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Multi-scale intelligent fusion and dynamic validation for high-resolution seismic data processing in drilling 认领 引用 被引量：1
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作者 YUAN Sanyi XU Yanwu +2 位作者 XIE Renjun CHEN Shuai YUAN Junliang 《Petroleum Exploration and Development》 SCIE 2025年第3期680-691,共12页
During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resol... During drilling operations,the low resolution of seismic data often limits the accurate characterization of small-scale geological bodies near the borehole and ahead of the drill bit.This study investigates high-resolution seismic data processing technologies and methods tailored for drilling scenarios.The high-resolution processing of seismic data is divided into three stages:pre-drilling processing,post-drilling correction,and while-drilling updating.By integrating seismic data from different stages,spatial ranges,and frequencies,together with information from drilled wells and while-drilling data,and applying artificial intelligence modeling techniques,a progressive high-resolution processing technology of seismic data based on multi-source information fusion is developed,which performs simple and efficient seismic information updates during drilling.Case studies show that,with the gradual integration of multi-source information,the resolution and accuracy of seismic data are significantly improved,and thin-bed weak reflections are more clearly imaged.The updated seismic information while-drilling demonstrates high value in predicting geological bodies ahead of the drill bit.Validation using logging,mud logging,and drilling engineering data ensures the fidelity of the processing results of high-resolution seismic data.This provides clearer and more accurate stratigraphic information for drilling operations,enhancing both drilling safety and efficiency. 展开更多
关键词 high-resolution seismic data processing while-drilling update while-drilling logging multi-source information fusion thin-bed weak reflection artificial intelligence modeling
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Optimized Up-Down Deconvolution for Source-Side Ghost Wave Suppression in Multi-Component Ocean-Bottom Seismic Data 认领 引用
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作者 WANG Xiangchun ZHAO Qingxian +2 位作者 LIU Zhendong HAN Meifeng ZHOU Ya 《Journal of Ocean University of China》 SCIE CAS CSCD 2026年第2期539-552,共14页
Multi-component ocean-bottom seismic data offer several advantages,including a high signal-to-noise ratio and comprehensive PP and PS wavefield information.However,the strong reflection characteristics of the sea surf... Multi-component ocean-bottom seismic data offer several advantages,including a high signal-to-noise ratio and comprehensive PP and PS wavefield information.However,the strong reflection characteristics of the sea surface generate various types of ghost waves that severely degrade data quality.The source-side ghost waves not only change the effective wave waveforms but also induce the notch effect in frequency spectra and even produce false structures in stacking profiles.Here,the optimization of the updown deconvolution method is addressed to suppress the source-side ghost waves.Based on this method,a complete source-side ghost suppression process is constructed.In traditional methods,the upgoing and downgoing wavefields are derived through vertical wavenumber calibration.However,the wavenumber-domain method is computationally demanding and relies on precise source-receiver geometry for accurate determination of vertical wavenumbers.To address these problems,the upgoing and downgoing wavefields are extracted using the matching method in this study.Then,the up-down deconvolution process is applied to improve the effectiveness of source-side ghost wave suppression.Finally,synthetic and field data case studies,with detailed discussion,are presented to illustrate that the algorithm of our optimized up-down deconvolution is effective. 展开更多
关键词 up-down deconvolution multicomponent ocean-bottom seismic data source-side ghost waves matching method
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Physics-informed neural network for reconstruction of seismic data under compressed sensing sampling 认领 引用
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作者 Yin-Shuo Li Wen-Kai Lu +2 位作者 Xiao-Gang Huang Ji-Cai Ding Cao Song 《Petroleum Science》 SCIE EI CAS CSCD 2026年第4期1829-1841,共13页
