pure and applied geophysics最新文献

The Uttarakhand state of the Indian Himalayan region is primarily occupied  by needle-leaf and broad-leaf forests Understanding the behavior of a land surface model (LSM) to different vegetation types and forcings is crucial to configuring LSM for this particular region, which is the aim of the study. Various reanalysis products (ERA5-Land, IMDAA, GLDAS, MERRA2, and NEP FNL) are validated against in situ observations at three stations (Kosi-Katarmal, Kantli, and Gangolihat stations) to find the best forcing, and ERA5-Land performed the best. Therefore, one-dimensional Noah-multi parameterization (Noah-MP) LSM is forced with ERA5-Land analysis and IMERG rainfall during 2011–2021. Three vegetation types (Deciduous needle-leaf forest, Evergreen broad-leaf forest, and Barren/Sparsely vegetated type) along with no-vegetation type are considered in LSM and referred to as EXP2, EXP3, EXP4, and EXP1, respectively. Note that the vegetation types in EXP2 and EXP3 are closely related to the actual vegetation observed at in- situ stations, while EXP1 and EXP4 provide sensitivity of land surface conditions to the tree density. The diurnal variation of soil temperature (ST) from EXP2 and EXP3 reasonably agrees with in-situ observations and is better than global/regional analyses, unlike EXP1 and EXP4. EXP2 and EXP3 are comparable for surface sensible and latent heat fluxes in pre-monsoon and southwest-monsoon seasons and could be due to matching with vegetation type and density. The Noah-MP soil moisture (SM) is overestimated (~ 0.09 to 0.15 m³ m⁻³) against observation, comparable with ESACCI and CYGNSS (− 0.065 to 0.03 m³ m⁻³) on daily and monthly scales. The SM variations are marginal among the seasons, unlike the ST and surface fluxes. The Noah-MP simulated evapotranspiration is comparable to in-situ observation in EXP2 and EXP3. The study demonstrates the value of LSM in simulating land-surface processes when driven by correct vegetation type, density, and best forcing.

The Late Cretaceous Naparima Hill Formation argillites are the primary source for most of the oil and gas production within the last century in Trinidad. Hydrocarbon production has declined in recent years, necessitating the exploration of new petroleum plays and the use of improved techniques to increase production. The Naparima Hill Formation is also considered as an unconventional reservoir due to its low permeability and high hydrocarbon content. Hydraulic fracturing, which depends on mechanical properties such as brittleness and fracturability, is necessary for the exploitation of this formation. In this study, we measured the unconfined compressive and tensile strengths of four organic-rich outcrop lithofacies (siliceous-calcareous argillite, calcareous argillite, carbonate rich siliceous argillite, and siliceous argillite), under dry and water-saturated conditions. The strength measurements were then used to determine the strength-based brittleness index and fracture toughness. A fracturability evaluation model that integrates brittleness index, fracture toughness, and minimum horizontal insitu stress was used to evaluate the fracturability. Our results showed that saturation reduced the uniaxial compressive and tensile strengths up to 51% and 59%, respectively. The siliceous calcareous argillite and siliceous argillite experienced the highest reductions in strength due to their relatively high porosities (21% and 31%, respectively) and lack of carbonate cement. The brittleness index and fracturability ranged from 60 to 75% and 0.17 to 0.43 MPa^-2.m^0.5, respectively, under dry conditions, and increased to 69 to 78% and 0.26 to 0.55 MPa^-2.m^0.5, respectively, when saturated. Our results imply that all the argillites are brittle, and only the siliceous calcareous argillite and siliceous argillite are easily fractured when water saturated for hydraulic fracturing operations. Thus, the siliceous calcareous argillite and siliceous argillite are more susceptible to tensile fracture initiation and propagation during hydraulic fracturing.

