Acta Geophysica最新文献

In this study, the seismic wave attenuation beneath the Southern Granulite Terrain (SGT), India has been investigated using the coda waves from 22 local earthquakes (2.0 ≤ M_L ≤ 3.6) recorded by 21 broadband seismic stations. The dependence of the attenuation ‘Qc’ on frequency was extracted using the single backscattering model at central frequencies 1.5, 2, 2.5, 4, 5, 8, 10, 12 and 16 Hz. Different lapse time windows, from 10 to 90 s with an interval of 10 s, were used to test the lapse time dependency. The Qc value usually ranges from 150 to 1000 within the frequency range of 2–8 Hz. However, at higher frequencies (12–16 Hz), Qc value variations range from 1500 to 2500. Estimated Qc values are comparatively lower at stations ELP and MVT across frequencies and these lower values are aligned with the previous seismic activity in the surrounding area. This correlation is further supported by the presence of shear zones, lineament, and other northwest-oriented faults, indicating a pronounced level of heterogeneity and complexity in the crust, ultimately contributing to the observed lower Qc values. The estimated Qo values range from 150 to 350, and N values range from 0.70 to 0.95. The observed Qo and N values are slightly lower than those of the other stable continental regions. The significant spatial variation in Qo values observed within the study region may be attributed to the potential existence of pore fluids, as supported by the reported Vp/Vs ratio and shear wave velocity models, which introduce heterogeneities within the crust. Taking into account all other existing studies, along with the current findings, it can be suggested that both scattering (g) and intrinsic attenuation (Qi) factors contribute to the observed attenuation of the study region. The estimated intrinsic and scattering factors align well with the global attenuation model. In the absence of detailed body wave attenuation studies in this region, the frequency-dependent Q relationships developed here are useful for the estimation of earthquake source parameters of the region. These relations may be used for the simulation of earthquake-strong ground motions, which are required for the estimation of seismic hazards and geotechnical and retrofitting analysis of critical structures in the region.

Foehn wind occurrence has generated great interest among researchers because of the destructive power and impact on the local climate. Based on anemometric data provided by a high-mountain station on Kasprowy Wierch in the Polish Tatra Mountains, the characteristics of the potential occurrence of foehn wind (referred to as halny in the Polish Tatras) are presented, including its speed and duration, as well as the frequency of occurrence on a multiannual, annual and daily basis. Halny winds occur most frequently in the cold period of the year (Oct–Feb), with the frequency peaking in November, and sporadically in the summer. The occurrence of foehn winds is strongly dependent on the synoptic situation. Therefore, the main aim of the study was to identify the circulation conditions conducive to their occurrence on the Polish side of the Tatra Mountains. Circulation conditions responsible for foehn formation were analysed using gridded sea level pressure (SLP) data from the NCEP-DOE (National Centers for Environmental Prediction-Department of Energy) reanalyses. The occurrence of foehn wind in the Tatra Mountains is associated with a low pressure system over north-western Europe, and above normal pressure over south-eastern Europe. The location and intensity of the centres of atmospheric influence on foehn days can vary, as indicated by the three types of pressure systems favouring the occurrence of the phenomenon, distinguished by the hierarchical grouping method. In type 1, the cyclonic centre spreads over northern Europe, in type 2 over western Europe and in type 3 over north-western Europe. In types 1 and 3, the air masses come from the south-west, and in type 2 more from the south. Type 3 is characterised by the greatest horizontal pressure gradients among the three circulation types and with the greatest SLP anomalies.

The incremental impacts of climate change on elements within the water cycle are a growing concern. Intricate karst aquifers have received limited attention concerning climate change, especially those with sparse data. Additionally, snow cover has been overlooked in simulating karst spring discharge rates. This study aims to assess climate change effects in a data-scarce karst anticline, specifically Khorramabad, Iran, focusing on temperature, precipitation, snow cover, and Kio spring flows. Utilizing two shared socioeconomic pathways (SSPs), namely SSP2-4.5 and SSP5-8.5, extracted from the CMIP6 dataset for the base period (1991–2018) and future periods (2021–2040 and 2041–2060), the research employs Landsat data and artificial neural networks (ANNs) for snow cover and spring discharge computation, respectively. ANNs are trained using the training and verification periods of 1991–2010 and 2011–2018, respectively. Results indicate projected increases in temperature, between + 1.21 °C (2021–2040 under SSP245) and + 2.93 °C (2041–2060 under SSP585), and precipitation, from + 2.91 mm/month (2041–2060 under SSP585) to + 4.86 mm/month (2021–2040 under SSP585). The ANN models satisfactorily simulate spring discharge and snow cover, predicting a decrease in snow cover between − 4 km²/month (2021–2040 under SSP245) and − 11.4 km²/month (2041–2060 under SSP585). Spring discharges are anticipated to increase from + 28.5 l/s (2021–2040 under SSP245) to + 57 l/s (2041–2060 under SSP585) and from + 12.1 l/s (2021–2040 under SSP585) to + 36.1 l/s (2041–2060 under SSP245), with and without snow cover as an input, respectively. These findings emphasize the importance of considering these changes for the sustainability of karst groundwater in the future.