Seismic exploration is one of the most critical methodologies and the highest-cost expenditures in the pre-exploration.The main cost of seismic exploration is acquiring seismic data,which can be significantly reduced ... Seismic exploration is one of the most critical methodologies and the highest-cost expenditures in the pre-exploration.The main cost of seismic exploration is acquiring seismic data,which can be significantly reduced through compressed sensing(CS)techniques.Traditional and deep learning(DL)CS methods offer unprecedented opportunities for cost optimization while maintaining data fidelity.However,CS methods rely on random acquisition,which performs poorly when the seismic data are not randomly acquired.This manuscript proposes a novel physics-informed neural network(PINN)framework for reconstructing 3D seismic data acquired via down-sampling from Ocean Bottom Seismometer(OBS)observation systems.The compressed sensing acquisition system of seismic data contains two types of sparsity:1)2D random missing traces,2)Dual random missing of source lines and source points.The proposed method employed move-out(MO)transformations with multiple constant velocities to mitigate aliasing artifacts and improve reconstruction accuracy.Then,a pre-interpolation process is utilized for the MO-transformed seismic data groups.Additionally,a semblance evaluation mechanism dynamically assigns weights to each MO dataset,generating optimized,pre-interpolated seismic profiles.Finally,the PINN architecture integrates physical constraints to refine the reconstructed data.The experimental results demonstrate the superior reconstruction performance and computational efficiency of the proposed method compared with the state-of-the-art. 展开更多
关键词 Physics-informed neural network Reconstruction of 3-D seismic data Move-out transformation Alias suppression
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Irregularly seismic data interpolation based on deep learning with integrated channel-spatial attention mechanism 认领 引用
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作者 Chao Ma Jian-Ping Huang +3 位作者 Zi-Xuan Qiao San-Fu Li Wen-Sheng Duan Gang-Lin Lei 《Petroleum Science》 SCIE EI CAS CSCD 2026年第3期1182-1196,共15页
To address the challenges of irregular sampling and insufficient spatial sampling in field seismic data,this study proposed a deep learning-based interpolation method incorporating dual channel spatial attention mecha... To address the challenges of irregular sampling and insufficient spatial sampling in field seismic data,this study proposed a deep learning-based interpolation method incorporating dual channel spatial attention mechanisms(CSAM).The proposed model establishes a collaborative framework of channel and spatial attention,enhancing feature representation by establishing connections between local reflection characteristics and global structural features.The performance of the method was evaluated through synthetic data experiments,including sparsity sensitivity tests,noise sensitivity tests,and field data validation,using metrics such as signal to noise ratio(SNR),mean absolute error(MAE),and structural similarity index(SSIM).Comparative analyses were conducted with Fourier projection onto convex sets(Fourierpocs),the classic U-net,and the efficient channel attention U-net(ECAUnet).Results demonstrate that the proposed method outperforms existing methods in reconstructing seismic reflection events and preserving amplitude fidelity,particularly in scenarios with extensive random data missing. 展开更多
关键词 Seismic data Deep learning Irregular sampling Channel-spatial attention mechanism Interpolation
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Seismic activity characteristics of the Sichuan-Yunnan region revealed by data fusion 认领 引用
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作者 Pei He Huai Zhang Yaolin Shi 《Earthquake Science》 CAS CSCD 2026年第4期370-386,共17页
Accurate characterization of regional seismic activity is crucial for assessing earthquake hazards.Seismic activity depends on multiple factors,including tectonic loading,fault geometry and distribution,crustal and ma... Accurate characterization of regional seismic activity is crucial for assessing earthquake hazards.Seismic activity depends on multiple factors,including tectonic loading,fault geometry and distribution,crustal and mantle structures,local topography,physical properties,and global environmental changes.Human influences,such as reservoir impoundment,enhanced geothermal systems,and shale gas extraction,further complicate these relationships.Establishing an integrated theoretical and methodological framework through data to analyze these natural and anthropogenic factors represents a frontier challenge in contemporary seismology and geodynamics.This study introduces three novel visualization methods for earthquake catalogs,efficiently capturing the complex relationships between magnitude,frequency,seismic origin time,and epicentral location.Utilizing these methods with comprehensive heterogeneous geophysical datasets from the Sichuan-Yunnan region,including over 420,000 earthquake records,160 three-dimensional active fault datasets,high-resolution topography,Moho depth,community velocity models,and crustal deformation data,the seismic characteristics of the region over the past 50 years were systematically analyzed.Results indicate that:(1)High seismic activity and hazard areas in the Sichuan-Yunnan region are primarily concentrated near deep major faults,block boundaries,and brittle transition zones with distinct low-and high-velocity anomalies,showing clear spatial banding,temporal clustering,and cyclicity;(2)Fault segments such as Longmenshan,Lijiang-Xiaojinhe,and Nujiang-Irrawaddy likely facilitate internal material exchange within the Qinghai-Xizang Plateau.Significant crustal thickening in their northwestern sections corresponds with lower seismic activity;(3)Crustal strain varies notably along the Xianshuihe-Anninghe-Zemuhe-Xiaojiang and Longmenshan fault zones,which delineate the boundary between regions of high-and low-velocity ratio anomalies.These zones host the majority of the regional earthquakes,requiring intensified monitoring due to frequent events despite moderate mainshock magnitudes.Overall,the proposed methodology provides a new reference for deepening our understanding of regional seismicity and developing an improved earthquake visualization technique. 展开更多
