pure and applied geophysics最新文献

Long-term assessment of near-surface air pollutants was done at the semi-arid region of Jaipur in the western part of India from 2018 to 2022. The annual mean mass concentrations of PM₁₀, PM_2.5, NO_x, SO₂, CO, and ozone were 121.37 ± 14.65 µg/m³, 54.37 ± 7.02 µg/m³, 43.65 ± 7.31 µg/m³, 12.36 ± 1.08 µg/m³, 0.94 ± 0.05 mg/m³, and 45.51 ± 3.35 µg/m³, respectively. The mass concentrations of PM_2.5, PM₁₀, and NO_x exceeded ~ 36%, 62%, and 11% of days to their respective annual National Ambient Air Quality Standards (NAAQS), while SO₂ and ozone remained within its permissible limits. A significant decline in pollutant concentrations was observed in 2020, attributed to the substantial reduction in various human-driven activities due to the nationwide lockdown amid COVID-19 pandemic. The sharp decline in anthropogenic emissions provided a rare glimpse into the environmental impact of human, industrial and economic operations. Seasonal variations indicated increased pollutant levels during the post-monsoon and winter seasons, attributed to a lower and stable boundary layer, constraining pollutant dispersion. Additionally, elevated pollutant concentrations coincided with high wind speed during the pre-monsoon, transporting large dust particles from the Thar Desert region to the west of Rajasthan. The potential source sectors and the transport pathways of pollutants at Jaipur were investigated with the air mass back-trajectory analysis. The enhanced pollutants over the region are found to be largely associated with the trajectories mainly from the west (~ 39%), west-southeast (~ 27%) and southwest (~ 15%) directions.

India is increasingly experiencing harsh and frequent heatwaves, making it one of the most vulnerable regions to extreme temperature events. The current study focuses on identifying heatwave instances observed during the years 1951 to 2022 using the Excessive Heat Factor index. For this purpose, two distinct regions, viz., the north central states and Maharashtra (NCM), and the south-eastern and eastern Indian states (SE) are considered. The results indicate that the SE region has seen a significant shift in the climatological daily maximum temperature in recent years, unlike the NCM region. However, the NCM region is found to be experiencing more heatwaves in recent years, especially in the western parts. The months of May and June are more prone to heatwaves, yet, April is found to experience increasing heatwaves in recent three decades. The relevant land-atmospheric parameters that impact intensification of heatwaves reflected an increase in near-surface air temperature during heatwaves, leading to a deficit of soil moisture and pushing the regional temperature to rise further. The dryness is more prevalent in the NCM region compared to SE. In the NCM region, the wind direction is westerly, which subsequently drives the hot, dry wind towards the SE region, enhancing the discomfort, besides the contribution of prevailing humid conditions. Also, future projections indicate higher heatwaves by 2100, characterized by longer and persistent occurrences, with some events lasting over 10 consecutive days. Given the growing risk, it requires an integrated approach combining forecasting, policy interventions, public awareness, and engagement to build resilience in the wake of a warming climate scenario, to address this challenge.

This study developed methods that use acoustic and electromagnetic waves for the early detection and characterization of cavities beneath structures. The model cavities were simulated using an acrylic crate filled with dry sand. Top plates fabricated from various materials (acrylic, wood, and concrete) were placed on the sand surface, and a controlled sand disposal valve facilitated incremental cavity formation (0–1500 g). Acoustic wave tests employed a microphone to measure the waves generated by hammer impacts using interchangeable hammer tips (aluminum, plastic, and rubber). Electromagnetic wave tests utilized an insulated wired probe to systematically measure the electromagnetic waves and reflections of the three plate materials. The experimental results showed a strong correlation between pitch frequency and cavity size. Early cavity formation caused significant transitions in the pitch frequency, which were influenced by the plate material, thickness, and tip properties. Wavelet analysis demonstrated high-frequency attenuation and increased prominence of lower frequencies as the cavities grew in size. Electromagnetic waveform analysis revealed a critical transition from early to open cavities at 70 g of sand disposal, showing reduced sensitivity to further cavity growth. Both the acoustic and electromagnetic responses were sensitive in the early stages and gradually converged as the cavities grew. This paper suggests that acoustic and electromagnetic responses are effective indicators of early cavity formation.

