Abbreviation (ISO4): Prog Geophy
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Based on the spatial and temporal matching data of Cloud Profile Radar(CPR)carried by CloudSat and Dual-frequency Precipitation Radar(DPR)on the Global Precipitation Measurement(GPM)satellite from 2014—2017, we examine the relationships among cloud top height(CTH), cloud base height(CBH), and precipitation layer thickness(△h), and their associations with surface precipitation intensity and microphysical properties of precipitation, across four types of precipitation systems—marine stratiform, marine convective, terrestrial stratiform, and terrestrial convective—in tropical and subtropical regions. The results reveal a strong positive correlation between CBH and surface precipitation rate for all four precipitation types, with correlation coefficients exceeding 0.55, surpassing the correlation between CTH and surface precipitation rate. An analysis of the mean precipitation layer thickness(△h)across different precipitation rate intervals reveals that, for all four precipitation types, the average △h tends to decrease as the precipitation rate increases. By analyzing the relative contributions of evaporation and warm rain processes during the descent of precipitation particles to the surface in different CTH intervals, it can be concluded that as the cloud top height increases, the complex interactions and variability of various microphysical processes such as evaporation and warm rain during the descent of raindrops lead to a weakened correlation between surface precipitation intensity and CTH. The statistical analysis of the relationship between CBH and surface precipitation drop size distribution parameters reveals that, with increasing CBH, the effective droplet radius (Dm) shows a significant increasing trend across all four precipitation types, whereas the droplet concentration parameter (dBNW) exhibits distinct variations depending on the precipitation type. In marine stratiform precipitation, the increase in both Dm and Dm suggests their joint contribution to surface precipitation enhancement. Conversely, the significant reduction in dBNW in terrestrial convective precipitation indicates that surface precipitation enhancement is primarily driven by larger droplets. This difference reflects that the relationship between CBH and the microphysical characteristics of surface precipitation varies significantly across different precipitation types.
Given that the traditional method for expressing precession is based on spherical trigonometry, its defect lies in the arbitrary nature in determining the positive or negative signs of the spherical angles and sides of spherical triangles when determining them, as well as the difficulty in determining the theoretical relationships between different precession angles, and it is not conducive to the theoretical derivation of both the precession-nutation matrix and other theoretical aspects. To address this issue, this paper uses the inner product and outer product of two vectors to jointly define an angle (the angle and side length of a spherical triangle), such that the angle thus determined is unique, thereby avoiding the ambiguity in determining the positive or negative signs of angles using the spherical trigonometry method. By means of the representation of a specific vector in different coordinate systems and the transformation between different coordinate systems, the theoretical relationships between different precession angles can be directly obtained. Meanwhile, this study provides the polynomial expansions of precession angles and their trigonometric functions, thereby establishing a complete precession model involving all precession angles.This study points out that, on the order of tens of microarcseconds, the different precession angles in the IAU2006 precession model or the P03 precession model are not mutually consistent, necessitating a re-examination of the numerical computation of different precession angles. The research methods and results can be applied to research in fields such as the astronomical geodetic datum and data processing of space geodetic technology.
Earthquake early warning systems are one of the effective means to mitigate earthquake disasters. In recent years, frequent earthquakes have caused enormous losses to human lives and property, making it extremely urgent to build a safe and reliable earthquake early warning system. The ShakeAlert system is an earthquake early warning system on the United States. West Coast. Since its official launch in 2019, it has now been updated to version 3.0. The monitoring network of the ShakeAlert system includes 1, 400 seismic stations, 1, 100 GNSS stations, as well as the MEMS, DAS, and BSM sensor networks under research. Its main algorithms consist of three seismic source algorithms: EPIC, FinDer, and GFAST-PGD, along with the Solution Aggregator (SA) module, Decision Module (DM), and ground motion estimation module (Eqinfo2GM). The operational process of ShakeAlert is closely linked: the data layer captures and transmits raw data, the production layer conducts collaborative analysis through multiple algorithms, and the alert layer conducts final review for release and dynamic updates, forming an efficient closed loop from perception to early warning. This paper studies its performance from the three dimensions of timeliness, accuracy, and reliability. The system can issue the first alert within 4 to 20 seconds in areas with dense stations, and successfully warned 41 out of 53 earthquakes with magnitude M≥4.5 from 2019 to 2023. However, the ShakeAlert system still has problems such as high system complexity, insufficient station distribution, shortcomings in algorithm performance, difficulty in identifying intensity anomaly zones, and failure of the public's scientific risk avoidance behaviors. As an internationally leading early warning system, ShakeAlert has important reference value for its technical experiences such as multi-source monitoring integration and dynamic algorithm weighting. In the future, China can improve the accuracy and social effectiveness of its earthquake early warning system by densifying stations in weak areas, optimizing algorithm fusion mechanisms, and strengthening public education.
Seismic monitoring stations are critical infrastructure for China's earthquake prevention and disaster reduction efforts, and their observational environments are legally protected. Meanwhile, natural gas transmission pipelines are vital facilities that drive national economic development and safeguard people's livelihoods. By 2023, the total length of long-distance natural gas pipelines nationwide had reached 124, 000 kilometers. The construction of such extensive pipelines inevitably overlaps with seismic observation sites, raising the urgent question of whether pipeline operations affect the seismic observation environment and to what extent. To address this, we conducted a series of seismic observation experiments at the Yongqing Compressor Station in Hebei and along the Shan-Jing Fourth Pipeline. Based on the observational data, we studied the propagation patterns of vibration noise generated by natural gas pipeline facilities. The experimental results indicate that near-field noise levels around compressor stations along the pipeline are relatively high, severely interfering with the normal operation of seismic monitoring stations and warranting separate consideration. In observation areas far from compressor stations, however, the existing environmental background noise did not clearly reveal vibration noise from the natural gas pipelines. Based on these findings, we recommend accelerating the establishment of a national database for ambient seismic background noise and incorporating environmental impact assessments of seismic background noise into the evaluation processes of large-scale construction projects. The results of this study will provide references for the formulation and revision of relevant national standards and contribute to a scientifically balanced approach between economic development and environmental protection.