关键词 data fusion seismic activity Sichuan-Yunnan region magnitude-frequency plot earthquake catalog
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Application of multi-scaled morphology in denoising seismic data 认领 引用 被引量：13
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作者 王润秋 李青 张明 《Applied Geophysics》 SCIE 2008年第3期197-203,共7页
In this paper, multi-scaled morphology is introduced into the digital processing domain for land seismic data. First, we describe the basic theory of multi-scaled morphology image decomposition of exploration seismic ... In this paper, multi-scaled morphology is introduced into the digital processing domain for land seismic data. First, we describe the basic theory of multi-scaled morphology image decomposition of exploration seismic waves; second, we illustrate how to use multi-scaled morphology for seismic data processing using two real examples. The first example demonstrates suppressing the surface waves in pre-stack seismic records using multi-scaled morphology decomposition and reconstitution and the other example demonstrates filtering different interference waves on the seismic record. Multi-scaled morphology filtering separates signal from noise by the detailed differences of the wave shapes. The successful applications suggest that multi-scaled morphology has a promising application in seismic data processing. 展开更多
关键词 Multi-scaled morphology structure element seismic data processing seismic data denoising.
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Seismic Data Reconstruction and Noise Suppression Based on an Improved Weighted Fast Projection onto Convex Sets Algorithm 认领 引用
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作者 Cheng Gao-peng Zhang Hua +3 位作者 Li Hong-xing Song Yu Yue Ming Zhang Kai-dong 《Applied Geophysics》 SCIE CSCD 2026年第1期336-351,433,434,共16页
In eld seismic data acquisition,seismic traces are often aected by substantial data gaps and strong noise interference due to environmental and instrumental factors,thus degrading the resolution and signalto-noise rat... In eld seismic data acquisition,seismic traces are often aected by substantial data gaps and strong noise interference due to environmental and instrumental factors,thus degrading the resolution and signalto-noise ratio(SNR)of the seismic profiles.Effective seismic data reconstruction and noise suppression techniques are therefore essential to recover missing signals and improve data quality.In this study,a fast projection onto convex sets(FPOCS)algorithm is proposed by incorporating an inertial parameter that involves a linear combination of the two preceding iterations based on the traditional projection onto convex sets(POCS)algorithm.Then,a weighting factor is introduced to achieve simultaneous data reconstruction and noise suppression using the weighted fast projection onto convex sets(WFPOCS)algorithm.To further suppress residual random noise in the updated solution,an optimization strategy is adopted by swapping the order of the iterative hard thresholding operator and the projection operator.The nal algorithm,termed the improved weighted fast projection onto convex sets(IWFPOCS),achieves high-efciency reconstruction and effective noise suppression.Compared with WFPOCS,the proposed method maintains fast reconstruction speed while demonstrating superior denoising performance on irregularly missing and noisy datasets.Field data experiments conrm that the proposed method signicantly improves the SNR and resolution of seismic data,oering strong practical potential for subsequent processing and interpretation. 展开更多
关键词 Seismic data reconstruction Fast projection onto convex set algorithm Curvelet transform Weighted fast projection onto convex set algorithm Noise suppression
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Cenozoic fault system and its control on gas hydrate accumulation in the eastern Dongsha Island area,northern South China Sea:Insights from high-resolution 3D seismic data 认领 引用
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作者 Gang Dong Yun-bao Sun +8 位作者 Zhi-lei Sun Ang Li Di Luo Qing Li Dong Zhang Feng Cai Bin Zhai Gui-jing Yan Jian-ming Gong 《China Geology》 CAS CSCD 2026年第3期489-501,共13页
