Abbreviation (ISO4): Prog Geophy
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The scattering coefficient of Lightning Whistler(LW)is a critical physical parameter for inverting space electron density. To achieve precise and automatic extraction of the scattering coefficient from massive observational data, this paper proposes an Automatic Extraction Algorithm for Lightning Whistler Scattering Coefficients(LWSC)utilizing computer vision techniques, which is applied to Zhangheng satellite data. Firstly, short-time Fourier transform processing is employed to obtain time-frequency maps. Then, a semantic segmentation network is used to detect the time-frequency trajectories of each LW. Subsequently, a quadratic polynomial regression analysis is performed on the time-frequency trajectory points to derive the scattering coefficients of each LW. Following this, satellite data from April 1st to April 5th, 2020, are utilized to evaluate the performance of the LWSC model, and experiments reveal that compared to manual extraction results, the mean absolute error is less than 0.0045 and the mean relative error is less than 0.0121.The average time of single piece detection is 0.98 s, which is better than the mainstream models in this field in terms of speed and accuracy.Finally, the algorithm is applied to Zhangheng satellite data from April 5th to April 10th, 2020, to automatically acquire the scattering coefficients of LWs over one orbital period. Analysis leads to the following conclusions: The overall distribution of LW scattering coefficients exhibits a bimodal pattern: the values of the scattering coefficients are higher near latitudes of ±20°, and they gradually decrease with increasing latitude, showing a similar trend to the distribution of electron density in the space ionosphere. This result further validates the effectiveness and practicality of the algorithm for probing space electron density, holding significant reference value and application potential for the automatic mining and analysis of physical parameters from vast amounts of space electromagnetic events.
The Antarctic continent plays a crucial role in global climate and geological research, profoundly influencing global sea levels, climate patterns, and Earth's energy balance. The crust and mantle structure of Antarctica preserves essential information about the planet's evolutionary history, extending back to the Precambrian era. This makes it a critical region for studying plate tectonics and other geological processes that have shaped the Earth's terrain over millions of years. However, the extreme Antarctic environment, particularly the thick ice sheet covering much of the continent, presents significant challenges for studying its crust and mantle structure. As a result, geophysical methods, especially seismic observations, have become indispensable tools for probing the deep Earth structure of this remote region.This paper reviews the progress made in understanding the crust-mantle structure of key Antarctic regions using passive-source seismic methods, such as ambient noise imaging, teleseismic surface and body wave tomography, and receiver function analysis. These methods have provided valuable insights into the variations and characteristics of the crust and mantle across different regions. In West Antarctica, separated by the Transantarctic Mountains, the crust is relatively thin and tectonically active, while East Antarctica features a thick and stable crust. The uplift mechanisms of the Transantarctic Mountains remain a subject of debate, with multiple theories. In Marie Byrd Land, West Antarctica, active magmatism is present, and mantle low-velocity anomalies are observed. The origin of the Gamburtsev Mountains in central East Antarctica is still controversial, with their formation linked to multiple tectonic events.In the future, as the number of seismic stations in Antarctica continues to grow and inversion methods are optimized, more precise and detailed information will be obtained. This will significantly enhance our understanding of the continent's deep Earth structure, improve our models of its geological evolution, and contribute to advancing the field of Antarctic geophysics.
The thermal transport properties of rocks and minerals are important for understanding the thermal state and geodynamic properties of the Earth's interior. Due to the influence of complex conditions, such as temperature and pressure, and rock composition, accurately measuring and characterizing rock thermal conductivity and diffusivity have always been challenging and hotspot in the study of deep rock physical properties. This paper first introduces the experimental techniques for measuring the thermal conductivity and diffusivity of upper mantle minerals under high-temperature and high-pressure conditions, especially the ÅngstrÖm Method, Transient Plane Source Method, and Laser Flash Analysis Method. Then, the study progress on the thermal conductivity and diffusivity of four typical upper mantle minerals, including olivine, orthopyroxene, clinopyroxene, and garnet, is summarized. The thermal conductivity and thermal diffusivity of the four minerals decrease rapidly with increasing temperature, stabilizing around 900~1000 K. While they increase linearly with increasing pressure. Cation substitution, an increase in water content, grain boundary thermal resistance, and serpentinization all contribute to decreased thermal conductivity and thermal diffusivity. The thermal conductivity and thermal diffusivity of olivine, orthopyroxene, and clinopyroxene exhibit anisotropy, while both the thermal conductivity and thermal diffusivity of garnet are lower than those of the three minerals. Finally, three prospects are proposed for future study on the thermal properties of deep earth rocks.
The fault geometry and the regional tectonic stress field form the basis for studying scientific issues such as crustal deformation and earthquake generation environments. This paper examines the fault structure and stress state of the seismic source region for the MW7.8 and MW7.6 earthquakes in Turkey on February 6, 2023. Based on fault distribution characteristics and the spatial distribution of focal mechanism solutions, the MW7.8 main shock was divided into the Pazarcik segment and the Naril segment for study, while the MW7.6 main shock was divided into the Savrun segment and the Çardak segment. For each segment, a clustering nodal planes analysis of focal mechanism solutions and an inversion of the stress field in the seismic source region were carried out, determining the seismic structural parameters and the tectonic stress field for the doublet earthquake sequence. The research findings are summarized as follows: (1) Based on the clustering of focal mechanism solutions, the strike and dip angles of the seismic fault in the Pazarcik segment of the MW7.8 earthquake are 56.2° and 85.8°, respectively; while in the Naril segment, the strike and dip angles are 39.7° and 85.2°, respectively. (2) For the Savrun segment of the MW7.6 earthquake, the strike is 75.3° with a dip angel of 82.1°. The strike and dip angles of the Çardak segment are 265.3° and 85.9°, respectively. (3) The stress field inversion results indicate that the stress field in the source region of the MW7.8 earthquake is complex, with regimes including strike-slip, normal faulting, and oblique-slip. On the Narli and Pazarcik segments, the stress field is strike-slip faulting stress regime, with the principal compressive stress axis of the Narli fault trending NNW-SSE and the Pazarcik segment's principal compressive stress axis trending nearly N-S, consistent with the dominant strike-slip movement of the East Anatolian Fault. From the Narli fault to the Amanos fault segment, the stress field transitions from strike-slip faulting to a combination of normal faulting and strike-slip, and then to a normal faulting stress field at the Amanos segment. (4) The overall orientation of the principal compressive stress axis of the MW7.6 earthquake is NNE-SSW, and the orientation of the principal tensile stress axis is NWW-SEE. The Savrun segment of the MW7.6 earthquake is dominated by tensional stress, while the Çardak segment is dominated by strike-slip faulting stress regime.