The high-precision characterization of shallow subsurface three-dimensional structures is a frontier topic for ensuring the safe development of urban underground space. The use of environmentally friendly, non-invasive geophysical methods to finely identify underground structures is a pressing issue that needs to be addressed. This paper takes the underground fine structure detection in Xiong'an New Area as an example, using the microtremor H/V spectral ratio method to probe the internal structure of thick sedimentary layers. The morphological characteristics of the H/V spectral ratio curve directly reveal the form of the thick sedimentary layer in Xiong'an New Area. Its bimodal shape represents the lithological interfaces of the underground structure in the area: the high-frequency peak corresponds to the interface between silty soil and clay; the low-frequency peak corresponds to the interface between the Quaternary and Neogene layers, with the associated lithology inferred to be silty clay and sandstone or gravel sandstone. The S-wave velocity structure obtained by inversion of the H/V spectral ratio curve using a particle swarm algorithm shows that the inversion results align with the actual stratigraphic structure, with a certain resolution for weak inter-layers. Experimental results demonstrate that the microtremor H/V spectral ratio method provides a scalable technical paradigm for urban geological transparency.
Abnormal changes in groundwater levels serve as crucial indicators of seismic precursors. Prior to earthquakes, groundwater levels typically exhibit varying degrees of anomaly, manifesting as sudden rises or falls that may persist for extended periods. To accurately identify seismic precursor anomalies, we categorize groundwater level data into Seismic Active (SA) and Non-Seismic Active (non-SA) periods, providing a basis for segmenting the dataset. We propose a novel network architecture termed Residual Multi-scale TCN Sparse Expert Network-iTransformer (RMMoTCN-iTransformer). This model integrates the strengths of Residual Multi-scale TCN Sparse Expert Network (RMMoTCN) and iTransformer, enabling effective capture of multi-scale local features and global dependencies in groundwater levels. RMMoTCN achieves sTable training through its residual structure and sparse multi-scale TCN expert network, enabling flexible modeling of complex temporal features and learning trends across different time step scales. Concurrently, iTransformer enhances long-sequence prediction performance via an improved self-attention mechanism. Additionally, we incorporate a Wavelet Noise Reduction (WNR) method to further boost the model's robustness and prediction accuracy. Experimental results demonstrate the model's robust capabilities in groundwater level prediction and seismic precursor anomaly detection. To enhance anomaly detection accuracy, this study employs an exponentially weighted moving average (EWMA) control chart to precisely identify anomaly onset times. Earthquake validation confirms the model's effective identification of groundwater level anomalies under diverse geological conditions, providing sufficient advance warning time for earthquake preparedness. This validates its broad adaptability and practical utility. This research contributes scientific innovation and practical value to seismic precursor analysis, offering novel technical support and analytical methods for earthquake early warning technology development and disaster prevention efforts.
In the West No.2 Mining Area of Longshou Mine, mastering the trends of surface subsidence and the development process of underground overburden is crucial for analyzing mining progress and assessing mining safety. This study proposes the use of SBAS-InSAR technology combined with bicubic interpolation to achieve continuous monitoring of surface subsidence trends. Based on the monitoring data, we analyze the rock stress changes induced by mining operations and further infer the formation and evolution of the underground overburden. The results of the study show that: (1)A typical subsidence funnel formed above the mining area, with significant uplift in the northeast direction of the mining area; (2)Different regions exhibited varying trend changes during the study period; (3)Through time-series analysis of characteristic points, key nodes of rock stress changes were identified, and the overburden formation process was divided into three stages: ①Early mining stage, where mining operations had a small impact and the overburden had not yet started to form; ②Formation stage, where the roof strata gradually collapsed and thickened to form the overburden, and surface subsidence accelerated significantly; ③Stable subsidence stage, when the overburden had been mostly formed, and the subsidence rate stabilized.The study concludes that SBAS-InSAR technology combined with bicubic interpolation can not only dynamically monitor surface subsidence trends but also indirectly reveal the evolution of the overburden. The results are highly consistent with those observed in GNSS and microseismic monitoring. This technology demonstrates unique advantages in mining safety analysis, providing a reliable and continuous time-series perspective for mining progress analysis and safety assessment.
In the later stages of oilfield development, monitoring remaining oil saturation is of great significance for increasing reserves, boosting production, and controlling water production. Resistivity Logging After Casing (RLAC) can measure the resistivity of the formation outside the casing, and is an electrical logging technique. Its interpretation is simple, its methods are mature, and it has a relatively large radial detection range, giving it distinct advantages over radioactive logging techniques in remaining oil monitoring. However, the original measurement data of RLAC has characteristics such as multiple values, discrete values, and uneven depth intervals, making it difficult to directly collaborate with open-hole well logging data for analysis. To address this issue, this paper proposes a systematic RLAC data processing and interpretation method: first, using the Grubbs test and box plot methods to automatically remove resistivity anomalies, then synthesizing the single resistivity data using the arithmetic mean method, and utilizing the Akima interpolation method to densify the single resistivity data into an equal-depth interval dataset. Furthermore, by combining the open-hole well resistivity logging data, the remaining oil saturation is calculated based on the Indonesian formula, and a waterflooded layer grading evaluation standard is established by introducing a depletion index. Practical applications show that both the Grubbs test and box plot methods can effectively eliminate resistivity measurement anomalies in RLAC. The Akima interpolation method can better retain the original data shape while ensuring the smoothness of the curve. In the study area, the RLAC performs well in measuring medium and low resistivity formations. The Indonesian formula and the waterflooded layer identification standard based on the depletion index are reasonable and reliable for evaluating remaining oil saturation and waterflood levels. The processing results of well G35 are consistent with the actual production situation, verifying the rationality and accuracy of the data processing and interpretation method presented in this paper, and providing reliable technical support for remaining oil saturation monitoring and waterflooded layer evaluation in oilfields.
Magnetotelluric (MT) inversion is a key technology for exploration underground electrical structures. However, conventional methods such as the Gauss-Newton method and Occam method are limited by high computational complexity, sensitivity to initial models, and strong non-uniqueness. In recent years, deep learning has significantly improved the efficiency and accuracy of inversion through end-to-end nonlinear mapping, multimodal data fusion, and noise enhancement strategies. This paper systematically reviews the core progress of deep learning in MT inversion: Firstly, it reviews five traditional MT inversion methods, including the Gauss-Newton method, quasi-Newton method, Occam method, conjugate gradient method, and nonlinear conjugate gradient method. These conventional MT inversion methods face problems such as high computational resource requirements, significant sensitivity to initial models, and strong non-uniqueness. Then, in response to these difficulties, this paper mainly reviews the application of deep learning methods such as MT2DInv-Unet and MT-MitNet in MT inversion. MT2DInv-Unet avoids the dependence on initial models through end-to-end nonlinear mapping, reduces the risk of falling into local optimal solutions, and thus alleviates the problems of initial model sensitivity and strong non-uniqueness; MT-MitNet accelerates forward modeling, significantly shortening the forward calculation time and effectively reducing computational complexity. The application of deep learning methods effectively improves the predicament of traditional inversion methods. Finally, in the face of challenges such as scarce measured data, non-Gaussian noise interference, and three-dimensional anisotropic modeling in complex geological scenarios, this paper combines the advantages of deep learning methods and proposes targeted suggestions for optimizing MT inversion.