The enrichment and accumulation of natural gas hydrates depend on sufficient gas supply and effective migration pathways.The upward migration of deep thermogenic gases through fault systems is critical for seepage-typ... The enrichment and accumulation of natural gas hydrates depend on sufficient gas supply and effective migration pathways.The upward migration of deep thermogenic gases through fault systems is critical for seepage-type hydrate formation.This study aims to elucidate the developmental characteristics of Cenozoic fault systems in the eastern offshore area of Dongsha Island and their influence on natural gas hydrate formation.Utilizing high-resolution 3D seismic data,this study conducted a detailed structural interpretation and seismic attribute analysis to systematically investigate the spatial distribution,developmental stages,and dynamic mechanisms of the Cenozoic fault systems in this region.In addition,this study explored the role of these fault systems in facilitating the migration of deep thermogenic gases to the shallow strata.The study area is dominated by extensional and transtensional normal faults characterized by inherited development and relatively small fault displacements.The Cenozoic strata exhibit a tectonic framework of block-faulted uplift and subsidence with alternating highs and lows.Faults on either side of the central uplift dip in opposite directions and commonly exhibit parallel,step-like patterns.Differences in fault system attitudes were observed between the southern and northern parts of the study area.In the south,fault strikes remained consistent from deep to shallow levels,predominantly trending NE and NEE.In the north,fault strikes varied significantly with depth,transitioning from predominantly NEE in deeper strata to EW and NWW in shallower strata.The study identifies two distinct phases of Cenozoic fault activity:(1)66–10 Ma,a regional extensional tectonic regime controlled fault development,resulting in the formation of NEE-trending normal faults;(2)10–2.6 Ma,the Dongsha Movement influenced fault activity,during which EW-and NW–W-trending transtensional faults with dextral strike-slip characteristics developed in the Miocene strata of the northern region.The Cenozoic fault system played a significant positive role in facilitating the migration of deep thermogenic gas to shallow levels,thereby enabling the formation of natural gas hydrates. 展开更多
关键词 Gas hydrate Combustible ice Clean energy Cenozoic Structural features Fault system Geodynamic mechanism Gas migration Hydrate accumulation High-resolution 3D seismic data Geological survey engineering Eastern Dongsha Island
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Seismic data extrapolation based on multi-scale dynamic time warping 认领 引用
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作者 Jie-Li Li Wei-Lin Huang Rui-Xiang Zhang 《Petroleum Science》 SCIE EI CAS CSCD 2024年第6期3981-4000,共20页
Seismic data reconstruction can provide high-density sampling and regular input data for inversion and imaging,playing a crucial role in seismic data processing.In seismic data reconstruction,a common scenario involve... Seismic data reconstruction can provide high-density sampling and regular input data for inversion and imaging,playing a crucial role in seismic data processing.In seismic data reconstruction,a common scenario involves a significant distance between the source and the first receiver,which makes it unattainable to acquire near-offset data.A new workflow for seismic data extrapolation is proposed to address this issue,which is based on a multi-scale dynamic time warping(MS-DTW)algorithm.MS-DTW can accurately calculate the time-shift between two time series and is a robust method for predicting time-offset(t-x)domain data.Using the time-shift calculated by the MS-DTW as the basic input,predict the two-way traveltime(TWT)of other traces based on the TWT of the reference trace.Perform autoregressive polynomial fitting on TWT and extrapolate TWT based on the fitted polynomial coefficients.Extract amplitude information from the TWT curve,fit the amplitude curve,and extrapolate the amplitude using polynomial coefficients.The proposed workflow does not necessitate data conversion to other domains and does not require prior knowledge of underground geological information.It applies to both isotropic and anisotropic media.The effectiveness of the workflow was verified through synthetic data and field data.The results show that compared with the method of predictive painting based on local slope,this approach can accurately predict missing near-offset seismic signals and demonstrates good robustness to noise. 展开更多
关键词 Seismic data reconstruction Multi-scale morphology Dynamic time warping Extrapolation Common-midpoint(CMP)gathers
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Technical Security Vulnerabilities and Protection Strategies for Seismic Monitoring Data Transmission Networks 认领 引用
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作者 YANG Xiaofeng 《外文科技期刊数据库（文摘版）自然科学》 2026年第1期026-031,共6页