The Anninghe fault zone is a near north-south fault in the fault active system of the Sichuan-Yunnan block, and its deep structure and seismic activity have attracted much attention. In order to evaluate the seismotectonic environment and study the "relics" of mantle plume action in the northern section of Anninghe fault zone, this paper, based on the Jiulong-Zhaojue magnetotelluric sounding data, processed the data by various means, established the deep electrical structure model of the study area, and analyzed the geological structure data, drawing the following conclusions: (1) The deep electrical structure of the area is relatively complex, the surface and shallow strata are mainly distributed with medium and high resistivity, with uneven depth and thickness, the low resistivity layer of the middle and lower crust is developed, and there are relatively upwelling high resistivity bodies in the upper mantle of the Anninghe fault zone.(2)The main fault zones in the area show obvious electrical interface/electrical gradient zone in terms of electrical structure, and the low resistivity of the region near the fault zone may be related to the saline fluid filled inside.(3) The seismic sources in the study area are mainly distributed at the edge of the high-resistivity body, the high-low resistivity contact zone, and inside the low-resistivity body. The high-resistivity body has stronger rock mechanical properties, and the high conductor corresponds to the weak material in the crust. Under the dynamic background of the continuous southeast expansion of the Qinghai-Xizang Plateau, these forces are transmitted through the weak material and concentrated at the edge of the high-resistivity body, causing it to brittle fracture, thereby promoting the occurrence of earthquakes. (4) The axial high resistance body in Panxi tectonic belt is also a high speed, high density and high magnetic anomaly body, which may be the eruption channel of the Emeishan large igneous province and the accumulation channel of the thick basic ultrabasic rocks.
High-precision Earth Orientation Parameter (EOP) forecasting, which encompasses parameters such as Polar Motion (PM), universal time, and length of day, is crucial for facilitating the transformation between terrestrial and celestial reference frames in various applications (e.g., satellite autonomous navigation, deep space exploration, and geodynamic research). However, the mainstream predictive methods (including Least squares, numerical decompositions) have serious limitations, such as tail effect, prior periods and poor estimations of model parameters. To improve the accuracy of EOP forecasting (1~360 days), this paper introduces an integrated model combining Singular Spectrum Analysis (SSA), Prony's method, and Autoregressive (AR) models, demonstrated through the case of PM parameter prediction: Firstly, the SSA is used to separate the principal components (e.g., trend, annual, and Chandler terms) and residual components from the original PM observations. Secondly, combined with the Prony method to model and extrapolate these principal components based on complex exponentials functions; and we combine the widely used AR method to predict their residuals. To verify this combined approach, we conduct multiple prediction experiments based on the IERS EOP 20 C04 data. The experimental results showed that the SSA+Prony+AR model effectively captures the time-varying characteristics and significantly mitigates the tail effects of the PM components. Compared with traditional LS+AR models and IERS Bulletin A forecast products, our proposed model exhibits superior performance in medium to long-term polar motion forecasting, particularly reducing forecast errors by nearly 40% in the X direction. These findings can also provide valuable insights into the forecasting of other EOP parameters.
Geophysical exploration is a vital methodology for investigating the structural characteristics of geothermal systems. In recent years, Beijing geothermal geophysical exploration has made significant progress in the study of fault structure scale and extension characteristics, pre-Cenozoic basement morphology, reservoir burial depth and caprock thickness, spatial range of intrusive rocks and their transformation characteristics to reservoirs. Distinct integrated geophysical explorations are necessary for two typically different systems-namely convective type systems in uplifted orogenic belts and composite sedimentary basin systems, due to their contrasting geological configurations. Emerging technologies, particularly wide field electromagnetic methods and microtremor surveys, provide effective solutions for resolving complex geothermal geological structures and should be prioritized for implementation. The systematic collection and testing of rock physical property samples have provided foundational data for the quantitative assessment of geothermal resources and detailed structural inversion in the Beijing region.
Geothermal heat flow is fundamental to understanding heat transfer and storage within the Earth's interior. However, it can only be measured at discrete locations, making continuous observation challenging and data acquisition costly. Consequently, the spatial distribution of available measurements is highly uneven. Traditionally, most heat flow maps have been constructed through direct interpolation of these measurement points, leading to significant biases due to uneven data distribution. Regions with dense measurements exhibit greater accuracy, whereas areas with sparse data suffer from reduced reliability in heat flow estimation. In contrast, machine learning techniques, when integrated with geological and geophysical parameters, provide a cost-effective and data-driven approach to generating accurate heat flow maps. This paper reviews the application of machine learning algorithms in geothermal heat flow prediction, systematically categorizing and summarizing existing methodologies. Key aspects of data preprocessing, feature selection, and model evaluation are examined, with particular emphasis on the impact of data quality and the selection of appropriate evaluation metrics. Additionally, the challenges associated with model underestimation are analyzed, and potential strategies for algorithmicimprovement and model optimization are discussed. In summary, advancing machine learning applications in heat flow prediction has significant implications for geothermal resource assessment, seismic hazard analysis, geodynamic research, and hydrocarbon exploration.