Traditional full waveform inversion uses the L2 norm as the misfit function and applies a local optimization algorithm in the inversion process. When the initial model is inaccurate or the low-frequency information is deficient, the inversion results converge to a local minimum. Elastic full waveform inversion needs to obtain multi-parameter information, the nonlinearity is stronger, and the parameter crosstalk problem exists. The graph space optimal transport distance can effectively alleviate the cycle skipping of waveform inversion due to the convexity in the signal time shift and amplitude change. Therefore, we take the graph space optimal transport distance as the metric and construct the objective function, use the Hungarian algorithm to solve the linear distribution problem, and use the automatic differential to obtain the gradient to realize the graph space optimal transport automatic differentiation elastic full waveform inversion. According to graph space optimal transport characteristics, we propose a graph space optimal transport automatic differentiation elastic full waveform inversion method based on point cloud allocation strategy to ensure the inversion effect and improve the computing efficiency. Model test results show that the proposed method has noise robustness and low dependence on the initial model and low-frequency information
Fractures play a crucial role in controlling hydrocarbon migration pathways and the spatial configuration of accumulation zones. Accurate and detailed characterization of fracture systems is essential for improving the efficiency of oil and gas exploration and development. However, conventional seismic attribute-based fracture identification methods are highly sensitive to data quality, and often suffer from limited resolution and low stability. These limitations restrict their ability to precisely delineate the geometric structure and spatial distribution of fractures, particularly in areas with complex geology or low signal-to-noise ratios. To address this challenge, this study proposes a high-precision fracture identification method based on multi-scale seismic data derived through matching pursuit decomposition. The matching pursuit algorithm exhibits excellent time-frequency localization capability, allowing seismic signals to be decomposed into frequency components that emphasize different structural scales. This process generates large, medium, and small scale seismic datasets, which better highlight fracture features of corresponding sizes. Based on the decomposed multi-scale seismic data, gradient structure tensor attributes are computed at each scale to extract fracture-related discontinuities. These attributes are then integrated through weighted fusion to form a comprehensive multi-scale fracture attribute volume that captures both the overall structural trends and fine scale fractures. Subsequently, the optimal surface voting algorithm is employed to enhance the continuity and clarity of the identified fractures. It refines fracture representation by scanning orientation and dip angles, selecting candidate fracture surfaces, and applying a voting mechanism to suppress noise and highlight true fracture features. Validation on synthetic models and real seismic data demonstrates that the proposed method significantly improves the spatial resolution and geometric integrity of fracture interpretation. Compared to traditional attribute-based approaches, this method achieves superior accuracy in locating fracture positions and delineating fracture boundaries, particularly for small scale fractures that are otherwise difficult to detect. The methodology presented in this work offers a robust and effective tool for fracture detection and imaging in structurally complex regions. It has strong potential for wide application in the fine scale interpretation of fractured reservoirs and supports improved decision-making in hydrocarbon exploration and production.
The strong-energy multiple wave interference developed within the Ordovician strata in the desert area of Tarim Basin has become a key factor restricting high-precision imaging of fault-controlled fracture-cavity reservoirs. This paper conducts systematic research on multiple wave issues in seismic exploration of Tarim Basin. Aiming at the composite interference problem of long-period surface multiples and short-period interbed multiples unique to desert areas, we propose a combined multiple suppression technology system suiTable for ultra-deep fault-karst reservoir exploration creatively. The cascaded processing workflow integrating 3D Generalized Surface Multiple Prediction (3D-GSMP) and adaptive plane wavefield continuation method breaks through the limitations of traditional single suppression methods. Application examples demonstrate that this combined approach achieves effective multiple suppression while preserving valid signals, improves imaging accuracy of fault-controlled fracture-cavity reservoir targets, and provides new solutions for multiple wave issues in ultra-deep carbonate oil and gas exploration in desert areas.
The Silurian reservoir in the Kataklonk Uplift area of the Tarim Basin is characterized by alternating sandstone and mudstone depositions, exhibiting strong heterogeneity with generally thin individual sand bodies. Traditional seismic waveform indication inversion techniques, while capable of identifying reservoir characteristics through impedance parameter calculations, lack sufficient sensitivity to minor lithological differences in complex formations with frequent sandstone-mudstone alternations, thus limiting reservoir prediction accuracy. To address this issue, this paper innovatively introduces a method combining Principal Component Analysis with Seismic Waveform Indication Inversion (PCA-SMI). First, through sensitivity analysis, Gamma Ray (GR) logging curves were determined as the input feature parameters for seismic waveform indication simulation inversion. Then, the first three principal components reflecting the main reservoir characteristics were extracted using principal component analysis to reconstruct the gamma ray curve, enhancing the feature curve's ability to identify different lithologies. Finally, the reconstructed gamma ray curve was used as input for lithological inversion via waveform indication simulation. Results demonstrate that this method significantly improves the identification accuracy of sandstone-mudstone interbedded reservoirs, optimizing the sandstone-mudstone discrimination threshold from 74.56 API to 57.6 API, effectively resolving issues of low vertical resolution and lateral discontinuity in inversion results. In practical applications, this method shows significant advantages in identifying individual sand bodies and precisely characterizing reservoir distribution, with prediction results achieving a 91.76% match with actual logging sandstone thickness. This achieves dual improvements in reservoir prediction accuracy and resolution, providing powerful technical support for oil and gas exploration under complex geological conditions.