The seismic monitoring data transmission network is the core infrastructure for emergency management departments to carry out seismic monitoring, early warning and emergency response. Its safe and stable operation is ... The seismic monitoring data transmission network is the core infrastructure for emergency management departments to carry out seismic monitoring, early warning and emergency response. Its safe and stable operation is directly related to the safety of people's lives and property and regional social stability. Combined with the actual seismic monitoring work in Botou City, based on the local base station equipment configuration and network operation status, this paper systematically analyzes the existing technical security vulnerabilities in the transmission link, terminal equipment, network management and environmental adaptation of the current seismic monitoring data transmission network. In line with the requirements of the 14th Five-Year Plan for the upgrading of the seismic backbone network and industry security specifications, targeted and implementable protection strategies are proposed to strengthen the coordinated connection between technical and management protection, avoiding the listing of construction and project plans. It provides theoretical and practical support for the Emergency Management Bureau of Botou City to optimize the network security system and improve risk prevention and control capabilities, ensuring the real-time, accuracy and security of monitoring data. 展开更多
关键词 Botou City seismic monitoring data transmission network technical security vulnerabilities protection strategies emergency management
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Pore pressure prediction based on conventional well logs and seismic data using an advanced machine learning approach 认领 引用 被引量：1
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作者 Muhsan Ehsan Umar Manzoor +6 位作者 Rujun Chen Muyyassar Hussain Kamal Abdelrahman Ahmed E.Radwan Jar Ullah Muhammad Khizer Iftikhar Farooq Arshad 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2025年第5期2727-2740,共14页
Pore pressure is a decisive measure to assess the reservoir’s geomechanical properties,ensures safe and efficient drilling operations,and optimizes reservoir characterization and production.The conventional approache... Pore pressure is a decisive measure to assess the reservoir’s geomechanical properties,ensures safe and efficient drilling operations,and optimizes reservoir characterization and production.The conventional approaches sometimes fail to comprehend complex and persistent relationships between pore pressure and formation properties in the heterogeneous reservoirs.This study presents a novel machine learning optimized pore pressure prediction method with a limited dataset,particularly in complex formations.The method addresses the conventional approach's limitations by leveraging its capability to learn complex data relationships.It integrates the best Gradient Boosting Regressor(GBR)algorithm to model pore pressure at wells and later utilizes ContinuousWavelet Transformation(CWT)of the seismic dataset for spatial analysis,and finally employs Deep Neural Network for robust and precise pore pressure modeling for the whole volume.In the second stage,for the spatial variations of pore pressure in the thin Khadro Formation sand reservoir across the entire subsurface area,a three-dimensional pore pressure prediction is conducted using CWT.The relationship between the CWT and geomechanical properties is then established through supervised machine learning models on well locations to predict the uncertainties in pore pressure.Among all intelligent regression techniques developed using petrophysical and elastic properties for pore pressure prediction,the GBR has provided exceptional results that have been validated by evaluation metrics based on the R2 score i.e.,0.91 between the calibrated and predicted pore pressure.Via the deep neural network,the relationship between CWT resultant traces and predicted pore pressure is established to analyze the spatial variation. 展开更多
关键词 Pore pressure Conventional well logs Seismic data Machine learning Complex formations
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A novel method for predicting formation pore pressure ahead of the drill bit by embedding petrophysical theory into machine learning based on seismic and logging-while-drilling data 认领 引用 被引量：1
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作者 Xu-Yue Chen Cheng-Kai Weng +3 位作者 Lin Tao Jin Yang De-Li Gao Jun Li 《Petroleum Science》 SCIE EI CAS CSCD 2025年第7期2868-2883,共16页
Formation pore pressure is the foundation of well plan,and it is related to the safety and efficiency of drilling operations in oil and gas development.However,the traditional method for predicting formation pore pres... Formation pore pressure is the foundation of well plan,and it is related to the safety and efficiency of drilling operations in oil and gas development.However,the traditional method for predicting formation pore pressure involves applying post-drilling measurement data from nearby wells to the target well,which may not accurately reflect the formation pore pressure of the target well.In this paper,a novel method for predicting formation pore pressure ahead of the drill bit by embedding petrophysical theory into machine learning based on seismic and logging-while-drilling(LWD)data was proposed.Gated recurrent unit(GRU)and long