The development and utilization of shallow geothermal resources is one of the important means to achieve carbon neutrality. A fine assessment of the shallow geothermal resource is crucial for the construction planning and sustainable development. The precise calculation of exploitable resource amounts and the delineation of target areas for distribution are of significant importance for the extraction of shallow geothermal resources using ground-source heat pumps, as well as for the stability and performance of the heat pump system. Currently, the volume method is widely used in the evaluation of geothermal resource. However, the calculation parameters of the reservoir (such as thickness and density) lack small block attributes, and the integration of geophysical results into the fine calculation of geothermal resource has not been effectively realized. Based on the traditional geothermal resource evaluation volume method, this study incorporates geophysical detection results of layer thickness and density parameters into the calculation of shallow geothermal resource reserves. Through targeted calculations with geological structure information, the evaluation accuracy has been significantly improved. Combining the stratigraphic characteristics, topographic variations, and other factors in the study area, the evaluation region is grid-based. Within each small block, high-frequency background noise is used to extract dispersion curvesofdifferentbandwidths, anddifferent lithology layer thicknesses and layer densities are then inverted to calculate the shallow geothermal resource amount in that block. The improved method not only allows for a more detailed evaluation of geothermal resource reserves, but also helps to delineate the most suitable target areas for the development and utilization of shallow geothermal resources, providing a refined and referenceable basis for subsequent decision-making.
Natural Remanent Magnetization (NRM) of geological samples contains critical records to understand the evolution of geomagnetic fields. Self-Reversed Remanent Magnetization (SRM) refers to the phenomenon that certain magnetic materials acquire a remanent magnetization opposite to the direction of external magnetic field. SRM may occur in various geological settings. Understanding the SRM mechanism is essential for accurately establishing magnetostratigraphy, reconstructing plate tectonics, and interpreting past geomagnetic field variations. However, current researches on SRM in geological samples are mostly case studies, lacking a comprehensive synthesis of existing knowledge. This paper reviews the historical development of SRM research on geological samples, highlighting representative case studies on both natural and synthetic samples. Emphases are laid on features and physical mechanisms of SRM in different geological contexts, outlining the methodologies and techniques used for investigating SRM, and addressing current challenges and knowledge gaps. Future research should utilize interdisciplinary approaches to better understand the microscopic physical mechanisms and to advance the study of SRM.
The Xiangshan uranium ore field in Jiangxi Province is the largest volcanic-type uranium ore field in China. Mineralization is primarily controlled by volcanic structures, and its origin is inseparable from volcanic activity. Therefore, the identification of paleovolcanic vents/volcanic conduits in Xiangshan is of great significance for deep uranium resource exploration. Combining the existing geological physical property data in the Xiangshan area with the latest rock magnetism results, and considering the advantages of multi-scale decomposition of wavelet analysis method in boundary identification, this paper employs rock magnetism and wavelet analysis to separate and interpret the fourth-order detail fields of gravity and magnetic anomalies in the Xiangshan area, Jiangxi Province. A detailed comparison was conducted with other gravity and magnetic boundary identification methods, and a systematic identification of paleovolcanic vents in the study area was achieved. The results verified a paleovolcanic vent on the left side of Xiangshan Main Peak, located at a depth of approximately 2 km underground. In addition to porphyroclastic lava, the Xiangshan paleovolcanic vent may also contain rhyolitic dacite with low magnetic susceptibility from the Daguding period. Meanwhile, the paleovolcanic vent in the northwestern part of Furong Mountain was verified, with its eruptive materials primarily composed of rhyolitic dacite. The eruptive materials of paleovolcanic vents in Niutouling, Julong'an, Zoujiashan, and the northeast of Yaogang are mainly porphyroclastic lava. Furthermore, it is speculated that a subvolcanic vent may exist southwest of Yunji, with its eruptive materials dominated by porphyroclastic lava and located at a depth of approximately 2 km underground. These results provide support for refining the understanding of the deep structure of Xiangshan and facilitating deep uranium exploration.
In the process of reconstructing the Digital Elevation Model (DEM) based on the TerraSAR-X/TanDEM-X bistatic system, there are a large number of spatially distributed random noises in the interferograms due to the influence of geometric distortion and incoherence factors. The existing methods exhibit filtering waves or weak filtering phenomena, which limits the reconstruction accuracy of TerraSAR/TanDEM-X DEM. Based on this, this paper proposes a PDV-Goldstein filtering method that combines the phase derivative variance and the Goldstein filtering method. The improved filtering method calculates the phase derivative variance of the interferogram. It uses the prior filtering factor to adaptively filter the regions with different intensities in the interferogram, so as to improve the filtering effect of the interferogram and the quality of subsequent DEM reconstruction. In this paper, simulated and real data are used to compare experiments with various filtering methods. The results show that the filtered interference pattern obtained by the PDV-Goldstein filtering method can significantly maintain the phase detail information. The residual point reduction rate is 87.83%, which is 13.92% higher than that of the traditional Goldstein filtering method. It can be proved that the improved PDV-Goldstein filtering method has superior performance in noise suppression and phase detail preservation, which can support the subsequent DEM reconstruction work.
Soil moisture is a key variable in natural ecosystems, playing an indispensable role not only in regulating the weather and climate systems but also in processes such as soil conservation, agricultural production, and carbon cycling. Therefore, accurately monitoring soil moisture is of significant scientific and practical importance for ecological environmental protection and global sustainable development. Microwave remote sensing, due to its capability to provide large-scale, all-weather, and all-day observation data, has taken a leading position in the field of soil moisture monitoring. However, systematic reviews of microwave remote sensing-based soil moisture retrieval methods and passive microwave spatial downscaling techniques are still relatively lacking. Based on a review of existing studies, this paper systematically reviews active microwave satellites commonly used for retrieving fine-resolution soil moisture and passive microwave satellites suitable for global-scale soil moisture retrieval. By classifying retrieval models and examining land cover types, this paper organizes the methods, principles, and applicability of active and passive microwave soil moisture retrieval. Meanwhile, although passive microwave remote sensing is the most effective method for monitoring global soil moisture, its relatively low spatial resolution limits its quantification and application in studies. Spatial downscaling is currently the main approach and research focus for improving the spatial resolution of passive microwave data. This paper outlines three common downscaling methods and their applicability, classified by model types: empirical models, semi-empirical models, and physical theory models. Finally, the paper discusses and summarizes the existing problems and challenges, aiming to provide a reference for the further development of microwave remote sensing-based soil moisture retrieval and downscaling research.