Underground Gas Storage (UGS) facilities in depleted hydrocarbon reservoirs have garnered significant attention due to their inherent advantages of low operational risk, substantial storage capacity, and cost-effectiveness. However, the precise characterization of internal gas migration distribution patterns within such UGS systems continues to present a substantial scientific and technical challenge. This study analyzes the feasibility of applying the wide-field electromagnetic method(WFEM) to UGS gas-water migration monitoring by using coupled field finite element. Firstly,we established the correlation mechanism between the gas-water distribution state in UGS and formation electrical properties through effective medium theory, thereby obtaining an electromagnetic field coupled three-dimensional geological model containing the two-dimensional equivalent anomaly structure of UGS. We use Finite Element Method (FEM) to solve the electromagneic model, thereby quantifying the impacts of gas migration on the reservoir's electrical properties. Subsequently, electromagnetic forward modeling was conducted to generate the WFEM apparent resistivity distribution response. Subsequently, two distinct WFEM apparent resistivity parameters were computed for UGS models under diverse gas injection schemes. We systematically investigated the response characteristics of injection rate, injection duration, and gas migration direction on both apparent resistivity distributions, while rigorously evaluating the sensitivity of WFEM apparent resistivity to fluid distribution variations. The results show that two types of apparent resistivity changes can effectively track the state of gas migration in UGS,and the apparent resistivity calculated using the electric field component is consistent with the distribution of gas-water saturation in the UGS. This result demonstrates the potential ability of the WFEM to be applied to on-site monitoring of UGS gas status. This study aims to deliver a robust technical methodology for the refined operational management of large-scale, long-cycle UGS facilities.
Elemental wireline logging and element mud logging, as key technologies in the field of oil and gas exploration, respectively achieve the measurement and analysis of formation elements through neutron-gamma reaction and X-ray fluorescence mechanisms, and play an important role in lithology identification, reservoir evaluation and engineering decision-making. This paper systematically reviews the physical principles, instrument development history and engineering application progress of the two technologies, compares their technical differences in measurement methods, resolution characteristics, interference factors and detection performance, and reveals their complementarity in spatio-temporal resolution, application scenarios and data value. Research shows that elemental wireline logging has a higher accuracy advantage in thin reservoir characterization and quantitative analysis of mineral components, while element mud logging is more engineering applicable in dynamic formation tracking due to its real-time characteristics. Elemental wireline logging and mud logging techniques are widely applied in lithology identification, sedimentary environment analysis, reservoir parameter calculation and geological guidance. By integrating high-precision point data from wireline logging with continuous big data streams from mud logging, a multi-dimensional reservoir evaluation system can be constructed, reducing the uncertainty of a single technology. In the future, high-precision detection technologies driven by intelligent algorithms, multi-source data fusion models, and passive innovations oriented towards green exploration will become the core directions for the development of elemental measurement and logging technologies. This article can provide certain ideas or guidance for optimizing the collaborative application of elemental wireline logging and mud logging technologies and enhancing the exploration efficiency of unconventional resources.
At present, imaging logging image recognition mainly focuses on fracture recognition, while the research on the recognition of pore-like reservoir types is relatively lacking, and there has not been a systematic recognition method for layer-segment reservoir types. To this end, this paper proposes a reservoir type recognition method based on computer vision and deep learning, adopting YOLOv8-S as the model framework, and improving the recognition prediction ability of image reservoir types by introducing the attention mechanism and optimizing the Neck module. In addition, a sliding window method combined with edge detection is proposed to segment the layer segment images, which effectively reduces the loss of feature information and improves the accuracy of model recognition. Compared with the existing identification methods, this experimental research method significantly reduces the subjectivity and workload of manual operation, and is able to identify both fracture and hole reservoir types, with an identification accuracy of 83.3%, which has a certain reservoir type identification and prediction capability, providing a technical guarantee for the accurate evaluation of fracture and hole reservoirs
Deep coal seams are classified as ultra-low permeability gas reservoirs, characterized by inherently low permeability and micro-scale pore-throat structures. During production, these reservoirs are highly vulnerable to external fluid intrusion, resulting in water-locking damage. To tackle water-locking damage associated with the low permeability of deep coal seams, this study elucidates the microscopic mechanisms of water-unlocking agents, addressing the prior lack of microscopic quantitative characterization. By integrating Low-Field Nuclear Magnetic Resonance (LF-NMR) with physical simulation experiments, we quantitatively analyze fluid retention patterns across various pore-throat scales. Five water-unlocking agents (nonionic, anionic, cationic, zwitterionic, and fluorocarbon) were selected, and their efficacy was evaluated via surface tension, contact angle, and permeability recovery tests. Results indicate that, at a bound water saturation of 53.30%, the water-locking damage rate reaches 87.04%, indicating severe water-locking. LF-NMR analysis shows that fluid retention within small pore-throats (0.001~2.5 ms) accounts for 86.51% of total retention, serving as the primary contributor to water-locking damage. Upon addition of water-unlocking agents, surface tension decreases while contact angles increase. The fluorocarbon-based agent (Type V) exhibited the best performance, reducing surface tension to 19.60 mN/m, increasing contact angle to 48.85°, and achieving a water-locking prevention contribution rate of 81.20%. Following Type V agent treatment, fluid retention in small pore-throats decreased by 31.51%, and permeability recovery reached 28.49%. Small pore-throats serve as the primary seepage channels. As fluid occupancy in smaller pores decreases, gas-phase seepage channels expand, resulting in increased macroscopic permeability. This study accomplishes microscopic quantitative characterization of water-locking damage via LF-NMR, demonstrating that water-unlocking agents alleviate capillary forces to unblock small pore-throat channels, thereby offering novel strategies for efficient deep coal seam development.
Surface wave dispersion curve picking is one of the important steps in surface wave data processing. At present, the dispersion curve picking mainly relies on a large number of manual processing and is time consuming when a large number of dispersion curves need to be picked up, and it is difficult to meet the demand of efficient processing of modern surface wave exploration. How to efficiently and accurately realize picking of surface wave dispersion curves automatically has become an urgent problem. In this paper, the picking of dispersion curves is regarded as an image segmentation problem, and a region splitting and merging algorithm is proposed to realize picking of dispersion curves from the dispersion energy map quickly and accurately. The algorithm uses the standard deviation and average value of the grayscale value of the pixels in the dispersion energy map region to establish the consistency criterion of region splitting and merging, uses the consistency criterion to split and merge them continuously to split the disperse energy region, and finally realizes the automatic picking of the dispersion curve by tracking the peaks of different frequencies in the disperse energy region. To verify the effectiveness of the algorithm, a soft sandwich-containing model and a velocity-increasing model are used for comprehensive testing. The results show that the automatically picking dispersion curves are highly consistent with the theoretical dispersion curves. Meanwhile, the actual surface wave data test further confirms the accuracy of the region splitting and merging algorithm.
