Acta Geophysica最新文献

Routine analysis of source parameters is mainly based on the assumption that stations are located in the far field, which is valid for most measurement conditions of natural seismicity. The study aims to examine the validity of this assumption for anthropogenic seismicity characterized by shallower earthquakes and smaller distances of stations from seismic sources. Data from three real cases were studied: reservoir-triggered seismicity in Vietnam, copper mining-induced seismicity in Poland and geothermal field The Geysers. The influence of the intermediate and near fields on the values previously used to calculate source parameters was assessed. The results indicate the need to improve tools employed so far. It is especially significant in the seismicity induced by copper mines and geothermal field, even when surface stations monitor the seismicity. These effects are less important in the case of the tested seismicity in Song Tranh 2 but should not be neglected.

Efficient arrival picking and precise source imaging are crucial components of seismic data processing in both active and passive seismology. In this study, we employ the unsupervised Fuzzy C-Means clustering approach to improve the picking of the arrivals from small magnitude earthquake events. The waveform-based source image is then computed using the grouped time-reversal approach, which first splits the receivers into groups and then backward propagates the wavefield. Once the arrivals are autopicked, P-wave signals are identified and extracted from the entire waveforms. These extracted segments are then used to construct a high-resolution source image through multi-dimensional cross-correlation of the wavefields, from which the event location is determined at the point of maximum coherence. The performance of the integrated approach is first tested on a suite of synthetic data and then on field datasets obtained from the North-West Himalayan region of Jammu and Kashmir. The estimated uncertainties reveal improvements in event locations compared to the conventional travel-time based inversion method. We demonstrate that the integrated approach can be effectively used to analyze seismological datasets in complex media, and can even work with sparse seismic networks for locating the small-magnitude earthquakes. Moreover, the present approach enables the location of seismic sources utilizing the single-component waveform data.

In this study, the geophysical environment and earthquake dynamics of the Sabalan volcano are explored using MT inversion and advanced spectrum analysis. The magnetotelluric profile, approximately 12.98 km in length, oriented SW-NE, is located about 5.31 km west of Mount Sabalan. The Occam and Bostick algorithms have succeeded in inverting two-dimensional sections of electrical resistivity, indicating conductive fault-controlled paths FLM1 to FLM3, which are linked to hydrothermally altered sections with fluid movement. These paths are located above larger and highly resistive sections that correspond to large volcanic or intrusions which may act as a heat source in geothermal environments. In addition to geoelectric modeling and interpretation, we are examined amplitude spectra of local seismic data through two methods classical Fourier analysis and a formulation derived from quantum mechanics theory. By classical means, via Fast Fourier Transform, we obtain the traditional results, whereas the quantum approach grounded in the Schrödinger equation exhibits greater resilience to noise and reveals distinct resonance features at low frequencies ( 5–10 Hz ) that classical methods fail to resolve. These quantized energy provide novel insight on source processes and energy accumulation zones. The combination of MT and quantum spectrum analysis highlights the connection among structural geology, geothermal activity, and seismicity at Sabalan, carrying significant implications for risk evaluation and the exploration of geothermal resources.

This study investigates the 2020–2021 seismic swarm in the Southern Alboran Sea region, providing new insights into the spatial and temporal dynamics of seismic events. By analyzing the b-values, Hurst exponents, and spatial fractal dimensions, we establish that the seismicity in this region exhibits both long-term memory and a consistent stress regime. The results highlight the complexity of seismic processes in Southern Alboran Sea, showing that the seismic swarm patterns align with previously observed seismic activity in the area. The persistence and clustering behaviors observed in the seismic data underscore the region’s ongoing tectonic activity, suggesting that seismic events are not randomly distributed but follow discernible patterns. The study’s findings are essential for improving seismic hazard models and can aid in better preparing for future seismic events in the Southern Alboran Sea region. This research contributes to a deeper understanding of the mechanisms driving seismic swarms and provides a framework for analyzing similar seismic events in other tectonically active regions.

The aim of this work is to provide a structural analysis of the Benue Trough, a complex fault zone extending from eastern Nigeria to northern Cameroon. To improve the understanding of its structure and tectonic evolution, the Bouguer anomalies derived from the Global Gravitational Model were used to characterize its subsurface using qualitative and quantitative methods, including digital filtering and 3D inversion. The objective is to characterize the subsurface of the trough, understand its geodynamic context, and assess the implications of these structures on the outcropping formations and geological features in northern Cameroon of Garoua Rift. The results reveal that the Benue Trough exhibits a complex intracrustal structure with several NE-SW and WE trending lineaments, as well as high-density blocks likely associated with magmatic intrusions in the subsurface. These structural features suggest an upper intracrustal and lower mantle origin, reflecting the tectonic evolution and rifting processes in the region. The study highlights the Benue Trough as a natural laboratory for understanding continental rift mechanisms and provides valuable insights into the region’s geological history and potential resources.