Investigating the evolution of ecosystem service functions and their trade-off and synergy relationships driven by land use change is crucial for the formulation of regional land spatial planning and ecological protection policies. This study focuses on the Hebei section of the Grand Canal, employing a suite of research methods including land use transition matrix, InVEST model, hot spot analysis, and trade-off and synergy analysis (Pearson) model. The study examines the land use changes and the spatiotemporal distribution, change characteristics, and interrelationships of four ecosystem services—water yield, water quality purification, soil erosion, and habitat quality—between 2000 and 2020 in the Hebei section of the Grand Canal. The findings reveal the following: (1) From 2000 to 2020, the predominant land use type in the study area was arable land, accounting for over 79%, with land use changes primarily characterized by the conversion of arable land to construction land; (2) The spatiotemporal distribution of the four ecosystem services exhibited certain spatial similarities. Over the two decades, the study area experienced a general decline in P output and habitat quality, while water yield, N output, and soil erosion showed an overall upward trend. The changes in ecosystem service functions were directly linked to the land use structure; (3) The relationships among the four ecosystem services were predominantly synergistic, with a particularly strong synergy observed between water yield and water quality purification. Only N output and habitat quality exhibited a relatively weak trade-off relationship, and the spatial distribution of trade-off/synergy relationships among the ecosystem services was associated with the distribution of hot and cold spots. By focusing on the Grand Canal and analyzing the evolution and trade-off/synergy relationships of its ecosystem services from the perspective of land use change, this study provides theoretical support for optimizing regional land spatial planning and constructing a "beautiful canal".
The cementation index is an important parameter for calculating water saturation. Its main influencing factors include the following aspects: formation temperature, pressure, formation water salinity, reservoir space type, mud content, wettability, dispersion characteristics, particle shape and size. This article provides a brief overview of the influencing factors and their characteristics and results. The results are: The cementation index of oil wet reservoirs is lower than that of water wet reservoirs. The higher the mud content, the smaller the cementation index. The higher the frequency, the higher the cementation index. The cementation index is influenced by the type of storage space and varies greatly. The larger the content of micropores, the smaller the cementation index. Rock with good pore structure has a lower cementation index. The smaller the pore throat ratio, the lower the cementation index. The cementation index decreases with the degree of particle sphericity, and the m value steadily increases. The smaller the equivalent particle, the higher the cementation index. In addition, the reasons were analyzed and practical application suggestions were provided, aiming to provide reference for the research and analysis of rock electrical experiments and cementation index, in order to obtain accurate cementation index and lay the foundation for accurate calculation of water saturation.
A high-precision three-dimensional forward simulation method based on optimized finite-difference coefficients is proposed to address the numerical dispersion problem in three-dimensional finite-difference forward simulation. Firstly, the spatio-temporal dispersion relationship is derived from the three-dimensional acoustic wave equation, and an infinite norm optimization problem is constructed using dispersion errors. Then, a new objective function is obtained by utilizing the symmetry of the error function. Finally, the optimization finite-difference coefficients which will vary with velocity is obtained by solving the objective function through Remez iteration method. Dispersion analysis shows that the optimized finite-difference coefficients obtained by the new method have the characteristic of equal ripple error, which means the error in the low wave number region changes regularly within the error limit. Compared with the finite-difference coefficients obtained in the spatial domain, the finite-difference coefficients obtained by the new method can effectively adapt to changes in the CFL (Courant Friedrichs Lewy) number, and can suppress time dispersion under large CFL number. Compared with the finite-difference coefficients obtained by the Taylor expansion method in the spatial domain, the finite-difference coefficients obtained by the new method have a wider frequency band and can reduce the spatial dispersion caused by the errors in high wave number region. The high-speed homogeneous model example shows that the time dispersion of the new method is invisible and the spatial dispersion is slight in high-speed formations, proving that the new method can suppress both time dispersion and spatial dispersion and has a high accuracy in simulating wave fields. The salt model shows that the new method can significantly suppress numerical dispersion caused by both low-speed layers and high-speed geological bodies in strata with rapidly changing velocity, significantly improve the resolution of seismic events in seismic records, and verify that the new method is suitable for complex models with drastic velocity changes.
With the increasing encounter rate of non-parallel formations during horizontal well construction, the applicability of existing response laws for parallel formations has significantly declined. Especially when non-parallel formations interact with complex wellbore conditions, it severely affects the accuracy of horizontal well logging interpretation, thereby interfering with the evaluation and development of oil and gas resources. To deeply explore the influence mechanism of wellbore conditions on natural gamma logging responses in non-parallel formations, this study established a horizontal wellbore condition model with inclined upper interfaces in non-parallel formations and conducted numerical simulations using the MCNP program. The research focused on analyzing the effects of key wellbore parameters, such as non-parallel interface angle, wellbore diameter, radioactive substance content in drilling fluid, wellbore position deviation, and well deviation angle, on the characteristics of natural gamma logging responses. The simulation results show that: as the non-parallel interface angle increases, the contribution of surrounding rock to the detection point increases, leading to a gradual increase in natural gamma response values, which tend to approach the surrounding rock characteristic values; an increase in wellbore diameter enhances the contribution of mud to the detection point while weakening the contributions of the target layer and surrounding rock, resulting in a decrease in response values; the response values increase significantly with the rise in radioactive substance content in drilling fluid, where the influence of drilling fluid dominates; downward deviation of the wellbore position can weaken the impact of non-parallel formations, and when the angle is greater than 30° with a deviation within 5 cm, the change in response values is too slight to be identified; variations in well deviation angle alter the shape and values of logging curves, with greater angles leading to more obvious curve asymmetry, even causing interface recognition errors. This study reveals the synergistic mechanism between non-parallel formations and complex wellbore conditions, providing basic technical support for horizontal well logging interpretation. It helps improve interpretation accuracy and efficiency, and holds important guiding significance for the optimal design and safe operation of horizontal wells in oil and gas field development.
