Acta Geophysica最新文献

The article by Al-Najjar et al. (2022a) possesses abundant flaws in geopolitical, geographical and hydrological contexts. The paper ignores a vast body of scientific literature about the study region of the Negev Desert, Israel, in general, and Nahal Besor (Wadi Gaza) in particular. The paper’s methodology lacks data collection from the field. These gaps and flaws lead to erroneous and geopolitically slandered research conclusions. Nahal Besor is a large transboundary ephemeral river shared between Israel, the West Bank (Palestinian and Israeli territories) in the northeast, and finally, its western outlet into the Mediterranean Sea is in the Gaza Strip. Despite the current political ordeal between the two nations to accurately portray and model the segment of the river in the downstream coastal plain of Gaza, it is crucial to use the data of upstream Israeli floods that in some events reach the Strip. In this comment, we utilize some of the main flaws of Al-Najjar et al. (2022a) to demonstrate that how the hypothesized potential flood geohazard of Gaza can be significantly reduced by binational and regional cooperation such as using upbasin bank-side reservoirs in the northwestern Negev, Israel.

The presence of noise in geophysical data poses significant challenges to accurate analysis and interpretation, impacting the reliability of geoscience research and exploration. In the context of inverting electromagnetic-sounding data, the objective is to derive a unique model for interpreting observations while acknowledging the non-uniqueness of solutions. The uncertainty introduced by unwanted signals complicates the selection of an initial model for inversion. This study emphasizes the heightened efficacy of the artificial neural network (ANN) model, prioritizing smoothness to mitigate overinterpretation and eliminate arbitrary discontinuities in layered models. The goal is to identify the smoothest model fitting experimental data within a defined tolerance rather than maximizing model roughness. A practical ANN scheme is developed, predicting subsequent values based on previous ones while optimizing for arbitrary discontinuities. Extensive evaluation using synthetic and real-world magnetotelluric data showcases the model's performance. Employing a sliding window technique for dataset preparation allows the extraction of local patterns and trends in time series data. The results demonstrate the remarkable noise reduction capabilities of neural networks, surpassing traditional methods like filtering and wavelet transform. The neural network model consistently produces predictions with relatively low Mean Absolute Error (MAE) values, indicating its ability to preserve underlying geological structures even in noisy conditions. Specifically, MAE for actual inverted data ranges from 0.49 to 5.95, while MAE for predicted values by the neural network model ranges from 6.19 to 7.75. Notably, the model outperforms wavelet transform, particularly in preserving short trends during noise reduction. This aligns with prior studies emphasizing neural networks' superior performance in handling complex data patterns. Further exploration applies the neural network model, revealing accuracy rates of approximately 93% along the east–west (EW) direction and 92.5% along the north–south (NS) direction for six diverse profiles. Robustness is demonstrated by introducing various noises into testing sample data, showcasing the model's resilience in inversion findings. This study underscores the profound effectiveness of neural networks in noise reduction, highlighting machine learning's vast potential in geophysical data analysis. Beyond conventional techniques, these insights offer valuable implications for the future of geophysics research and applications.

The rock physics template (RPT) represents a trustful method to understand the reservoir properties and attempts to predict the reservoir’s behaviour under various porosity levels, water saturation and clay content. However, many RPT applications were applied on clastic rocks compared to carbonates due to their complex pore systems. Therefore, this study attempts to construct a suitable RPT for the Beda carbonate reservoir, with 3D facies and petrophysical models based on well log data collected from 41 wells of North Beda Field in the western Sirt Basin. Seven wells include core permeability, porosity and lithology data. Two main bounding faults are identified based on well-correlation and divided the reservoir architectural structure into an exposed fault-block and also have controlled subsurface reservoir heterogeneities, lithofacies and petrophysical parameters distributions. However, core data analysis has revealed that the Beda reservoir is made up of four main types of lithofacies, which are classified as Lithofacies A (limestone), Lithofacies B (dolostone), Lithofacies C (shale) and Lithofacies D (shaly limestone) deposited in a wide range of shallow-marine environments. Lithofacies A and B are categorised as having good to excellent carbonate reservoir quality with an average 23% porosity and > 160 mD permeability. According to the log response characteristics, the reservoir consists of a series of electrofacies patterns, which are divided into nine isolated vertical units, reflecting differences in petrophysical properties. The AI-v_p/v_s crossplots show that the shale and clean carbonate line can easily be distinguished. A higher shale content zone is characterised by increased v_p/v_s ratios and decreased AI. Additionally, crossplots with porosity colour-scale successfully divided the reservoir into three porosity zones: excellent, fair to very good and poor. Crossplots comparison between two wells from hanging and footwall sides shows that the shale volume and the water saturation have increased in the hanging wall well and decreased in the footwall well, which indicates an increasing in the hydrocarbon saturation with a relative decrease in acoustic impedance and v_p/v_s values. Results prove that faults and fractures played pivotal roles in enhancing reservoir quality in the high area by enabling larger volumes of fluids to be filtrated and migrated. Besides, the RPT provides robust control of well log data and elastic properties to differentiate the lithology and fluid content in the carbonate reservoir.