Drought, which is defined as a decrease in average rainfall amounts, is one of the most insidious natural disasters. When it starts, people may not be aware of it, which is why droughts are difficult to monitor. Scientists have long been working to predict and monitor droughts. For this purpose, they have developed many methods, such as drought indices, one of which is the Standardized Precipitation Index (SPI). In this study, SPI was used to detect droughts, and machine learning algorithms, including support vector machines (SVM), artificial neural networks, random forest, and decision tree, were used to predict droughts. In addition, 3 different statistical criteria, which are correlation coefficient (r), root mean square error (RMSE), and Nash–Sutcliffe efficiency (NSE), were used to investigate model performance values. The wavelet transform (WT) was also applied to improve model performance. One of the areas most impacted by droughts in Turkey is the Konya Closed Basin, which is geographically positioned in the center of the country and is among the top grain-producing regions in Turkey. The Apa Dam is one of the most significant water resources in the area. It provides water to many fertile fields in its vicinity and is affected by droughts which is why it was selected as a study area. Meteorological data, such as monthly precipitation, that could represent the region were obtained between 1955 and 2020 from the general directorate of state water works and the general directorate of meteorology. According to the findings, the M04 model, whose input structure was developed using SPI, various time steps, data delayed up to 5 months, and monthly precipitation data from the preceding month (time t − 1), produced the best results out of all the models examined using machine learning algorithms. Among machine learning algorithms, SVM has achieved the most successful results not only before applying WT but also after WT. The best results were obtained from M04, in which SVM with WT was used (NSE = 0.9942, RMSE = 0.0764, R = 0.9971).

Single-channel sparker seismic reflection systems are currently preferred in offshore geo-engineering studies due to their cost-effectiveness, ease of use in shallow areas, their high-resolution data, and straightforward data processing. However, the distinctive characteristics of sparker data introduce specific challenges in the processing of single-channel seismic datasets. These include (i) unavailability of the stacking process for single-channel seismic data, (ii) inability to derive subsurface velocity distribution from single-channel seismic profiles, (iii) limitations imposed by ghost reflections and bubble effects as well as random noise amplitudes, and (iv) the suitability of only predictive deconvolution for suppressing multiple reflections. Applications demonstrate that the inability to apply the stacking process to single-channel seismic data poses a significant challenge in suppressing both random and coherent noise, and increasing the signal-to-noise (S/N) ratio. The F-X prediction filter has proven highly effective in mitigating random noise in sparker data. Appropriate determination of operator length and prediction lag parameters allows predictive deconvolution to effectively suppress multiple reflections, despite some residual multiple amplitudes in the output. Spiking deconvolution significantly eliminates ghost reflections and bubble effects, enhancing temporal resolution by eliminating the ringy appearance of the input signal. Trace mixing is a crucial data processing step for enhancing sparker data resolution. The method can be applied as weighted mix for random noise suppression or as trimmed mix for suppressing high-amplitude spike-like noises. This study incorporates a comprehensive analysis of the various noise components embedded in sparker seismic data. It delineates the processing flow and parameters utilized to effectively mitigate these specific noise types.

The study focuses on the bias correction of Coupled Model Intercomparison Project Phase 6 (CMIP6) hydrologic variables for the Indian region. The performance of two widely accepted bias correction methodologies, namely Quantile Mapping (QM) and Bias Correction Spatial Disaggregation (BCSD), is compared. The study undertakes to evaluate the application of these popular bias correction methodologies on four important hydrologic variables viz. precipitation, temperature, and surface wind. The QM methodology is employed and compared with BCSD based bias corrected variables obtained from NEX-GDDP-CMIP6 dataset. The selected GCM historical bias corrected climate variables using QM are compared with the NCEP reanalysis variables. The objective is to improve the reliability and accuracy of climate projections by minimizing biases present in the GCM outputs. Through a comprehensive comparative analysis, it is determined that QM exhibits superior performance in reducing biases when compared to BCSD. Thus, use of QM demonstrates higher efficacy by effectively capturing the statistical distribution characteristics of observed data and transferring them to the GCM outputs. The future climate change over the Indian region is observed for both QM and BCSD algorithms for SSP5-8.5, SSP2-4.5, and SSP1-2.6. The result emphasizes the importance of selecting an appropriate bias correction methodology to enhance the reliability of climate projections in the Indian region. Ultimately, the findings of this study contribute to the broader field of climate modeling and impact assessment, providing valuable insights into the selection and application of bias correction techniques for CMIP6 datasets in the Indian subcontinent region.