Geochemical logging has emerged as a cornerstone in the exploration and evaluation of unconventional oil and gas resources, owing to its ability to provide comprehensive data on reservoir characteristics. This paper aims to present a comprehensive review of the theoretical foundations, technological advancements, and future prospects in this field. Utilizing the Litho Scanner tool as a representative example, this study investigates its high-resolution and high-accuracy capabilities, enabling precise measurements of elemental concentrations, mineral compositions, and Total Organic Carbon (TOC). Recent progress in instrument development, including pulsed neutron sources and advanced scintillation crystals like LaBr3 and CeBr3, has significantly improved the reliability and resolution of geochemical logging tools. Additionally, the integration of Artificial Intelligence (AI) and Machine Learning (ML) algorithms has revolutionized data processing methodologies, enhancing mineral quantification efficiency and reducing uncertainty. Case studies demonstrate the successful application of geochemical logging in complex reservoir characterization, highlighting its superiority in evaluating mineralogy, porosity, and hydrocarbon saturation while minimizing reliance on traditional core analyses. This paper also discusses emerging trends, such as the miniaturization and multifunctionality of logging instruments, the development of intelligent detectors, and the increasing use of real-time data analysis through cloud-based platforms. The synthesis between geochemical logging and other advanced logging techniques, such as nuclear magnetic resonance (NMR) and dielectric dispersion, is projected to further enhance reservoir evaluation capabilities. In conclusion, geochemical element logging is poised to play an increasingly critical role in unconventional resource exploration, environmental monitoring, and mineral prospecting. Driven by technological innovation and interdisciplinary integration, its continued evolution is expected to create new opportunities for accurate, cost-effective, and sustainable resource development.
The Tarim basin is rich in oil and gas resources, where fault-controlled fracture-cavity systems represent crucial reservoir types in Ordovician carbonate formations. However, the ultra-deep burial depth of these reservoirs (exceeding 7, 000 meters), coupled with complex fracture-cavity interactions and low-resolution seismic data, poses significant challenges in precisely characterizing strike-slip faults, thereby constraining exploration and development efforts. This study presents an integrated approach: First, texture attribute volumes are extracted, utilizing texture entropy to delineate fracture-cavity systems with high signal-to-noise ratio characteristics, effectively identifying their contour features. During extraction, parameters including time window size and gray level are optimized to enhance the overall continuity and clarity of fault-controlled fracture-cavity systems. Subsequently, leveraging the anisotropic tracking advantages of ant colony algorithm, a cascade methodology combining texture entropy with directional tracking enables precise identification of main strike-slip faults. The optimization process systematically considers key parameters such as ant movement direction, search step length, and termination criteria to ensure comprehensive path traversal and rational fracture information extraction. Finally, data volume fusion technology integrates original texture entropy attributes with main faults extracted through ant colony algorithm, significantly enhancing the spatial distribution characteristics of fault-controlled fracture-cavity systems. This methodological framework demonstrates practical value for the exploration and development of ultra-deep fault-controlled fracture-cavity reservoirs.
In the current field of log interpretation based on machine learning, due to the difference in data distribution, it is difficult to directly apply the model trained on the existing log data to the new log interpretation. This paper focuses on the intelligent interpretation of geophysical logging, and proposes a semi-supervised model fine-tuning method, Log2FT, with the help of machine learning. In this method, the model is trained on the source domain, and then fine-tuned with a few labels in the target domain to improve the adaptability of the model. In order to verify the effectiveness of this method, we selected four wells D, E, F and G located in Jiyang Depression, Bohai Bay Basin, and conducted four groups of experiments D→E, E→D, F→G, and G→F, respectively. Through a series of experimental designs, including parallel repeated experiments, contrast experiments, ablation experiments and related interpretation analysis, the effectiveness and practicability of the proposed method are fully verified. The experimental results show that this method significantly improves the accuracy of logging interpretation.Thisresearchhelpsto overcome the problem of data distribution difference in the existing logging interpretation, and provides a feasible and effective method for interpreting new logging data.
As a key geological factor affecting coal mine safety production, the accurate identification of fault structure is crucial for hazard prevention and efficient mining during coal exploration and development. With the enhancement of exploration and development of deep coalbed methane, the requirements for fault identification in underground coal-bearing strata are getting higher and higher. Therefore, this paper carries out research on edge detection technology for seismic faults. Based on the analysis of previous research results, a novel edge detection method using an LBP/VAR composite operator is proposed, which combines rotation invariant Local Binary Pattern (LBP) and Rotation Invariant Variance (VAR). Firstly, a theoretical geological model with continuous variation of vertical fault displacement is established, and different operators are used to test the result of forward modeling, which proves that the recognition accuracy of LBP/VAR operator is better. then, different intensities of noise are introduced to simulate the real seismic acquisition environment. Then, the noise with different signal-to-noise ratios is introduced. By comparing with the rotation invariant local binary pattern and the traditional edge detection operators such as Canny and Roberts, the stability of the LBP/VAR operator under noise interference is verified. Subsequently, the proposed method is applied to the real coalfield seismic data, and compared with the detection results of conventional seismic attributes, traditional edge detection operators and rotation invariant local binary pattern. The results show that the LBP/VAR operator has the best recognition effect, which is basically consistent with the fault edge in the original image. The recognition effect of the Canny operator is better than that of the Roberts operator. The experimental result show that the LBP/VAR operator shows significant advantages in fault recognition accuracy and noise immunity through the collaborative analysis of texture features and contrast. Especially under complex geological conditions with low signal-to-noise ratio of the seismic data, this method can still maintain high accuracy for fault boundary location. The study result can provide reference for the qualitative identification of coal seam faults, and have certain guiding significance for future exploration and development in coal seam.