The frequency limitation of 3D seismic data leads to the insufficient recognition accuracy of small-scale geological bodies such as thin interbeds and coal seam bifurcation and merging. For this reason, a Joint Lateral Constraint Compressed Sensing Multi-Trace Sparse Inversion Method (JL-CSMTSI) is proposed. Under the framework of compressed sensing sparse inversion, this method constructs lateral continuity constraints by combining TV regularization terms with geological structure constraints, enabling collaborative inversion of multi-trace reflection coefficients. This approach effectively broadens the frequency band of seismic data and enhances seismic resolution. In terms of algorithm implementation, an adaptive parameter selection strategy is proposed to overcome the limitations of empirical assignment. To objectively evaluate resolution improvement, quantitative metrics—Effective Bandwidth Ratio (EBR) and Wavelet Compression Ratio (WCR)—are established. Tests on wedge models and the Marmousi2 model demonstrate the method's effectiveness in high-resolution processing of seismic data with complex structures such as thin interbeds, steep dips, and faults. In practical seismic data processing, the method not only preserves the quality of the original data but also improves the identification accuracy of thin interbeds and microstructures, exhibiting strong reliability. The results demonstrate that the JL-CSMTSI method provides a high-resolution processing technique for 3D seismic exploration in coal mining areas, with good potential for engineering applications, and is also applicable to high-precision seismic exploration in other complex geological conditions.
Accurately predicting reservoir porosity and permeability is of great significance for evaluating the storage capacity of tight sandstone reservoirs. Traditional methods for petrophysical evaluation often yield significant errors. To fully utilize existing well logging data and improve prediction accuracy, a literature review revealed that conventional methods have limitations and perform poorly in predicting the petrophysical parameters of tight sandstone reservoirs. Therefore, this paper proposes a model that integrates the Particle Swarm Optimization (PSO) algorithm to globally iteratively search for the optimal penalty factor (c) and kernel function parameter (g) for a Support Vector Machine (SVM) prediction model. This PSO-SVM model is constructed for predicting the petrophysical parameters of tight sandstone reservoirs. In practical application, Pearson correlation coefficients were calculated to select well logging curves highly sensitive to porosity and permeability. Five curves—Spontaneous Potential (SP), Gamma Ray (GR), Caliper (CAL), Acoustic (AC), and Compensated Neutron Log (CNL)—were chosen as input features to predict porosity and permeability, respectively. Research demonstrates that the PSO-SVM model achieved prediction accuracies of 96.7% for porosity and 93.6% for permeability, outperforming other similar algorithms. It proves to be a reliable and advantageous method for predicting petrophysical parameters in the tight sandstone reservoirs of the Shanxi Formation in Block Q, providing robust technical support for oil and gas exploration and development.
To address the issue of high water cut in the Gasikule Oilfield, a series of laboratory experiments was conducted to examine the electrical response characteristics of water-flooded layers. Core samples representing high, medium, and low porosity and permeability clastic rocks were collected from the N1-N21 formations. The study focused on analyzing changes in lithology, petrophysical properties, pore structure, and cation content before and after water flooding, aiming to clarify the mechanisms responsible for the observed differences in electrical response. Experimental results indicate that in medium-to high-porosity and permeability reservoirs, when the salinity of the injected water exceeds 20, 000 mg/L, the resistivity-water saturation relationship follows an L-shaped curve. However, when the injected water salinity is below 20, 000 mg/L, the curve exhibits a U-shaped trend. In low-permeability reservoirs, the transition from an L-shaped to a U-shaped curve occurs at a much higher salinity of around 80, 000 mg/L. During freshwater flooding, as the degree of water invasion increases, the rock resistivity initially decreases and then increases, resulting in a clear U-shaped curve. The lower the water salinity, the more pronounced the U-shaped behavior becomes. Additionally, reservoirs with better petrophysical properties show lower water saturation values at the inflection point of the resistivity curve, indicating earlier changes in electrical response. These changes are primarily attributed to the hydration, swelling, dispersion, and migration of clay minerals during water flooding. These mineralogical processes lead to reductions in clay content and cation exchange capacity, while simultaneously improving pore connectivity and increasing both porosity and permeability. This research providesessential technical insights into the behavior of clastic reservoirs under water flooding and highlights the influence of salinity and reservoir quality on resistivity responses. The findings offer valuable guidance for the identification of water-flooded zones and the evaluation of remaining oil saturation. They also support the development of improved resistivity interpretation models and waterflood management strategies in similar sandstone reservoir settings.
As global energy demand continues to grow and conventional oil and gas resources become increasingly depleted, hydrocarbon exploration is progressively extending into areas characterized by complex geological structures and unconventional reservoirs. Within geological settings featuring developed fault/fracture zones, variable formation dips, and significant reservoir heterogeneity, 3D seismic data processing faces the challenge of achieving the "Three Highs" (high signal-to-noise ratio, high resolution, and high fidelity). The Qinshui Basin, a crucial strategic energy region in China, serves as a typical complex structural area and a demonstration zone for deep Coalbed Methane (CBM) extraction. Its interior hosts multiple fault systems and complex stratigraphic contacts. Particularly, seismic signals from deep coal seams exceeding 800 meters burial depth suffer severe attenuation, making conventional seismic exploration techniques inadequate for detailed reservoir characterization requirements. Preliminary resource assessment in the Hengling Block within this basin indicates substantial coal seam thickness, high CBM content, and significant development potential. This study employs well-controlled and Offset Vector Tile (OVT) domain processing techniques on the 3D seismic data from the Hengling Block, Qinshui Basin. Key technologies, including well-controlled deconvolution, OVT-domain five-dimensional interpolation, Pre-stack time migration, and azimuthal anisotropy correction, were applied to obtain seismic data meeting the "Three Highs" criteria. Leveraging the full-azimuth and azimuth-sectored seismic data products generated through OVT-domain processing, prestack inversion for gas prediction was conducted within the Hengling Block. The inversion results, specifically the profiles based on Lambda-Rho (λρ, Lamé constants multiplied by density), were validated using well log information. This approach successfully predicted the gas content of the No. 15 coal seam, revealing generally high gas content ranging between 15 cm3/g and 36 cm3/g. A comparison of these predictions with drilling results shows a high degree of consistency. The methodology provides a systematic solution for seismic data processing and reservoir prediction in this region and offers valuable guidance for the next phase of CBM development in the area.
