Acta Geophysica最新文献

A proper investigation of geomechanical properties of reservoir sediments allows accurate prediction of both magnitude and direction of different stress regimes. These are crucial for hydrocarbon production and development particularly in deformed and structurally controlled petroliferous basins. The rift basins in the north Western Desert (NWD) provide analogues for these structurally controlled prolific regions, where the syn-rift Jurassic sedimentary successions host prolific reservoir targets. However, a detailed assessment of the geomechanical behavior of the Jurassic reservoirs has never been investigated. Thus, the present study utilizes wireline log data to determine the geomechanical properties of the syn-rift Jurassic facies of the Khatatba Formation in Shushan Basin in the NWD. Mechanical earth model was constructed and formation microimager logs (FMI) were interpreted for to investigate the geomechanical behavior and wellbore stability of the studied deformed facies. The stress direction was determined from FMI by identifying drilling-induced fractures, breakout zones and formation fractures. Resistive, conductive and partially conductive fractures are oriented in NE–SW, ESE–WSW, NW–SE, NE–SW and SW–NE directions coinciding with the Jurassic syn-rift structures. The ENE–WSW direction of breakout and minimum horizontal stress (SH_min) are the same as that of the primary fault in the study region contrasting with the NNW–SSE direction of maximum horizontal stress (SH_max) and induced fractures. The present study highlights the significance of integrating wireline logging results in interpreting the critical and non-critical stress orientations which are necessary for optimal production plans in structurally controlled prolific basins around the globe.

Waterlogging and floods are among the most recurring and devastating natural hazards likely to occur more frequently in cities due to climate changes and rapid urban growth. High-intensity precipitation and subsequent waterlogging arouses negative physical and socio-economic challenges in urban areas. Mainstreaming disaster risk assessment is fundamental to reduce the related loss. In the lieu of changing characteristic of meteorological, hydrological and socio-economic condition of Delhi city, this study entails much needed analysis of daily rainfall intensity, frequency and duration, waterlogging area estimation, hazard and vulnerability mapping and spatial risk susceptibility mapping in frequently affected area of North Delhi as a study region. Microspatial scale assessment at urban municipal wards using analytical hierarchy process for weight criteria assignment was done by selecting 19 parameters. The final risk susceptibility map revealed that the north and north-western part of North Delhi are at very high risk which is evident with frequent waterlogging incidences too. An area of 282.56 square kilometres accounting 52.75 per cent is estimated to be at high- and very high-risk category. The high-risk areas demand employing pumping stations at locations precisely such as Jahangirpuri, Begampur, Burari, Bawana along with Rohini Sector 20, 21, 23 and 24 as immediate mitigation measure. The result also suggests that the moderate (39.8%)- and low-risk zones (7.57%) have comparatively lesser significant portion of the total area, but the problem intensifies due to encroachment of drains, dense informal settlement neighbourhood and increase in urban built-up increasing the impervious surfaces. The study also demonstrated that the city system demands regular maintenance of its sewage pipes, cleaning of inlets and taking care of waste disposal as it clogs the drain and increases risk of waterlogging. This study models the microlevel comprehensive investigation for disaster risk reduction to be used further for cities worldwide.

Graphical abstract

We estimated the source parameters for local earthquakes near the Idukki reservoir, Kerala. The region falls under seismic zone III, indicating moderate seismicity, and is reported to have witnessed several small to moderate size magnitude earthquakes. Eight local earthquakes with magnitudes ranging between 2 and 3.6 were used during the data analysis of this study. Four key parameters were primarily estimated from the earthquake signals, providing an overall idea about the source characteristics, i.e., seismic moment, stress drop, corner frequency, and source radius. Our estimated moment magnitudes (M_w) range between 2 and 3.4, which are consistent with the reported local magnitude (M_L) scale, indicating a minor difference between M_W and M_L scale. The estimated variations in seismic moment align well with the global model of micro-earthquakes, ranging between 1.2E + 15 and 1.1E + 17 dyn-cm. The source radius mostly varies between 110 and 220 m, with seismic moment exhibiting a linear increase as source size grows. This suggests a clear dependence of seismic moment on the radius of the source. It is likely that the brittle shear-failure mechanism on the fault segment and/or the presence of weak zones would contribute to local earthquakes with smaller source radius. Stress drops for most of the events are relatively low in the study region, ranging from 0.3 to 4.5 bars. The initiation of rupture is evident along an existing fault plane, potentially acting as a contributing factor to the observed lower stress drop values. The stress drop variable with a positive correlation to the seismic moment of the event might indicate a wide range of strength of the crustal rock in the region. Interestingly, both the corner frequency (f_c) and maximum frequency (f_max) decrease as seismic moment increases, indicating that both are related to the source process and possibly influenced by the site effects. Finally, we can suggest that the derived source parameters can be utilized to simulate ground motion parameters of historical events, thereby enhancing seismic hazard assessment and facilitating earthquake engineering analyses in future research initiatives.