short-term memory(LSTM)models were developed and validated using data from three wells in the Bohai Oilfield,and the Shapley additive explanations(SHAP)were utilized to visualize and interpret the models proposed in this study,thereby providing valuable insights into the relative importance and impact of input features.The results show that among the eight models trained in this study,almost all model prediction errors converge to 0.05 g/cm3,with the largest root mean square error(RMSE)being 0.03072 and the smallest RMSE being 0.008964.Moreover,continuously updating the model with the increasing training data during drilling operations can further improve accuracy.Compared to other approaches,this study accurately and precisely depicts formation pore pressure,while SHAP analysis guides effective model refinement and feature engineering strategies.This work underscores the potential of integrating advanced machine learning techniques with domain-specific knowledge to enhance predictive accuracy for petroleum engineering applications. 展开更多
关键词 Formation pore pressure Prediction ahead of the drill bit Seismic and logging-while-drilling data Machine learning Model interpretation
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Multi-scale simulation model of air system based on cross-dimensional data transmission method 认领 引用 被引量：6
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作者 Lei WANG Junkui MAO +2 位作者 Song WEI Longfei WANG Jin PAN 《Chinese Journal of Aeronautics》 SCIE EI CAS CSCD 2023年第5期157-174,共18页
The Secondary Air System(SAS)plays an important role in the safe operation and performance of aeroengines.The traditional 1D-3D coupling method loses information when used for secondary air systems,which affects the c... The Secondary Air System(SAS)plays an important role in the safe operation and performance of aeroengines.The traditional 1D-3D coupling method loses information when used for secondary air systems,which affects the calculation accuracy.In this paper,a Cross-dimensional Data Transmission method(CDT)from 3D to 1D is proposed by introducing flow field uniformity into the data transmission.First,a uniformity index was established to quantify the flow field parameter distribution characteristics,and a uniformity index prediction model based on the locally weighted regression method(Lowess)was established to quickly obtain the flow field information.Then,an information selection criterion in 3D to 1D data transmission was established based on the Spearman rank correlation coefficient between the uniformity index and the accuracy of coupling calculation,and the calculation method was automatically determined according to the established criterion.Finally,a modified function was obtained by fitting the ratio of the 3D mass-average parameters to the analytical solution,which are then used to modify the selected parameters at the 1D-3D interface.Taking a typical disk cavity air system as an example,the results show that the calculation accuracy of the CDT method is greatly improved by a relative 53.88%compared with the traditional 1D-3D coupling method.Furthermore,the CDT method achieves a speedup of 2 to 3 orders of magnitude compared to the 3D calculation. 展开更多
关键词 Air system Data transmission Disk cavity Multi-scale simulation 1D-3D coupling
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Evaluation of Multi-Scale Full Waveform Inversion with Marine Vertical Cable Data 认领 引用 被引量：10
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作者 Aifei Bian Zhihui Zou +1 位作者 Hua-Wei Zhou Jin Zhang 《Journal of Earth Science》 SCIE CAS CSCD 2015年第4期481-486,共6页
Seismic illumination plays an important role in subsurface imaging. A better image can be expected either through optimizing acquisition geometry or introducing more advanced seismic mi- gration and/or tomographic inv... Seismic illumination plays an important role in subsurface imaging. A better image can be expected either through optimizing acquisition geometry or introducing more advanced seismic mi- gration and/or tomographic inversion methods involving illumination compensation. Vertical cable survey is a potential replacement of traditional marine seismic survey for its flexibility and data quality. Conventional vertical cable data processing requires separation of primaries and multiples before migration. We proposed to use multi-scale full waveform inversion （FWI） to improve illumination coverage of vertical cable survey. A deep water velocity model is built to test the capability of multi-scale FWI in detecting low velocity anomalies below seabed. Synthetic results show that multi-scale FWI is an effective model building tool in deep-water exploration. Geometry optimization through target ori- ented illumination analysis and multi-scale FWI may help to mitigate the risks of vertical cable survey. The combination of multi-scale FWI, low-frequency data and multi-vertical-cable acquisition system may provide both high resolution and high fidelity subsurface models. 展开更多
关键词 full waveform inversion vertical cable illumination multi-scale geometry optimization,low-frequency data velocity model.
