Acta Geophysica最新文献

Distributed acoustic sensing (DAS) technology has gained widespread use in vertical seismic profiling (VSP) data acquisition due to its efficiency. However, high-energy noise introduced by complex geological conditions significantly degrades data quality, posing challenges for traditional denoising methods. While deep learning offers new approaches for seismic denoising, its reliance on large-scale training data and high computational resources remains a limitation. To address this, we propose a Weight Fusion Adversarial Network based on a Self-Collaborative Strategy (SC-WFAN). This network dynamically fuses features from different processing stages, incorporating a weight fusion (WF) module between the encoder and decoder to preserve contextual information and enhance detail recognition. Additionally, the denoising network replaces the generator in generative adversarial networks (GANs), optimizing the process through adversarial training, while the self-collaborative strategy further improves training efficiency. A training dataset comprising 483 pairs of field DAS-VSP records covering four dominant noise types (random background, fading, horizontal, and optical noises) was constructed. Experimental results demonstrate that SC-WFAN excels in suppressing strong noise and recovering weak signals from thin and deep layers, requiring only 66.56G floating-point operations (FLOPs) and 1.87 M parameters, outperforming traditional methods and mainstream deep learning models (e.g., DnCNN, AttU-Net). Its efficiency and robustness provide an innovative solution for processing complex DAS-VSP seismic records, particularly suited for high-precision data processing in unconventional oil and gas resource exploration.

During tunnel boring machine (TBM) tunnel construction, abrupt variations in shear strength can cause TBM jamming. Predicting the shear strength of rock is essential for both optimizing TBM tunneling parameters and improving construction efficiency. However, conventional methods for determining engineering properties are invasive, costly, and time-consuming. Additionally, because the TBM occupies the tunnel face, assessing changes in the surrounding rock ahead is challenging. Water content and porosity are the primary factors influencing the shear strength. The induced polarization (IP) method is sensitive to the response of water-bearing structures and porosity, making it suitable for determining the surrounding rock conditions ahead of the tunnel face. In order to investigate the shear strength distribution in front of tunnel face, a predictive model was established to relate IP multi-parameters and shear strength parameters including cohesion and internal friction angle based on petrophysical relationship. Specifically, the IP multi-parameters include relaxation time, resistivity, and normalized chargeability and shear strengths parameters include cohesive forces and angle of internal friction. Therefore, the 41 granite samples drilled from the Gaoligongshan tunnel were experimentally determined. Based on the experimental data, models were developed to describe the relationships between cohesion and IP parameters, as well as between the internal friction angle and IP parameters. Research has indicated that shear strength parameters increase with resistivity but decrease with increasing relaxation time. Moreover, the internal friction angle, compared to cohesion, has a stronger correlation with IP parameters. However, normalized chargeability has little correlation with shear strength. Finally, porosity is utilized as an intermediary to validate the dependability of the multi-parameter model that connects shear strength parameters with IP, offering novel methodological insights into the prediction of the shear strength of rock ahead of the TBM face.

Channel wave seismic exploration is an important means to detect geological structure anomalies in coal seam working faces. The coupling of geophones has a great impact on channel waves. At present, the commonly used coupling method is to connect the geophone to a bolt outcrop. However, the coupling state between the bolt and coal wall can vary greatly, resulting in poor channel wave data consistency, thus affecting the imaging effect. At present, there are few studies on eliminating the effects of bolts. In addition, the excitation conditions of each shot are different, and the effects of shots also need to be eliminated. This article proposes an algorithm to eliminate the effects of geophone-bolt coupling and excitation conditions. An influence factor is set for the effect of each shot or each geophone on the channel wave, and a matrix equation is established for the data of all traces based on the channel wave attenuation formula. The overdetermined equation is solved to obtain the influence factor, which can be eliminated to obtain the true amplitude, thus achieving consistent correction of the channel wave energy. The algorithm is verified with theoretical data and achieves good results when using the field data, thus making up for the shortcomings of the current channel wave construction method.