To study how the soils, respond to an earthquake, seismic waves are frequently utilized. The purpose of this work is to build the porosity graphs based on the geotechnical parameters of the soils and forecast the porosity of shallow clay soils using seismic wave velocities that analyze the dynamic features of the soil. Compressional (P) and shear (S) wave velocities, seismic velocity ratio, Poisson ratio, bulk modulus, and shear modulus are the factors used to calculate porosity. In this work, porosity values are calculated using grain and dry densities of core samples taken from different boreholes within the study region, and bulk modulus, shear modulus, and Poisson ratio are calculated using P- and S-wave velocities obtained by utilizing the seismic-refraction method, as well as porosity values. The research region is in Iran; Isfahan Metro Line 2 and mostly consists of clay, silt, sand, and gravel deposits. Based on the values of the Poisson ratio, seismic P-wave velocity, seismic velocity ratio (V_p/V_s), and the stiffness of the clay soils, the data of the clay soils in the region were individually sorted. These characteristics were used to create novel multi-parameter relationships between clay soil porosity, seismic velocities, shear modulus, and the Poisson ratio. Using the error norm approach, the errors in the parameters utilized for each relationship were identified. The error norm technique's findings show that the shear wave velocity and shear module have the lowest error when calculating porosity. Therefore, it is advised to estimate porosity of shallow clay soils using the given correlations. These relationships can be used to assess the porosity of clay soil and to determine if the soil's pores are saturated with liquid.

Understanding the impact of hydroelectric development in the Brazilian Amazon is critical for maintaining ecological balance. This research evaluates the impact of the Balbina Dam on the Uatumã River's flow and landscape. We employ a method to measure flow regime changes, scoring these changes from 0 to 1. Additionally, we analyze landscape changes through satellite imagery. Results indicate a significant dam-induced alteration in downstream water flow, with a score of 0.638. However, the river's in-stream shape has not notably changed since the dam construction. Assessing lateral water movement in the adjacent forested areas is challenging with satellite data, necessitating on-ground studies for accurate mapping. By integrating flow measurement techniques with landscape analysis, this study provides insights into sustainable water management and ecosystem rehabilitation in the Amazon.

The processing of potential field datasets requires many steps; one of them is the inverse modeling of potential field data. Using a measurement dataset, the purpose is to evaluate the physical and geometric properties of an unidentified model in the subsurface. Because of the ill-posedness of the inverse problem, the determination of an acceptable solution requires the imposition of a regularization term to stabilize the inversion process. We also need a regularization parameter that determines the comparative weights of the stabilization and data fit terms. This work offers an evaluation of automated strategies for the estimation of the regularization parameter for underdetermined linear inverse problems. We look at the methods of generalized cross validation, active constraint balancing (ACB), the discrepancy principle, and the unbiased predictive risk estimator. It has been shown that the ACB technique is superior by applying the algorithms to both synthetic data and field data, which produces density models that are representative of real structures and demonstrate the method’s supremacy. Data acquired over the chromite deposit in Camaguey, Cuba, are utilized to corroborate the procedures for the inversion of experimental data. The findings gathered from the three-dimensional inversion of gravity data from this region demonstrate that the ACB approach gives appropriate estimations of anomalous density structures and depth resolution inside the subsurface.

