Acta Geophysica最新文献

This study compared earthquake location estimation using grid search and manta ray foraging optimization algorithm for synthetic and real earthquakes data from Van, Turkey. Both locating methods worked well, and they achieved similar results. The horizontal coordinates (latitude and longitude) of the earthquake were obtained successfully with both methods, from the inversion of the arrival times calculated from the noisy and noise-free synthetic earthquake data. However, there was some deviation in depth parameter for the noisy data. The location parameters obtained from the inversion of the real earthquake data using grid search and manta ray foraging optimization methods were in accordance with the solutions presented in previous studies. The depth parameters for the Van earthquakes did not fully match those in the previous studies, possibly due to differences in crustal velocity models. The depth parameters obtained for both Van earthquakes using both methods performed in this study are self-consistent at around 24 km. In addition, Disaster and Emergency Management Presidency and German Research Centre seismology centres also reached depth solutions near those in this study. The grid search method has some disadvantages compared with the manta ray foraging method, as it must be applied gradually, and delays reaching a solution. The manta ray foraging method is an easy, fast way to determine the kinematic location of earthquake hypocentres.

The short-term rockburst prediction in underground engineering plays a significant role in the safety of the workers and equipment. Due to the complex link between microseismicity and the rockburst occurrence, prediction of short-term rockburst severity is always challenging. It is, therefore, necessary to develop an intelligent model that can predict rockbursts with high accuracy. Besides the predicting capability, it is essential to understand the model’s interpretability regarding the decisions to ensure reliability, trust and accountability. Accordingly, this paper employs the knowledge of explainable artificial intelligences (XAI) by proposing a novel glass-box machine learning model: explainable boosting machine (EBM) to predict the short-term rockburst. Microseismic (MS) data obtained from the underground engineering projects are utilized to build the model, which is also compared with the black-box random forest (RF) model. The result shows that EBM can accurately predict the rockburst severity with high accuracy, while providing with the underlined reasoning behind the prediction from the global and local perspectives. The EBM global explanation reveals that MS energy followed by MS apparent volume and the MS events is the most contributing factor to determining the Rockburst severity. It also gives insights into the relationship between MS factors and rockburst risks, delivering how various MS parameters impact the model predictions. The local explanation extracts the understanding of wrongly predicted samples. The interpretability and transparency of the proposed method will facilitate understanding the model’s decision which adds effective guidance evaluating the short-term rockburst risks.

Aflaj refers to a traditional irrigation system found in Oman, which has been used for centuries to sustainably manage groundwater resources. These resources play a vital role in meeting various consumption needs, including agriculture, domestic, and industrial. The article, for the first time, introduces the concept of “hydraulics of aflaj”, emphasizing the importance of accurate information about interaction of falaj and aquifer and also flow within their tunnels. The study utilizes the mechanisms of horizontal wells to simulate the interaction between the aquifer and the falaj tunnel, employing the meshless local Petrov–Galerkin numerical model to compute groundwater head of aquifer. The model is applied to a real test case in the Loba aquifer of Malaysia, demonstrating improved accuracy compared to previous models based on evaluation indices such as MAE, RMSE, MAPE, NSE and p-bias. The findings of the proposed model show good agreement.

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.