This study investigates seismic quiescence (Z-value) and b-value anomalies preceding the 6th February 2023 earthquake doublet in Kahramanmaraş, Türkiye (M_W 7.8, M_W 7.6), via compiled earthquake catalog from various sources, including Tan (Nat Hazard 21: 2059 2073,2021), KOERI, ISC, and USGS. We converted the catalog to both moment magnitude scale (M_W) and das magnitude scale (M_Wg) and conducted a comparative analysis—following which we adhered to the magnitude scale (M_W) for further study. Temporal completeness was assessed using the cumulative visual inspection method (CVI), while magnitude completeness was determined through the maximum curvature method (MAXC). The estimation of the b-value was carried out using the maximum likelihood method (MLM). Analyzing the spatial distribution of b-value revealed a low b-value region (b < 1) well before the events. Similarly, the temporal decline in the b-value curve was noted before the occurrence of the Türkiye earthquakes (M_W 7.8, M_W 7.6). Furthermore, the spatial distribution of the z-value indicated seismic quiescence, with the epicenters of the recent Kahramanmaraş, Türkiye, earthquakes (M_W 7.8, M_W 7.6) located in the positive z-value region which considerably aligned with the observed decrease in seismic activity from 2015 to 2023. Utilizing the Gumbel extreme value approach, we estimated seismic parameters, including maximum likelihood magnitudes, average recurrence intervals, and the probability of different magnitude occurrences for four sections of the East Anatolian Fault Zone (EAFZ): Amanos fault section, Pazarcık fault section, Erkenek fault section, and Çardak fault section. The study anticipates that the maximum annual earthquake magnitude for the Amanos fault segment exceeds that of the Pazarcık, Erkenek, and Çardak fault sections. Similarly, it expects the Amanos fault segment to experience major earthquakes (M_W ≥ 6.5) more frequently compared to the Pazarcık, Erkenek, and Çardak fault sections. Over the next century, the study projects a higher probability of major earthquakes for the Amanos fault segment compared to the Pazarcık, Erkenek, and Çardak fault segments. This study emphasizes the significance of these parameters in seismic hazard analysis, providing essential insights for evaluating seismic hazards in the East Anatolian fault region.

During the construction of deep and large foundation pits in floodplain areas, it is inevitable to cause stratum disturbance and endanger the safety of the surrounding environment. This paper focuses on the influence of deep foundation pit excavation on surrounding environment based on a soft soil deep foundation pit project in Nanjing floodplain area. A series of laboratory tests were conducted to obtain the parameters of the small strain hardening (HSS) model for the typical soil layers. Then PLAXIS 3D software is used to simulate the excavation process of the foundation pit. On the basis of field measurement and numerical model, the deformation characteristics of deep foundation pit and surrounding environment are analyzed. The HSS model and the appropriate model parameters can effectively simulate the deformation behavior during the excavation of the foundation pit. Aiming at the problem of excessive deformation of foundation pit and surrounding pipelines, the reinforcement effect of reinforced soil in active and passive areas under different reinforcement parameters is analyzed. The optimal reinforcement width and depth should be determined after reasonable analysis to obtain the best economic benefits.

A closed relationship between the Gutenberg–Richter b-value (or (beta =b text{ln}10)) and the information or Shannon entropy is found and checked through numerical evaluation of the entropy using exact probabilities derived directly from the magnitude exponential distribution. Comparison of the numerical evaluation of the entropy over a finite magnitude range makes it possible to assess the possible contribution to the entropy of real or hypothetical very large magnitudes, and these contributions are found to be quite small. The relationship is also compared with entropies calculated from synthetic data, and Monte Carlo simulations are used to explore the behavior of entropy determinations as a function of sample size. Finally, it is considered how, for the usual case of having data from a single realization, i.e., a single magnitude data set, since estimates of the entropy and of the Aki–Utsu b-value are measured in different ways, they are not redundant and may be complementary and useful in determining when a sample is large enough to give reliable results.