Abnormal pressure is closely related to the generation, migration and accumulation of oil and gas and the preservation of oil and gas reservoirs, so accurate prediction of formation pressure is essential in oil and gas exploration.The prediction of formation pressure, which is generally developed with high pressure in Xihu sag, cannot meet the exploration demand in this area. In this paper, the relationship between shear wave velocity and formation pressure is established based on petrophysical model, and further applied to pre-stack simultaneous inversion seismic data for pressure prediction, so as to improve the multi-solutionof formation pressure prediction and improve the prediction accuracy. Based on Hertz-Mindlin model considering pressure and Gassmann fluid replacement equation, the relationship between shear wave velocity and effective stress is established by combining mineral composition pore fluid to simulate elastic characteristics of underground rock strata. Combined with effective stress theorem, formation pressure is predicted on the basis of petrophysical model, the single well formation pressure prediction of Pinghu Formation in Pinghu structural belt on the west slope of Xihu sag is compared with the conventional Eaton method. The proposed method is more accurate Then the relationship between effective stress and the product of shear wave velocity and shear wave impedance is applied to seismic data to predict seismic formation pressure. According to the well profile of predicted formation pressure, the well bypass is relatively matched with the well formation pressure, and the predicted formation pressure coefficient is consistent with the measured results, which accords with the pressure coefficient distribution law of the west slope of Xihu sag. In this paper, the seismic formation pressure prediction based on petrophysical theory is realized by combining logging petrophysical seismology.
Landslides, as a common geological hazard, often occur with enormous destructive power and loss of life and property. Non explicit landslides are difficult to detect and warn of in a timely manner due to their lack of deformation signs and landslide terrain features. Therefore, detecting potential landslide risks in advance through non-contact detection methods is of great significance for preventing geological disasters. The semi aerial transient electromagnetic method has advantages over other geophysical exploration methods in terms of detection depth, detection efficiency, resolution, and applicability to complex terrain. It has shown great potential in the detection of non explicit hidden slope bodies. However, existing research and applications have not clearly defined the effects of different terrain conditions, differences in landslide structures, and different emission observation parameters on the semi airborne transient electromagnetic detection capability. Therefore, we conducted forward numerical simulations based on a typical non explicit landslide model and analyzed the semi airborne electromagnetic response characteristics of non explicit landslides. Firstly, three typical three-dimensional geoelectric models of non explicit landslides were constructed. Then, finite element simulations were used to obtain semi aerial transient electromagnetic responses under different device parameters. Furthermore, the response characteristics were analyzed and summarized based on measured data. The final location of the source will have a significant impact on the detection results, and the source should be placed on a gentle and sloping terrain during detection; And when there is a sudden change in the electrical interface underground, the forward modeling results will undergo a sign change phenomenon, which can point to the boundary of the landslide. The experimental results show that the forward calculation results are reliable, and the conclusions obtained can lay the foundation for the application of semi aerial transient electromagnetic method in landslide detection in the future.
Residual Curvature Analysis (RCA) based on common image point gathers(offset domain) can significantly improve the accuracy of migration velocity modeling and Pre-Stack Time Migration (PSTM). By picking up the points with the maximum energy in the spectrum data, one can achieve a new time domain migration velocity. However, the traditional methods for instance: migration velocity modeling based on manual picking of γ spectrum, or migration velocity analyzing based on Human-Computer Interaction etc. Which are very labor-intensive and time-consuming. To help address this concern, we proposed a kind of migration velocity modeling method based on deep learning. First, A series of random γ values are applied to the target velocity model, and after performing PSTM, then common image point gathers are obtained. Second, based on common image point gathers, we can calculate corresponded γ spectrum through γ scanning, which can be taken as the sample data-set and input γ can be used as label data. In terms of constructing a γ value prediction network, we implement a neural network based on the architectures of U-Net3+, and we chose Mish as the activation function, and we chose Log_Cosh as the misfit function. We can train the network using data-sets above, and corrected migration velocity model can be predicted though inferring input γ spectrum. Method test based on Marmousi model has verified the correctness of the method proposed in this paper. And a large amount of field data processing results show that the method proposed in this paper can obtain more accuracy velocity model than manual picking, while its efficiency is dozens of times higher than manual picking, which turns out our method has great potential in industrial application.Although certain achievements have been made in this paper, continuous efforts are still required in terms of the generalization of the neural network model, the pre-processing of CRP gather data, and the completeness of the data-set.
The computational accuracy and efficiency of the finite-difference forward modeling in the frequency domain determines the quality of the waveform inversion, and choosing a suitable difference format is the basis of the frequency domain forward modeling. At present, the optimization differential format of the rotating coordinate system is widely used in actual production, but the optimization differential format of the rotating coordinate system is limited by the condition of the equal spacing sampling. For the optimized differential format of the average guide method, it can not only be applied to different sampling intervals, but also improve the sampling accuracy. Therefore, this paper based on the traditional 9-point finite difference, it used the Average-Derivative Method (ADM) for the two-dimensional scalar wave equation to develop a 25-point finite difference optimized difference scheme and applied to Laplace-Fourier domain performance simulation. After optimization, the ADM-25 finite difference optimized difference scheme only needs about 5 points per minimum pseudo-wavelength to achieve a normalized error of less than 1%. At the same time, it adds a complete matching layer at the border to absorb boundary(Perfect Matched Layer, PML). According to the above condition. The rectangular grid test can be seen that the 25-point format of the average guidance method derived in this article can not only be applied to a square grid with equal spacing sampling, but also the complex model of a rectangular grid with different sampling spacing. The 25-point optimized difference coefficients are calculated and obtained under different spatial sampling interval ratios, it can accomplish the non-uniform numerical simulation in both the longitudinal and transverse directions and draw figures to convenient in this paper. Numerical example results show that the 25-point difference scheme based on the average derivative method has higher simulation accuracy than the classical 9-point difference scheme and proves the accuracy and stability of the ADM-25 point method.
