Abbreviation (ISO4): Prog Geophy
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Based on ROCSAT-1 satellite observations from 2000 to 2003, this study uses a new method to identify plasma irregularities (plasma bubbles and plasma blobs) and their longitudinal ranges in low and middle latitude ionosphere. Furthermore, the distributions of longitudinal ranges of plasma irregularities are analyzed depending on local time, season, geographic longitude, and geographic latitude. The results show that, during different seasons, mesoscale plasma bubbles and plasma blobs (0.45°~°, about 50~1000 km) constitute over 90% of the plasma irregularities in low and middle latitude ionosphere, with longitudinal ranges primarily concentrated within 1° ~4°, exceeding more than 70% of the total irregularities. Moreover, plasma blobs and its longitude ranges exhibit noticeable variations with local time, geographical longitude and season. The peak occurrence time of plasma blobs is slightly delayed compared to that of plasma bubbles, occurring approximately one hour later. The occurrence frequency of plasma blobs is highest during the summer solstice (June solstice), primarily distributed in the -180°E to -100°E, -30°E to 60°E, and 90°E to 180°E longitude sectors. The second-highest occurrence is during winter solstice, predominantly appearing in the -180°E to 0°E longitude sectors. Additionally, plasma blobs occur preferentially in the winter hemisphere rather than in the summer hemisphere, and they also show a preferential occurrence in the autumn hemisphere compared to the spring hemisphere. Comparing the characteristics of plasma blob with that of plasma bubble, this study suggests a potential connection between plasma bubbles and plasma blobs, indicating that plasma blobs may appear with the evolution of plasma bubbles. Moreover, plasma blobs can also be generated and appear independently.
The high-frequency mass variations of the atmosphere and oceans have significant impacts on the inversion of time-variable Earth gravity fields using GRACE. This paper comprehensively compares and analyzes the atmospheric and oceanic components, as well as their combination, of the AOD1B RL06 and RL07 products released by GFZ using methods including spectral domain analysis, comparison of low-order terms, spatial analysis, principal component analysis, and inversion of time-variable gravity field models. The results show that the differences between the two sets of products in the spectral domain are relatively small, with the main differences being reflected in the oceanic component. However, the comparison results in the spatial domain indicate significant differences in the equivalent water heights of the oceanic component, reaching the decimeter level. The differences between the 60th order time-variable gravity field models inverted based on RL06 and RL07 are relatively small, with differences in the spectral domain mainly concentrated in medium to high orders. The RMS differences of the KBRR residuals after validation for both sets of time-variable models are less than 3.912 nm/s (correlation coefficient: 0.999). However, the KBRR residuals computed based on RL07 products are generally smaller, demonstrating the slight advantage of RL07 in the inversion of time-variable Earth gravity field models.
Owing to variations in data sources, inversion methods, and data processing strategies in global geopotential models (GGMs) derived by different institutions, there exists divergence in precision across different regions. Consequently, integrating information from various GGMs holds significant practical value for deriving a more robust GGM with consistent precision. This study combines GGMs such as XGM2019e_2159 and EIGEN- 6C4 through arithmetic mean, weighted mean aggregation, and variance component estimation (VCE) techniques, with respecting to a reference model. Moreover, the evaluation encompasses GNSS leveling assessments alongside DTU17 marine gravitational anomalies and regional geoid modeling analyses. The results of the comparative analysis demonstrate that: (1) Whether to rescale with respect to the reference model has a relatively minor impact, with a maximum impact of 1.4 mm on height anomalies while affecting gravimetric anomalies by 0.05 mGal; (2) VCE (Scheme a) and arithmetic mean methods yield comparatively stable GGMs showcasing strong performance across all regions according to external validation from multiple sources.
The spatial and temporal distribution of the palaeomagnetic data of the South China Block is uneven, which limits our understanding of the paleogeography position of the South China Block in the Early Paleozoic. To search for suitable strata for systematic study of paleomagnetism, this study carries out a combined study including petrography, rock magnetism, and magnetic fabric on the mudstone-siltstone of the Cambrian strata from the Zhenba area in the northwestern edge of the South China. The results show that the magnetic minerals of the strata are mainly composed of titanium-bearing hematite or magnetite and magnetic pyrite, while the magnetic fabric is characterized by depositional-weakly deformed magnetic fabric. It is concluded that most of the Cambrian strata in this area have not been tensely modified by tectonic activities, and it is expected to retain the primary magnetic component acquired during deposits, which makes it possible to obtain reliable paleomagnetic data.
The Wudalianchi volcanic group is one of the youngest volcanic groups among the Cenozoic ones. As a typical example of intracontinental monogenetic volcano, it is characterized by clusters of small volcanic cones. A 1:5000 high-precision gravity survey is carried out in the western part of Wudalianchi to obtain the fine distribution features of regional magma chambers. This paper introduces the gravity density inversion method constrained by spectrum characteristics to obtain a three-dimensional underground density model of the region, and provides the spatial distribution of magma chambers based on their low-density features. There are three magma structures extending along northeast faults 5~10 km west of the Wudalianchi volcano, which appear as medium-high mountain landforms. There are two large magma chambers below the crater, and a large magma chamber at the depth of 30 km, which effectively supplies the formation of Wudalianchi volcano. Based on the density inversion results of this high-density gravity survey, it is revealed that magma structures exist at different depths in the Wudalianchi volcanic area, and it is proved that there are different stages of magma activities in this area, and that the formation of volcano is closely related to its faults. Such discoveries will provide important basic data for the further studies of the volcano.
