a physical explanation based on the effect of the fluid flow process in triggering seismicity. For all seismic sequences, the b-value time series show a gradual decrease from a few months to one year before mainshocks. The gradual decrease in the b-value is interpreted as a gradual increase in earthquake activity due essentially to the short-term to intermediate-term pore-fluid fluctuations. The temporal variation of the b-value during Amatrice-Norcia and Ridgecrest foreshock sequences is characterized by a double b-value minimum separated by a short-lived b-value increase as observed in laboratory experiments on water-saturated rocks. The observed fluctuation of the b-value is presented here as an accelerating crack growth due essentially to the fluid flow instability. Even though seismic precursors could have been predictable in areas with high dense seismic networks, the different b-value time series reveal the difficulty in establishing a correspondence between the duration of the foreshock activity and the magnitude of the next largest expected earthquake. This may suggest that the fluid migration controls the size of the ruptures.

The Indian Ocean Dipole (IOD) can significantly influence wind speed (WS) patterns in the Tropical Indian Ocean (TIO). However, the capability of climate models to replicate the impact of IOD on WS still remains inadequately quantified. This study evaluated the performance of 24 Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating WS responses to IOD over the Indian Ocean, based on historical simulations spanning the period 1958–2014 during the JJA and SON seasons. The evaluation employed four skill metrics—interannual variability score (IVS), M-Score, root mean square error (RMSE), and Taylor skill score (TSS) in order to assess the model accuracy. A Comprehensive Rating Index (CRI) was used to rank the model performance across different regions and seasons in the study domain. Results indicate that IOD plays a pivotal role in modulating WS in the Bay of Bengal (BOB), Arabian Sea (AS), and Northern Indian Ocean (NIO), with CMIP6 models exhibiting varying skill levels in simulating these responses. AS exhibited the highest variability and uncertainty during JJA, characterized based on larger RMSE and IVS values and lower M-Score, while the NIO showed superior performance with minimal variability and higher consistency. During SON, the model performance improved across all regions, though NIO remains the most consistent. Study indicates that top-performing models for JJA are MIROC6 (AS), MME (BOB), and CESM2 (NIO), while for SON, TaiESM1 (AS), MIROC6 (BOB), and CESM2-WACCM (NIO) ranked the highest. Overall, the models MIROC6, CESM2, and CESM2-WACCM demonstrated consistently higher performance across different regions and seasons, highlighting the robustness in capturing WS responses to IOD. Additionally, the CMIP6 models exhibit significant uncertainty over the AS during JJA in the Indian Ocean.