Abbreviation (ISO4): Prog Geophy
Editor in chief:
Previous studies indicate that the amount of astronomical solar radiation received at any location on Earth varies with season and latitude. The distribution of solar radiation across seasons and latitudes serves as a primary driver of climate change, reflecting a functional interplay between time and space. To further investigate this "spatiotemporal functional interplay" and explore the relationship between climate change on sub-orbital and shorter time scales and solar radiation, we calculated annual-resolution sequences of astronomical solar radiation energy at different latitudes in the Northern Hemisphere by integrating seasonal duration and solar radiation intensity. The characteristics of these sequences were analyzed, and the connection between Holocene solar radiation and climate change was examined. The results reveal that: (1) The variation in astronomical solar radiation at the equator and 30°N differs from that at 65°N. At 65°N, the annual trend in radiation energy aligns with that of the summer half-year, both peaking around 9, 000 years ago before gradually declining. In contrast, at 30°N, the annual trend matches that of the winter half-year, with a trough around 9, 000 years ago followed by a gradual increase. The summer half-year trend at this latitude runs counter to both the annual and winter half-year trends. At the equator, radiation energy troughs for the winter half-year, annual, and summer half-year occurred around 9, 000, 7, 500, and 6, 000 years ago, respectively. (2) The variations in astronomical solar radiation exhibit periodicities of 2.7, 3.6~4.5, 8.0~8.8, 14~16, 24~29, 47~52, 130~170, 240~260, and 280~300 years. These cycles coincide with those observed in climate change, suggesting a linkage between astronomical solar radiation fluctuations and Earth's climate variability. (3) At 65°N, 30°N, and the equator, and for both annual and half-year (summer and winter) radiation energy, the amplitude of centennial-scale variations has gradually decreased from 12, 000 years ago to the present. This implies that the greater climate variability in the early Holocene was not solely due to glacial feedback, but may also be associated with the larger amplitude of astronomical solar radiation variations at that time.
Earthquake dynamic inversion aims to infer the tectonic stress and friction parameters on the fault from the rupture process or observed waveforms related to an earthquake. Earthquake dynamic inversion has been developing for more than thirty years and its research methods have undergone significant changes. In this review, the history of earthquake dynamic inversion is summarized from the aspects of the friction law, the inversion strategies, the data used in the inversion, the parameterization, and the methods of spontaneous rupture simulation. Some researches are taken as examples to further illustrate the trade-offs among these aspects. The methods on earthquake dynamic inversion are classified as two strategies, one based on repeated spontaneous rupture simulation and the other based on the calculation of stress time history. In the former strategy, various inversion algorithms were introduced in 2004 to directly invert observed waveforms, while the classical trial-and-error approach has continued to be used. A large number of dynamic models are visited in this strategy, making it possible to provide an ensemble of plausible models, which can be further utilized to assess the sensitivity to individual model parameters as well as the trade-offs among them. However, inversion algorithms lead to a great number of spontaneous rupture simulations, requiring the number of model parameters to be limited and an efficient method of simulation to be employed. The latter strategy, popular in the 1990s, does not need to repeat spontaneous rupture simulations many times, allowing a computationally expensive simulation method and a general parameterization to be used. Nevertheless, with the latter strategy, one can only invert the kinematic rupture process, which may introduce bias in the dynamic parameters. In the 1990s, the slip-weakening friction law became widely used and contributed to the development of the latter strategy. The history of earthquake dynamic inversion provides vital insights. When investigating a specific earthquake, the inversion strategy as well as the parameterization and the simulation method should be carefully decided. The choice of the simulation method mainly depends on the velocity structure, the fault geometry and the computing resource available. The parameterization should be decided considering the magnitude of the earthquake as well as the resolution of the data.
The Stochastic Green's Function (SGF) method, developed as an extension of the Empirical Green's Function (EGF) technique by Kamae, was a stochastic approach for ground motion simulation considering the influence of fault rupture process. By adhering to the scaling law governing large and small faults, this method enabled the synthesis of a large earthquake while extending ground motion simulation to broader frequency range. The SGF method is extensively employed in Japan to simulate history earthquakes and assess seismic hazards associated with potential fault rupture areas. Given that this method has been successfully applied to ground motion simulation in areas lacking small earthquake records, it can provide a valuable reference for ground motion simulation approach that accounts for the influence of fault rupture processes. This paper first outlines the theoretical basis of the SGF method and the methodology for ground motion simulation. Secondly, it reviews advancements in ground motion simulation with the SGF method over the past two decades, mainly including amplitude spectrum, radiation pattern, phase simulation of small earthquakes and synthesis method of ground motion, evaluating the validity and practical applications of these advancements. Finally, to provide reference for future improvement, several issues which remain to be solved are proposed.
Using a deterministic physics-based method, a simulated near-fault ground motion dataset by considering magnitude uncertainty, focal mechanism parameters, and focal depth was constructed, and the pulse-like ground motion and its parameters were obtained by using Baker's wavelet method. A pulse occurrence probability model with different components was established for various magnitudes and focal mechanisms, and it was further compared with Iervolino and Cornell's empirical prediction model. Meanwhile, a mathematical model was established for the spatial azimuth factor of pulse period, and the relationship between the spatial azimuth factor with the pulse-like station's location was discussed. The results show that the pulse period in the simulated near-fault ground motion dataset generally increases with the increase of magnitude, and the mean value of the pulse period under different magnitudes is close to the value predicted by the empirical model. The pulse area estimated by the pulse occurrence probability model in this paper also increases with the increase of magnitude. For strike-slip, the pulse area is mainly concentrated on both sides of the fault, while for dip-slip, the pulse area is a belt-like distribution with the direction parallel to the fault strike. The prediction results in this paper are generally consistent with Iervolino and Cornell's empirical prediction model. Especially for dip-slip, the predicted shape in this paper is more consistent with the distribution of the simulated PGV, while the pulse occurrence probability on both sides of the fault can be overestimated by the empirical model. Finally, both the magnitude and the pulse-like station's spatial location have a significant impact on the spatial azimuth factor. The established spatial azimuth factor of pulse period can be used to estimate the pulse period of horizontal components under different magnitudes.
How to quantitatively clarify the frequency-dependent barometric responses of borehole strain is a very challenging problem in the fixed-point observation of crustal deformation and geodynamic research. Since the 1970 s, China has had a large sustained program to make continuous measurements of four-component borehole strain; however, the study of signature and physical mechanism of four-component borehole strain in response to barometric pressure at different frequencies are seldom investigated in this day. For this reason, we take the Shenchi station in Shanxi as a typical case to systemically uncover the frequency-dependent barometric responses of four-component borehole strain. Both the coherence function and the transfer function are applied in this diagnostic work. The obtained results show that: (1) The values of coherence in the frequency band 0.1 to 40 cpd are almost greater than 0.9, which imply that the borehole strain are strongly correlated with barometric pressure in this frequency band. (2) The barometric pressure responses are relatively stationary in the low-frequency band (0.1~0.5 cpd), and remained stable in the intermediate-frequency band (0.5~8 cpd), but the frequency-dependent responses are very strong in the high-frequency band (8~40 cpd). (3) In the high-frequency band, the spectra of barometric pressure coefficients and phase shifts for the four-component borehole strain decrease and increase exponentially with increasing frequencies, respectively. (4) In the whole frequency band (0.1~40 cpd), the frequency-dependent barometric responses are obvious for the four-component borehole strain, and the main trends of spectra of phase responses increase exponentially with increasing frequencies; furthermore, the partial and entire features of barometric responses for the four-component borehole strain are slightly different from each other. The above findings will be useful for correcting the nonlinear barometric response for the four-component borehole strain records, as well as for advancing our understanding of physical mechanism of crustal deformation at different periods.