The assessment and analysis of ecosystem service functions are of great significance for the rational allocation and optimization of regional resources. Taking Cangzhou City as the research object, the ecosystem service functions in 2005, 2015, and 2020 were evaluated based on the InVEST model. The Spearman correlation coefficient was used to analyze the trade-off and synergy between ecosystem services, and the self-organizing mapping method was used to identify ecosystem service bundles. The research results indicate that the water yield in Cangzhou City shows a trend of first increasing and then decreasing, while the N/P output first decreases and then increases, while carbon storage and habitat quality remain stable with some improvement. There is a significant synergistic relationship between water yield and N output, water yield and P output, N output and P output, carbon storage and habitat quality. There is a significant trade-off between water yield and carbon storage, water yield and habitat quality, N output and carbon storage, N output and habitat quality, P output and carbon storage, and P output and habitat quality. There are three ecosystem service bundles in Cangzhou City, namely ESB1 focused on water yield and N/P output, ESB2 focused on habitat quality and carbon storage, and ESB3 focused on cultural tourism.The research results provide decision-making basis for improving the overall efficiency of ecosystem services and differentiated management of ecological functional areas in Cangzhou City.
CT images of rocks could reflect the structural characteristics of pores and fractures. Processing of CT images and extracting geological information are crucial for studying the pore structures of rocks and analyzing their physical responses, which would enhance our understanding of rock characteristics aiding in resource exploration and geophysical research. The principles, applicable conditions, application effects, and influencing factors of rock CT image processing methods were all reviewed. The image processing algorithms are categorized into three types, digital image-based processing methods, machine learning-based processing methods and super-resolution image processing methods. The results indicate that: (1) The digital image processing methods are straightforward and easy to implement, with a wide range of applications, however, rely heavily on prior knowledge from human operators; (2) Machine learning-based image processing methods could automatically, objectively, accurately, and quickly extract information from images, nevertheless, there is a shortage of rock CT image data and the generalization ability of models needs to be improved; (3) Super-resolution image processing methods could effectively improve the clarity and details of the image, but their effectiveness in enhancing resolution is limited by the imaging conditions of the rock samples. Integrating artificial intelligence methods offers the potential for the intelligent analysis and automatic recognition of rock CT images, which would accelerate the acquisition and interpretation of geological information.
In the process of horizontal well drilling, timely adjustment of the stratigraphic model based on Logging While Drilling (LWD) data is crucial for optimizing the drilling process and improving efficiency. In this paper, we propose an automatic adjustment method for the stratigraphic model while drilling. The method establishes an initial geological model based on pilot well logging data and extracts logging response characteristics between formations. During the drilling process, wavelet transform is used to segment the real-time logging response curves. The logging curves are reconstructed by integrating multiple types of information from time series, and then the Dynamic Time Warping(DTW) lower bound function and fastDTW algorithm based on early abandonment are applied to quickly search for the optimal matching segment within the constructed candidate sequence dataset, achieving formation comparison between the horizontal well and the pilot well. Finally, the stratigraphic model is adjusted based on the results of the formation comparison, thereby obtaining the true subsurface geological structure. The application results show that after adjustment using the method proposed in this paper, the morphological trend changes of the measured and simulated curves on the horizontal channel match closely, meeting the accuracy and real-time requirements for stratigraphic model adjustment while drilling.
Seismic exploration has gradually turned to deep formation and complex geological structures. Because the heterogeneity of the formation medium affects the propagation of reflected waves, the seismic data have the property of complex interference. It is difficult to achieve the requirements of high-precision image. The related methods of data processing have been widely researched and applied with the development of EMD algorithm. This algorithm has also improved CEEMDAN algorithm, which effectively solves the mode mixing problem of EMD. But CEEMDAN algorithm still has shortcoming in the ability to filter noise. The algorithm based on CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise) and permutation entropy is using by the seismic data having the property low signal-to-noise ratio. Firstly, the seismic signals were decomposed by CEEMDAN to obtain Intrinsic Mode Functions (IMF) from high frequency to low frequency components. Then, the IMF components are sorted using permutation entropy to retain useful information for reconstruction.Subsequently, the reconstructed signal is decomposed using CEEMDAN, and the IMF components suitable for reconstruction are selected by comparing parameters such as approximation degree, smoothness, and objective function value. This achieves effective suppression of complex interference signals by combining the permutation entropy algorithm. The results show that compared with the traditional EEMD algorithm, the CEEMDAN-PE algorithm has higher computational efficiency, not only solves the mode mixing problem, but also effectively protects the high-frequency effective signal of the reflected wave while suppressing noise. Moreover, it can effectively screen out abnormal signals by determining whether they exceed the permutation entropy value and whether the objective function value is closer to zero, ensure the accuracy and stability of the reconstructed signal, and provide an effective processing method for improving the signal-to-noise ratio of seismic signals.
Impedance inversion based on convolution models generally involves inversion of reflection coefficients before impedance inversion, and there are few studies that directly invert impedance using wave equations. Given that wave equation based methods have higher accuracy, this paper proposes a wave impedance inversion method based on wave equations. To ensure the lateral continuity of the impedance inversion model, Fourier series expansion is used in this paper to represent the wave impedance model using Fourier coefficients, which are used as inversion parameters; In the inversion process, an inversion strategy was designed to transition from low wave numbers to high wave numbers and to partition data groups. This not only ensures the inversion from the background wave impedance model to the fine wave impedance model, but also effectively avoids data redundancy, greatly improving computational accuracy and efficiency. The test results of synthetic seismic records and actual seismic data show that the method proposed in this paper has a significant ability to characterize the local edge features of "bead like" reservoirs, which can provide data support for the characterization and early reserve evaluation of fractured reservoirs.
Multi-stage faults are developed in the Shanxi Formation-Taiyuan Formation, the main strata in the northwestern margin of the South North China Basin. In this area, the heterogeneity is strong, the fracture formation mechanism is complex, and the small-scale hidden faults are relatively developed. The small-scale fracture boundary characteristics obtained by conventional prediction methods are fuzzy and the accuracy is low, which seriously restricts the development process of deep coal measure gas. Therefore, it is urgent to find a concealed structure prediction method suitable for the study area. Taking JF1 well area as an example, this paper proposes a method of hidden fracture identification based on texture analysis of gray level co-occurrence matrix and Mini Batch K-means deep clustering. Firstly, this paper uses time-varying frequency division deconvolution technology to carry out frequency expansion processing, and obtains broadband post-stack seismic data. Then, by optimizing the scale and gray level of the three-dimensional sliding window, the gray level co-occurrence matrix is generated in the sliding window according to the specified true dip angle and azimuth angle, and the texture features of entropy, difference, uniformity and energy are extracted respectively. The texture attributes based on true dip angle and azimuth angle constraints are used as sample data. The initial clustering center of Mini Batch K-means is set, and the small batch data subset is optimized to establish a Mini Batch K-means deep learning model suitable for JF1 well area. Finally, based on intelligent ant colony algorithm optimization.