Dictionary learning methods have been successfully applied to seismic data reconstruction and denoising. However, these methods, such as K-singular value decomposition (K-SVD) algorithm, divide the data into small patches without considering the global data in the reconstruction process. In contrast, Convolutional Sparse Coding (CSC), which processes signals globally, has advantages in extracting structural features of seismic data. Therefore, we propose a CSC model based Projection onto Convex Sets (POCS) for denoising and interpolating seismic data. By introducing CSC into Penalized Weighted Least-Squares (PWLS) framework, we design an effective sparse coefficient updating method, which significantly improves the computational efficiency of the algorithm. In addition, it uses multi-iteration Projection onto Convex Sets algorithm to supplement some missing features of seismic data in CSC, so as to realize the reconstruction and denoising of seismic data. Finally, numerical experiments on synthetic data and field data demonstrate that the proposed method has good application effect.
Carbonate rock fractures and caves are well-developed and highly heterogeneous. The pore size and porosity of these fractures and caves determine to some extent the oil and gas storage capacity of the reservoir. The higher the pore size and porosity, the better the reservoir's storage and permeability capabilities. Electrical imaging plays an indispensable role in the effectiveness evaluation of carbonate reservoirs due to its high resolution and high coverage. In order to more accurately evaluate the effectiveness of reservoirs, the idea of using core scale electrical imaging images is proposed. Firstly, the electrical imaging images are preprocessed and fracture hole extraction is carried out through techniques such as Filtersim, grayscale reconstruction transformation, watershed algorithm, and morphological algorithm. Then, the core fracture holes are marked using tools such as Photoshop, and the fracture hole parameters of the two are calculated separately. Different models are used to fit the relationship between the electrical imaging and the rock core fracture hole parameters. Finally, different relationship equations are applied to actual well data. The results indicate that the scale model fitted by a quadratic function can better reflect the complex relationship between the fracture and cavity parameters in electrical imaging and the true fracture and cavity parameters in the rock core. Moreover, the absolute error of the extracted fracture and cavity parameters from electrical imaging corrected by this model is controlled within 3.5%. The accurate extraction and correction of fracture parameters can provide a more accurate data basis for the effectiveness evaluation of reservoirs.
Accurate identification of deep carbonate microfacies is crucial for reservoir characterization and sweet spot prediction. Deep carbonate reservoirs usually exhibit complex compositions and numerous microfacies types, leading to dramatic challenges and low accuracy in microfacies identification. This study employs conventional logging curves, elemental mud logging curves, and processed mineral interpretation logs from deep carbonate reservoirs as input. A Residual Long Short-Term Memory (ResLSTM) network-based supervised model is developed to establish nonlinear mapping relationships between logging data and carbonate microfacies for intelligent reservoir microfacies identification. The test results demonstrate that: (1) The residual structure incorporated in the ResLSTM network effectively mitigates gradient vanishing and explosion issues during network training. Compared with traditional LSTM networks, the proposed ResLSTM achieves over 10% improvement in prediction accuracy for deep carbonate microfacies. (2) For thin interbedded layers within the reservoir, the ResLSTM model achieves 92.4% microfacies prediction accuracy, demonstrating its strong robustness. These findings highlight the ResLSTM's superior capability in handling the heterogeneity and complex patterns inherent in deep carbonate reservoirs. Furthermore, the tests also demonstrate that the distribution of training data exerts a significant influence on prediction accuracy of ResLSTM. Specifically, in scenarios of input data imbalance, the ResLSTM tends to develop a pronounced predictive bias toward the majority lithofacies categories due to their numerical dominance in the training set. The systemic bias introduced by imbalanced lithofacies distributions presents a critical challenge in petrophysical machine learning applications, demanding urgent methodological innovations to enhance model generalizability across minority facies classes.
Both monopole and dipole acoustic logs measure the mode waves in the fluid of borehole. The shear-wave slowness of formation is obtained from the frequency dispersive curves of borehole. The SH-wave vibrates in the circumferential direction, and the shear-wave slowness is measured along the z axis for isotropy. There is no coupling wave of all P-wave and surface waves. It can be used to measure the shear-wave slowness to vibrate in the circumferential direction for anisotropy. It is different from sonic logging. For casing well, it is used to test the banding qulitity, when there is water ring, the tangential stress in the circumferential direction of the outer wall of the casing or the outer wall of the Cement Ring is 0, and the SH wave excited from the inner wall of the casing is cut by the water ring. SH wave can not continue to spread out and be reflected back into the solid (casing or cement ring), so that SH-wave in the radius direction reflected back and forth, along the z-direction become mode waves. When there is no water ring in the borehole wall, SH waves measured along the z-direction of the waveform are obvious different. In this paper, the SH wave is excited in the open hole or casing hole by axisymmetric excitation, and the calculation formula of SH wave is deduced by the method of real axis integration. The two-dimensional spectrum of SH-wave received along the z direction in the borehole wall is studied, and the two-dimensional spectrum distribution and acoustic waveforms of SH-wave in open hole and casing well are given. The propagation law of SH-wave in shaft wall is discussed. The structure of the probe for SH wave excitation and reception is briefly introduced.
Semi-airborne transient electromagnetic method is a novel exploration method which is beneficial for deep sounding under complex terrain and geological conditions. Complex terrain will lead to electromagnetic response distortion. At present, there is still insufficient theoretical basis for topographic effect recognition in grounded-source semi-airborne transient electromagnetic data. Therefore, this paper studies the topographic effect of grounded-source semi-airborne transient electromagnetic method based on finite element method. In this paper, different three-dimensional terrain models are constructed, multi-azimuth flight lines are designed, and the abnormal response of magnetic field and induced electromotive force is quantitatively evaluated and analyzed. Three-dimensional terrain can cause anomalies in the observed responses over a wide area, with the anomalous regions not being limited to the vicinity of the actual terrain locations. The influence of terrain on dBz/dt response is more complex than it on hz response. There are also notable distinctions in the characteristics of how terrain at the source location and terrain away from the source location influence the observed responses, with the former having a more pronounced effect on the observed responses. Additionally, the anomalies caused by ridge and valley in the observed responses are opposite.