Graphical abstract

The roughness feature of a natural rock fracture surface is an important factor affecting the shear and poromechanical behavior of rock. The scale effect and spatial distribution characteristics of the fracture surface roughness are notable challenges at rock engineering sites. In this article, morphological data of a large-scale field rock fracture surface were collected using a 3D scanner. Then, the original surface was divided into several small fracture surfaces. With the use of a 2D roughness statistical index, the 2D roughness (JRC^2D) of the fracture profile was evaluated. The 3D roughness (JRC^3D) of the fracture surface along different directions was obtained via the weighted averaging method. Based on four oblique analysis schemes, the elevation statistical trend and roughness scale effect of fracture surfaces with different widths were examined. With increasing fracture size, the average elevation ((mu)) and the standard deviation of elevation ((sigma)) showed different typical change patterns. The impact of size variation on the fracture surface roughness includes four types and exhibits significant anisotropy. Based on small fissure surfaces without mutual coverage, the spatial distribution characteristics of the fracture roughness were analyzed and were proven to exhibit high dispersion and anisotropy. With increasing width of the analyzed small fracture, the roughest position on the fracture surface basically remained the same, but there was a significant change in roughness anisotropy.

Water saturation estimation faced a great challenge in tight gas sandstone reservoirs because of the effect of pore structure and strong heterogeneity. The classic Archie’s equation cannot be always well used. To quantify the effect of pore structure to rock resistivity in tight gas sandstones, taking Triassic Xujiahe Formation of northwestern Sichuan Basin as an example, 35 core samples were recovered and applied for resistivity experiments in laboratory under the simulated formation temperature and pressure environment, and 18 of them were simultaneously applied for nuclear magnetic resonance (NMR) and high-pressure mercury injection experimental measurements. Relationships between pore structure and resistivity parameters were analyzed. The results clearly illustrated that cementation exponent (m) and saturation exponent (n) were heavily affected by pore structure. Rocks with superior pore structure contained relatively higher cementation exponent and lower saturation exponent, and vice versa. Afterward, we raised a parameter of pore size index, which was defined as the ratio of macropore and micro-pore percentage contents, to characterize rock pore structure, and established a model to calculate optimal saturation exponent from NMR data. Meanwhile, cementation exponent prediction model was also raised by combining with porosity and irreducible water saturation (S_wirr). Combining with calculated cementation exponent and saturation exponent, we optimized the Archie’s equation to predict water saturation in our target tight gas sands. Field examples illustrated that the predicted cementation exponent and saturation exponent matched well with core-derived results. The absolute errors between predicted cementation exponent and saturation exponent with core-derived results were lower than 0.05 and 0.07, separately. By using optimized Archie’s equation, water saturations were precisely predicted from well logging data in our target tight gas sandstone reservoirs; whereas, the classic Archie’s equation underestimated formation water saturation.

Most of the studies in the literature focus on analyzing water flow within earthen dam under static condition. The objective of this study is to analyze water flow mechanisms within earthen dam under seismic condition. To achieve this purpose, a series of numerical simulations were conducted to model earthen embankment based on a real case scenario based on dam built in eastern province of India (i.e., South 24 Parganas in West Bengal). Further the effect of sheet pile as a seepage cutoff has been evaluated with variations in sheet pile length and location under steady-state settings for both static and seismic conditions. The study was carried out using FLAC2D version 5.0 and SEEP/W version 12.0 for a dam. The results show that pore pressure is high on the upstream side of the sheet pile during continuous seepage and quickly decreases along the sheet pile itself for all sheet pile positions. In seismic instances under steady-state conditions, when pore water pressure increases, the factor of safety decreases by 45% to 50% as compared to similar static cases. This is due to an increase in seepage force. As the sheet pile is cut off, the overall factor of safety increases as compared to the condition with no sheet pile. This study is though simplified; however, it provides insights into water flow pattern within earthen dam that need to be considered for preliminary design in regions, which are subjected to seismic loads.

The Global Association model is a crucial tool in seismic data analysis at the International Data Centre (IDC) of the Comprehensive Nuclear-Test-Ban Treaty Organization. However, it faces challenges due to its limitations in accurately associating seismic events on a global scale. Over the past years, attempts have been undertaken to tackle these issues by introducing the Network Processing Vertically Integrated Seismic Analysis (NET-VISA) algorithm, specifically designed to enhance seismic event association across the globe. NET-VISA uses a machine learning Bayesian approach to solve the automatic association problem. NET-VISA has been implemented in operation as an additional automatic event scanner tool since January 2018. In this study, we assess the effect of the NET-VISA automatic scanner on the IDC output REB and LEB bulletins. We used three distinct time periods to evaluate the NET-VISA performance. The results show a 4.6% increase in the number of LEB events after including the NET-VISA scanner in operation, with an average of 7 additional events per day, and an increase of 17.90% in the number of scanned events. A comparison between the different bulletins in distinct periods shows NET-VISA is beneficial to build more valid events, providing opportunities to improve nuclear-test-ban monitoring. However, NET-VISA exhibits slightly reduced performance for events occurring at depths exceeding 300 km.