Seismic impedance inversion is an effective method for oil and gas reservoir prediction and has been widely used in industry. Although the technique is very mature, it still faces the problems such as strong ill posedness, poor lateral continuity, and low resolution. The structure-oriented regularization proposed by predecessors can effectively reduce ill posedness in the least-squares inversion process and enhance the lateral continuity of the inversion results, but it cannot fully preserve the geological boundaries and may even cause damage to discontinuous signals such as faults. In order to enhance the ability of inversion algorithms to depict geological structure details and highlight the "block" characteristics of the impedance model, we introduce edge-preserving filter during the inversion iteration process, and use a weight parameter to control the sharpness of model update. For each sampling point in the impedance model, edge-preserving filter searches for the most uniform window around it and assigns the average value of all samples within that window to that point. This processing can preserve sharp interfaces from blurring and significantly improve the resolution of inversion results. The results of model and field data testing show that the proposed method can significantly improve the ability to characterize the structure boundaries and has excellent performance in enhancing the discontinuity features of impedance inversion results.
Research has shown that seismic waves usually undergo different degrees of velocity dispersion and attenuation when they encounter hydrocarbon-bearing reservoirs during propagation, which also leads to a close correlation between the reflection coefficient and frequency. Therefore, we can utilize the velocity dispersion property extracted by hydrocarbon-bearing reservoirs AVO inversion for fluid identification. Frequency-dependent AVO inversion is performed based on the amplitude spectrum obtained from the time-frequency analysis of seismic data. The resolution and accuracy of the time-frequency analysis are critical factors influencing the results of dispersion attribute inversion. In recent years, time-frequency analysis methods based on sparse representation have gained attention due to their high time-frequency resolution. This paper proposes a more flexible sparse time-frequency analysis method based on compressed sensing theory and constrained by the LP quasi-norm. Numerical models demonstrate that this method achieves higher resolution time-frequency distributions, making it suitable for seismic signal analysis. By integrating this LP quasi-norm sparse time-frequency analysis method with frequency-dependent AVO inversion, it is possible to accurately extract P-wave dispersion attributes, thereby identifying fluids in reservoirs. Field data validation shows that the frequency-dependent AVO inversion method based on sparse time-frequency analysis not only provides high resolution but also offers reliable fluid indicators for hydrocarbon reservoirs, offering strong technical support for the identification of complex reservoirs.
This study addresses the challenges in logging evaluation of the Saling Formation reservoirs in Block X of the Songliao Basin, where the outdated logging suite lacks NMR data and exhibits significant discrepancies in measured parameters. Fundamental curves such as natural gamma ray, lateral resistivity, and acoustic time difference are absent in partial wells, imposing substantial limitations on reservoir evaluation. Through analyzing the correlations among lithology, physical properties, oil-bearing characteristics, and electrical responses, calculation models for mineral components (shale/calcium contents) were established. Additionally, porosity, permeability, and original water saturation models were constructed for different logging series, adapted to various logging series. Physical property thresholds for effective thickness (porosity ≥20%, permeability ≥4×10-15 m2) and electrical property boundaries were determined using oil testing and oil occurrence methods, enabling reservoir classification into Types Ⅰ, Ⅱ, and Ⅲ. Results demonstrate that micro-potential resistivity can effectively substitute for natural gamma ray in mineral component calculation. The sweet spot area identified by multi-parameter overlay reaches 23.14 km2, providing a scientific basis for sweet spot optimization and well location deployment. This research establishes a systematic logging evaluation framework to overcome data scarcity, offering practical guidance for similar reservoir exploration in mature oil basins.
Microseismic signals generated by minor fracturing or deformation in rock masses are often weak and significantly affected by environmental noise, making it challenging to accurately identify effective signals and locate the fracturing source spatially. To eliminate noise superimposed on the fracturing signals and improve the Signal-to-Noise Ratio (SNR) of weak microseismic signals, this paper proposes a denoising method that combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Singular Spectrum Analysis (SSA). First, CEEMDAN is used to obtain the Intrinsic Mode Functions (IMFs) of the signal, and energy entropy is employed to optimize the signal components, removing low-frequency noise. Then, SSA is applied to the reconstructed signal to decompose it into components corresponding to different singular values. Using singular values, the reconstructed components are determined, and the final reconstructed signal achieves secondary filtering. The study is of significant importance for analyzing the location of weak microseismic events induced by fracturing in rock slopes and monitoring the dynamics of landslide hazards.Based on the theoretical and experimental results, the following conclusions can be drawn: (1) The traditional EMD method shows poor frequency separation effect when decomposing weak signals. Due to the strong coupling between microseismic weak signals and random noise, modal aliasing occurs in the components.(2) The simulation results of noisy sinusoidal function waveforms indicate that the SNR of the simulated waveform before denoising was 11.34 dB. After applying this method, the SNR improved to 21.53 dB, the root mean square error was reduced by 74.24%, and the signal energy was maintained at 98%. This method demonstrates a significant denoising effect.(3) Denoising of microseismic signals generated by hydraulic fracturing in the SF-6 well of the Fuling shale gas field in Chongqing shows that the high-frequency band denoising effect is superior to that of EMD and EMD-wavelet threshold methods.(4) Denoising experiments on three microseismic signals effectively removed the background noise, preserving the characteristics of the microseismic weak signals.