Rayleigh wave exploration predominantly exploits the dispersion characteristics inherent in Rayleigh waves to elucidate the properties and structural modifications of subsurface media.In recent years, this methodology has been extensively employed to address critical issues, including geological structure identification, medium characterization analysis, earthquake risk assessment, and mineral resource exploration.Nonetheless, the inversion of Rayleigh wave dispersion curves encounters significant challenges, including slow convergence rates, noise interference, and the propensity to converge to local minima.This paper introduces a Tikhonov regularization and optimization algorithm specifically designed for the inversion of Rayleigh wave dispersion curves.This methodology incorporates the L1 norm of the model to impose sparse regularization constraints throughout the inversion modeling process.This approach enhances the model's generalization capacity, mitigates errors arising from layer refinement, improves inversion accuracy, and aligns the inversion results more closely with the real geological model.In the context of sparse regularization models, the Alternative Direction Method of Multipliers (ADMM) is utilized to address the minimization problem and ascertain the optimal solution.Empirical testing conducted with theoretical geological models and real data demonstrates that the method proposed in this study exhibits enhanced inversion accuracy and superior stability compared to the traditional least squares method.
The development and utilization of urban underground space present significant challenges due to complex and unknown geological conditions as well as potential engineering risks. Ground Penetrating Radar (GPR), with its high resolution, adaptability, and non-invasive detection capabilities, has become one of the primary tools for underground space exploration. However, GPR data collected by antennas of different frequencies exhibit distinct advantages and limitations: high-frequency antennas provide higher resolution, which is critical for detecting small-scale or shallow targets, but their detection depth is significantly limited. In contrast, low-frequency antennas offer greater penetration depth, enabling the detection of deeper targets, but at the cost of reduced resolution. This trade-off limits the comprehensive application of GPR technology in urban underground exploration, where both resolution and depth are crucial. To address this limitation, our study proposes a low frequency match filter method. This method first performs spectral analysis on two sets of GPR signals collected by antennas with different frequencies to identify their respective advantageous frequency bands and determine the boundary point between them. The signal collected by the high-frequency antenna is then used as the input, with its high-frequency components remaining unchanged, while the low-frequency components are replaced by the low-frequency signal as the desired output. Based on this, a frequency-segmented matching filter is constructed. Finally, the filter is applied to all radar data collected by the high-frequency antenna, achieving low-frequency compensation for the high-frequency signals. The results from both model experiments and real-world applications demonstrate that the proposed frequency-segmented matching filter significantly broadens the spectrum of high-frequency GPR data, enabling more effective extraction of deep signals while preserving the high resolution of shallow layers. This method greatly enhances the overall capability of GPR in urban underground space detection, addressing the limitations of traditional high-frequency GPR systems with insufficient detection depth in complex geological conditions. In conclusion, the low frequency match filter technology proposed in our study offers an efficient and cost-effective approach for extracting deep signals from GPR, showing promising application potential.
Data processing in the diapir fuzzy zone of the Yinggehai Basin faces challenges, specifically manifested as a small effective offset for shallow seismic data, a low signal-to-noise ratio for deep seismic data, and significant spatial velocity variations. These factors render it challenging for purely data-driven tomographic inversion to effectively invert the formation velocity, resulting in unclear delineation of shallow faults and chaotic imaging of deep formations in seismic images.In response to the above issues, this paper proposes a velocity modeling method based on multi-information constraints. Firstly, a deep neural network is trained by integrating multiple aspects of information which includes geological horizons, seismic velocities, seismic attributes, the trend laws of velocity changes inside and outside the fuzzy zone, and original migration velocities. After that, we used the deep neural network to construct a seismic velocity model with multi-information constraints. Then, the velocity in the fuzzy zone is updated based on high-precision grid tomography velocity inversion technology, and finally, an optimized velocity model is obtained.The application of practical data demonstrates that this method not only enhances the consistency and accuracy of velocity and seismic imaging outcomes, but also markedly improves the imaging of shallow tomography and deep fuzzy regions. Consequently, this approach exhibits high practicality and effectiveness.
Fiber Bragg Grating (FBG) sensing technology serves as a crucial approach for monitoring groundwater seepage velocity in boreholes, where the accurate determination of flow field distortion coefficient is a key factor for its precise measurements. This paper based on a single-hole velocity correction model for FBG seepage monitoring, conducts experimental investigations on the distortion coefficient in FBG monitoring seepage velocity, aiming to explore the variation patterns of the distortion coefficients in underground seepage through geotechnical media under different seepage conditions. Firstly, a distortion correction model for FBG monitoring seepage velocity was derived based on the principle of single-borehole velocity correction, and the three main influencing factors of the distortion coefficient, including seepage velocity, filter pipe diameter and type, were determined. Subsequently, an in-situ groundwater seepage monitoring device was designed and fabricated. Combined with the self-made FBG seepage monitoring device, followed by experimental investigations on FBG-based seepage velocity monitoring under various borehole layout forms. The influence of different seepage velocity, filter tube diameters and types on the distortion coefficient were studied. The experimental results showed that by applying the distortion coefficient to correct the borehole-measured seepage velocities obtained from the FBG seepage monitoring device under various borehole layout forms, the actual seepage velocity within the aquifer can be determined with improved accuracy. When the seepage velocity exceeded 0.05 mm/s, the distortion coefficient exhibited a well-defined pattern: the distortion coefficient increased with higher seepage velocity, decreased with larger filter tube diameter, and was greater for bridge filter tubes compared to circular filter tubes under the same layout. The influence of the seepage velocity (the permeability coefficient of aquifer), filter tube diameter, and type on the distortion coefficient varied, with the order of sensitivity being the seepage velocity (the permeability coefficient of aquifer) > filter tube type > filter tube diameter. The findings of this study provide valuable reference for the practical application of FBG technology in subsurface seepage monitoring.
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