pure and applied geophysics最新文献

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.

Amid rapid economic and social development, countries worldwide are heightening their focus on addressing climate change induced by greenhouse gas emissions.CO₂ geological storage has become an increasingly mature technology and is currently recognized as one of the most effective ways to reduce CO₂ emissions on a large scale globally. The shale reservoir CO₂ geological storage combined with enhanced shale gas recovery technology (CO₂-ESGR) is a new type of CO₂ geological storage and shale gas development technology. This technology uses supercritical or liquid phase CO₂ instead of hydraulic fracturing of shale, utilizing the stronger adsorption capacity of CO₂ on shale than CH4 to displace CH4, thereby increasing the yield and production rate of shale gas while achieving geological storage of CO₂. To study the impact of different fracture parameters on CO₂ displacement of CH4, this research selected shale from the southern slope of a certain basin as the target reservoir and established a homogeneous dual-porosity and dual-permeability model. GEM was used to simulate eight scenarios for CO₂-enhanced methane extraction from shale layers, analyzing the effects of three influencing factors: half-length of fractures, fracture spacing, and number of fractures on CO₂ displacement of CH₄. Additionally, six different fracture patterns were simulated to analyze the influence of fracture patterns on CO₂ displacement of CH₄. The study found that increases in the number of fractures, fracture spacing, and half-length of fractures all increase the amount of CO₂ displaced by CH4, but the degree of influence decreases gradually. Furthermore, the average fracture pattern yields better results for both CH₄ production and CO₂ sequestration compared to unevenly distributed fractures, providing strong evidence for improving shale gas production rates and achieving geological storage of CO₂.

Joint inversion of different geophysical data sets has demonstrated its effectiveness in mitigating the limitations associated with individual methods and maximizing their respective advantages. This study investigates the groundwater aquifers in the western area of Ismailia city, Egypt, which faces water scarcity and relies heavily on groundwater. Vertical Electrical Sounding (VES) and Transient Electromagnetic (TEM) data were collected at 35 points along five parallel profiles to assess the primary aquifers in this region. These data sets, gathered at nearly identical locations, were inverted separately before performing joint inversion to enable objective comparison. The joint inversion, conducted using the Curupira Program, revealed two aquifer systems in the Pleistocene and Miocene units. The Pleistocene aquifer, identified as the primary aquifer, consists of gravel, sand, and clay lenses with depths ranging from 10 to 83 m and thicknesses from 129 to 236 m. Its resistivity ranges from 4 to 95 Ωm. The Miocene aquifer is underlying the Pleistocene aquifer at depths between 130 and 291 m and primarily consists of marine marly sandstone and limestone, with resistivity values of 6 to 22 Ωm, indicating predominantly saline groundwater. Overall, this study provides valuable insights into the hydrogeological characterization of the study area and highlights the potential and limitations of joint inversion techniques in the accurate assessment of aquifer systems.

This research examines how significant atmospheric fluctuations affect drought conditions, in the Kızılırmak Basin in Türkiye. We studied the impact of climate indices like NAO, Niño, AMO, PDO, ONI, and SOI by using the Standardized Precipitation Evapotranspiration Index (SPEI) as an indicator of drought. The findings reveal an increase in both the frequency and severity of droughts after 2015. In the 2000s, short-term droughts lasted from 1 to 3 months. However, after 2020, longer-term droughts lasting between 6 and 24 months have become more severe. Correlation and lead-time analyses reveal ENSO indices, particularly Niño 3.4 and ONI, as primary drivers of drought, with a positive impact. The SOI emerged as a significant predictor of future drought conditions. While PDO and AMO influence drought, their effects are less pronounced. Understanding these complex relationships is crucial for developing effective regional drought management strategies.

In late February 2024, a swarm–like seismic activity took place north of Kefalonia Island, in the area of central Ionian Islands. Following a machine-learning aided workflow, we compiled an enhanced, relocated seismic catalog of 2495 low- to moderate magnitude earthquakes during a 2–month period. Spatiotemporal analysis reveals a narrow epicentral distribution of nearly E-W alignment, approximately 5 km long, much longer than the length anticipated by common scaling laws for the aftershock area extension of the stronger earthquakes that did not exceed M4. Seismic activity decays at a rate slower than mainshock-aftershock sequences, providing evidence of swarm-like behavior. Fluid diffusion appears to be the critical driving force behind this sequence, effectively reproducing the spatiotemporal diffusion of the analyzed activity, whereas cascade triggering due to stress changes and transfer by the combined effect of the two relatively strongest earthquakes promote the triggering of most of the weaker earthquakes that follow in the sequence. Our ML-enhanced spatiotemporal analysis, along with the computation of 17 focal mechanisms of the stronger earthquakes using waveform modeling, support the presence of a population of smaller faults that strike obliquely in respect to the Kefalonia Transform Fault Zone (KTFZ) forming a strike slip duplex in the area between them.