The Transient electromagnetic method is widely used in coal mine goaf exploration. However, data inversion is a complex problem requiring nonlinear solution equations, and the electromagnetic response of double-layer goafs is complex, leading to low accuracy in identifying their types and ranges. A three-dimensional model conforming to the geological characteristics of the study area is constructed, and the transient electromagnetic induction electromotive force data at each measurement point are obtained via numerical simulations. Based on the smoke ring inversion and depth correction of the data, apparent resistivity and its gradient along the apparent depth, as well as the logarithm of induced electromotive force and its gradient along the time plane, are selected as the sample parameters, which are sensitive to the electrical reflection and abnormal characterization of goafs. Principal component analysis of the multilayer depth parameters yields the standard values of each parameter at the depth of the target layer. Training samples for different goaf types are established, and the backpropagation (BP) neural network algorithm is used to train the samples to construct a neural network model of each goaf type. The training samples are back-estimated, and the type of goaf in the entire area is predicted. Results show that the back-estimation prediction accuracy of the No.2 coal seam samples is 84.7%, and the prediction accuracy of the entire area is 84.3%. Meanwhile, the back-estimation prediction accuracy of the No.3 coal seam samples is 94%, and the prediction accuracy of the entire region is 87.87%. The proposed method is used to identify the type of double-layer goaf in coal mines, and the distribution characteristics of the goaf type in the entire area are rapidly obtained. The back-estimation prediction accuracies of the No. 2 and 3 coal seam samples are 91.1% and 93.3%, respectively. Discrimination results are confirmed via drilling. Findings show that the proposed BP neural network discriminant model based on the transient electromagnetic multiparameters realizes the rapid and accurate identification of double-layer goaf types in coal mines and avoids multiple solutions and low recognition caused by the inversion of the transient electromagnetic data.

Thénia fault is well characterized in its eastern part whereas in its western part, between Cap-Matifou and Boumerdes, the fault trace, although invisible, is marked by a Plio-quaternary scarp with a direction N110°. Questions persist regarding its existence, its continuity and its exact position in its western segment which remains poorly documented. In this work, we study the spectral ratios (HVSR) and horizontal polarization of ground motion using ambient vibration noise recordings made in the western part of the Thénia fault, with the objective of asserting or not its continuity. Clustering of HVSR curves based on their trends, their frequency peaks and their amplitudes highlighted three distinct zones: both the plateau area north of the scarp and the lower part of the plain to the south are characterized by high frequency curves while the scarp area is characterized by a mix of low and high frequencies. Moreover, polarization analysis of the ambient vibration noise recordings, carried out by the covariance matrix method, indicates well-polarized signals on the scarp and weak ones on the northern and southern parts. This analysis also made it possible to highlight a mean direction of predominant polarization between 10° and 30°, whose transverse projection coincides perfectly with the direction of the scarp which is N110°. This polarization direction is observed for low and high frequencies which represent the same soil response for different depths. All of these results support the hypothesis of the extension of the Thénia strike-slip fault in its western part, which until now only showed a banal escarpment. The results obtained will make it possible to refine the mapping of the Thénia fault zone at the seismic microzoning scale and to have a better assessment of the regional seismic hazard and good management of the seismic risk, particularly in urbanized areas.

Full waveform inversion (FWI) is a prevalent method for estimating subsurface model parameters, typically employing a frequency-multiscale serial inversion strategy to achieve the required resolution. However, this approach is computationally costly and often yields imprecise results due to the frequency-dependent resolution of velocity model. To enhance both the efficiency and accuracy of FWI, this study introduces a modified spatial multiscale serial high-resolution inversion strategy underpinned by deep learning. Initially utilizing a coarse grid for low-frequency inversion to capture the general subsurface structure, this strategy employs super-resolution generative adversarial networks (SRGAN) to map coarse grid data onto a fine grid as the inversion frequency increases, facilitating lossless data enhancement. This transition provides superior model details for high-frequency inversion on the fine grid, achieving a scalable, frequency-sequential serial inversion from lower to higher scales, while effectively reducing data space consumption at lower frequencies. Furthermore, the incorporation of a residual network (ResNet) enhances the recovery of high-frequency details and physical property boundaries. Experimental results using the Overthrust and Marmousi-II benchmark standard models demonstrate that the revised spatial multiscale FWI method not only boosts inversion efficiency but also significantly improves inversion stability and data resolution.