Magnetic fabric provides highly sensitive tracing indicators for inverting geodynamic processes. In this paper, based on a multi-scale analysis of the mechanisms of rock magnetic fabric formation, we systematically explore the relationship between magnetic fabric and the formation-evolution processes of sedimentary rocks, igneous rocks, and metamorphic rocks: (1) In sedimentary rocks, magnetic fabric is controlled by the depositional environment during the formation of sedimentary rocks. Its parameters can reconstruct the depositional environment such as paleoclimate, paleocurrent and paleo-wind directions, while also revealing the tectonic characteristics and stress directions during the synsedimentary and diagenesis stage. (2) The magnetic fabric of igneous rocks is dominated by magmatic hydrodynamic processes. Through the spatial configuration of the principal axes of Anisotropy of Magnetic Susceptibility (AMS), the magmatic intrusion vector field can be accurately analyzed, providing crucial dynamic constraints for magma chamber localization and the reconstruction of multi-stage emplacement sequences. (3) The magnetic fabric of metamorphic rocks is closely related to the metamorphic and deformational processes of rocks. Its parameters can be used as a quantitative scale for metamorphic grade, and the distribution of AMS principal axes is applicable to interpreting regional tectonic, especially demonstrating unique advantages in shear zone kinematic analysis. The application of magnetic fabric can follow four steps: geological survey, identification of the dominant magnetic minerals controlling AMS, analysis of the genetic origins of the magnetic fabric, and a multi-method discussion of its geological significance. By constructing a matrix for the conversion between magnetic susceptibility tensor and strain tensor through deformation simulation experiments, combined with techniques such as Scanning Electron Microscope-Electron Back Scatter Diffraction for micro-scale magnetic fabric imaging, and various magnetic fabric separation techniques, a multi-scale observation system can be established to achieve quantitative decoupling of multiple components in magnetic fabric, significantly improving the accuracy and reliability of geological interpretation of magnetic fabric parameters.
Due to the lag in the calculation of polar motion, it fails to meet the real-time requirements in space engineering fields such as deep space exploration and satellite orbit determination. Therefore, it is of great significance to obtain predicted values of polar motion. This study proposes a hybrid prediction model that integrates the Least Squares (LS) method and a two-stage attention mechanism, aiming to achieve a 30 days short term prediction of polar motion. First, the LS model is used to perform trend fitting and extrapolation on the polar motion observation sequence. Then, a neural network model based on the two-stage attention mechanism is constructed to train and predict the residual part. Finally, the final predicted value of polar motion is obtained by adding the LS extrapolated value and the predicted residual value. Experimental results indicate that, when compared with the prediction effects of the least squares and autoregressive methods, the accuracy of the proposed hybrid method is significantly improved in the short-term prediction of the polar motion in the Y-direction. Meanwhile, the experiment reveals that increasing the amount of basic data can further enhance the prediction performance of this hybrid model, verifying that an increase in data volume can improve the short-term accuracy of the hybrid model. The hybrid prediction method of LS and the two-stage attention mechanism proposed in this study provides new technical ideas and method references for improving the prediction accuracy of Earth rotation parameters.
In the realm of SAR-based sea surface wind field retrieval, the conventional Fast Fourier Transform (FFT)-based spectral analysis method, which relies on a single maximum value, faces challenges in integrating effective regions within the spectrum that contain wind streak features. Consequently, this study proposes an optimized wind direction extraction method based on bivariate polynomial fitting of the spectrum. Through effective denoising and fitting techniques, this method integrates the effective regions with wind streak characteristics in the spectrum and yields more accurate results. Comparative analyses with ECMWF Re-Analysis 5 (ERA5) data and National Data Buoy Center (NDBC) buoy data reveal that the absolute deviations of the retrieved wind direction and wind speed are 3.90° and 2.33 m/s compared with ERA5 data, and 6.82° and 1.19 m/s compared with NDBC data, with reductions of 11.74° and 0.10 m/s in deviations respectively, relative to those between ERA5 and NDBC data. The experimental results indicate that the bivariate polynomial fitting method exhibits excellent wind direction extraction capability and even outperforms ERA5 data in specific regions, thereby contributing to the research on sea surface climate change.
With the acceleration of urbanization, anthropogenic activities have induced pronounced modifications of surface morphology, posing severe challenges to the sustainable development of historic cities. This study aims to characterize the spatiotemporal distribution of surface deformation in the old urban area of Xi'an and to elucidate its driving factors, and further to develop a single-feature prediction model based on cumulative subsidence to reveal deformation trends and spatial disparities over the coming year. Using Sentinel-1A imagery acquired between 2019 and 2024, we applied PS-InSAR and SBAS-InSAR techniques to extract time-series deformation and construct a comprehensive subsidence dataset, cross-validating the correlation and reliability of the two InSAR results through a dual-technology mutual-checking approach. Long-term deformation sequences derived from PS-InSAR were then employed to train and validate two forecasting frameworks—N-BEATS and LSTM. The results indicate: (1)The deformation fields retrieved by PS-InSAR and SBAS-InSAR exhibit a high degree of linear correlation and display a "northwest subsidence and southeast uplift" spatial pattern, with a maximum annual subsidence rate of 9.283 mm/a and a maximum cumulative subsidence of 61.16 mm; and a maximum annual uplift rate of 5.585 mm/a and a maximum cumulative uplift of 49.727 mm. (2) Comparative evaluation of the forecasting models demonstrates that N-BEATS, with superior single-feature learning capability, predictive accuracy, and stability, outperforms LSTM. Forecasts from the N-BEATS model project a maximum cumulative subsidence of 74 mm and a maximum cumulative uplift of 47.466 mm over the next year, with an amplified contrast between northwest and southeast deformation.
With the multi-source nature of remote sensing scientific data, the automation of processing, and the widespread application, the evolution of methods such as PS-InSAR, SBAS, and SqueeSAR, as well as the maturity of processing tools like GAMMA and StaMPS, have led many scholars and researchers to face concerns regarding the selection of multi-source data preprocessing, platform compatibility, and result accuracy evaluation during the data processing process. This paper selects three platforms, namely AI Earth, SARvey, and SARscape (as the reference benchmark), and conducts an applicability analysis of the platforms from multiple dimensions, including processing accuracy, operation efficiency, and functional integrity. We found that: SARscape has comprehensive functions and a high degree of visualization in the processing process; AI Earth achieves efficient computing relying on a cloud-based architecture, but there are limitations in terms of spatial and temporal scope; the open-source platform SARvey performs excellently in extracting time-series information, but it needs to rely on external ISCE for preprocessing. We propose a data processing collaborative framework that integrates the advantages of cloud-based processing and open-source analysis, aiming to provide technical references for the processing of multi-source remote sensing data and platform selection in different application scenarios.