With the escalating demand for underground mining and infrastructure construction, the optimization of tunnel construction has emerged as a primary concern for researchers. The geological conditions encountered during the excavation of hard rock tunnels using tunnel boring machines (TBM) significantly impact construction efficiency and cost-effectiveness. The existing lithology testing methods need to be more efficient in aligning with TBM operational efficiency. In recent years, the rapid advancement of artificial intelligence has paved the way for its integration into numerous domains, including tunnel engineering. To address this issue, this study proposes three innovative hybrid RF-based intelligent models, namely PSO-RF, ALO-RF, and GWO-RF, for the precise prediction of lithology in hard rock tunnels using TBM working parameters. The TBM operating parameters of the Jilin Yinsong Water Supply Project serve as the basis for this investigation. Twelve distinct characteristic parameters relevant to the lithology of the tunnel working face were carefully selected as input parameters for lithology prediction. Comparative analysis of the three hybrid models reveals that GWO-RF demonstrates exceptional lithology prediction performance (ACC = 0.999924; PRE_A = 0.0.9999976; REC_A = 0.999775; F1_A = 0.999876; Kappa = 0.999911), whereas PSO-RF and ALO-RF exhibit slightly inferior performance. Nonetheless, all three hybrid models exhibit a significant improvement in prediction accuracy compared to the unoptimized RF model. The research findings presented herein facilitate the swift determination of TBM working surface lithology, enabling timely adjustment of TBM working parameters, reducing equipment wear and tear, and enhancing construction efficiency.

Given the shortcomings of inadequate vacuum transfer effectiveness, susceptibility to clogging, and environmental impact, i.e., white pollution, associated with standard plastic drainage boards, this paper introduces an innovative solution termed combined vertical drain (CVD). This CVD is composed of biodegradable straw materials and tailings sand. Through controlled indoor model experiments, the drainage and consolidation performance of the CVD is investigated across varying proportions of straw content. The findings indicate that increasing the concentration of straw within the mixture of straw particles and tailings sand (referred to as MST) within the drain distinctly mitigates the diminishment of the vacuum condition during the vertical drainage transfer. Optimal elevation of the straw component in MST can notably reduce the duration required for vacuum preloading and enhance displacement up to a certain value. Nonetheless, an excessive increase in the straw composition not only fails to enhance drainage effectiveness but also raises the organic matter level within the soil. Furthermore, compared with the average water content difference of soil in different model box test units after vacuum preloading, the average divergence in vane shear strength of test soil exhibits a more pronounced variation.

To enhance the seismicity analysis within a given seismic region, it is crucial to establish a unified earthquake catalog with minimized uncertainties. The preparation of such a unified catalog needs scaling relationships to convert different magnitude types to a homogeneous magnitude. Among the plethora of magnitude types, the moment magnitude (M_w) stands out as a widely utilized metric in modern earthquake risk and recurrence analysis. Hence, the key objective of this study is to expand the M_w earthquake dataset specifically for the Northern Algeria region and its surrounding areas, providing a valuable resource for researchers investigating seismicity in this region and for earthquake magnitudes homogenization. To achieve this objective, surface wave (M_s) and body wave (m_b) magnitudes obtained from international agencies were standardized to M_w using newly developed regional empirical relationships based on the general orthogonal regression method (GOR). The use of GOR for magnitude conversions has gained popularity in recent years. However, a critical aspect when employing the GOR method is estimating the standard deviations associated with different magnitude types and subsequently determining the error variance ratio. To address this, the present study leverages recent research works to approximate the standard deviations associated with various magnitudes. By calculating the error variance ratio, derived from the estimation of magnitude uncertainties, the general orthogonal regression method was effectively applied to achieve the desired earthquake magnitude homogenization. Notably, this study fills a significant gap in research conducted in Algeria by developing regional empirical relationships using GOR with appropriate values of the error variance ratio. The expanded M_w dataset serves as a dependable resource used for other earthquake magnitudes homogenization, hence the preparation of a more extensive and unified M_w earthquake catalog for Northern Algeria and its neighboring areas.