Reservoir pore structure has an important influence on seepage capacity and oil production capacity. Mercury injection experiment is an important method to study reservoir pore structure, but it cannot be carried out in large quantities due to factors such as limited number of cores, high experiment cost and mercury toxicity. Through the analysis of the mercury injection experiment data, it is found that a series of mercury injection pressure is generally a fixed distribution, and the adjacent mercury injection saturation has a good correlation. As long as a mercury injection saturation of a depth point is predicted, and the entire pseudo capillary pressure curve can be predicted for that depth. XGBoost is adopted to predict the mercury saturation, and then the pore throat radius spectrum is obtained. The rock surface relaxation rate is determined by overlapping the pore-throat radius spectrum of the mercury injection experiment and the T2 spectrum of the NMR experiment, and the two cut-off values for distinguishing small, medium and large pores on the T2 spectrum are converted into the two cut-off values of the pore-throat radius spectrum. Using two cut-off values to divide the pore throat radius spectrum into three parts, the pore structure index Rc_index is proposed. This parameter has a good correlation with the oil layer production measured by the cable formation tester. It is concluded that the pore structure index Rc_index predicted by conventional logging curves can continuously predict reservoir production and guide follow-up measures such as test layers selection.
Due to the lack of shear wave time difference data in the study area and the poor accuracy of conventional shear wave time difference prediction methods as a result of coal seam hole enlargement, the AdaBoost algorithm was introduced to optimize BP neural network method for predicting shear wave time difference. The prediction model of shear wave time difference is established by selecting the sensitive logging curves and setting the best model parameters to improve the prediction accuracy of shear wave time difference. Also, the prediction effect of shear wave time difference by multiple linear regression method, BP neural network method and AdaBoost optimized BP neural network algorithm is compared. The rock mechanical parameters and brittleness characteristics of coal seam are evaluated by the predicted shear wave time difference, and the types of coal structure are classified according to the relationship between the brittleness index and the Young's modulus. The results show that the BP neural network optimization model based on AdaBoost can predict the shear wave time difference efficiently, and the average relative error of the prediction results is 2.7%. The brittleness index is calculated by the predicted shear wave time difference and the coal structure type is identified, which is in good agreement with the core description. This approach can effectively improve the prediction accuracy of shear wave time difference and provide reliable data support for coal seam brittleness evaluation and coal structure identification.
Amplitude spectrum is one of the most commonly used mathematical quantitative analysis tools in seismic data analysis, and its theoretical basis is the Fourier transform. Through the amplitude spectrum analysis, we can convert the time domain data to the frequency domain to observe and compare the data from the two aspects of amplitude and phase. At present, according to the different calculation methods, the spectral analysis of the time domain signal after the Fourier transform mainly has different forms, such as amplitude spectrum, power spectral density, and noise density. This paper according to the corresponding calculation formula of three frequency spectrum dimensions of the form firstly, and then classify the normalization method commonly used in data analysis, and an analysis was conducted on the amplitude spectra of velocity\acceleration geophone, combination effects, and coupling effects. It is believed that when conducting spectral analysis, the dimensions of spectral analysis should be clearly defined, and then compared to reveal the physical properties of spectral analysis results, clarify the mathematical relationship between data performance and physical background, ensure data quality, and improve exploration effectiveness.
The shale oil of Fengcheng formation is key target of exploration and development in Junggar Basin. Dipole acoustic logging shows strong anisotropy which matches the massive microfractures and small amount beddings in core. So far, we do not know the anisotropic characteristics and mechanism of shale oil matrix so that we can't understand the contribution of shale oil matrix through the orientation arrangement of minerals. We measured the velocity anisotropy of 20 samples and analyzes the mechanism. The results showed a weak anisotropy: the range of ε was from 0 to 0.17, the range of γ was from 0.01 to 0.116 and the range of ζ was from 0.008~0.11. The tuff and tuffaceous sandstone had the weakest anisotropy and crystal powder dolomite had the strongest anisotropy. The mechanism was the orientation arrangement of calcite, felsic mineral and calcite, and cinerite and calcite. There was negative correlation between anisotropic parameter and porosity. Thus, it is necessary to define new anisotropic parameters by divided by porosity in order to eliminate the effect of porosity. There was positive correlation between ε and γ for all types of shale. This study can be used for comprehensive interpretation of well logging and seismic inversion.
Wide Field Electromagnetic Method(WFEM) is an important means of middle and deep mineral exploration. According to the characteristics of WFEM data, a laterally constrainted pseudo-two-dimensional algorithm (SD-WLCI) based on skin depth weighting is proposed in this paper. Through two two-dimensional theoretical models, it is proved that the algorithm can effectively improve the horizontal continuity of deep inversion results in WFEM and improve the resolution of layer interface. In addition, the influences of three regularization factor search methods (linear search method, cooling method and adaptive regularization factor method) on this algorithm are compared in detail. The results show that the linear search method is superior to the other two methods in terms of work efficiency and stability within the comparison range. Finally, this paper applies the skin-depth weighted lateral constraint algorithm to the inversion of wide-field electromagnetic data measured in a mining area of Dongguashan, Tongling. The inversion results are highly consistent with the actual geological conditions, which provides a reliable geophysical basis for the subsequent prediction of middle-deep mineral resources and borehole location. By comparing with the traditional lateral constraint inversion results, The practicability and necessity of this algorithm in the inversion of the measured data of wide-field electromagnetic method are proved.
Low-permeability tight gas reservoirs are rich in reserves, but their natural gas spatial distribution prediction is extremely challenging due to the complex factors such as reservoir heterogeneity, anisotropy, low porosity, and low permeability. Especially under conditions of limited well data, the lack of core test data, unclear logging and seismic response mechanisms, and insufficient geological understanding restrict the accuracy of gas content identification in low-permeability tight reservoirs. Therefore, this paper proposes a method for gas content tight reservoirs based on the Modern TCN deep learning algorithm under conditions of few wells.First, the sensitive parameters for gas content response are analyzed using well log data, such as sonic time difference (DT), shear sonic time difference (DTS), and density (ρ). Second, the Modern TCN (Modern Temporal Convolutional Network) deep learning network is constructed, with the sensitive parameters as the input for model training and testing. Finally, the decoupled design is used to separate the temporal and feature information of sensitive parameters, fully capturing the gas content characteristics of the reservoir and predicting the spatial distribution characteristics of the reservoir. This method was applied to the gas content identification of tight clastic gas reservoirs in the Huangyan structural belt of the Xihu Sag in a certain sea area, achieving a good well-seismic matching effect. It proves that this method can provide support for exploration and development of low-permeability tight clastic gas reservoirs under few-well conditions.