As a critical water regulatory region and ecological barrier in the middle reaches of the Yangtze River, understanding sub-basin scale Terrestrial Water Storage Anomaly (TWSA) is crucial for regional water resources management and early warning of droughts and floods in the Dongting Lake Basin. However, the relatively short and discontinuous records from the Gravity Recovery and Climate Experiment (GRACE) and GRACE-Follow On (FO) satellite missions limit long-term hydrological analysis. This study reconstructed monthly TWSA from 1980 to 2024 at the sub-basin scale by integrating multi-source data, including GRACE/GRACE-FO satellite observations, global reanalysis (MERRA-2), the Global Hydrological Model (WGHM), and meteorological data (precipitation and temperature). Methodologically, the Seasonal-Trend decomposition using Loess (STL) method was applied to isolate different temporal components of the input variables, which were then reconstructed using two machine learning approaches: the Generalized Regression Neural Network (GRNN) and the Long Short-Term Memory (LSTM) network. Results demonstrate that the GRNN method consistently outperformed LSTM across most sub-basins. The GRNN model driven by MERRA-2 data achieved the most accurate reconstruction, demonstrating strong agreement with the GRACE-derived TWSA in both phase and amplitude. Among the sub-basins, the Xiangjiang River Basin, which has the largest spatial extent, showed the highest accuracy, whereas the Lishui River Basin, being the smallest sub-basin, presented the largest uncertainty due to increased data noise at the smaller scale. Long-term trend analysis revealed a significant increasing trend in TWSA (+1.57 mm/a) for the entire basin and the sub-basins over the past 45 years, which is consistent with the increasing trend in precipitation (+0.87 mm/a) and reservoir water storage (+1.12 mm/a). Short-term TWSA dynamics often exhibited an inverse trend against evapotranspiration. Notably, sharp declines in TWSA were detected during major historical drought years (1985, 2003, 2011, 2022), aligning strongly with negative precipitation anomalies and extreme climatic conditions.Although the terrestrial water storage in the Dongting Lake Basin shows an increasing trend, droughts still occur frequently, indicating that the water resource situation in the basin is becoming increasingly severe. The reconstructed TWSA time series provides scientific supports for sub-basin scale water resources management and prediction of extreme hydrological events in the Dongting Lake Basin.
Lithology identification serves as the foundation for reservoir evaluation and prediction. However, due to the high cost of drilling coring and the relatively low reliability of mud logging lithology identification, utilizing well logging data for identification becomes crucial. The compositional and structural variations among different lithologies result in distinct well logging response characteristics, creating an urgent need to systematically evaluate the applicability of various well logging interpretation methods for lithology. This aims to maximize the utilization of well logging data for accurate lithology identification, thereby establishing a basis for sedimentary reservoir evaluation. This paper first employs the core-calibrated logging method to analyze and summarize the well logging response characteristics of typical lithologies, including sedimentary rocks, volcanic rocks, and metamorphic rocks. It then reviews how integrated utilization of conventional well logging cross plots charts enables qualitative lithology identification, while further application of well logging mineral component calculation allows for quantitative lithology discrimination. Additionally, it summarizes how emerging logging technologies such as elemental logging and imaging logging, combined with conventional methods, enhance the accuracy of lithology interpretation. Simultaneously, incorporating AI-based methods can improve the efficiency of lithology identification. Finally, the paper discusses the significance of well logging lithology interpretation in studying the reservoir "four properties" relationships. It proposes selecting appropriate comprehensive lithological interpretation through the fusion of "multi-method, multi-paramater" approaches. This study aims to provide theoretical guidance and technical support for hydrocarbon resource assessment and exploration development.
Depth-domain tomography and Full Waveform Inversion (FWI) are the primary methods for subsurface velocity model building. Tomography relies on travel-time information, featuring high computational efficiency but with insufficient resolution. FWI utilizes the travel-time, amplitude and waveform information of seismic field data, providing high-precision, but is highly dependent on the initial model and seismic data quality, and prone to non-convergence issues during inversion. To comprehensively leverage the advantages of aforementioned techniques, we constructed a practical workflow and processing method for joint velocity modeling integrating depth-domain tomography and FWI under geological constraints. This approach involves using well logging data to constrain the construction of background velocity models and incorporating fault control information during both depth-domain tomography and full waveform inversion to constrain the updating direction. Comparative analysis of modeling results and actual data from the Dongpu exploration area demonstrates that the geologically constrained joint modeling method can: (1) obtain relatively accurate subsurface velocity models in complex fault-block areas; (2) suppress the influence of noise on inversion results; (3) achieve better fault positioning and seismic imaging in obscured zones; (4) significantly improve the focusing and flattening of image gathers; and (5) remarkably reduce well-to-seismic discrepancies.
The Benxi Formation coal bed in the Ordos Basin represent one of the most important reservoirs for Coalbed Methane (CBM) in China. However, the heterogeneous distribution of gangue significantly affects coal bed continuity, gas content, reservoir quality, and the design of horizontal well trajectories. Accurately characterizing gangue distribution is therefore essential for improving CBM exploration and development efficiency. This study aims to establish an integrated geological-geophysical workflow for high-precision geomorphology reconstruction and gangue prediction within the Benxi Formation, addressing the limitations of conventional seismic data such as insufficient vertical resolution and blurred stratigraphic boundaries. To achieve this objective, we introduce the Steerable Pyramid seismic micro-attribute enhancement method, which effectively improves the clarity of seismic reflection geometries and enhances vertical resolution while preserving the original structural framework. Based on the enhanced seismic volume, the "imprint method" is employed to reconstruct the paleogeomorphology of the 8 # coal bed in eastern Yulin. Log data analysis indicates that natural gamma (GR) is the most sensitive indicator for gangue development; thus, GR is selected as the key constraint in seismic inversion. By integrating high-precision paleogeomorphologic boundaries into the inversion workflow, a refined three-dimensional gangue distribution model is constructed. Result demonstrates that the development of gangue within the Benxi Formation is predominantly controlled by subtle paleogeomorphologic undulations. Areas corresponding to gentle paleodepressions exhibit minimal gangue content and better coal continuity, representing favorable zones for CBM enrichment and production. The gangue prediction outcomes show high consistency with log and data across multiple well profiles, confirming the reliability and geological validity of the proposed workflow. In conclusion, this study establishes a robust and practical method for accurately characterizing gangue spatial distribution in coal bed through integrated paleogeomorphology analysis and seismic inversion. The findings not only clarify the coupling relationship between paleogeomorphology and gangue development but also provide valuable method support for well placement optimization, sweet-spot identification and efficient CBM development in the Ordos Basin.
Reflection seismic exploration, based on layered medium theory and regular acquisition systems, effectively solves large-scale geological body imaging through multiple coverage observations and Common Midpoint (CMP) stacking. However, it has limitations in precise imaging of small-scale, non-layered, and concealed geological targets. Full-wavefield seismic technology, which jointly images reflection waves, diffractions, and scattered waves, can compensate for the insufficient spatial resolution of reflection seismic data. Irregular seismic acquisition is a full-wavefield seismic acquisition method that enables discrete common midpoint sampling. It ensures sufficient sampling of scattered and diffracted waves while allowing for fine bin processing. To enhance the effectiveness of irregular discrete sampling, this study proposes an evaluation method, analyzes the impact of randomization patterns and parameters, and compares irregular vs. conventional acquisition with real data. Results show that the method quantitatively assesses CMP discreteness, guiding irregular acquisition layout. Irregular data facilitates finer bin processing and clearer fracture delineation, proving effective for late-stage oil/gas exploration and development.