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Assessing the data quality and seismic monitoring capabilities of the Belt and Road GNSS network 认领 引用
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作者 Yu Li Yinxing Shao +2 位作者 Tan Wang Yuebing Wang Hongbo Shi 《Earthquake Science》 CAS CSCD 2025年第1期56-66,共11页
The Belt and Road global navigation satellite system(B&R GNSS)network is the first large-scale deployment of Chinese GNSS equipment in a seismic system.Prior to this,there have been few systematic assessments of t... The Belt and Road global navigation satellite system(B&R GNSS)network is the first large-scale deployment of Chinese GNSS equipment in a seismic system.Prior to this,there have been few systematic assessments of the data quality of Chinese GNSS equipment.In this study,data from four representative GNSS sites in different regions of China were analyzed using the G-Nut/Anubis software package.Four main indicators(data integrity rate,data validity ratio,multi-path error,and cycle slip ratio)used to systematically analyze data quality,while evaluating the seismic monitoring capabilities of the network based on earthquake magnitudes estimated from high-frequency GNSS data are evaluated by estimating magnitude based on highfrequency GNSS data.The results indicate that the quality of the data produced by the three types of Chinese receivers used in the network meets the needs of earthquake monitoring and the new seismic industry standards,which provide a reference for the selection of equipment for future new projects.After the B&R GNSS network was established,the seismic monitoring capability for earthquakes with magnitudes greater than MW6.5 in most parts of the Sichuan-Yunnan region improved by approximately 20%.In key areas such as the Sichuan-Yunnan Rhomboid Block,the monitoring capability increased by more than 25%,which has greatly improved the effectiveness of regional comprehensive earthquake management. 展开更多
关键词 Belt and Road multi-system GNSS data quality seismic monitoring capability
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Basic processing of the InSight seismic data from Mars for further seismological research 认领 引用
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作者 Shuguang Wang Shuoxian Ning +4 位作者 Zhixiang Yao Jiaqi Li Wanbo Xiao Tianfan Yan Feng Xu 《Earthquake Science》 CAS CSCD 2025年第5期450-460,共11页
The InSight mission has obtained seismic data from Mars,offering new insights into the planet’s internal structure and seismic activity.However,the raw data released to the public contain various sources of noise,suc... The InSight mission has obtained seismic data from Mars,offering new insights into the planet’s internal structure and seismic activity.However,the raw data released to the public contain various sources of noise,such as ticks and glitches,which hamper further seismological studies.This paper presents step-by-step processing of InSight’s Very Broad Band seismic data,focusing on the suppression and removal of non-seismic noise.The processing stages include tick noise removal,glitch signal suppression,multicomponent synchronization,instrument response correction,and rotation of orthogonal components.The processed datasets and associated codes are openly accessible and will support ongoing efforts to explore the geophysical properties of Mars and contribute to the broader field of planetary seismology. 展开更多
关键词 Mars Insight seismology data process seismic noise
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Seismic energy dispersion compensation by multi-scale morphology 认领 引用 被引量：1
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作者 Yu Junqing Wang Runqiu +5 位作者 Liu Taoran Zhang Zhenglong Wu Jian Jiang Yongyong Sun Lipeng Xia Pei 《Petroleum Science》 SCIE CAS CSCD 2014年第3期376-384,共9页
Seismic energy decays while propagating subsurface, which may reduce the resolution of seismic data. This paper studies the method of seismic energy dispersion compensation which provides the basic principles for mult... Seismic energy decays while propagating subsurface, which may reduce the resolution of seismic data. This paper studies the method of seismic energy dispersion compensation which provides the basic principles for multi-scale morphology and the spectrum simulation method. These methods are applied in seismic energy compensation. First of all, the seismic data is decomposed into multiple scales and the effective frequency bandwidth is selectively broadened for some scales by using a spectrum simulation method. In this process, according to the amplitude spectrum of each scale, the best simulation range is selected to simulate the middle and low frequency components to ensure the authenticity of the simulation curve which is calculated by the median method, and the high frequency component is broadened. Finally, these scales are reconstructed with reasonable coefficients, and the compensated seismic data can be obtained. Examples are shown to illustrate the feasibility of the energy compensation method. 展开更多
关键词 Seismic wave multi-scale morphology dispersion compensation high resolution median method spectrum simulation
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Optimal fault detection from seismic data using intelligent techniques:A comprehensive review of methods 认领 引用
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作者 Bhaktishree Nayak Pallavi Nayak 《Journal of Groundwater Science and Engineering》 2025年第2期193-208,共16页
