Acta Geophysica最新文献

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.

The sub-bottom profiler is a valuable tool for obtaining high-resolution shallow stratigraphic data in marine geological and geophysical surveys. To detect and acquire the structural characteristics of small submarine objects, we developed a data processing method that utilizes 2D data to construct a 3D structural model. We conducted application experiments using sub-bottom profile detection data from Chuanshan Islands, which were explored using China’s most advanced unmanned exploration platform and commercial shallow formation profiling system. To create high-resolution 3D seafloor structure models from recorded 2D sub-bottom profile datasets, an optimized data processing sequence was devised, comprising two stages: 2D data processing and 3D data processing. The 2D data processing stage involved spectrum analysis, band-pass filtering, matching filtering, time-varying gain, and surge correction. The subsequent 3D data processing stage encompassed ping location reallocation, static correction, and extraction of feature layer information. Notably, the final pseudo-3D sub-bottom profile time slice exhibited significant amplitude variations near the target body. This methodology represents an extension of the application of 2D sub-bottom profile data, enhancing the target recognition capabilities of such data. To further improve the precision of target body characterization, we used ArcScene 10.0 to create a 3D sub-bottom profile formation model spatial database. We constructed a submarine 3D formation structure model to show the 3D structural characteristics of the target body in detail and identified a seabed target body measuring 6.4 × 9.2 × 10 m.

Seismic interpretation is a critical aspect of hydrocarbon exploration, where geoscientists often struggle to accurately recognize patterns and anomalies in large datasets. Machine learning techniques offer a promising solution by allowing for the quick and accurate analysis of multiple and large-size seismic volumes. This study leverages seismic facies analysis, seismic attribute analysis, and supervised machine learning to identify and characterize turbidite deposits in the Dangerous Grounds region, an underexplored area recently revealed by high-resolution broadband seismic data. Through seismic stratigraphy, two distinct phases of turbidite deposition were identified: a lower unit showing higher amplitude and signs of faulting effect, and an upper, present-day unit characterized by lower amplitude and continuous reflectors. The attribute expression of these turbidites shows strong amplitude response, high relative acoustic impedance, and high gray-level co-occurrence matrix entropy emphasizing their distinctiveness from surrounding facies, with variations in reflector continuity and spectral decomposition providing further insight into their depositional processes and sediment characteristics. By applying nine machine learning classifiers with twenty seismic attributes as input, this study achieved over 99% accuracy in distinguishing turbidite facies from background, with the neural network, random forest, K-nearest neighbors, decision tree, and support vector machine exhibiting optimal performance. The study contributes significantly to the regional understanding of turbidite deposits through detailed machine learning-aided seismic characterization. It underscores the value of integrating domain knowledge with machine learning techniques in enhancing subsurface interpretations, offering a comprehensive methodology for seismic facies analysis in similarly complex and underexplored regions.

Previous studies have suggested prominent variations in the seismic wave behavior at the 5 s period when traveling across the Valley of Mexico, associating them with the crustal structure and contributing to the anomalous seismic wave patterns observed each time an earthquake hits Mexico City. This article confirms the variations observed at 0.2 Hz by analyzing the Green tensor diagonal retrieved from empirical Green functions (EGF) calculated using seismic noise data cross-correlations of the vertical and horizontal components. We observe time and phase shifts between the east and north EGF components and show that they can be explained by the crustal structure from the surface up to 20 km depth; we also observe that the time and phase shifts are more significant if the distance between the source and the station increases. Additionally, the article presents an updated version of the velocity model from receiver functions and dispersion curves (VMRFDC v2.0) for the crustal structure under the Valley of Mexico. To validate this model, we compare the EGFs with synthetic Green functions determined numerically. To do so, we adaptatively meshed this model using an iterative algorithm to numerically simulate the impulse response up to 0.5 Hz. Finally, the comparisons between noise and synthetic EGF showed that the VMRFDC v2.0 model explains the time shifts and phase differences at 0.2 Hz, previously observed by independent studies, suggesting it correctly characterizes the crustal structure anomalies beneath the Valley of Mexico.

Due to the varieties, random distributions, and rich visual characteristics of the volcanic disaster scene, traditional methods fail to fully express the complex features of volcanic disaster scenes in remote sensing images. To tackle this problem, a new multi-instance network framework with the Shift Windows Transformer (i.e., Swin-T) and attention mechanism is used to classify the volcanic disaster scene from remote sensing images (MI-STA). Firstly, via aggregating the global contextual information of remote sensing image features, the Swin-T extracts the multi-scale hierarchical features of volcano disaster scenes from remote sensing images. Secondly, the channel attention module and spatial attention module fuse to extract the features of volcanic disaster scene to enhance the description and representation for the local details and global information in volcanic disaster scenes. Last, the importance weight of different example characteristics is scored to calculate the attributive probabilities of each instance. This study elaborates an experiment on the xBD dataset and gives comparisons with the commonly used deep network models. The results show that the overall classification accuracy of the proposed method achieves 92.46% and has good performance on the test dataset. Then, we further utilize our model to classify the volcanic disaster scenes of the specific Hunga Tonga-Hunga Ha’apai on January 15, 2022, and the classification images have good consistency with the existing literature. It provides a new approach for volcanic disaster monitoring by means of remote sensing image and has broad application prospects.

The Italian Seismological Instrumental and Parametric Database (ISIDe) is the recipient of earthquake data collected in real-time by the Istituto Nazionale di Geofisica e Vulcanologia (INGV), and used by the studies of earthquake forecasting and seismic hazard assessment in Italy in the last decade. When it went online, following a significant improvement of the seismic acquisition system of INGV, it was including only data since the second fortnight of April 2005. About ten years later, the data since the beginning of 1985 suddenly appeared without any prior notice than the updating of the starting date of the dataset. However, the characteristics of the added data appeared clearly different from the following period both in terms of the numbers of located earthquakes and of types of magnitudes provided. After having analyzed the numerical consistency and the calibration of magnitudes of ISIDe as a function of time from 1985 to 15 April 2005, we can say that such a dataset is incomplete and poorly calibrated compared to other catalogs of Italian seismicity (CSTI, CSI, and HORUS) available for the same period. Hence, we suggest not using it as is for statistical analyses of Italian seismicity. However, it provides some magnitudes that are missed by other catalogs and thus might be used for improving such catalogs.