Conventional Transient Electromagnetic (TEM) apparent resistivity calculations typically rely on approximate formulas valid only in specific time intervals, such as the late-time approximation. As a result, their effectiveness is confined to narrow time windows, making it difficult to accurately characterize the complete subsurface resistivity structure from shallow to deep regions. Moreover, existing algorithms often assume an ideal step-off current waveform, neglecting the ramp-off effect that occurs during the actual turn-off process. This simplification can introduce blind zones and spurious high-resistivity anomalies in the early-time responses. To address these issues, this study proposes a full-space, full-time apparent resistivity calculation method for TEM that explicitly incorporates the ramp-off effect. The method is based on the canonical electric dipole response excited by an ideal step-off current, combined with precise spatial numerical integration along the actual transmitter loop to construct a forward model applicable to arbitrary observation points. A differential correction is then applied to transform the ideal response into an induced electromotive force waveform consistent with the ramp-off condition encountered in field measurements. The method iteratively fits measured data channel by channel across the entire time domain to obtain apparent resistivity values that honor the ramp-off effect in both space and time. This approach effectively eliminates distortions and false anomalies caused by neglecting the turn-off duration, thereby improving the accuracy and reliability of TEM data interpretation. Application of the method to a goaf detection case in the Ayiguozi Mine demonstrated promising results, successfully delineating multiple mined-out zones and assessing their reliability. This provides a new technical pathway for TEM surveys in geologically complex environments.
To address the challenge of predicting fracture-cave structures in coal mine goafs, this paper proposes a novel identification technique based on seismic attribute fusion. First, extraction algorithms for multiple sensitive seismic attributes were implemented, and corresponding modules were developed on a self-developed software platform. Second, to overcome the lack of supervised information and background noise in goaf areas, unsupervised machine learning algorithms—including Fuzzy C-Means Clustering (FCM) and Principal Component Analysis (PCA)—were integrated to analyze and fuse the extracted seismic attributes. Then, the unsupervised learning results were innovatively constrained using multi-source information data to extract supervised samples. A deep learning model based on the Backpropagation (BP) neural network was constructed and trained to achieve seismic attribute fusion and fracture-cave structure identification in goafs. Finally, the proposed method was applied to a goaf area in the Modi Gully, Hejin, Shanxi Province. The results demonstrated effective fusion of multiple seismic attributes, with predicted fracture locations and goaf boundaries showing strong agreement with drilling data, This study provides a reliable technical approach for fracture-cave identification in goaf areas.
Determination magnetization direction is essential for processing and interpretation of magnetic data. However, traditional methods for estimating magnetization direction are often sensitive to noises and to effects of low latitude, leading to large deviations in the estimated directions. In contrast, deep learning methods can effectively mitigate the effects of the above problems. Therefore, we propose a multi-scale dilated convolution with squeeze-and-excitation neural network(MSDC-SENet) for magnetization direction estimation. This model extracts both local details and global features of magnetic data simultaneously through a multi-scale dilated convolution module. In addition, SE (squeeze and excitation networks) attention modules are added to enhance the focus on key features of magnetic anomalies. Synthetic data experiments show that test accuracy of MSDC-SENet model outperforms that of the original CNN model under most conditions. Particularly, on diverse datasets with different depths and locations, the inclination test accuracies of MSDC-SENet are improved by 4.82% and 14.22% to 91.56% and 89.47%, respectively, while the declination test accuracies are improved by 1.24% and 6.17%, respectively. The model is applied to synthetic data tests as well as magnetization direction estimation of aeromagnetic data from southern Australia. Compared with the existing methods, the model in this paper shows higher accuracy and robustness under complex conditions, providing an efficient and reliable method for magnetization direction estimation.
The Leikoupo Formation marine tidal-flat dolomite gas reservoir in western Sichuan Basin is characterized by deep burial depth and high sulfur content. Reservoir pores, vugs, and fractures are developed to varying degrees, resulting in strong heterogeneity, and economically efficient development requires relatively high single-well productivity. However, reservoir classification schemes and lower-limit criteria established using conventional methods show inconsistencies between logging interpretation results and well test outcomes in some wells. To address this issue, combined Nuclear Magnetic Resonance-CT scanning experiments before and after core acidification were designed. By measuring the Nuclear Magnetic Resonance T2 spectra and CT imaging of cores before and after acidification, and extracting petrophysical and pore structure parameters using digital core technology, the effects of petrophysical changes induced by acidification on pore structure improvement in the Leikoupo Formation dolomite reservoirs were comparatively analyzed. The petrophysical thresholds for economically efficient development were further investigated.The results indicate that: (1) core NMR-derived porosity, permeability, and the peak value of movable fluid T2 spectra exhibit positive correlations with pore-throat parameters, and both pore structure and connectivity of the cores are improved to varying degrees after acidification; (2) when porosity ranges from 4% to 8%, pore structure parameters and connectivity show significant improvement, whereas the enhancement is relatively limited when porosity is ≥8% or < 4%. Through integrated experimental analysis and typical well case studies, petrophysical classification criteria for evaluating tidal-flat dolomite reservoirs of the Leikoupo Formation were established, providing important technical support for gas reservoir development.
In order to solve the problems of poor detection effect of conventional seismic data interpretation on small and medium-scale faults and insufficient evaluation of reservoir fault effectiveness, a reservoir fracture prediction and effectiveness evaluation method combining the fault connectivity evaluation method and the fault opening evaluation method based on curvature attribute was proposed. This method was applied to the WY Longmaxi Formation shale reservoir working area. Gradient Structure Tensor(GST)curvature and energy ratio coherence attributes were used to identify major fracture development zones, highlighting small-and medium-scale fractures, thus providing a clearer and more comprehensive characterization of fracture features in the target reservoir. Using the fracture openness prediction method based on maximum positive curvature and the curvature along strike attributes, orientation strength attributes for six different directions (0°, 30°, 60°, -30°, -60°, -90°) were calculated to accurately evaluate fracture openness and emphasize open fractures. Combined with the fracture connectivity prediction method based on the three-dimensional steady-state saturated fluid equation, absolute flow values were used to highlight connected fractures, allowing for a comprehensive evaluation of effective fracture development zones. The results indicate that numerous small-and medium-scale fractures developed in the reservoir. The openness and connectivity of effective fractures are both high, mainly concentrated in the central, southwestern, and northeastern parts of the reservoir, with orientations trending northwest-southeast, north-south, and northeast-southwest.