Seismic data plays a pivotal role in fault detection,offering critical insights into subsurface structures and seismic hazards.Understanding fault detection from seismic data is essential for mitigating seismic risks ... Seismic data plays a pivotal role in fault detection,offering critical insights into subsurface structures and seismic hazards.Understanding fault detection from seismic data is essential for mitigating seismic risks and guiding land-use plans.This paper presents a comprehensive review of existing methodologies for fault detection,focusing on the application of Machine Learning(ML)and Deep Learning(DL)techniques to enhance accuracy and efficiency.Various ML and DL approaches are analyzed with respect to fault segmentation,adaptive learning,and fault detection models.These techniques,benchmarked against established seismic datasets,reveal significant improvements over classical methods in terms of accuracy and computational efficiency.Additionally,this review highlights emerging trends,including hybrid model applications and the integration of real-time data processing for seismic fault detection.By providing a detailed comparative analysis of current methodologies,this review aims to guide future research and foster advancements in the effectiveness and reliability of seismic studies.Ultimately,the study seeks to bridge the gap between theoretical investigations and practical implementations in fault detection. 展开更多
关键词 Seismic data Fault detection Fault Segmentation Machine learning Deep learning Adaptive learning
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Study on Key Techniques for Multi-scale Expression of Laneway Traverse Data in MGIS 认领 引用 被引量：5
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作者 ZHANG Hai-rong YU Jing-song-di LIU Wei 《Journal of China University of Mining and Technology》 2007年第4期508-512,共5页
The multi-scale expression of enormously complicated laneway data requires differentiation of both contents and the way the contents are expressed. To accomplish multi-scale expression laneway data must support multi-... The multi-scale expression of enormously complicated laneway data requires differentiation of both contents and the way the contents are expressed. To accomplish multi-scale expression laneway data must support multi-scale transformation and have consistent topological relationships. Although the laneway data generated by traverse survey-ing is non-scale data it is still impossible to construct a multi-scale spatial database directly from it. In this paper an al-gorithm is presented to first calculate the laneway mid-line to support multi-scale transformation; then to express topo-logical relationships arising from the data structure; and,finally,a laneway spatial database is built and multi-scale ex-pression is achieved using components GIS-SuperMap Objects. The research result is of great significance for improv-ing the efficiency of laneway data storage and updating,for ensuring consistency of laneway data expression and for extending the potential value of a mine spatial database. 展开更多
关键词 multi-scale expression traverse data MGIS laneway mid-line
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Rapid Seismic Damage Quantification for Reinforced Concrete Frames using Minimal Strain Inputs and Neural Networks Trained via Pushover Analysis 认领 引用
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作者 Mohammadreza Vafaei Sophia C.Alih Abdirahman Abdulkadir 《Computer Modeling in Engineering & Sciences》 SCIE EI 2026年第3期509-537,共29页
Rapid quantification of seismic-induced damage immediately following an earthquake is critical for determining whether a structure is safe for continued occupation or requires evacuation.This study proposes a novel da... Rapid quantification of seismic-induced damage immediately following an earthquake is critical for determining whether a structure is safe for continued occupation or requires evacuation.This study proposes a novel damage identification method that utilizes limited strain data points,significantly reducing installation,maintenance,and data analysis costs compared to traditional distributed sensor networks.The approach integrates finite element(FE)modeling to generate capacity curves through pushover analysis,incorporates noise-augmented datasets for Artificial Neural Network(ANN)training,and classifies structural conditions into four damage levels:Operational(OP),Immediate Occupancy(IO),Life Safety(LS),and Collapse Prevention(CP).To evaluate the method’s accuracy and efficiency,it was applied to two reinforced concrete(RC)frames;a single-story frame tested experimentally under cyclic loading and a three-story frame analyzed under various lateral load patterns.Strain data from selected beam and column ends were used as ANN inputs,while the corresponding damage classes served as outputs.Confusion matrix results demonstrated high true positive rates(>85%for the single-story and>90%for the three-story frame),even with a reduced number of sensors.The model also exhibited strong robustness to White Gaussian Noise(SNR=2.5-5 dB)and generalized effectively to nonlinear time-history analyses under scaled ground motions(PGA=0.1-1.0 g).Feature selection using the MRMR and ANOVA algorithms further enhanced computational efficiency.Overall,the proposed ANN-based framework has strong potential for real-time structural health monitoring applications. 展开更多
关键词 Damage detection seismic structural health monitoring reinforced concrete frame neural networks strain data
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