In the process of in-situ leaching sandstone Uranium deposit exploration, Uranium reservoir heterogeneity can indicate the change law of Uranium ore layer lithology, physical properties and connectivity in the vertical and horizontal directions, which is an important content of studying Uranium mineralization process and spatial positioning prediction. At present, scholars have conducted preliminary research on Uranium reservoir heterogeneity, but there is no unified understanding on heterogeneity classification and quantitative evaluation methods. Different Uranium reservoir heterogeneity classifications have different evaluation methods. Based on previous research, this paper summarizes the classification and evaluation methods of Uranium reservoir heterogeneity, introduces the imbalance measurement theory for the first time to quantitatively characterize Uranium reservoir heterogeneity, and uses the interlayer coefficient and logging data theoretical model as imbalance measurement characterization parameters, which can achieve the purpose of quantitatively characterizing Uranium reservoir heterogeneity. This method was applied to the Shawan Formation Uranium reservoir in the Chepaizi area. The heterogeneity of the Shawan Formation was evaluated from three aspects: plane, interlayer and intralayer. The standard for dividing the degree of heterogeneity of sand bodies was clarified in combination with the imbalance measurement index, and the degree of heterogeneity was divided into five categories: weak, relatively weak, medium, relatively strong and strong. It is believed that the good plane continuity of the sand bodies in the lower and upper sections of the Shawan Formation can provide a good ore-bearing space for Uranium mineralization. The medium interlayer heterogeneity and the weak and medium intralayer heterogeneity are favorable places for Uranium mineralization. The process of heterogeneity from weak to strong is accompanied by the increase of reducing substances, and the oxidized ore-bearing fluid gradually "unloads" and deposits to form Uranium ore bodies.
Seismic data inversion plays a central role in exploration geophysics and subsurface medium modeling, yet conventional single-trace methods frequently exhibit limited noise robustness and non-uniqueness, producing unstable solutions. To address these shortcomings, this paper proposes a multichannel basis pursuit inversion that incorporates spatial reflectivity structure constraints. By constructing a structural characterization matrix based on the spatial distribution of reflected wavefields and incorporating it into the regularization term of the inversion equation, the approach couples vertical sparsity priors with lateral structural coherence, forming a synergistic constraint that enhances both geological continuity and spatial correlation. The algorithm is designed to balance convergence and noise suppression. Numerical experiments and field data case studies demonstrate that the proposed method substantially improves noise tolerance, enhances vertical resolution, recovers more thin-bed information, and strengthens lateral continuity and consistency, yielding more stable and reliable results than traditional basis pursuit inversion. The proposed framework thus offers a practical pathway for reflection-structure-based inversion and advances subsurface imaging and interpretation.
The group velocity in a general Transversely Isotropic (TI) medium is a function of the phase slowness direction angle. However, in practical calculations, it is often necessary to compute the group velocity along a specific ray direction. Thus, the corresponding phase slowness direction angle must first be derived from the ray direction, and then the group velocity in that direction can be calculated. Traditional ray velocity calculation methods for general TI media typically discretize the phase slowness angle with small angular intervals, compute the ray direction angle and ray velocity for each discrete angle, and then use interpolation based on the ray direction angle-ray velocity database to obtain the ray velocity for the actual ray direction. Nevertheless, this approach suffers from time-consuming computations and insufficient accuracy. This paper proposes a calculation method for ray velocity in the context of the general TI medium Thomsen parameter model. Firstly, the phase slowness vector is represented using the phase slowness direction vector and phase velocity. Subsequently, a system of equations is directly established between the ray direction and the phase slowness direction angle. Finally, the phase slowness direction angle obtained from solving the system of equations is substituted into the group velocity formula to derive the ray velocity for that ray direction. In the numerical simulations section, this ray velocity calculation method was used to obtain correct traveltime isosurfaces for homogeneous Vertical Transverse Isotropy (VTI) and Tilted Transverse Isotropy (TTI) medium models. Additionally, when applying the ray velocity formula to the traveltime calculation of an overthrust TTI model, the traveltime results were identical to those obtained from an elastic parameter model. Furthermore, by introducing an interpolation method to solve the phase slowness direction angle along the ray direction and applying it to the shortest path ray tracing method, the corresponding seismic wave traveltime field was obtained, providing a reliable comparison for the traveltime results of qP-waves and qSV-waves in the overthrust TTI model. Comparative tests show that the ray velocity calculation method for the Thomsen parameter model of general TI media proposed in this paper is correct, and compared with traditional interpolation methods, this method significantly improves the computational efficiency.
The inversion of fracture parameters has important theoretical significance for identifying fractured reservoir types. Based on the relationship between anisotropic parameters and fracture parameters, an approximate formula of PSV wave reflection coefficient for HTI medium with upper isotropic medium and lower vertical fracture under weak anisotropic condition is derived. By comparing the response characteristics of PSV wave and PP wave reflection coefficient in identifying water-bearing sandstone and gas-bearing sandstone, it is concluded that PSV wave has more advantages in identifying water-bearing sandstone and gas-bearing sandstone. Based on the derived reflection coefficient approximation formula, the inversion objective function of fracture parameters is established, and the advantages of particle swarm optimization algorithm and differential evolution algorithm are combined to form PSO-DE algorithm, which effectively overcomes the premature convergence problem of single algorithm and improves the global optimization performance. Gauss random noise with different signal-to-noise ratio is added to the theoretical model to test the noise immunity of the inversion method. The results show that the inversion error is small, the convergence speed is fast and the noise immunity is good. The velocity, density and fracture parameter profiles are obtained by inversion of the improved Marmousi2 model, and the fluid factor prediction is realized. The inversion and prediction results are consistent with the theoretical model, which verifies the effectiveness of the method. The research results will provide technical support for improving the inversion accuracy and reliability of seismic data.
Aiming at the challenge of ambiguous boundary identification of anomalous bodies in coal fields using the transient electromagnetic method under complex geological conditions, this study proposes the introduction of the DBSCAN clustering algorithm to determine the boundaries of target bodies in mine transient electromagnetic detection. Taking three typical concealed disaster-causing bodies in mines—low-resistivity goafs, high-resistivity layered coal seam dislocation faults, and composite anomalous bodies such as coal seam collapse columns and water-filled roadways—as research objects, the response characteristics are obtained through transient electromagnetic forward modeling and apparent resistivity imaging. The DBSCAN algorithm is then applied to cluster analyze the apparent resistivity data, achieving precise identification of anomalous body boundaries. By selecting the neighborhood radius based on the inflection point in the k-distance graph and establishing clustering criteria, the algorithm effectively enhances the contrast between anomalous bodies and the background field. This significantly reduces the boundary positioning error of typical anomalous bodies such as water-rich goafs and greatly improves the identification accuracy of complex geological bodies. Finally, transient electromagnetic detection data from the 5130 working face of the Yanjiahe Coal Mine are selected for anomalous boundary identification. The results demonstrate that the DBSCAN clustering analysis algorithm can effectively identify anomalies in transient electromagnetic imaging results, clearly determining anomalous boundaries. This provides a new approach for the precise identification of water-rich goaf ranges and other disaster-causing body boundaries.