Clarifying the relationship between the gas-bearing characteristics of tight sandstone and the lithological characteristics is very important for identifying and evaluating the gas-bearing characteristics. In order to clarify the characteristics of the tight sandstone types and its gas-bearing in He8 Member of the western part of the Jingbian gas field, based on the thin sections, physical properties, gas testing data and well logging data, the analysis of the lithological characteristics of sandstone was carried out, then, the physical characteristics and gas-bearing characteristics of quartz sandstone, lithic quartz sandstone and lithic sandstone were discussed. The sandstone type of He8 Member is mainly lithic quartz sandstone, followed by lithic sandstone and quartz sandstone. The characteristics of the gamma-ray curve and photoelectric absorption cross section index of the three types of sandstone are obviously different. Based on the two logging parameters, the constructed sensitive litho-parameter can better indicate the sandstone type. Meanwhile, the corresponding analysis of the gas testing conclusion and the characteristics of sandstone type reveals that the gas-bearing of quartz sandstone is generally good, and the single test/commingled test conclusion are the gas layer and the gas-bearing layer; the gas-bearing of lithic quartz sandstone is generally good, and the commingled test conclusion are the gas layer, the gas-bearing layer, the gas-water layer, a small amount of water layer, and the lithic quartz sandstone with high mud content or relatively high lithic content is mostly dry layer; the gas-bearing of the lithic sandstone is generally poor, and the gas-bearing of the commingled test conclusion are mainly dry layer, a small amount of gas-bearing layer and gas layer; the quartz sandstone is favorable for gas-bearing in He 8 member. And then, Gas-I, a gas-sensitive signal combination enhancement factor based on the combination of deep lateral resistivity, acoustic time difference, compensation neutron and gamma-ray parameters that is sensitive to gas response, can better characterize the gas-bearing of sandstone. In addition, due to the lithology, physical properties and pore structure of the tight sandstone reservoir, which affect its gas-bearing, the gas-bearing characteristics of sandstone in the mixed area in the plot of litho-parameter and Gas-I are complex, It is necessary to further carry out targeted analysis and in-depth research in combination with other well logging data and special well logging data.
Distributed Acoustic Sensing (DAS) technology has been widely applied in seismic exploration due to its advantages of low cost and high accuracy. However, DAS data is contaminated by various types of noise, among which fading noise is ubiquitously distributed. To address this issue, this paper proposes a joint denoising framework that integrates fast dictionary learning with Sequential Generalized K-means (SGK) and target-oriented median filtering. This framework utilizes SGK to replace the K-Singular Value Decomposition (K-SVD) process, specifically substituting the Singular Value Decomposition (SVD) update step. Subsequently, a statistical classification scheme is employed to effectively identify fading noise atom sequences through the construction of novel statistical parameters. Finally, targeted noise suppression is achieved via median filtering using a physics-parameterized threshold selection strategy. The denoising performance of the proposed method was validated through tests on both synthetic and real field data. By enabling precise noise localization via physics-driven statistical parameters and combining efficient dictionary learning with targeted filtering, our method significantly enhances DAS data quality, offering a novel approach for seismic signal extraction in complex noise environments.
Shale gas resources in marine-continental transitional facies are abundant, and exploration practices have shown that they possess promising prospects for exploration and development. However, complex geological conditions of shale strata in marine-continental transitional facies—such as complicated lithologic combinations, rapid lateral variations, and significant differences in gas content—have restricted the efficient and large-scale development of shale gas. Thus, there is an urgent need to carry out work on shale gas sweet spot prediction and parameter evaluation for target area selection. Taking the typical marine-continental transitional facies shale gas in the Shan23 sub-member of the Shanxi Formation in the Daning—Jixian Block on the eastern margin Ordos Basin as an example, this study first selects the key evaluation parameters for shale gas sweet spots and conducts logging quantitative characterization of each parameter. On the basis of petrophysical analysis, it applies pre-stack simultaneous inversion and facies-controlled waveform indication simulation seismic prediction methods to realize the spatial prediction of key evaluation parameters, and uses the fuzzy optimization evaluation method to achieve the quantitative prediction of shale gas sweet spots. Results show that: based on the basic geological characteristics of shale strata in transitional facies, key evaluation parameters for shale gas sweet spots were selected, including shale thickness, Total Organic Carbon (TOC), porosity, total gas content, and brittleness index. On the basis of the effective seismic prediction of each parameter, a classified evaluation system for sweet spots was established through fuzzy optimization evaluation, and three types of shale gas sweet spots were identified. Overall, the sweet spots in the lower section are superior to those in the upper section. Combined with the results of production testing, the reliability of the sweet spots prediction results was verified. The research method provides a significant reference for the exploration and development of marine-continental transitional facies shale gas in the study area and other areas under similar geological conditions.
The sandy conglomerate reservoirs of the W oil-bearing formation in A sag are characterized by medium-to-low porosity, low-to-ultra-low permeability, and strong heterogeneity. Their complex pore-throat structures pose significant challenges to accurate permeability prediction. To establish a refined prediction model suitable for the study area, this paper integrated core data, well logs, and mercury injection capillary pressure tests systematically analyze petrological and pore structure characteristics. On this basis, the application effects of six modeling approaches were comparatively evaluated: conventional porosity-permeability regression, reservoir grain size, sedimentary microfacies, pore-throat difference constraint, Flow Zone Index (FZI) flow unit, and Particle Swarm Optimization-Extreme Gradient Boosting (PSO-XGBoost) machine learning models. The results demonstrate that the conventional porosity-permeability regression model fails to effectively characterize the complex nonlinear features of the reservoir. The reservoir grain size model provides limited prediction accuracy because extensive diagenetic modifications, such as late-stage dissolution and cementation, have significantly weakened the correlation between current pore-throat structures and original sedimentary grain sizes. Although the sedimentary microfacies and pore-throat difference-constrained models improve accuracy to a certain extent, they are hindered by the qualitative nature of microfacies boundary definition and the difficulty of acquiring high-cost experimental data, respectively. Among the data-driven methods, the PSO-XGBoost model achieves the highest statistical precision; however, its application is restricted by ambiguous geological mechanisms and a heavy reliance on large datasets. In contrast, the FZI flow unit model effectively quantifies the strong heterogeneity by utilizing the Flow Zone Index to divide the macroscopically heterogeneous reservoir into evaluation units with uniform internal physical properties. Overall, the FZI flow unit model attains the optimal balance among predictive accuracy, data requirements, and geological mechanisms. It is confirmed as the most applicable permeability prediction method for the study area, providing reliable technical support for the exploration and development of low-permeability reservoirs in the Pearl River Mouth Basin and similar offshore regions.
For the automatic identification of seismic faults, this study adopts a U-Net model integrating the CBAM (Convolutional Block Attention Module) attention mechanism and residual structures. Based on the U-Net encoder-decoder framework, the model introduces residual modules to enhance feature transmission stability and employs the CBAM module to strengthen fault-related feature responses, thereby improving the model's ability to identify fault boundaries and spatial continuity. Experimental results show that the model performs well in terms of fault continuity and recognition accuracy. Validation on 2D field seismic data and 3D seismic data further demonstrates that the model can effectively identify major fault structures while maintaining good structural consistency. The results indicate that the model can provide a useful reference for automatic seismic fault interpretation.
Simultaneous-source acquisition technology can improve acquisition efficiency and reduce exploration costs, and its core is the deblending method. Traditional deep learning-based deblending methods are prone to losing detailed boundary information during the down-sampling process, thereby reducing the signal-to-noise ratio of the separation results. This paper proposes a multi-scale UNetPlus (MsUNetPlus) deblending method. Based on the UNetPlus network, an independent "left leg" path is introduced in the encoder to extract shallow features of the input image at different resolutions, which are then closely fused with the main encoder path. This helps supplement edge details at deeper levels and alleviates the edge blurring caused by down-sampling. Separation tests on physical simulation data show that the proposed method achieves higher separation accuracy compared to the UNet, MsUNet, and UNetPlus methods, and can better preserve image detail information. Finally, the proposed method was applied to field data and achieved satisfactory separation results.