Piping is one of the main forms of seepage damage, which can pose a serious threat to hydraulic structures such as dams. The phenomenon of particle transport is a manifestation of piping. The migration of fine particles in pores will generate elastic waves due to collision, friction and other factors, which makes it possible to use acoustic emission signals to evaluate particle transport. Using AE technology as a means, considering the influence of flow rate, the filling proportion of small particles and skeleton particles, and the particle size of small particles, we simulated the transport phenomenon of small particles in the process of seepage in porous media, and analyzed the AE signal characteristics under various influencing factors. The phenomenon of small particle transport develops in stages with the change of particle quantity, and the change of AE signal characteristic parameters also conforms to this phased development. High flow rate makes small particles obtain larger initial velocity and energy, which increases the possibility of collision between particles, and it can be reflected in the change of AE signal. Through the test, it is found that the filling proportion of small particles and skeleton particles will have a great impact on the AE signal. Excessive filling of small particles will cause pore blockage, limited movement of small particles, reduced AE signal strength. On the other hand, too few small particles can reduce the likelihood of collision, which can also lead to a decrease in AE signals. When there is a particle transport phenomenon in the seepage process, the AE signal is significantly stronger than the blank control group, and the size of small particles will also have a certain impact on the AE signal. It is possible to use acoustic emission technology to predict and forecast the occurrence of piping.

Türkiye is threatened with drought due to climate risk since it is vulnerable to water resources derived from rainfall. This paper was generally carried out in drought-affected areas in a semi-arid agricultural watershed in the provinces of The Western Black Sea Basin, including Düzce, Bolu, Zonguldak, Çankırı, Karabük, Bartın, Kastamonu, and Sinop. It was aimed to meet the need for a coordinated examination of agricultural areas across different basins with hydrological and meteorological data. Life-touching results were revealed by overlay mapping the flow observation station data analysis results and irrigation areas. The importance of agriculture in the world economy, which is in a bottleneck regarding food needs and access to food, is understood better daily. This study evaluated future risks and opportunities in agricultural production in the basin. A better understanding of the future state of water throughout the basin is essential in protecting and improving the basin's economic, social, and demographic structure. The irrigation areas of 43% are currently in operation, and with the addition of the areas under construction, 60% of the currently identified areas will become irrigable. It was observed that land principally occupied by agricultural areas increased by 26.54% with the transformation of irrigated and non-irrigated lands in the basin between 1990–2008 according to CORINE database. Considering the irrigation areas included in the preliminary examination and master plan, planning, and project stages, irrigated agriculture in the basin is not yet at the desired level. By implementing better agricultural policies, the welfare level of the people of the region and the development level of the basin can be increased in general. One of the primary purposes of the study is to shed light on decision-makers. The results were designed to form the basis for basin-wide planning studies.

Utilizing the comprehensive dataset from the Lightning Imaging Sensor (LIS) aboard the International Space Station (ISS) spanning 2018 to 2022, the present study deals with the interactions between climatic factors and the lightning activity across the tropical and subtropical regions of India. The primary aim of the present study is to analyse the temporal variability of lightning occurrence and the atmospheric conditions governing these patterns. The results indicate that the distinct seasonal cycle, with peak lightning activity aligning with the Indian summer monsoon season. A comparative assessment highlights notable meteorological differences between the two regions, with the tropics exhibiting more intense convective activity, higher moisture content, and stronger surface energy fluxes, contributing to high lightning activity. On the other hand, the subtropical region displays more consistent patterns with higher aerosol optical depth (AOD), surface sensible heat flux and less supply of moisture to the lower atmosphere which leads to comparatively low lightning occurrences. The meteorological parameters were analysed using multiple linear regression analysis, revealing that approximately 80% of the variance in the dependent variable for the subtropical region and 84% for the tropical region is explained by the regression model. Both the multiple linear regression models demonstrate high statistical significance, indicating a robust relationship between the predictor variables and lightning activity. These findings support in understanding the connection between atmospheric conditions and regional factors which is crucial for predicting and mitigating climate related risks over India. By understanding the relationship between the climatic factors such as AOD, surface heat fluxes, precipitation and instability, and their impact on lightning intensity, one can develop more accurate forecasting models and early warning systems. These advancements will help mitigate the risk factor associated with lightning strikes, especially for vulnerable populations in rural areas which are distributed in tropical and subtropical regions of India.