The strongest in Western Carpathians (WC) in XXI century M5 earthquake occurred on 9 October 2023 near Humenne (Slovakia). It was widely felt in Slovakia and Poland, what is rare. It occurred in complex tectonic setting formed with overthrusted frontal nappes and rotated internal lithospheric units. The present tectonic regime of the WC is resulting with vertical movements related to convergence of the WC and of the stable European Platform. Since the complex tectonic setting may influence the estimates of the source parameters, we propose to use local velocity model and regional and temporary seismic stations available within the time of the event occurrence from different projects: AdriaArray, Polish Geological Institute—National Research Institute monitoring network and broadband stations available from Polish, Slovak and Hungarian national seismological networks. Basing on the local velocity model derived from the earlier seismic experiments we obtained similar location and magnitude estimates as EMSC and NEIC, however our focal mechanism is significantly different. Usage of the above-described data improved the precision of the focal mechanism solution and of the depth location of the studied event. Obtained solutions suggest that focus was located at 10–15 km depth and had orientation of strike-slip fault with significant reverse fault component of strike parallel to the main discontinuities in this region and to the PKB (Pieniny Klippen Belt), which follow the trend of the Carpathian Mountains arc or perpendicular to above-mentioned main structures NE-SW strike in agreement with minor discontinuities and main compressional trend in this area.

This study focuses on developing region-specific seismic attenuation relationships for North-west Iran, a tectonically active area prone to frequent and destructive earthquakes. By analyzing a robust dataset of seismic events, we identify breakpoints in attenuation behavior at distances of 85 km and 175 km, attributed to crustal features such as the Moho and Conrad discontinuities. Using nonlinear optimization and inversion methods with explicit parameter bounds, we estimate frequency-dependent parameters, including geometric spreading coefficients, quality factor (Q), and magnitude-dependent terms. The geometric spreading coefficients for velocity data show slight variations across frequencies, reflecting the complex crustal structure in the region. Negative values of these coefficients indicate a significant velocity contrast at the Moho discontinuity, leading to substantial energy reflection. The observed amplitude decay trend remains consistent between breakpoints, with a notable change at approximately 175 km, likely due to the superposition of reflective phases from the Conrad and Moho discontinuities and multiple reflections within the S-wave window. Crustal stratification ensures continuous energy reflection, resulting in geometric spreading attenuation coefficients that exceed theoretical predictions. These empirically derived coefficients are intended for regional hazard assessment and may not be directly portable to other tectonic settings. The calculated average shear wave quality factor (Q) for the region is empirical and reflects the area’s structural characteristics and high seismicity. The findings provide practical insights for seismic hazard assessments and support the design of resilient infrastructure in North-west Iran.

A bridge abutment is the most critical civil engineering infrastructure directly exposed to floodwater. Numerous studies have been conducted to mitigate scour around bridge abutments; however, limited research has focused on assessing flow dynamics and energy reduction around bridge abutments using eco-friendly methods. Therefore, the current research investigates energy reduction and flow dynamics around bridge abutments with recycled materials (brick waste (BW) and marble waste (MW)) under subcritical flow conditions. Experiments were conducted in a controlled laboratory setting to investigate various parameters, including water surface profile, energy reduction, reduction of fluid force index (RFI%), moment index (RMI%), and delay in floodwater arrival time. These parameters were investigated under different conditions, including without waste (WW) and with recycled materials. The result demonstrates that energy reduction increases as the Froude number (Fr) is increased from 0.13 to 0.22. Energy reduction increases up to 5.95, 6.5, and 6.27% in the case of WW, MW, and BW, respectively. The use of MW resulted in a maximum energy reduction, with an average energy reduction of 4.38%. The highest RFI% of 8.86% and RMI% of 12.44% were recorded when using MW during the experiments. The findings also show that a significant reduction in floodwater arrival occurred in the case of MW up to 68% compared to the case without an abutment in the channel. These findings offer valuable insights into the flow characteristics and energy dissipation around bridge abutments, thereby contributing to the design of sustainable and resilient hydraulic infrastructure.