Accurate porosity prediction in oil and gas reservoirs is crucial for reservoir characterization. However, traditional methods like core measurements and logging data are expensive, inefficient, and fail to fully capture large-scale 3D porosity distributions. To address the limitations of petrophysical models with specific assumptions, this paper introduces an Explainable Artificial Intelligence (XAI) approach that combines machine learning algorithms, SHapley Additive exPlanations (SHAP) analysis, and petrophysical theory. Using the F3 Block in the European North Sea Basin as a case study, we built 3D porosity prediction models with four mainstream algorithms—multilayer perceptron (MLP), Random Forest (RF), Support Vector Regression (SVR), and Linear Regression (LR)—using various 3D seismic attributes as inputs. SHAP values were employed to uncover the decision-making logic behind the predictions. All four algorithms achieved high accuracy, with support vector regression performing best (R2=0.9669, RMSE=0.0055), surpassing traditional linear regression. The seismic inversion acoustic impedance, given its clear petrophysical meaning, was the most influential input feature across all algorithms. Low-pass filtering of logging curves further improved prediction accuracy. This study proposes an efficient, reliable XAI method for porosity prediction by integrating machine learning, petrophysical theory, and SHAP analysis. This approach enhances model interpretability and transparency in reservoir characterization, ensuring results are petrophysically sound.
The deep gas reservoirs in the southern margin of the Junggar Basin have tight and poor physical properties, and the water layer resistivity increases due to the invasion of oil-based mud. The accuracy of fluid property identification using resistivity logging is only 50%, making it necessary to explore fluid property identification methods other than electrical logging. Based on theoretical analysis and experimental measurement, a method for identifying the fluid properties of gas reservoirs using acoustic parameters was established. Firstly, the volume modulus and shear modulus of rocks with different gas saturations were obtained based on the GASSMAN equation, SCA model, and Gassman-Hill equation, and the sensitivity of five parameters IΔK, IK, IVp/Vs, Iv, and IZp to fluid properties was theoretically calculated. Secondly, experiments were conducted to measure the longitudinal and transverse wave velocities under varying saturation conditions and different pressure and temperature in the strata. The experiments examined the sensitivity of five parameters IΔK, IK, IVp/Vs, Iv, IZp to the fluids. Finally, the logging identification criteria for gas and water layers were given based on crossplot technology, and the fluid property identification work was completed for 7 wells. The results show that the sensitivity of acoustic parameters to fluid properties, from high to low, is IΔK>IK>Iv>IZp>IVp/Vs; the criteria for identifying gas and water layers can be IΔK < 0.5, IK < 0.85, and VP/VS < 1.8; the fluid property identification results of 10 layers in 7 wells show that the identification accuracy based on acoustic parameters is 90%, which can meet the needs of well testing and layer selection in this gas reservoir.
Due to the limitations of seismic data acquisition and processing, real seismic data often suffer from low resolution, noise contamination, and other issues, which pose significant challenges for subsequent seismic interpretation. This paper proposes a visual perception-guided seismic image super-resolution reconstruction method to address the task of enhancing low-resolution seismic images to high resolution. The proposed method is based on the U-Net deep learning model and incorporates a perceptual loss function to guide the reconstruction process perceptually. Additionally, L1 loss and Total variable difference loss are combined to balance the quality and smoothness of seismic image reconstruction, thereby reducing high-frequency noise and checkerboard artifacts. Moreover, sub-pixel convolution is introduced to recover high-frequency detail information and mitigate the occurrence of checkerboard artifacts. To address the challenge of processing large-size images using the U-Net model, an overlapping patch cutting strategy is adopted to divide seismic images into multiple smaller patches with different features. Data augmentation techniques, such as random flipping and rotation, are applied to enhance the model's generalization ability and robustness. Experimental results show that, compared to existing super-resolution models, the proposed model demonstrates better performance, effectively recovering high-frequency details of seismic images, reducing noise, enhancing the continuity of seismic events and geological features such as faults, and facilitating subsequent manual annotation and automatic recognition.
The deep to ultra-deep platform margin reservoir is an important target of marine carbonate oil and gas exploration in continental basin, However, this kind of reservoir faces many challenges in prediction. This paper reviews the progress of geophysical exploration technology, such as seismic data processing, facies controlled reservoir prediction and other geophysical exploration technologies, and summarizes the application effects and limitations of geophysical prospecting technology reservoir prediction. The research shows that amplitude preserving and frequency extension, facies controlled inversion and other technologies have improved the accuracy of reservoir identification, but the exploration is still limited by the lack of seismic data quality, the uncertainty of geological model, and the inapplicability of geophysical exploration technology. To achieve breakthroughs in reservoir prediction accuracy, it is recommended to promote interdisciplinary collaboration, optimize acquisition parameters, refine the analysis of seismic response characteristics, and advance high-precision inversion technologies. Then improving the accuracy of reservoir prediction. The purpose of this study is to provide technical applications and development suggestions for further exploration and development in the field of deep ultra deep platform margin reservoir.
The Antarctic ice sheet covers an extensive area and reaches enormous thicknesses, which has long severely constrained our understanding of its internal structure and the underlying lithosphere. Seismic exploration is an important approach for investigating the ice sheet and subglacial geological environment. However, due to the extreme environmental conditions and logistical constraints in Antarctica, conventional seismic acquisition methods suffer from low operational efficiency, high labor intensity, and difficulties in conducting large-scale surveys.This study introduces a towed geophone seismic acquisition system, which was preliminarily tested on the ice sheet of Larsemann Hills during the 42nd Chinese Antarctic Expedition. Towed by a snow vehicle and combined with an electromagnetic vibroseis source, the system enabled rapid and continuous seismic data acquisition.The acquired surface-wave data were successfully applied to Multichannel Analysis of Surface Waves (MASW), and a high-resolution near-surface shear-wave velocity structure of the ice sheet was obtained through inversion. Strong reflections from the ice-bedrock interface were clearly identified within the 100~155 Hz frequency band, and the reflection profiles revealed significant undulations in the subglacial topography beneath the Larsemann Hills ice sheet. The experimental results demonstrate that the towed geophone system is well suited for efficient and lightweight seismic exploration on the Antarctic ice sheet. This study provides a promising technical solution for future large-scale seismic investigations of Antarctic ice sheets and subglacial geological environments.