Because of its geotectonic location at the boundary between the Indian plate and the Burma subplate, Bangladesh and its surrounding regions have historically experienced numerous devastating earthquakes. Seismic hazard maps are important for mitigating seismic risk and disasters in the context of sustainable development. In this study, we used a classical probabilistic seismic hazard assessment approach to calculate the peak ground acceleration (PGA) and spectral acceleration (SA) for 10% and 2% probability of exceedance (PE) in 50 years. We utilized area seismic source and smoothed grid seismic source models, Vs30 local site effects, and seven ground motion prediction equations for the shallow crust and subduction zone. Seismicity parameters were calculated using the maximum likelihood method from the declustered and unified earthquake catalogs. The standard logic tree framework was used to integrate different source models and minimize epistemic uncertainties. The sensitivity of input parameters on hazard assessment and the uncertainty bands (fractiles) of logic tree branches were also evaluated. The result showed a similar lateral distribution of PGA or SA values followed the background seismicity. The ranges of PGA values for 10% and 2% PE were 0.15–0.65 g and 0.31–1.35 g, respectively. The SA values varied from 0.74 to 2.54 g, and from 0.39 to 2.03 g at 0.2, and 1.0 s, respectively, at 2% PE. The eastern and western regions are zones with the highest and lowest hazards, and site conditions affect these hazard values. These results support to sustainable, earthquake-resilient development in this tectonically active region, which lies between the Indian plate and the Burma subplate. To more accurately capture the spatial hazard variations, future seismic hazard assessments should incorporate region-specific Ground Motion Prediction Equations, refined Vs30 values and updated seismic source zonation within a logic-tree framework.

The Bayan Obo deposit is a world-class REE-Fe-Nb (rare earth element-iron-niobium) resource and the most representative carbonatite-type rare earth deposit, holding immense economic and strategic value. However, traditional single geophysical methods have struggled to accurately delineate the deep 3D geometry of its ore bodies, limiting effective resource evaluation and exploration targeting. To clarify the subsurface distribution of the ore-bearing carbonatite, this study proposes an integrated approach that leverages petrophysical properties as a critical link between geological interpretation and multi-geophysical data. Based on a comprehensive dataset of 4019 samples, we systematically analyzed the density and magnetic susceptibility of three major rock types: slate, dolomite, and iron ore. A lithology prediction model was developed using the K-Nearest Neighbors (KNN) algorithm, which was selected after a comparative evaluation against alternatives (e.g., Random Forest, SVM) for its effectiveness in handling small-to-medium datasets and capturing local feature structures. The model achieves high prediction accuracies of 97.7% for ore bodies and 90.9% for wall rocks. Applied to 3D geophysical inversion results, the model reveals that the ore bodies exhibit an east–west strike and dip southward. The deepest ore bodies occur between the main and east mining pits, with the burial depth shallowing toward the eastern and western flanks. Significant ore-hosting carbonatites are also identified in the Dongjielegele area. This work delineates the 3D boundaries of deep-seated ore-hosting carbonatite in the Bayan Obo deposit, while extending the known mineralization depth to 2000 m, effectively translating geophysical anomalies into quantifiable resource potential. The resulting 3D model provides critical insights for formulating future exploration strategies, evaluating resource spatial distribution, and optimizing comprehensive utilization.