Least-squares reverse time migration is a method of updating the reflection coefficient through fitting and multiple iterations based on reverse time migration, so as to improve the imaging quality to the greatest extent, so that the acquired imaging results have higher accuracy and better resolution. The traditional elastic wave least squares reverse time migration uses the conjugate gradient method or L-BFGS (Limited-memory BFGS) to update the reflection coefficient during the iteration process. The operation speed of the conjugate gradient method is slightly insufficient; and the L-BFGS method occupies a large amount of computing memory. This paper studies the least-squares reverse time migration based on the decoupled elastic wave equation, and updates the imaging value through a new hybrid conjugate gradient method based on memoryless variable BFGS (M-BFGS). The advantage of this method is that it can improve computational efficiency while ensuring imaging accuracy and occupying less memory. Compared with traditional methods, the advantage of this method is that it can improve computational efficiency while occupying less memory, and can also improve imaging accuracy with the same number of iterations.
The channel sandstone of Qianfoya Formation in northeastern Sichuan Basin has good exploration prospects. However, sandstone reservoirs have strong horizontal and vertical heterogeneity and complex spatial distribution characteristics. At the same time, the longitudinal wave impedance of logging curves is not sensitive to sandstone and mudstone.Conventional post-stack seismic inversion has difficulty solving the problem of quantitative prediction of lithology and reservoirs in this area. Therefore, in this paper, seismic information from different frequency bands and geological sedimentary knowledge are fully utilized to explore a geologically constrained frequency division inversion method for predicting complex sandstone and reservoirs. Firstly, we use known logging data to clarify the parameter characteristics of lithology and reservoirs. Then, based on the frequency division of seismic data using the matching pursuit algorithm, the neural network algorithm is used to establish a nonlinear mapping relationship between lithology and frequency division volumes to directly predict lithology. At the same time, characteristic attributes related to the development of channel sands are introduced as constraints during the learning process to reduce the risk of over-fitting and make the results more predictive. Finally, based on sandstone prediction, conventional longitudinal wave impedance inversion is combined to further predict reservoirs with high porosity inside sandstone. The results of practical application exhibit high resolution in the longitudinal and transverse directions of the profile, high consistency with wells, and reasonable sedimentary patterns in the plane. This method explores a new technological approach for predicting similar complex sandstone reservoirs in other blocks.
Magnetotelluric Sounding (MT) is an important technique for deep geophysical exploration, and the accuracy of its forward modeling directly impacts the reliability of inversion interpretations. This paper considers the dual parameters of conductivity and magnetic permeability, as well as anisotropy, and proposes a one-dimensional forward modeling approach based on Physics-Informed Neural Networks (PINNs). First, a complex-domain extension framework based on PINNs is introduced. Then, by incorporating the Wirtinger operator, we enable backpropagation of complex-valued operations in the neural network, constructing constraint-based physical information equations that support both conductivity anisotropy and dual magnetic permeability parameters. Innovatively, the balance factor is treated as a learnable parameter for adaptive optimization, combined with an adaptive residual refinement sampling strategy, to establish a joint training model for the MT forward problem using PINNs. Numerical experiments demonstrate that the relative error in electromagnetic field calculations for typical resistivity models is less than 2%, showing high consistency with both finite element solutions and analytical results. This validates the method's effectiveness and its potential for engineering applications in simulating complex anisotropic strata.
Ion-adsorbed rare earth ore is a relatively rare earth ore resource, and in-situ leaching is the main way of mining ion-type rare earth ore. Monitoring the diffusion of leaching liquid in deep depth can provide important support for efficient mining of rare earth ore and environmental control. The leaching liquid presents relatively low resistance in the regolith, and it is theoretically feasible to monitor the diffusion of leaching liquid by using the time-lapse change of resistivity. This study focuses on a rare earth ore exploration area in Fujian Province. First, a high-density resistivity exploration line is arranged in the target liquid injection area, and a shallow resistivity structure model of 50 meters along the measurement line is obtained. An area with low resistance regolith and high resistance granite is selected for liquid injection. Then, the time-shifted high density resistivity method was used to collect data on the line. The collection time lasted for 22 days, and a total of 11 groups of original observation data were obtained. Through fine processing, information extraction and inversion calculation of time-shifted high density resistivity method data, the time-shifted variation characteristics of apparent resistivity and inversion resistivity during liquid injection were systematically studied, the diffusion range of liquid injection solution was effectively identified, and the influence of underground structure on the seepage direction of liquid injection solution was discussed. The comprehensive research results of the time-shifted high-density resistivity method show that the deep resistivity of the injection area decreases gradually with the extension of the injection time. The characteristics of resistivity change can effectively identify the diffusion of liquid injection solution. Under the blocking effect of deep intact granite, the leaching liquid diffused evenly in both sides of the weathering layer. The fluctuation shape of the top surface of granite has obvious influence on the diffusion range of the leaching liquid, and the diffusion rate of the leaching liquid is greater on the side with the larger buried depth of the top surface. In addition, the detection also revealed the resistivity structure of the deep surface recovery roadway, showing the longitudinal diffusion of leaching liquid along the deep fracture. The experimental results demonstrate that it is feasible to monitor the deep diffusion range of leaching liquid through the resistivity variation law, and the research results have important reference value for the accurate layout of liquid injection holes and recovery roadways in ion-type rare earth mines.