Aiming at the problems of low efficiency in measuring electrical parameters of rock and ore by frequency conversion method and narrow frequency band of existing special equipments, an experimental system for rapid measurement of broadband electrical parameters of rock and ore specimens based on Invert-Repeated m-Pesudo random Binary Sequence(IRmPRBS) is designed. The frequency distribution characteristics of pseudo-random coded signals and multi-frequency data processing methods are used to achieve broadband rapid measurement. The impedance measurement accuracy of this system was verified by comparison with the resistance-capacitance (RC) model measurement results from a Zurich MFIA impedance analyzer. Furthermore, cylindrical specimens were self-made using materials such as cement, tailings sand, graphite, and copper particles. Experiments on measuring the complex resistivity of these specimens were conducted, and the differences in complex resistivity characteristics between dry specimens and specimens treated with water immersion were discussed. The results show that: The effective frequency band range of the system covers 1 mHz to 110 kHz; The impedance measurement results of the RC model are accurate and reliable; when a 5th-order signal is used, the measurement efficiency is 2.3 times that of the frequency conversion method, and the measurement efficiency can be further improved by adjusting signal parameters; The multiple measurement results of the self-made specimens show good consistency; the magnitude of complex resistivity of the specimens before and after water immersion differs by dozens of times, and the extreme point of the phase curve of the water-immersed specimens shifts to the right; Among the measured specimens, the graphite specimen exhibits the most obvious polarization phenomenon, followed by the silicon particle specimen, which also shows a distinct double-peak feature in its phase curve. This system provides a wide-band and rapid measurement method as well as technical support for the measurement of electrical parameters of rock and mineral specimens.
This study aims to address key limitations of traditional formation testers in complex reservoir evaluation, specifically the inability to perform multi-azimuth pretests at a single depth for in-situ anisotropy characterization, and the lack of an efficient recovery mechanism after probe seal failures. To bridge these gaps, a novel integrated formation tester (EFDT-Union) was developed, which enables pretesting, fluid sampling, and site-wall coring in a single downhole run. The core innovation lies in a newly designed adapter module enabling a "dynamic probe rotation-stationary tool anchoring" mechanism. This allows the probe module to rotate freely by 0°~360° circumferentially while the main tool body remains azimuthally fixed, representing a pioneering technology nationally and internationally. To underpin the data interpretation workflow for this tool, an analytical solution for circular-probe pressure diffusion equations incorporating anisotropic probe coefficient formulas was established and validated with numerical simulations. A combined mobility estimation strategy synergizing the drawdown method (based on steady-state flow principles) and the buildup method (utilizing pressure transient analysis) was proposed. The buildup method employs derivatives of pressure with respect to specific time functions (characterized by slopes: -0.5 for spherical flow, 0 for radial flow) to identify flow regimes and employs linear regression to extrapolate formation pressure. Validation results confirmed high consistency between analytical and numerical solutions with pressure derivative deviations less than 5% during spherical and radial flow regimes. Field applications at an oilfield in the South China Sea successfully demonstrated: (1) Multi-azimuth pretesting at identical depths (e.g., at depth of 3370.3 m, a mobility ratio of approximately 1.67 between azimuths 172° and 350° was observed, providing direct evidence of near-wellbore in-situ permeability anisotropy). (2) The capability for rapid probe reorientation and resealing post-seal failure (e.g., a successful 89° rotation followed by a pretest at depth of 3369.9 m after an initial seal failure at 350°), ensuring complete data acquisition efficiently. Pressure transient analysis (PTA) at these depths illustrated significantly different spherical flow development times and mobilities between azimuths (e.g., ~301.6% mobility difference at 3370.3 m). Importantly, formation pressure values derived from drawdown and buildup methods are in very good agreement at the identical depth with average absolute errors less than 0.007 MPa (specifically 0.0069 and 0.0015 MPa in the field application), and mobility values derived from drawdown and buildup methods exhibited order-of-magnitude consistency with relative errors less than 31% (specifically 7.0%, 28.9%, and 30.8% in the field application), jointly corroborating the reliability of the interpretation methodology and the effectiveness of the tool design. In conclusion, the EFDT-Union system equipped with its novel probe rotation mechanism and its integrated interpretation methodology, significantly enhances pretest efficiency and data integrity in complex heterogeneous reservoirs through multi-azimuth testing at identical depths and relocation-enabled probe resealing at matching depths following initial seal failures, thereby effectively mitigating operational risks and reducing costs.
As an essential technique for near-surface structure investigation, the uphole survey has traditionally employed a "downhole excitation-surface reception" acquisition approach.In contrast, the "surface excitation-downhole reception" mode offers distinct advantages including flexible source selection, lower operational costs, and enhanced safety. However, the coupling issue between downhole geophones and formations has long hindered the widespread adoption of this method. This study proposes an innovative downhole geophone coupling method based on a multi-polar pushing system with circular spring tubes, effectively addressing the adaptability limitations of conventional airbag inflation and mechanical pushing approaches. By implementing cable relaxation, cable wave interference was successfully eliminated. The adoption of a geoelectric signal triggering method resolved the time-delay issues associated with hammer source short-circuit triggering. High-quality deep uphole survey data acquisition was achieved using hammer sources under complex surface conditions in loess tablelands and desert areas. The designed heavy hammer source meets the energy requirements for ultra-deep uphole surveys while maintaining excellent wavelet consistency, providing high-quality raw data for Q-surveys in loess tableland ultra-deep wells..
In the processing of Ocean Bottom Node (OBN) seismic data, suppressing surface multiple is one of the key factors affecting the quality of imaging. In OBN acquisition, due to the special geometry where seismic sources are positioned near the free surface and receivers are deployed on the seafloor, conventional surface multiple suppression methods based on wave theory (such as Surface-Related Multiple Elimination, SRME) are not applicable, as they generally assume that sources and receivers are coplanar and evenly spaced, and rely on full wavefield data. To overcome these limitations, this paper proposes a surface multiple prediction method based on demigration theory. By incorporating information such as migrated sections and velocity fields, the method uses one-way wavefield extrapolation in common node gathers to simultaneously predict all orders of surface multiples generated between the seabed, sub-seabed interfaces, and the free surface. Additionally, Phase Shift Plus Interpolation (PSPI) is introduced to handle the lateral velocity variations of complex subsurface geology. The results from synthetic models and field data examples demonstrate that the surface multiple models predicted by this method are highly consistent with those observed in the original data, highlighting the method's significant practical value for the suppression of multiples in OBN seismic processing.
The fault-fracture reservoirs in Chengdao oilfield contain faults and fractures, with excellent oil sources and high single-well production. Due to their great depths, the poor quality of conventional migration data, and inadequate imaging of the buried hill interiors, there are significant difficulties in accurate reservoir prediction and characterization. To address this challenge, this study proposes a novel method for characterizing buried-hill fault-fracture reservoirs using full-azimuth angle data with the ES360 processing system. The workflow begins by comparing reservoir responses across different reflection and azimuth angles to select optimal seismic gathers. Following flattening along the strong reflection interface at the top, wavenumber-spatial domain filtering is applied to remove the weathering crust and overlying/underlying sedimentary layers, thereby enhancing features related to oil-and gas-filled fractures and cavities within the buried hills. Subsequently, the first eigenvalue is computed from the background-removed data volume, converting reflection event characteristics into three-dimensional structural representations of the fault-fracture system. Finally, the eigenvalue volume is structurally restored to its original stratigraphic configuration via inverse flattening, achieving effective interpretation of the fault-fracture reservoirs. Application to actual seismic profiles and attribute analysis along the target layer demonstrates that the proposed method effectively highlights the characteristics of steeply dipping buried-hill fault-fracture reservoirs. This approach provides a new conceptual framework applicable to other geologically similar regions.