Natural degassing geological systems have important implications for human safety, ecosystems, groundwater quality, and volcanic hazard. Therefore, their investigation and monitoring are essential for assessing the hazards associated with the upward migration of soil gases. Electrical resistivity is widely recognized as a key parameter for characterizing these systems, as it is sensitive to water saturation, gas content, and fluid temperature. However, interpreting changes in measured resistivity in terms of system dynamics remains challenging due to the complex relationships between thermodynamic and petrophysical parameters and resistivity. Most existing relationships have limited geological applicability and often rely on empirical coefficients derived from laboratory analysis. To contribute to this issue, a new machine learning approach based on a Random Forest algorithm is proposed to predict subsurface resistivity values from numerical simulations of the system dynamics. The aim is to establish a relationship between the petrophysical/thermodynamic variables of the numerical model and the 3D electrical resistivity imaging of the study system obtained from field geophysical surveys. Such a relationship could be used to predict temporal variations in resistivity distribution in response to changes in simulated thermo-petrophysical conditions. Comparison between predicted and field resistivity data would ultimately validate the current dynamics of the system, providing a powerful additional tool for resistivity monitoring of natural degassing systems. The application of the proposed approach to two CO₂-dominated degassing areas in southern Italy resulted in good resistivity prediction accuracy for both the test datasets, showing a significant improvement in resistivity prediction compared to the use of conventional techniques.

Hourly streamflow prediction is a critical component in various fields, including water resource management, energy production, network optimization, and flood risk mitigation. In flood management, accurate short-term discharge forecasts are essential for anticipating river surges and implementing preventive or mitigation strategies. This study proposes a forecasting approach for hourly streamflow at multiple time horizons (t, 1 h, 3 h, 6 h, 12 h, 24 h) using Extreme Learning Machine (ELM) models optimized by five bio-inspired algorithms: Grey Wolf Optimizer (GWO), Bat Algorithm (BAT), Differential Evolution (DE), Whale Optimization Algorithm (WOA), and Genetic Algorithm (GA). The models leverage temporal dependencies from past discharge values (t−1 to t−6) to improve predictive performance. Experimental results show that model performance varies significantly across time horizons. For immediate forecasts (t + 1h), the ELM-GWO1, ELM-BAT1, and ELM-DE1 models achieved the best accuracy, with R = 0.991, 0.990, and 0.988, respectively, and NSE values above 0.97. At longer horizons (t + 24h), predictive accuracy decreases, with models such as ELM-GA6 and ELM-WOA6 showing the lowest performance (R = 0.415 and 0.435, NSE = 0.201 and 0.206). The BAT and GWO-optimized models generally provided more stable and reliable forecasts across different horizons, outperforming traditional methods in terms of accuracy. These results confirm that bio-inspired optimization enhances ELM-based hourly discharge forecasts, particularly for short- to mid-term predictions. This approach provides a reliable solution for real-time streamflow prediction, improving decision-making for sustainable water resource management and flood risk mitigation.

In West Africa (WA), socioeconomic sectors such as agriculture, water management, electricity production, disaster risk management, and healthcare are heavily dependent on rainfall. It is therefore imperative to have reliable seasonal precipitation forecasts available sufficiently in advance to facilitate effective planning and informed decision-making across climate-sensitive sectors. The performance of 14 seasonal precipitation forecasts from the North American Multi-Model Ensemble (NMME) and European Centre for Medium-Range Weather Forecasts fifth-generation seasonal forecasting system (ECMWF-SEAS5) is evaluated for the June–September (JJAS) season. The Global Precipitation Climatology Centre (GPCC) and the African Rainfall Climatology Version 2 (ARC2) datasets are used as observational references. We began by verifying the ability of the forecasts to reproduce the climatology, and then assessed the performance of each model and the multi-model ensemble in predicting rainfall in WA at timescales ranging from 0 to 5 months lead time. The NMME and SEAS5 models successfully capture the average JJAS seasonal precipitation, estimated at around 11 mm/day in central and southeastern part of WA. Forecast skill is highest at shorter lead times but declines rapidly with increasing lead time, as reflected by the decreasing Heidke Skill Score values. Most models detect normal seasons with over 55% probability of Detection, but struggle to identify seasons above and below normal (probability of Detection < 42%). The performance of the multi-model ensemble is not systematically superior to that of individual models, suggesting the need for more advanced weighting approaches. According to our results, the NMME and SEAS5 models represent a valuable tool during the first three lead times (i.e., leads 0–2) in WA, allowing for the anticipation of key seasonal features before the onset of the JJAS season, and thereby enhancing the integration of weather phenomena into decision-making processes.