The carboniferous igneous rock reservoir in the Dixi X well area contains a total of seven rock types, namely Basalt andesite, Basaltic volcanic breccia, Andesite volcanic breccia, Monzoporium, Tuff volcanic breccia, Granite porphyry, Rhyolite. The logging response characteristics are complex. The conventional intersection mapping method is difficult to distinguish between Basalt andesite and Basaltic volcanic breccia, Monzoporium and Tuff volcanic breccia, Granite porphyry and Rhyolite. ECS logging cannot effectively distinguish Granite porphyry and Rhyolite. Based on the technical principles and acquisition conditions of conventional logging, ECS logging and imaging logging data, and taking the lithology identification of thin sections as the benchmark, the lithology logging identification methods of igneous rock reservoirs were established by applying the intersection graph method, convolutional neural network method, "composition+acidity and alkalinity" method and "composition+structure" method. Firstly, a qualitative identification method for Andesite volcanic breccia, Basalt andesite and Basaltic volcanic breccia was established based on the intersection graph method and convolutional neural network method by conventional logging. At the same time, two lithological combinations were identified: the lithological combination of Monzoporium and Tuff volcanic breccia, and the lithological combination of Granite porphyry and Rhyolite. Secondly, the identification methods of Monzoporium and Tuff volcanic breccia were established based on the "composition+acidity and alkalinity" method by conventional logging and ECS logging. Finally, the identification methods of Granite porphyry and Rhyolite were established based on the "composition+structure" method by conventional logging and imaging logging. The results show that when the data of conventional logging, ECS logging and imaging logging are complete, the coincidence rate of identifying lithology by applying this method is about 86%. When there are conventional logging data and ECS logging data, Granite porphyry and Rhyolite can't be effectively identified, and the coincidence rate of lithology identification is about 66%. When there are conventional logging data and imaging logging data, it is impossible to effectively identify Monzoporium and Tuff volcanic breccia, and the coincidence rate of lithology identification is about 71%. When only conventional logging data are available, the coincidence rate of lithology identification is about 61%. It is recommended to measure ECS and imaging logging as much as possible to improve the coincidence rate of lithology identification.
The attenuation and dispersion characteristics of seismic waves in fluid-saturated porous media are one of the key indicators for identifying hydrocarbon reservoirs. This study proposes an elastic wave propagation model for dual-phase HTI media by integrating Biot's dual-phase theory with HTI anisotropic medium theory, deriving a three-dimensional first-order velocity-stress equation, and constructing a 12th-order staggered-grid finite-difference algorithm with PML boundary conditions. Numerical experiments demonstrate that: The fluid-phase parameter (R) exhibits a significant positive correlation with the slow P-wave velocity, while its energy attenuation rate increases exponentially with higher R values. The solid-fluid coupling coefficients (Q1/Q3) show a linear regulatory relationship with the energy attenuation of fast/slow P-waves but have no significant impact on shear wave propagation characteristics. Simulations using the Marmousi complex model verify the numerical stability of the algorithm under strongly heterogeneous geological conditions.
In view of the problem that the limited wave velocity in logging measurements makes it difficult to accurately describe the elastic characteristics of orthotropic (ORT) shale formations, this paper first applies rock elastic theories such as the Self-Consistent Model (SCA) and the Differential Effective Medium model (DEM) to construct a background medium with vertically transverse isotropic (VTI) elastic characteristics. On this basis, by introducing the Schoenberg linear slip model, an anisotropic rock physics model capable of describing the ORT elastic characteristics of shale is established. Finally, practical application is carried out in Well x1. The research results show that obtaining the stiffness matrix of the shale background medium with VTI properties and avoiding the influence of fluids in vertical fractures on the P-wave velocity are the keys to the rock physics modeling of ORT shale. Through the inversion of the ORT rock physics model, it is obtained that the fracture weakness in the Longmaxi Formation shows a relationship of △V>△N>△H. The compressive stiffness coefficients satisfy c11>c22>c33, and the shear stiffness coefficients satisfy c66>c44>c55. In the same Tsvankin plane, the anisotropy coefficients of P-waves and S-waves usually vary in magnitude. An increase in shale content will increase the anisotropy of P-wave and S-wave velocities, decrease Young's modulus, and increase Poisson's ratios ν12, ν13, ν21, ν23. Through this study, basic data can be provided for subsequent in-situ stress modeling of orthotropic shale, wellbore stability analysis, and optimized design of fracturing stages.
Aiming at the problems of the traditional Dual-Frequency Induced Polarization(IP) transmitter only sends the combined rectangular wave, the output energy utilization rate is not high enough, and the received back is affected by inductive coupling peak interference and harmonic pollution, a new dual-frequency IP signal transmission scheme was designed and the experimental device was made. The transmission of dual frequency combined sine waves and dual frequency square wave-sine combined waves has been achieved. Furthermore, the comparison experiments were carried out with the SQ-5 Dual-Frequency Induced Polarization Instrument produced by Hunan Geosun High-technology Co., Ltd.. By comparing the results of RC induced polarization simulation tests and field experiments, it was proved that the new dual-frequency signal is equivalent to the observed results of traditional dual-frequency combined rectangular wave transmission, the transmission signal energy utilization rate is very high. It can suppress or eliminate the inductive coupling peak and harmonic pollution at the receiving end, improve the signal-to-noise ratio at the receiving end. This method has a good application prospect.
Aerial magnetic surveying serves as a critical technical approach in various fields, including mineral resource exploration, geological structure analysis, detection of abandoned oil and gas wells, and localization of underground magnetic anomalies. With the rapid advancement of Unmanned Aerial Vehicle (UAV) technology, its application has expanded significantly into the domain of aerial magnetic gradient detection. Considering the limitations of current UAV-based magnetic gradient detection systems—such as narrow magnetic field measurement ranges, substantial magnetic interference from the carrier, and pronounced low-frequency noise, which collectively hinder high-precision detection—this study proposes a quadrotor UAV-based vertical magnetic gradient detection system capable of effectively acquiring vertical magnetic gradient data. The system establishes an airborne magnetic measurement platform by integrating a quadrotor UAV with a Bartington Mag-03 fluxgate magnetometer and a data acquisition system. A specially designed variable vertical linkage structure enables the magnetometer to be vertically mounted 6 meters beneath the UAV. During flights under wind conditions of level 4 or lower, the magnetometer remains sTable and vertically oriented, with magnetic interference from the UAV motors being negligible. To address the challenge of magnetic compensation under higher wind conditions, which necessitates shortening the connecting rod, field experiments were conducted, including UAV magnetic interference tests and pipeline detection trials. The collected magnetic data were processed and analyzed to implement magnetic interference compensation. The experimental results demonstrate that all system performance indicators meet the technical specifications required for aeromagnetic surveys, enabling safe, efficient, and high-quality vertical magnetic gradient detection. The issues of magnetometer instability and magnetic interference compensation difficulties have been effectively resolved, resulting in significantly improved magnetic measurement data quality. This system provides advanced technical support for China's aerial vertical magnetic gradient detection capabilities and holds great promise for future applications.
ISSN 1004-2903 (Print)
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