pure and applied geophysics最新文献

High accuracy repeated gravity observations within Vrancea active seismic zone, where upper mantle seismicity occurs within full intra-continental environment and no evidence on an active subduction, have succeeded to outline the overall lowering of the gravity over the epicentres area, unexpectedly associated with a local relative subsidence of topography, superposed on the overall trend of Carpathians uplift following denudation and erosion of the mountains catena. A particular gravity decrease related to seismicity was also unveiled by successive gravity campaigns conducted prior and after some significant earthquakes (M 5+). As the vertical deformation of the crust may not explain the gravity change in the area, attempts have been made to model and understand genesis of the mass deficit responsible for the observed gravity. Solutions provided by gravity inversion and some 2D and 3D forward modelling have been interpreted in terms of vertical stretching of the upper part of the crust under the gravity pull generated by eclogitization of the lower crust penetrating the upper mantle. Given the subduction-related scenarios, often used to explain the Vrancea seismicity, may not offer the mechanical environment to justify the lithosphere stretching, some alternative, non-conventional geodynamic models like Rayleigh–Taylor gravitational instability, or the unstable triple-junction are suggested to explain the phenomenon.

Marchenko algorithms retrieve the wavefields excited by virtual sources in the subsurface, these are the Green’s functions consisting of the primary and multiple reflected waves. The requirements for these algorithms are the same as for conventional imaging algorithms; they need an estimate of the velocity model and the recorded reflected waves. We investigate the dependence of the retrieved Green’s functions using the Marchenko equation on the background velocity model and address the question: “How well do we need to know the velocity model for accurate Marchenko focusing?”. We present different background velocity models and compare the Green’s functions retrieved using these models. We show that these retrieved Green’s functions using the Marchenko equation match the exact Green’s function with a high accuracy. We also examine the presence of refracted waves in the retrieved Green’s function. Marchenko focusing algorithm produces refracted waves only if the initial velocity model used for the iterative scheme is sufficiently detailed to model the refracted waves. We show with numerical examples that the average slowness between the surface and the depth of the focal point is required for an accurate reflected wave retrieval. However, substantially more accurate velocity model knowledge is required in the presence of refracted waves.

The Comprehensive Nuclear-Test-Ban Treaty (CTBT) relies on the International Monitoring System (IMS) to detect and verify nuclear explosions worldwide. A key component of the IMS is its network of noble gas radionuclide stations, which measure isotopes such as ¹³³Xe. Station 69 (Hal69) plays a vital role in the Antarctic region, faces logistical and environmental challenges, prompting consideration of alternative locations. This study evaluates the potential relocation of Hal69 to two proposed sites, Rothera and Troll, using atmospheric transport modeling and network performance analysis. The results indicate that repositioning the station to Rothera would significantly enhance network capability across multiple spatial and temporal scales. On average, Rothera improves annual global coverage by 0.48%, with peak regional increases reaching up to 12%, particularly during the austral summer months of December (1.24%) and February (1.19%). In contrast, relocation to Troll leads to a performance decline, with an average annual reduction of 0.16% and localized losses up to 10%. These findings underscore the critical role of strategic site placement in maintaining and strengthening the effectiveness of the IMS verification regime.

Droughts are intensifying worldwide under climate variability, yet their regional dynamics remain poorly understood. This study provides the first century-scale (1902–2022) spatio-temporal assessment of meteorological droughts in the Mahanadi River Basin, India, using high-resolution (0.25° × 0.25°) gridded rainfall data, the Standardized Precipitation Index (SPI), and Mann–Kendall Trend (MKT) test. The results highlight that, (1) the escalating drought frequency as major short-term droughts (SPI-3) occurred in 1954, 1974, 1989, 1996, 2002, 2009, and 2017, while long-term droughts (SPI-12) were recorded in 1902, 1966, 1979, 1989, 2000, 2010, and 2016. Since 1954, the recurrence interval has shortened dramatically, with > 30% of basin area affected during several events. Finding reveals, (2) the emerging drought hotspots as the middle basin faced the highest drought frequency (> 4 events per 30 years), with Chhattisgarh identified as the most vulnerable sub-region. (3) Rising severity of drought intensity regularly exceeded SPI ≤ –1.5 (severe) and reached SPI ≤ –2.0 (extreme) in recent decades, with drought extent peaking at ~ 35% of the basin. (4) Seasonal and multi-decadal shifts was observed before 1982, > 30% drought coverage was restricted to pre-monsoon months; after 1982, nearly all months except May–June showed similar impacts. The humid subtropical (Cwa) zone endured persistent dry spells lasting 30–35 years. (5) The outcome of MKT trend evolution detected statistically significant drying (90–99% confidence) from the 1930s onward, intensifying after 2010. Also, (6) anthropogenic amplification as land-use change, deforestation, and groundwater over-extraction further reduced hydrological resilience. By linking long-term climate variability with land-use pressures, this study reveals how historically water-secure regions are transitioning to drought hotspots. The findings provide a transferable framework for drought risk reduction in South Asia and other monsoon-dependent basins facing accelerated climate stress.