The aim of this article is to detail the Bayesian approach to estimating the parameters of the Gutenberg–Richter relation modeling the rate of occurrence of an earthquake of a given magnitude. The parameters posterior distributions are known up to a factor of proportionality. They are usually estimated relying on Monte Carlo Markov Chain (MCMC) methods. These simulation techniques can be used to generate realizations of the posterior distributions, which enables to calculate useful characteristic quantities (mean, variance, quantiles, etc.). MCMC methods require fine-tuned parameterization to ensure both independence of the realizations and generation according to the desired, imperfectly known distribution. It is difficult to validate the correct parameterization of the algorithm and it requires a full simulation-based inference procedure. This article shows how the parameters posterior distributions can be computed essentially exactly (i.e. with machine precision), through a carefully rescaled version of the likelihood. Getting such an exact posterior distribution avoids the above-mentioned problems and enables highly precise evaluations of posterior quantities such as conditional expectations. Finally, the computation of the posterior normalizing constant considerably extends the applicability of the Bayesian approach to any prior distribution, hitherto often limited to the conjugate prior distribution of the model. This article applies the methodology to the Alps in France and compares the set of Bayesian results to the frequentist results based on the maximum likelihood of the Poisson process.

Climate change has a significant impact on hydrological systems, particularly in vulnerable semi-arid regions like Algeria’s Mekerra River basin. Since the 1970s, frequent droughts have reduced dam inputs, lowered groundwater levels, and depleted wells, underscoring the need for effective water resources management. This study aims to evaluate drought propagation in agricultural and hydrological systems, analyze drought trends, and assess climate change’s hydrological impacts using drought indices, trend analysis, and hydrological modeling. Hydrometeorological data from 1980 to 2012 were used to calculate the Effective Reconnaissance Drought Index (eRDI) and the Standardized Streamflow Index (SSI) on 3- and 12-month scales, respectively, to assess drought propagation in agricultural and hydrological systems. Trend analysis was performed using the Improved Visualization of Innovative Trend Analysis (IV-ITA) to examine drought patterns via IV-ITA drought classification. The semi-distributed HBV-light model was applied to determine climate change’s hydrological impact across upper, middle, and lower Mekerra sub-basins. The results indicate that the eRDI-3 revealed seasonal and spatial fluctuations in agricultural drought, with an extreme event in 1994 identified at Sidi Bel Abbes. The SSI-12 indicated significant flow variations between stations and reveal the longest and most extreme hydrological drought from 2002 to 2006. The IV-ITA indicated positive eRDI-3 trends at Hacaiba and Sidi Bel Abbes, non-monotonic trends elsewhere, while SSI-12 trends were negative at Sidi Ali Ben Youb, positive at Hacaiba, and non-monotonic at Sidi Bel Abbes. These trends clarified drought complexity in semi-arid contexts. The HBV-light model accurately reproduced the flow dynamics during calibration with acceptable validation performance for all the sub-basins, revealing low soil recharge that highlights drought’s impact on water resources, particularly during the calibration period. These results provide robust methodologies and in-depth knowledge of the mechanisms of agricultural and hydrological drought in our semi-arid region and others similar, by enhancing trend assessment, supporting adaptive policies, and establishing early warning systems to strengthen resilience against climate change.