Acta Geophysica最新文献

The Poisson’s ratio of hard rock exhibits a marked stress dependence, which is contrary to its mechanical definition as an elastic constant. Thus, it is of great importance to determine the Poisson’s ratio through a reasonable method. To investigate the Poisson effect of multiple types of hard rocks (sandstone, basalt, granite, and marble), the uniaxial loading–unloading tests are carried out. The test results indicate that whether the tangent Poisson’s ratio or the average Poisson’s ratio, all gradually increases with the stress level. And the stress dependence of the average Poisson’s ratio under the unloading path is reduced, which is significant in the low and medium stress intervals. Appropriately increasing the number of loading–unloading cycles can also improve the stability of the average Poisson’s ratio to some extent. Based on this, a new method for testing the average Poisson’s ratio is proposed, which can effectively exclude the effect of irreversible displacement of rocks and improve the stability of the average Poisson’s ratio. The test procedure is simple and has good application prospects.

This paper aims to present the mechanism of scour and empirical equations for evaluating local scour with and without a countermeasure around the bridge pier. A critical review of scour countermeasures, mainly hydraulic, structural, and biotechnical, extending to the present time is done. Hydraulic countermeasures consist of river training structures and bed armoring. Structures placed parallel, perpendicular, or at an angle to the flow aiming to modify it is the purpose of river training works. Armoring is done through the use of riprap, partially grouted riprap, cable-tied blocks, grout-filled containers, and gabions. Structural countermeasures include foundation strengthening and pier geometry modifications. Extending footings, underpinning, and pile- underpinning are related to foundation strengthening, while pier geometry modifications include different pier features such as shapes, textures, slots, and collars. Biotechnical countermeasures include using vegetation riprap, geosynthetic polymer, live staking, and bio-stabilization using extracellular polymeric substances. Different combinations of countermeasures are also discussed. In hydraulic and structural countermeasures, riprap and collars are most commonly used due to their efficiency in scour reduction and economic feasibility. Bio-stabilization using extracellular polymeric substances is a novel measure for scour prevention. From the literature, it is concluded that pier modifications are the most effective and active area of research in which lenticular pier shape, lenticular hooked, and airfoil-shaped collar are best suited for reducing the local scour around the pier. Finally, the limitations of the countermeasures mentioned above are presented.

This paper aims to explore the application of artificial intelligence in the petroleum industry, with a specific focus on oil well production forecasting. The study utilizes the Zananor field as a case study, systematically organizing raw data, categorizing different well instances and production stages in detail, and normalizing the data. An individual long short-term memory (LSTM) neural network model is constructed with monthly oil production data as input to predict the monthly oil production of the experimental oilfield. Furthermore, a multivariate LSTM neural network model is introduced, incorporating different production data as input sets to enhance the accuracy of monthly oil production predictions. A comparative analysis is conducted with particle swarm optimization optimized recurrent neural network results. Finally, gray relational analysis and principal component analysis methods are compared in feature selection. Experimental results demonstrate that the LSTM model is more suitable for the study area, and the multivariate model outperforms the univariate model in terms of prediction accuracy, especially for monthly oil production. Additionally, gray relational analysis exhibits higher accuracy and greater applicability in feature selection compared to principal component analysis. These research findings provide valuable guidance for production forecasting and operational optimization in the petroleum industry.

This article provides a comprehensive analysis of CO₂ injection monitoring in the Sleipner Field. Ensuring the safe storage and containment of CO₂ in geological formations or assigned storage sites, especially in the carbon capture and storage (CCS) projects. In this study, a seismic inversion method incorporating linear programming sparse spike inversion was employed to observe and analyze the CO₂ plume in the Sleipner field, Norway. This approach enhances the understanding of the dynamics and behavior of the CO₂ injection, providing valuable insights into the monitoring and assessment of CCS operations in the Sleipner field. The foundational dataset includes 3D post-stack seismic data from the year 1994, with special emphasis on the monitoring data collected in 1999, following four years of CO₂ sequestration. The analysis utilized synthetic data to investigate alterations in seismic amplitude, highlighting that amplitude variations were more prominent compared to variations in velocity and density. The findings highlight noticeable shifts in P-wave velocity, signifying a significant 29% reduction, with the most substantial decrease occurring within the 0 to 30% CO₂ saturation range. Correspondingly, density changes align with trace variations, demonstrating only a 2–3% reduction in density as gas saturation increases from 0 to 30%. Beyond 30% saturation, density exhibits a further decrease of 30%. The traces collectively reveal a consistent trend, showcasing a 32% reduction in impedance as CO₂ saturation levels rise. Through the cross-equalization process, it was observed that the initial data repeatability was low, indicated by a normalized root mean square (NRMS) value of 0.6508. However, significant improvement was achieved, bringing the NRMS value to a more satisfactory level of 0.5581. This improvement underscored the alignment of features both above and below the reservoir, underscoring the efficacy of the cross-equalization technique. The outcomes of the 4D inversion provided insights into the distribution of CO₂ within the reservoir, revealing upward migration. Importantly, the results confirmed the secure storage of CO₂ within the reservoir, affirming the integrity of the overlying cap layer.

Climate change is one of the most important global challenges of this century, with significant impacts on water resources, economic development and ecological health. This study aimed to investigate the effect of climate change on streamflow in Joumine watershed, upstream the Ichkeul Lake, a RAMSAR wetland and the most productive ecosystems in Tunisia and the Mediterranean. The hydrologic response of the basin was simulated based on Hydrologic Modelling System HEC-HMS. Climate data were generated from the emission scenarios RCP4.5 and RCP8.5 from the Irish Regional Climate Model (RCM) for the periods 2030–2060 and 2061–2100. The statistical analysis showed that model performance is satisfactory, with Nash–Sutcliffe efficiency of 0.7 and 0.64 for calibration and validation, respectively. The climate projections exhibited a declining trend in precipitation during the two future periods with more frequent extreme rainfall events in dry season and a rise in temperature which is more accentuated during the period 2061–2100. Climate change is expected to have profound impacts on water resources and resilience of ecosystems. Results showed that Joumine basin is projected to experience reduction in streamflow which is more pronounced under RCP8.5. The frequency and magnitude of hydrological extremes are expected to be intensified, notably during the far future period, leading to pressure on water availability in the end of the twenty-first century. Hence, sustainable water resources management is needed to close the water demand and supply gap in the Joumine river basin.

Seismic data analysis often faces the challenge of random noise contamination from various sources. To overcome this, innovative noise attenuation methods utilizing seismic signal properties are needed. This study focuses on efficiently suppressing random noise in the domain of time and frequency by accurately estimating instantaneous frequency using the single-valued group delay characteristic of seismic signals. The time-reassigned synchrosqueezing transform (TSST) and its second-order variant (TSST2) offer high-resolution time-frequency representations (TFRs) for noise suppression. Expanding on these advancements, we propose an efficient noise suppression method that integrates the adaptive thresholding model into the TSST2 framework and employs sparse representation of the TFR through low-rank estimation. This method effectively attenuates noise while preserving essential signal information. The proposed approach operates trace by trace on recorded data, initially transforming it into a sparse subspace using TSST2. The adaptive thresholding model then decomposes the resulting TFR into sparse and semi-low-rank components, achieving a high-resolution and sparse TFR for efficient separation of noise and signal. After noise suppression, the seismic data can be fully reconstructed by inversely transforming the semi-low-rank component data into the time domain. This method addresses previous limitations in noise attenuation techniques and provides a practical solution for enhancing seismic data quality.