This study investigates the fracture behavior of limestone samples subjected to varying thermal treatment conditions, including different peak temperatures and heating rates. The experimental process evaluated the mechanical response of the rocks by analyzing the correlation between Crack Mouth Opening Displacement (CMOD) and applied load. The results demonstrate that thermal exposure significantly alters the fracture toughness (KIC) and crack propagation behavior. Higher temperatures and faster heating rates were found to accelerate crack initiation and propagation due to induced thermal stresses and microstructural degradation. Mineralogical composition and petrographic characteristics, particularly grain size and porosity, strongly influenced thermal sensitivity. The findings indicate that crack propagation is significantly more rapid in the fine-grained limestone with some coarse calcite (M1) than in the small-grained, amorphous calcite and quartz limestone (L1). This behavior strongly depends on both the heating rate and the peak temperature. This indicates that thermally induced damage is largely dependent on the intrinsic properties of the rock, which must be considered in engineering applications involving high-temperature exposure or thermal fatigue.

Random noise is present in seismic data acquisition and affects subsequent data processing and interpretation. Traditionally, it is difficult to completely suppress or separate the random noise using time domain or frequency domain methods. Although some deep learning methods can improve computational efficiency and parameter adaptation, they still fail to completely separate random noise from effective signals. Obtaining sufficient training samples remains a problem. We introduce the Diffusion Model, which adds noise to clean data in a step by step forward diffusion process, and removes noise in the backward direction based on a neural network. The Diffusion Model predicts the noise and reconstructs clean effective signals. Combined with stationary-phase migration, we can obtain training samples and successfully remove the random noise while preserving effective signals. This improves the accuracy of subsequent processing and structure interpretation. The effectiveness of our method has been verified by field imaging profile and synthetic common-shot datasets.

Climate change poses significant risks to Sarawak, Malaysia, a region renowned for its ecological diversity, where accurate projections of temperature patterns are essential for effective adaptation and resource management. Existing studies often rely on limited global climate models (GCMs) without systematic evaluation, and the high variability in the tropics further complicates model selection. This underscore the need for machine-learning approaches to identify top-ranked GCMs for projecting future temperature changes in Sarawak. Climatic Research Unit (CRU) datasets were used as observational references, and machine learning-based ranking methods, including compromise programming (CP), entropy gain (EG), gain ratio (GR), random forest (RF), and symmetrical uncertainty (SU), were employed to assess and rank GCM performance. Group decision-making (GDM) and an envelope approach were then applied to identify representative GCMs, followed by the development of a RF-based ensemble for robust projection, featuring IPSL-CM5A-MR, IPSL-CM5A-LR, FIO-ESM, HadGEM2-AO, HadGEM2-ES, MIROC-ESM-CHEM, and GISS-E2-R as key contributors for different temperature indices. The results show that the successive Coupled Model Intercomparison Project Phase 6 (CMIP6) models, such as HadGEM3-GC31-LL, HadGEM3-GC31-MM, and FIO-ESM2-0, demonstrate better alignment with observed records compared to CMIP5, with notable improvements in reducing biases and capturing temporal variability. Ensemble projections reveal consistent warming across Sarawak, with CMIP6 generally indicating stronger warming trends than CMIP5, especially during the Southwest Monsoon (SWM). Spatially, the warming is uneven, highlighting persistent thermal contrasts across different parts of the region. These findings underscore the importance of systematic model evaluation and ensemble approaches in improving regional climate projections, providing critical guidance for climate adaptation, water resource management, and biodiversity conservation in Sarawak.

This study presents a geographical and temporal analysis of drought in the Tigris-Euphrates Basin from 1960 to 2023, utilizing the high-resolution TerraClimate dataset. Two primary drought indices, the Standardized Precipitation Index (SPI) and the Standardized Precipitation-Evapotranspiration Index (SPEI), were employed to assess the dynamics of meteorological and hydrological droughts. The results indicate an increased intensity and frequency of drought events, especially after the year 1990. The SPEI index highlights the critical negative impact of increased evapotranspiration on the climatological soil–water balance due to the increased evaporative demand as a function of the increased air temperatures in the study region. SPI analyses revealed that changes in precipitation patterns have increased over time, resulting in more frequent regional drought events. SPEI, on the other hand, reflects the drought trend and drought severity more precisely because it takes into account the increasing trend in evapotranspiration as a function of rising air temperatures. SPI and SPEI provided insights into long-term hydrological drought patterns, emphasizing the need for region-specific drought mitigation and adaptation strategies. These findings underscore the value of integrated drought monitoring and assessment for water management and climate adaptation planning in semi-arid and dry sub-humid regions.