Severe freshwater mud invasion in the sandy-shaly reservoirs of the A’nan Fault Block in the Erlian Oilfield has led to the pronounced development of low-contrast oil layers within the study area. This phenomenon significantly increases the difficulty of fluid identification and constrains efficient exploration and development. Targeting the A’nan 31 Block, this study integrates well-logging and well-testing data to propose an intelligent fluid identification method based on SMOTE-SABO-BiLSTM, aiming to improve the identification accuracy of low-contrast oil layers in complex sandy-shaly reservoirs. Eight key well-logging curves, including 90-inch array induction resistivity (AT90), acoustic compressional slowness, 30-inch array induction resistivity (AT30), photoelectric factor, microgradient resistivity, compensated neutron log, microelectrode resistivity, and spontaneous potential. They were selected as input features through feature importance analysis. To address the issue of imbalanced reservoir types, the SMOTE (synthetic minority oversampling technique) algorithm was employed to oversample minority-class samples (e.g., water layers, oil–water layers). This approach was combined with a bidirectional long short-term memory network (BiLSTM), leveraging its strength in processing sequential data, and the subtraction-average-based optimizer (SABO) was introduced to automatically optimize model hyperparameters. Experimental results demonstrate that when the SABO population size is 50, the model achieves an optimal hyperparameter combination (learning rate: 0.01, Dropout rate: 0.10, LSTM units: 64/127). This configuration yields a fluid identification accuracy of 92.24% on the test set, significantly outperforming the unoptimized BiLSTM (87.59%), SABO-LSTM (84.83%), and basic LSTM (76.72%) models. The proposed SMOTE-SABO-BiLSTM hybrid model effectively addresses the dual challenges of imbalanced reservoir types and low-resistivity oil layer identification in sandy mudstone reservoirs. By utilizing SMOTE to handle data imbalance and incorporating SABO-optimized BiLSTM, it substantially enhances the identification accuracy for complex sandy mudstone reservoir fluids. This method provides a valuable reference for identifying low-contrast oil layers with similar characteristics.

As seismic acquisition systems advance, how to effectively suppress complex noise and restore weak signals in seismic data has become a key task. Seismic data denoising through deep learning approaches has grown significantly in recent years, with U-Net showing great potential, yet limitations remain in its ability to adequately address both complex noise and weak signal restoration. This study resolves these challenges through a refined U-Net network based on multi-scale double-layer convolution and boundary enhancement combined with an attention mechanism for seismic data denoising. The proposed model replaces standard U-Net convolutions with multi-scale double-layer convolutions to capture richer hierarchical features; a boundary enhancement module is used to recover weak seismic signals, and an attention-based fusion module is designed to effectively combine multi-scale features with enhanced edge information. When testing synthetic data and field seismic data with complex noise levels, we evaluated our algorithm against competing methods that utilize both convolutional neural networks and transformer architectures. The results indicate that the algorithm significantly improves denoising performance while effectively preserving seismic details.

This study analyzed 135 teleseismic earthquakes with high signal-to-noise ratios between 2018 and 2024 with the new local network. Time domain iterative deconvolution applied to the rotated radial-transverse-vertical components, after Gaussian low-pass filtering, yields receiver functions; crustal thickness and Vp/Vs ratio are then determined through H-κ stacking across finely sampled grids, with theoretical Ps, PpPs, and PsPs phase travel times calculated for each stacking parameter pair. Our results reveal Moho depths from ~ 26.9 km near the Tuzla Fault zone to ~ 36 km beneath the northern Menderes-Kula region. Within this range, representative H-κ solutions include H ≈ 29.7 km and κ ≈ 1.56 at the Kiraz Basin station (DKRZ), H ≈ 27.6 km and κ ≈ 1.80 beneath the Karaburun Peninsula (KARB station), and H ≈ 31.0 km and κ ≈ 2.07 at Simav region (SIMV), with Poisson’s ratios of ~ 0.16, ~ 0.28, and ~ 0.35. Across the network, κ spans 1.50–2.07 and Poisson’s ratio 0.10–0.35. An eastward increase in Moho depth mirrors the extensional tectonic framework of Western Anatolia, while lateral variations in κ reveal localized thermal and compositional anomalies, with cooler, silica-rich crust beneath the Kiraz Basin (DKRZ) and a hotter, mafic-influenced lower crust indicated by the high κ values at the SIMV. Comparison with the CRUST1.0 model shows Moho depth discrepancies of up to ~ 5 km. We interpret these crustal profiles in the context of the region’s deformation and magmatic history, integrating constraints from previous geological and geophysical studies. By combining receiver function analysis, global crustal models and geodynamic interpretation, this work refines the crustal velocity structure beneath Western Anatolia and provides improved input for regional seismic hazard assessment.

The study presents the results of the investigation of the behavior of long-range correlations and self-similarity in solar energetic particles (SEPs). SEPs are high-energy, charged particles that originate from the solar atmosphere and solar wind. They consist of protons, electrons, and heavy ions with energies ranging from a few tens of keV to several GeV. The correlations and persistency have been examined for two distinctly different phases of Solar Cycle 23, one corresponding to solar maximum while the other solar minimum. The global scaling exponent ((boldsymbol{alpha })-exponent) using a method called robust detrended fluctuation analysis (r-DFA) is calculated for proton flux measured at various energy levels (ranging from > 1 MeV to > 100 MeV) for different years, which compare the features of SEP flux during solar maximum and minimum years. This analysis establishes a relationship between the behavior of SEPs at different energy levels and the corresponding phases of solar activity, gaining insight into the dynamics of energetic particle transport and modulation in the interplanetary medium.