OBN (Ocean Bottom Node) seismic exploration technology, as an important innovative method in the field of marine oil and gas exploration, boasts significant advantages over traditional towed-streamer seismic exploration: it not only effectively compensates for the exploration blind areas that are difficult to cover by traditional technologies, but also provides richer subsurface geological information for oil and gas resource detection under complex geological conditions through its multi-wave and multi-component acquisition capability, wide-azimuth observation characteristics, and flexible mode supporting fixed-point and repeated observations. However, OBN seismic acquisition technology still has prominent limitations in practical applications: constrained by the engineering difficulty and cost control of seabed node deployment, the distribution of receiving nodes is often relatively sparse, and direct reception of seabed reflection signals is not possible due to the influence of acquisition geometry. These two issues directly lead to poor imaging effects of the seabed interface and shallow geological bodies in OBN seismic data, characterized by insufficient resolution and poor event continuity, which severely restricts the promotion and application of OBN technology in scenarios such as shallow oil and gas reservoir exploration and seabed geological hazard assessment.To address this key technical bottleneck, this paper proposes a downward-wave migration method based on shot-domain Kirchhoff integral. The core innovation lies in fully exploring the application value of multiple reflections—As signals formed by multiple reflections of seismic waves at subsurface interfaces, multiple reflections not only carry abundant information about the lithology and structure of subsurface media but also have a wider subsurface coverage than primary waves, which can just make up for the coverage shortcomings of OBN primary wave acquisition. The method achieves imaging optimization through three core technical steps: Firstly, the complex wavelet domain dual-sensor (hydrophone and geophone) merging technology is adopted, leveraging the time-frequency localization advantage of complex wavelet transform to accurately separate the upgoing and downgoing wavefields in seismic data, providing high-purity wavefield data for subsequent migration processing. Secondly, aiming at the common non-coplanar distribution of shot points and receiver points in OBN exploration, a step-by-step calculation strategy is designed to separately solve the travel time from the shot end to the reflection interface and from the receiver end to the reflection interface, effectively adapting to the complex undulating seabed terrain and avoiding imaging deviations caused by terrain effects. Finally, an optimized approximate weighting coefficient is introduced to simplify the calculation process, ensure processing efficiency, and maximize the retention of seismic wave amplitude information, achieving amplitude-preserved imaging and providing a reliable basis for subsequent reservoir parameter inversion.To verify the effectiveness and practicality of the method, this paper conducts forward model data testing and actual OBN exploration data validation: the forward model is constructed based on a typical complex seabed geological model, simulating exploration scenarios containing shallow faults, seabed uplifts and other geological bodies; the actual data is derived from an OBN acquisition project in a deep-sea oil and gas exploration block. The test results show that compared with traditional effective wave imaging methods, the method proposed in this paper uses water-layer first-order multiple reflections for migrated imaging, which can significantly improve the clarity of the seabed interface and the imaging resolution of shallow geological bodies, effectively fill the shallow blind area of OBN primary wave imaging, and ultimately obtain seismic profile data with better quality and more complete information. This research achievement successfully solves the core problem of poor imaging effects of the seabed and shallow layers in OBN technology, provides reliable technical support for oil and gas resource exploration under complex seabed geological conditions, expands the application scenarios of multiple reflections in seismic imaging, and strongly promotes the large-scale application and development of OBN technology in the field of marine oil and gas field exploration.
Against the backdrop of the global oil and gas industry's strategic shift towards deepwater exploration, marine acquisition technologies are continually evolving. Ocean Bottom Node (OBN) acquisition has emerged as a key technique, offering advantages over conventional towed-streamer methods such as flexible deployment, rich low-frequency content, and full-azimuth coverage, making it essential for imaging deep and complex reservoirs. However, the benefits of multi-component OBN acquisition also introduce significant processing challenges, notably the shear-wave noise unique to the vertical geophone (Z) component and the ghost wave interference caused by sea-surface reflections in both the hydrophone and geophone data. This paper presents targeted techniques for jointly attenuating shear noise and ghost waves: firstly, a 3D sparse transform and complex wavelet domain joint shear-wave noise model is established to achieve precise noise attenuation; subsequently, based on the high-fidelity characteristics of the hydrophone and geophone data, a novel refracted wave matching algorithm is innovatively employed to perform dual-sensor combination for effective deghosting. The proposed methods were applied to real data processing from a block in the western South China Sea, delivering effective noise suppression and significantly improving the imaging quality of weak reflectors in the mid-to-deep layers.
Seawater, being a highly conductive medium, generates induced electromagnetic fields in space when moving within the geomagnetic field. This phenomenon increases the difficulty of electromagnetic exploration for offshore oil, gas, and mineral resources. To quantitatively calculate the distribution of the wave-induced magnetic field generated by ocean waves at different sea depths, this study establishes a three-layer model (air layer, seawater layer, and seabed layer) based on marine electromagnetic theory, describing the wave motion system. By applying the boundary condition that the normal component of the magnetic field is continuous across interfaces, a model for the monochromatic wave-induced magnetic field in an ocean of arbitrary finite depth is derived. When the total ocean depth approaches infinity, the calculated results from this model coincide with those of Weaver's infinite-depth ocean wave model, demonstrating the model's validity. Utilizing this validated model, the influence of total ocean depth on the monochromatic wave-induced magnetic field is analyzed. Furthermore, building upon the monochromatic wave model, a wave spectrum is introduced to investigate polychromatic wave-induced magnetic fields. The distribution characteristics of these polychromatic fields, possessing identical total energy but varying frequency ranges, are computed. The results indicate that: (1) For monochromatic waves at different total ocean depths, the wave-induced magnetic field exhibits a maximum value as a function of depth within the water column, and the variation of the field with wave period is significantly affected by the total ocean depth; (2) The distribution characteristics of polychromatic wave-induced magnetic fields across different total ocean depths are similar to those of monochromatic waves but are markedly influenced by the frequency range of the wave spectrum. The modeling approach proposed in this study provides an effective means for predicting the distribution of wave-induced magnetic fields in oceans of varying total depths. This offers a reliable theoretical basis for advancing marine electromagnetic detection technology and enhancing the precision of marine resource exploration.
The hidden geological disasters that occur after mining in mining areas, affected by factors such as site topography and surrounding electromagnetic environment interference, pose challenges to conventional ground geophysical prospecting methods, including difficulties in construction, low efficiency, and poor anti-interference ability. The Drone Airborne Transient Electromagnetic (DATEM) method possesses characteristics of high efficiency, convenient construction, and strong adaptability, offering broad application prospects in the fine detection of concealed disasters. This paper applies the drone airborne transient electromagnetic method to the fine detection of concealed disasters in metal mines for the first time. Firstly, the principle and data processing method of the drone airborne transient electromagnetic method are introduced. The planned terrain-following flight technology is used to ensure field flight safety and signal quality. The nonlinear optimization inversion method can effectively distinguish low-resistance anomalies. Then, the effectiveness analysis of the drone airborne transient electromagnetic method in the detection of concealed disasters in metal mines is carried out. Combined with the results of ground detection methods, it shows that this method is accurate and reliable. Finally, the drone airborne transient electromagnetic method is applied to the detection of concealed disasters in metal mines. Combined with the known data of the mining area, it is concluded that the drone airborne transient electromagnetic method can better distinguish the spatial distribution of concealed disaster bodies such as goaves and ore body enrichment areas. The detection results have high anomaly resolution, and the low-resistance anomaly detail effect is more obvious. The research work provides a fast, efficient, and accurate method and technology for fine detection of concealed disaster bodies.
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