Time-lapse seismic technology plays a crucial role in monitoring changes in the fluid reservoir of offshore oil and gas fields. Conventional time-shifted seismic data collection is required to be repeated, but the actual data collection is difficult to be completely repeated due to different purposes. A lot of 2D and 3D datasets can meet the requirements of time-shift seismic comparison through consistency processing. With the development of multi-component seismic exploration technology, research on time-lapse seismic monitoring by combining multi-component seismic data with early towed streamer data is still limited. In order to further study in time-shift seismic research, This paper conducts a study on non-repetitive time-lapse seismic matching processing based on early towed streamer data and recent Ocean Bottom Cable (OBC) seismic data collected from an oil field in the South China Sea. Due to significant differences in acquisition time, acquisition methods, parameters, and seismic geometry between the two datasets, noise, multiples, and ghost waves exhibit distinct differences. After performing denoising, multiple removal, and ghost wave suppression on the two datasets, the study analyzes their differences, focusing on specific factors such as amplitude, frequency, signal-to-noise ratio (SNR), wavelet phase, and time difference. A mutual equalization method is selected for consistency matching processing of both datasets before pre-stack depth migration. Subsequently, based on the migrated results, it is analyzed whether post-migration consistency matching is required. The results of the study indicate that the raw OBC data has stronger amplitude energy, narrower frequency bandwidth, higher signal-to-noise ratio, and smaller wavelet sidelobes compared to the raw towed streamer data. The consistency processing workflow can effectively match the differences in amplitude energy, frequency bandwidth, signal-to-noise ratio, wavelet phase, and time difference between the two datasets. After consistency matching, both datasets can be used for time-lapse comparison analysis. This research provides a feasible matching processing method for non-repetitive time-lapse seismic studies using multi-component OBC seismic data in conjunction with towed cable data, laying the foundation for subsequent time-lapse seismic studies based on multi-component data.
Shearing wave velocity is a crucial parameter for lithology prediction and fluid identification, playing a significant role in elastic parameter inversion and detailed reservoir characterization. However, due to constraints in exploration costs, direct shearing wave velocity measurements through well-logging are relatively rare and are typically estimated using empirical formulas or rock physics models. The hydrocarbon reservoirs in the Xihu Sag of the East China Sea are characterized by thinly interbedded sand-mud layers with abundant coal seams. The complex mineral composition and pore structure, along with rapid vertical variations in lithology and petrophysical properties, make it challenging to achieve high-accuracy shearing wave velocity predictions using empirical formulas or rock physics models, which often rely on simplified assumptions that fail to capture the complexity of the subsurface. Deep learning, with its powerful nonlinear representation and feature extraction capabilities, can effectively learn the intricate relationships between logging parameters and shearing wave velocity. In this study, a feedforward deep learning artificial neural network is employed to predict shearing wave velocity. Six logging parameters including reservoir depth, P-wave velocity, density, natural gamma, shale content, and porosity are used as input features to construct the deep learning neural model. A multi-layer network is designed to establish a complex nonlinear mapping between these parameters and shearing wave velocity, and an appropriate optimization strategy is implemented for artificial neural model training. This study focuses on shearing velocity prediction in the Xihu Sag of the East China Sea, where the reservoir is characterized by interbedded sandstone and mudstone with thin coal seams. The complex structural features and rapid vertical lithological variations present significant challenges for accurate shearing wave velocity estimation. Training and testing with actual well log data demonstrate that the feedforward deep learning neural network enables high-precision shearing wave velocity prediction for the complex thinly interbedded sand-mud reservoirs with coal enrichment in this region.
Electrical Resistivity Imaging (ERI), a geophysical method based on resistivity contrasts between different media and their sensitivity to water, has become a vital tool for investigating subsurface structures in shallow aquatic environments such as rivers and lakes. Known for its non-invasive nature, environmental friendliness, and high-resolution imaging capabilities, ERI is particularly effective in mapping sediment layers and subsurface strata. However, the complexity of aquatic environments, influenced by factors such as water depth, resistivity, flow velocity, and sediment properties, poses significant challenges in obtaining high-resolution data. To address these challenges, this study explores the performance of ERI in two distinct aquatic settings: the Xinbian River, an artificial river in Suzhou, and the Qingshui Lake, a small inland lake in Yinchuan. This study established five ERI profiles using both suspended and floating electrode configurations. Key prior information, including water depth and resistivity, was integrated as constraints during the inversion process to enhance the accuracy of the resistivity distributions. The results delineated detailed spatial distributions of water, sediment layers, and subsurface formations. In the Xinbian River, the upstream section exhibited a relatively uniform depth with thicker sediment layers and a higher groundwater level compared to the downstream section. The downstream area, significantly affected by seasonal flooding, showed increased riverbed scouring, resulting in thinner sediment layers and a groundwater level approximately 1 meter lower than upstream. In Qingshui Lake, the maximum water depth reached 6 meters, with minimal bottom undulation and an average sediment thickness of about 1 meter. The sediments primarily consisted of clay layers, acting as aquicludes, with a distinct lens body identified in the western region. The underlying sand layer exhibited high water content but showed no significant groundwater activity. The findings underscore the effectiveness of ERI in resolving fine-scale sedimentary and subsurface structural features in shallow aquatic environments. The method excels in identifying sediment thickness, fine-grained lithological distributions, and their physical properties, offering high-resolution imaging without the need for invasive techniques such as drilling. The incorporation of prior information as inversion constraints significantly improves reliability while reducing uncertainties. This study demonstrates the broad applicability of ERI in investigating shallow rivers, lakes, and other aquatic systems, providing critical technical support for pollution monitoring, groundwater research, and hydrogeological and ecological restoration efforts. Future research could focus on optimizing electrode configurations and developing advanced inversion algorithms to further improve the resolution and reliability of ERI in complex aquatic environments.