The scarcity of surface water resources has a significant impact on Mediterranean basin. This study aims to assess the climate change impacts on surface water resources in the Mitidja plain in Algeria. Two pre-calibrated monthly water balance models, namely, the GR2M model and the abcd model, were used. These models were driven by bias-corrected datasets from the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) under two Representative Concentration Pathway scenarios (RCP4.5 and RCP8.5) and two Shared Socioeconomic Pathways (SSP2 and SSP5). The combined Box–Cox transformation and bootstrapping procedure was used to aggregate the multiple runoff projections generated. The results revealed significant variations in the runoff patterns across the different sub-basins. In addition, all scenarios indicated a reduction in projected runoff across all sub-basins of the Mitidja plain, spanning from 26 to 74.32%. Furthermore, CMIP6 simulations showed more intense changes over the Mitidja basin.

The Euphrates-Tigris River Basin, which spans Turkey, Syria, Iraq, and Iran, is one of the most vulnerable zones to climate change. This study quantifies the impacts of changing climate on streamflow and suspended sediment load rates in the most threatened highlands region of the Euphrates-Tigris Basin, with the case of Bitlis Creek. In this evaluation, the multi-model ensemble approach is utilized to produce precipitation and temperature projections by analyzing the simulation performances of 24 global circulation models (GCMs) from the coupled model intercomparison project phase 6 (CMIP6). The Soil and Water Assessment Tool (SWAT) is used to estimate future streamflow and suspended sediment load rates over 25-year periods under the medium- and high-forcing shared socio-economic pathway (SSP) scenarios of SSP2-4.5 and SSP5-8.5. The results illustrate that the mean annual streamflow and suspended sediment load rates are expected to decrease by up to 8.5 and 21.4% under the SSP2-4.5 scenario, and by up to 20.9 and 40.7% under the SSP5-8.5 scenario, respectively. The projected shift from snowy to rainy winters leads to significant increases in winter streamflow and suspended sediment load rates, anticipated to reach 39.1 and 73.5%, respectively, during the 2075–2099 period for the SSP5-8.5 scenario. In contrast, declines in spring streamflow and suspended sediment load rates are projected to reach 40.9 and 60.0%, respectively, during the same period under the SSP5-8.5 scenario. These results suggest that the riparian countries should incorporate adaptive measures into their water resources management plans to ensure a sustained water supply in the coming decades.

The accuracy of the velocity field stands as the foremost determinant impacting the quality of migration imaging, thus underscoring the significance of establishing a robust velocity model in the context of complex geological body imaging. However, within the realm of volcanic rock development, the intricate lithology and lithofacies of volcanic rock, alongside spatial overlap and scale variability, have presented formidable challenges for conventional manual methodologies in explicating the spatial distribution of volcanic rock masses. Consequently, these methods have struggled to furnish precise structural interpretation models suitable for velocity modeling techniques like grid tomography. In this study, we have leveraged deep learning tools to effectively and precisely delineate the spatial distribution range of volcanic rock masses from seismic data within the imaging domain, thereby reshaping the technical process of grid tomography velocity modeling. Addressing the problem of volcanic rock mass detection as a semantic segmentation task, we trained a network to execute pixel-by-pixel prediction aimed at identifying pixels corresponding to a high likelihood of volcanic rock mass presence. To enhance the network’s recognition accuracy, this study introduced a cavity convolution and two functional modules, augmenting the performance of a conventional U-Net. The proposed methodology utilizes three-dimensional reflection seismic data for network training and validation. Ultimately, a practical dataset is employed to substantiate the reliability and efficacy of the method.

The Marmara Region is an active tectonic region in northwestern Türkiye, which comprises some important strike-slip active fault mechanisms and important tectonic units, located near the western part of the North Anatolian Fault Zone. In the historical and instrumental period, the Marmara Region experienced large/devastating earthquakes. Considering this continuous activity, in this study, we investigate the tectonic structure and performed future seismic hazard estimation of the region based on some seismotectonic parameters. For this evaluation, we plot the Coulomb stress change maps of 1912 Mürefte-Şarköy, 1953 Yenice-Gönen and 1999 İzmit mainshocks with the earthquakes (M_W ≥ 4.5) that occurred in the study region after 2003. For the estimation of b-value, occurrence probabilities and return periods of earthquakes, we used a homogenous local seismicity catalogue consisting of 119.029 events for the period between 1912 and 2023. In the findings of this study, the lower b-values and increasing Coulomb stress changes which are trigger stress failure compatible are observed in the west and northwest of the Marmara Sea. In contrast, the higher/moderate b-values and decreasing Coulomb stress values are observed in the east and southeast of the Marmara Sea. The results of probability assessments show that an earthquake with Mw = 6.5 may occur with a probability of 98% in the west of the Marmara Sea after 2025. As a remarkable fact, a comprehensive assessment of these types of variables will supply important findings for earthquake hazard and potential in the study region.