Sustainable water management is particularly important in mountainous areas, where access to surface water is limited and drilled wells often remain the only reliable source of fresh water. Locating aquifers in such regions is challenging due to the complex geological conditions. In this context, geophysical methods, especially electrical resistivity tomography (ERT), can provide valuable support in identifying zones with higher groundwater potential in areas such as the Carpathian flysch, composed mainly of sandstones and shales occurring in varying proportions. The paper presents case studies from the Magura and Silesian Nappes, demonstrating how ERT surveys, verified by borehole data, helped indicate aquifer locations and assess hydrogeological conditions. The application of ERT in the specific geology of the Carpathian flysch allowed for the identification of the influence of lithological proportions and water mineralisation on the values of electrical resistivity and the summary of the limitations and possibilities of the ERT method in difficult mountain conditions.

Although heterogeneous geological settings may limit the precision of interpretations, the results confirm that ERT is an effective tool for improving the recognition of groundwater resources in mountainous flysch areas and thus giving people access to water.

Extensive research has been conducted on the effects of anthropogenic practices on lowland rivers and floodplains; particularly regarding planform changes, only a few studies have utilized detailed riverbed elevation data. This study focuses on the Apalachicola River, one of the largest lowland rivers in the southeastern United States. The navigation project by the United States Army Corps of Engineers, which began in the late 1950s and continued till 2002, significantly impacted the Apalachicola River. The dredging and disposal, artificial cutoffs, and snag removal carried out as part of the navigation efforts significantly altered the Apalachicola River. Using bathymetric survey data from 1960 to 2010, we developed a high-resolution digital elevation model (DEM) to analyze geomorphic changes in the lower Apalachicola River and conduct a DEM of differences analysis for the 50-year period. We assessed the river’s net sediment gain and loss patterns using the DEMs and geostatistical approaches. We quantified the cumulative sediment volume change and gross change (cumulative absolute change) per river mile of the lower Apalachicola River between 1960 and 2010. The study revealed that the entire reach (RM ~ 45-0) experienced a loss of 8.36 million m³, a gain of 6.99 million m³, a gross change of 15.35 million m³, and a net change of 1.37 million m³. The reach upstream of the juncture with the Lower Chipola (~ RM 28), where several artificial cutoffs were present, experienced a net loss of 4.52 million m³. In this reach and just downstream of the juncture between RM 30 and 27, multiple pools deepened markedly. These morphological alterations have significantly compromised natural river–floodplain connectivity and altered critical aquatic habitats, particularly affecting the spawning and nursery areas essential for the region’s diverse freshwater mussel populations and other endemic species. However, downstream of RM 28, the Apalachicola had a net gain of 3.14 million m³, probably associated with sediment supply from downcutting and lateral erosion occurring upstream. This comprehensive sediment budget analysis provides essential quantitative evidence for river managers and restoration practitioners, demonstrating that navigation-induced modifications can redistribute over 15 million m³ of sediment across a 45-mile reach, with direct implications for habitat restoration planning, flood risk assessment, and sustainable waterway management in similar modified lowland river systems globally.

Accurate prediction of formation fracturing pressure is crucial for drilling safety and reservoir protection. In this study, a long short-term memory (LSTM) neural network model integrated with geomechanical constraints (Physical-LSTM) is proposed, which achieves deep coupling of data-driven approaches and physical laws through a physical constraint correction layer and a multi-objective loss function. Based on logging-while-drilling and drilling data from three wells in the Bohai Sea area, 15 key parameters were selected as the model inputs. The physical constraints include: the fracturing pressure must be greater than the pore pressure, less than the overburden pressure, and monotonically increasing with well depth. Bayesian optimization was employed to determine the weights of physical constraints and data fitting ((alpha) = 0.8, (beta) = 0.6). The experimental results show that the Physical-LSTM model achieves a mean squared error (MSE) of only 0.0015, a mean absolute error (MAE) of 0.0261, a coefficient of determination (R²) of 0.981, and a normalized Nash–Sutcliffe efficiency (NNSE) of 0.978 on the test set, which is significantly superior to the baseline models including LSTM, GRU, LightGBM, XGBoost, and RF. Compared with the traditional Eaton model, the Physical-LSTM not only maintains consistency in prediction trends but also substantially reduces prediction errors and eliminates physically unreasonable outliers. This study confirms that embedding physical constraints into machine learning models can significantly improve the accuracy, physical rationality, and engineering reliability of formation fracturing pressure prediction.