Road cracks, a common type of road distress, compromise structural integrity, accelerate deterioration, induce secondary disasters, and shorten service life. Ground Penetrating Radar (GPR) is currently the primary technological method for long-distance, engineering-scale, efficient, and non-destructive detection of the internal development of road cracks. The paper systematically reviews the formation mechanisms of road cracks and their impacts on road performance. It analyzes the key technical challenges faced by GPR in detecting road cracks and provides a detailed overview of the latest research achievements and application potential of GPR in detecting attributes of road cracks, such as depth, width, and dip. The review offers technical support and practical guidance for the precise detection and quantitative analysis of road cracks using GPR. It also promotes the application of GPR in detecting hidden road distress and implementing precise remediation measures.
Rayleigh wave inversion is a key method for obtaining the shear wave velocity structure. However, the current traditional inversion method has problems such as relying on the initial model, easy to fall into the local extreme value, and difficult to calculate the Hessian matrix. At the same time, the difference between the Rayleigh wave phase velocity and polarization rate dispersion curves on the sensitivity of the velocity structure parameters limits the accuracy of a single inversion result. Due to the certain characteristics of orderliness and ergodicity of shear wave velocities in various strata, the Markov decision algorithm is thus adopted to simulate all underground structural situations, and the velocity structure is expressed precisely through the adaptive layering method proposed in this paper. On this basis, finally, the dispersion curves of phase velocities and polarizability derived from the forward modeling of the stratum structure are fused in characteristic segments to construct a sample data set; Considering that the dispersion curves of phase velocity and polarizability corresponding to the formation velocity structure possess certain temporal characteristics, a convolutional neural network concatenated with long short-term memory recurrent neural network was built as the backbone network model for joint inversion; After the completion of the model construction, comprehensive and systematic supervised training was carried out on this network. Eventually, a model that can meet the actual requirements and has a relatively good inversion effect was obtained.The model test shows that the relative errors of the joint inversion results are all reduced compared with the single inversion results; the addition of noise in some sample data not only effectively improves the model inversion accuracy but alsoincreases the generalization performance of the model. In practical applications, the method proposed in this paper is applied to the measured data of the 2008 Wenchuan earthquake in Hongkou Shenxigou, Dujiangyan City, Sichuan Province, and good inversion results are obtained, which provide scientific evidence for the localization effect of "co-seismic deformation".
Ground Penetrating Radar (GPR) has been widely used to detect soil structure, soil moisture content, and soil texture. In this article, we establish models of 3D stochastic media in GPR based on soil fractal characteristics and perform GPR forward modeling. We use a 3D Fractal Brownian Motion (FBM) spectral density function to simulate stochastic media and discuss the effects of Hurst exponent and scale coefficients on the modeling results. The Hurst exponent is an important indicator in FBM, which reflects the smoothness of the medium as well as the degree of disturbance. Hurst's exponent is also used to describe the self-similarity of the soil, i.e. the degree of fractal. Soil Water Content (SWC) is a key factor affecting the heterogeneity of soil media, and the distribution of soil dielectric properties also depends on SWC. Therefore, we use SWC data to obtain the Hurst exponent through Rescaled range (R/S) analysis. Finally, a 3D stochastic medium model is established based on actual SWC data, and GPR forward simulation is performed on the established model by the Finite-Difference Time-Domain (FDTD) method. The simulation results show that the proposed stochastic medium modeling based on soil fractal characteristics can provide an effective modeling tool for GPR soil detection.
Ground Penetrating Radar (GPR) has become a pivotal tool in plant root studies owing to its non-destructive nature and operational efficiency. However, rapid and precise identification of root's hyperbolic reflections and wave velocity estimation from GPR data remain constrained by challenges including noise interference, complex hyperbola morphologies, and limited field-measured datasets. To address these limitations, this study introduces Yolov4-HPV (Hyperbolic Position and Velocity), an enhanced deep learning model built upon the Yolov4 framework. The proposed methodology integrates a key-point detection algorithm that identifies five characteristic points of hyperbolic signatures, enabling supplementary wave velocity calculations with improved accuracy. To mitigate training data scarcity, a synthetic data generation framework was developed using gprMax forward simulation software. The framework employs two strategies: (1) a Merge protocol to streamline simulated image synthesis, and (2) a Multi-CycleGAN approach for style transfer, substantially augmenting dataset diversity and model generalizability. The results show that Yolov4-HPV's capability to detect hyperbolas and estimate wave velocities with high precision. The key-points method further improves the accuracy of wave velocity estimation. The key-points method further reduced RRMSE of wave velocity estimation to 3.43%, outperforming Yolov4-HPV's 4.76% for the testing datasets. In control experiments, the average absolute errors of root depth positioning were 4 cm and 3 cm, with average relative errors of 15% and 11%, respectively, confirming the model's high accuracy and robustness. This work advances GPR-based root investigation by enhancing automatic target identification and wave velocity quantification while optimizing computational cost, offering significant methodological improvements for ecological and hydrological applications.
With the rapid development of tunnel transportation in China, the workload of routine maintenance of tunnels has increased dramatically. The use of Ground Penetrating Radar (GPR) to detect tunnel lining diseases is becoming a common method due to its economic, efficient and non-destructive characteristics. But the reflection feature recognition and interpretation from massive radar data collected is time consuming and highly dependent on human experiences. In order to improve the detection efficiency and accuracy, this paper proposed an identification method of the tunnel lining diseases based on LSTM neural network. This method integrated the advantages of LSTM neural networks in the field of image recognition and the characteristics of GPR reflection signals from tunnel lining diseases. Then LSTM neural networks were applied to detect and recognize tunnel lining diseases for the first time. First, the reflected radar signals corresponding to different tunnel disease models were obtained by FDTD numerical simulation. Secondly, the dataset was constructed through different ways, such as changing the type, the thickness and the buried depth of the disease area, and adding noise to the radar signals. Then the whole dataset was randomly divided into the training dataset, the testing dataset and the validation dataset. Thirdly, a neural network was built based on LSTM and the network model parameters and structures were optimized. A conclusion was drawn that the three-layer Bi-LSTM network has higher accuracy and more stable training process. Finally, the neural network was verified, and three parameters (the accuracy rate, the precision rate and the recall rate) were used for the result evaluation. The results show that the identification method based on LSTM neural network can quickly identify two tunnel diseases with high accuracy and stable process. This research provides a new option for the popularization and development of intelligent identification methods of tunnel lining diseases in the future. It also broadens the application area of LSTM neural network.
The reserve and production of oil and gas exploration and development in the global sea areas have been steadily increasing. The growth rate of ultra-deepwater oil and gas production has exceeded that of deepwater, making it a strategic replacement area for global oil and gas resources. Offshore oil and gas are a crucial part of China's oil and gas energy. With the advancement of offshore exploration towards deepwater and ultra-deepwater, it is extremely urgent to improve the technical service guarantee capabilities for exploration and well logging in these areas. Due to the special characteristics of the offshore exploration environment, such as complex structures, diverse lithologies, high temperatures and high pressures, there are special and complex requirements for the functions and performance of well logging instruments, and the technical difficulty is extremely high. Most offshore drillings are cluster wells or multi-branched wells, featuring large deviation angles, large displacements or horizontal wells. This requires well logging instruments to have higher operational efficiency and measurement accuracy. This paper sorts out and summarizes the technical bottlenecks and challenges faced by China's deepwater well logging technology and equipment. Combining with the development status, it puts forward development trends and suggestions, aiming to provide technical support for the exploration and development of China's deepwater oil and gas resources.
The fluxgate sensor is a kind of important equipment to measure the vector magnetic fields. Its working principle is based on the non-linear properties of soft magnetic material. It is used to detect weak magnetic fields. In this paper, we introduce the basic working principle, structure, and main applications of fluxgate sensor. We also discuss the research development of fluxgate sensor, focus on the progress of fluxgate sensor in miniaturization, intelligence, digitization, as well as reducing noise. Some further research directions are also provided.
Airgun source is an important tool for marine geophysical exploration, the construction of the gun body will be pre-injected into the release of high-pressure air into the water column, resulting in continuous oscillation until the rupture of the bubble, artificially generated energy-controllable seismic sub-wave, acquisition and analysis of sub-wave signals propagated underwater, high-resolution deep-earth exploration, and then complete the important task of oil and gas deposits prospecting and other important tasks. After decades of development, the international theory of airgun seismic source is becoming more and more mature, and a number of airgun seismic source products with excellent performance have been born, but the domestic start in this field is relatively late, and there is still a gap in technology compared with foreign countries, and there is no available domestic airgun seismic source. To address this situation, this paper firstly introduces the development history of airgun vibration source and underwater bubble motion theory, including the types and working principles of the existing mainstream airgun vibration source, introduces in detail the domestic and foreign geological exploration research based on airgun vibration source in recent years, and combines the current situation of airgun vibration source with its limitations, summarizes the current limitations of the development of China's airgun vibration source problems, and the future of the airgun vibration source of marine exploration. It also puts forward the prospect of the difficulties that may be faced in the process of localization of core equipment such as airgun seismic source in the future.
With the rapid development of China's economy, the consumption of shallow mineral resources continues to intensify, and the geological exploration work in China continues to advance to deep and complex areas, the exploration of deep geological resources in our country is also confronted with multiple challenges, such as detection depth, anti-interference ability, and measurement accuracy. The demand for deep exploration is also increasing, and the demand for the domestic distributed magnetotelluric acquisition station is becoming increasingly urgent. In this paper, a domestic ultra-wideband distributed acquisition station DMT-V1 for magnetotelluric detection is designed and developed. Combined with LORA autonomous network and 4G networking communication technology, remote real-time data monitoring and remote data download are realized. The developed acquisition station consists of 2 electric field channels and 3 magnetic field channels, and supports low noise fluxgate sensor, long period induction magnetic sensor, magnetic sensor field calibration; MT (Magnetotelluric) and LMT (ultra-long period Magnetotelluric) methods are supported. The AD converter uses a 32-bit high-precision ADC chip CX1282, and is equipped with a low-power hardware processor. Under ARM intermittent operation mode, the power consumption of the acquisition station can be less than 1W@12VDC. Field detection experiments were carried out on this system. Comparative tests with advanced foreign instruments showed that the performance indicators of the DMT-V1 acquisition station generally reached the international advanced level. Field application tests of the DMT-V1 acquisition station were conducted respectively in different regions, and its detection frequency could reach up to 100000 seconds, supports MT and LMT methods, and has the characteristics of high stability, low power consumption, high precision, light weight and portability. The application scope of this equipment covers shallow mineral exploration, deep and ultra-deep oil and gas exploration, and the investigation of the electrical structure of the crust and upper mantle.
Conventional geophones face challenges such as insufficient sensitivity and poor low-frequency response in deep seismic exploration. Integrated grating interferometry, with its high displacement resolution, shows great potential in optical interferometric geophones. This study investigates the application of integrated grating interferometry in optical interferometric geophones through the following steps: First, a mathematical model for displacement detection based on grating interferometry is established using scalar diffraction theory. Next, the influence of key parameters—including light source wavelength, grating period, duty cycle, and tilt angle—on the relationship between interference fringe intensity and displacement is systematically analyzed. Finally, through the analysis of simulation results, quantitative references were provided for the optical path parameter design of the MOEMS seismic sensor. Based on the parameter design, estimated values for the key performance parameters of the MOEMS seismic sensor were derived. The results show that the displacement-to-voltage conversion sensitivity Sd can reach 2×108 V/m, and the dynamic range Dr can achieve 140 dB.
The principle restoration of Zhang Heng's Seismoscope and the realization of its seismic detection function are crucial for the seismological community to recognize and accept Zhang Heng's Seismoscope as a scientific instrument. Pillar and Copper-instrument are the two most critical information in historical literature records. The Pillar must support the Copper-instrument, and the Copper-instrument must be placed on the top of the Pillar, otherwise it cannot be called Pillar; Understanding this relationship of support and positioning leads to the emergence of the principle model of the Seismoscope. The "secondary structure excitation model of primary-secondary structure resonance system" is proposed as the principle model of the Seismoscope. By utilizing the resonance amplification effect of the primary-secondary structures (at least 5.0 times), and the lever amplification effect of the trigger mechanism (at least 4.0 times), a relative displacement amplification of at least 20 times for most seismic motions is achieved, with some amplifications exceeding 50 times. Theoretically, this enables the effective excitation of Zhang Heng's Seismoscope under microseisms (imperceptible to humans). Coupled with an automatic locking system, the Seismoscope achieves an automatic seismic detection function. The primary structure (Ground-Motion) can be simplified as a "cantilever structure with a large concentrated mass at the top and supported at the bottom on a horizontal elastic foundation." The secondary structure (Wind-Observation) consists of 8 pendulums corresponding to 8 directions. Each pendulum is suspended using a "pin", and the pendulum rod is made of copper, serving as a tension-compression rod. This design ensures that the pendulum's swing direction is essentially perpendicular to the axis direction of the pin, allowing for the detection of seismic motion direction. The seismic motion direction measured by the Seismoscope is the one caused by microseisms that initially excites the secondary structure to undergo a significant displacement relative to the primary structure. If the direction aligns with the earthquake-source direction, it may be possible to measure the earthquake-source direction. Due to the significant differences in stiffness and mass between the primary and secondary structures, they can be separated and calculated separately as small-damping ideal linear elastic single-degree-of-freedom systems. The relative displacement of the secondary structure given by this simplified method is slightly smaller than the precise calculation results of the ANSYS finite element model, with a deviation of no more than 10%, indicating that the simplified method has high accuracy and credibility. The relative displacement amplification coefficient of the secondary structure is the primary indicator for whether the seismograph is easy to be excited. The relative displacement amplification coefficient spectrum of the secondary structure is proposed as the basic technical diagram for designing the Seismoscope. A statistical analysis was conducted on the calculation results of 18 sets of far-field seismic records. If requiring the relative displacement amplification coefficient of the secondary structure to be no less than 5.0, it is preliminarily believed that the optimal natural vibration period of the primary-secondary structures ranges from 2.1 s to 2.6 s. The on-site installation and adjusting process to control the natural vibration period range of the primary-secondary structures of the Seismoscope for achieving resonance effects is provided. Further field tests are required to verify its seismic detection function. The principle restoration and instrument design of Zhang Heng's Seismoscope have been preliminarily achieved. Using modern seismic observation results and structural dynamic analysis technology, the principle model of the Seismoscope proposed in this paper conforms to historical records and can achieve the (micro-)seismic detection function
The Transient Electromagnetic Method (TEM) is a geophysical exploration technique with significant potential for widespread application. Its good adaptability to various terrains and capability for non-invasive detection have made it a mainstream technology in urban underground space surveys in recent years. To enable effective TEM exploration in spatially constrained areas such as urban underground spaces and tunnels, this paper addresses the issues associated with small loop devices, such as high mutual inductance and complex structural design, by designing a transceiver integrated small coil and a matching buffer circuit. Firstly, this paper models the transceiver integrated coil and analyzes it from three aspects: equivalent resistance, inductance, and capacitance. Solutions to these issues are then proposed. To address the resonance problem in the coil, a buffer circuit is designed. Finally, the performance of the coil and its matching buffer circuit is verified using both high and low transmission currents. Testing indicates that even with a transmission magnetic moment of 28.75 Am2, the coil's effective resistance, inductance, and capacitance remain very low, measuring 494 mΩ, 1.03 mH, and 260 pF, respectively. Moreover, the coil demonstrates impressive turn-off times, reaching 38 μs and 5 μs for transmission currents of 14 A and 1.2 A, respectively. When the coil is used in conjunction with the buffer module, the system effectively reduces the interference from the primary field response, thereby enhancing the secondary field information. Additionally, since the designed coil device integrates transmission and reception functions and is small in size, it is highly suitable for exploration in spatially constrained areas, significantly improving portability and exploration efficiency.
In response to the current situation where the acquisition of electrical spectrum parameters of rock and ore mainly relies on indoor observation of samples, and there are few experiments on obtaining electrical spectrum parameters of outcrops in the field, a self-developed measurement device was used to conduct field observation experiments. The device is designed with two switchable signal transmission modes of Stable voltage and current, as well as 8 current output levels ranging from 20 μAto 400 mA. Based on indoor measurement experiments of the standard resistance capacitance network model to ensure the functionality and stability of the device, consistency experiments, spectrum observation comparison experiments, and depth measurement device spectrum observation experiments were conducted using a symmetrical quadrupole device in Daweishan, Liuyang City, Hunan Province to obtain the electrical spectrum parameters of the formation outcrop. The experiment proves that the device can correctly obtain the observed spectrum and obtain the electrical parameters of the underground conductive medium through reasonable analysis of the spectrum. The results indicate that the device has the advantages of simple operation and strong anti-interference ability, providing an independent and practical measurement device for the measurement of the electrical spectrum parameters of geological outcrops. Through reasonable geological interpretation of the observed spectrum, it plays a certain supporting role in related geophysical exploration work and has good application value.
Detecting the quality of cement bonding and the corrosion damage condition of the casing is an important measure to maintain the integrity of the wellbore. However, wellbore integrity logging faces challenges such as poor circumferential resolution, low measurement accuracy, and poor evaluation effect of light weight cement. To solve these problems, an ultrasonic vertical incidence and oblique incidence combined measurement design scheme was proposed, and an ultrasonic Lamb-wave scanning imaging logging tool was developed. The tool is composed of a rotary-scanning acoustic assembly, a roller elastic centralizer, a drive mechanism and measuring sensor assembly, and an electronic bin sub. The tool adopts a fast logging operation mode, mainly relying on downhole storage with cable transmission as a subsidiary, and clearly demonstrates the cement bond quality and casing corrosion damage degree with high-resolution images. Field applications indicate that the full 360° scanning measurement of the tool completely covers the entire circumference of the wellbore, and the azimuth imaging map can directly present the circumfluence position of cement loss, channels, and casing corrosion. It holds significant advantages in the evaluation of light cement cementing quality, providing technical support for the safe and stable production of oil and gas wells.
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