Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground mines remains a big challenge. Reliable P-phase onset time picking is necessary for accurate source location needed ...Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground mines remains a big challenge. Reliable P-phase onset time picking is necessary for accurate source location needed for planning and rescue operations in the event of failures. In this paper, a new technique based on the discrete stationary wavelet transform （DSWT）and higher order statist!cs, is proposed for processing noisy data from underground mines. The objectives of this method are to （1） Improve manual detection and tPicking of P-phase onset; and （ii） provide an automatic means of detecting and picking P-phase onset me accurately. The DSWT is first used to filter the signal over several scales. The manual P-phase onset detection and picking are then obtained by computing the signal energy across selected scales with frequency bands that capture the signal of interest. The automatic P-phase onset, on the other hand, is achieved by using skewness- and kurtosis-based criterion applied to selected scales in a time-frequency domain. The method was tested using synthetic and field data from an underground limestone mine. Results were compared with results obtained by using the short-term to long-term average （STA/LTA） ratio and that by Reference Ge et al. （2009）. The results show that the me!hod provides a more reliable estimate of the P-phase onset arrival than the STA]LTA method when the signal to noise ratio is very low. Also, the results obtained from the field data matched accurately with the results from Reference Ge et al. （2009）.展开更多
In seismic data processing,picking of the P-wave first arrivals takes up plenty of time and labor,and its accuracy plays a key role in imaging seismic structures.Based on the convolution neural network(CNN),we propose...In seismic data processing,picking of the P-wave first arrivals takes up plenty of time and labor,and its accuracy plays a key role in imaging seismic structures.Based on the convolution neural network(CNN),we propose a new method to pick up the P-wave first arrivals automatically.Emitted from MINI28 vibroseis in the Jingdezhen seismic experiment,the vertical component of seismic waveforms recorded by EPS 32-bit portable seismometers are used for manually picking up the first arrivals(a total of 7242).Based on these arrivals,we establish the training and testing sets,including 25,290 event samples and 710,616 noise samples(length of each sample:2 s).After 3,000 steps of training,we obtain a convergent CNN model,which can automatically classify seismic events and noise samples with high accuracy(>99%).With the trained CNN model,we scan continuous seismic records and take the maximum output(probability of a seismic event)as the P-wave first arrival time.Compared with STA/LTA(short time average/long time average),our method shows higher precision and stronger anti-noise ability,especially with the low SNR seismic data.This CNN method is of great significance for promoting the intellectualization of seismic data processing,improving the resolution of seismic imaging,and promoting the joint inversion of active and passive sources.展开更多
Microseismic phase detection and arrival picking are critical steps in the processing of hydraulic fracturing microseismic monitoring data.As the signal-to-noise ratios of P-and S-waves diff er because of the infl uen...Microseismic phase detection and arrival picking are critical steps in the processing of hydraulic fracturing microseismic monitoring data.As the signal-to-noise ratios of P-and S-waves diff er because of the infl uence of focal mechanisms,geometry,and background noise,diffi culties are introduced in the eff ective discrimination of seismic phases and the accurate acquisition of arrivals in conventional processing methods.In this paper,we propose a method for identifying microseismic phase arrival time by comprehensively analyzing the variation of moveout curves and combining the intra-event waveform similarities with the energy ratio of multitrace signals.First,a curve-fi tting formula is constructed with perforation arrivals,and event detection is achieved by adopting an energy-weighted similarity coeffi cient that seeks plausible fi tting curves with a sliding time window in continuous microseismic recordings.Then,the P-and S-waves are separated by the fitting parameters.The known arrival time trend of the microseismic phase is employed to calculate residual time corrections.Finally,the accurate arrival results of the microseismic phases can be obtained by picking the arrivals of stacked traces.The reliability and eff ectiveness of the proposed method for microseismic phase detection and arrival picking were determined through tests using field data.Arrival results indicate that the proposed method can improve accuracy compared with the traditional energy ratio method.展开更多
Accurately detecting the arrival time of a channel wave in a coal seam is very important for in-seam seismic data processing. The arrival time greatly affects the accuracy of the channel wave inversion and the compute...Accurately detecting the arrival time of a channel wave in a coal seam is very important for in-seam seismic data processing. The arrival time greatly affects the accuracy of the channel wave inversion and the computed tomography （CT） result. However, because the signal-to-noise ratio of in-seam seismic data is reduced by the long wavelength and strong frequency dispersion, accurately timing the arrival of channel waves is extremely difficult. For this purpose, we propose a method that automatically picks up the arrival time of channel waves based on multi-channel constraints. We first estimate the Jaccard similarity coefficient of two ray paths, then apply it as a weight coefficient for stacking the multi- channel dispersion spectra. The reasonableness and effectiveness of the proposed method is verified in an actual data application. Most importantly, the method increases the degree of automation and the pickup precision of the channel-wave arrival time.展开更多
基于微机电系统(Micro-Electro-Mechanical System,MEMS)技术研制的MEMS强震仪具有易集成、维护成本低和低功耗等优点,在地震监测领域应用广泛.然而,MEMS强震仪集成的软、硬件资源有限,并且受仪器自身噪声等因素干扰较大,地震信号测量...基于微机电系统(Micro-Electro-Mechanical System,MEMS)技术研制的MEMS强震仪具有易集成、维护成本低和低功耗等优点,在地震监测领域应用广泛.然而,MEMS强震仪集成的软、硬件资源有限,并且受仪器自身噪声等因素干扰较大,地震信号测量结果质量较低,对嵌入算法要求更高.针对这一问题,本文提出一种更适用于MEMS强震仪的改进长短时窗均值比(Short Term Average/Long Term Average,STA/LTA)算法.首先,通过构建抗干扰(Anti-interference,AR)特征函数抑制基线漂移和低频噪声的干扰,提高STA/LTA算法拾取地震事件的抗干扰能力;其次,提出采用“延时长窗”的方式,提高STA/LTA算法的计算效率和拾取精度,减少STA/LTA算法对MEMS集成资源的占用;最后,结合时窗位置进一步探究不同时窗大小对STA/LTA算法拾取效率的影响.实际地震资料处理结果表明,本文提出的改进STA/LTA算法计算效率更高,实时性和抗干扰能力更强,更适用于集成资源有限的MEMS强震仪.展开更多
Passive microseismic monitoring(PMM)serves as a fundamental technology for assessing hydraulic fracturing(HF)effectiveness,with a key focus on accurate and efficient phase detection/arrival picking and source location...Passive microseismic monitoring(PMM)serves as a fundamental technology for assessing hydraulic fracturing(HF)effectiveness,with a key focus on accurate and efficient phase detection/arrival picking and source location.In PMM data processing,the data-driven paradigm(deep learning based)outperforms the model-driven paradigm in characteristic extraction but lacks quality control and uncertainty quantification.Monte Carlo Dropout,a Bayesian uncertainty quantification technique,performs stochastic neuron deactivation through multiple forward propagation samplings.Therefore,this study proposes a deep learning neural network incorporating uncertainty quantification with manual quality control integration,establishing an optimized workflow spanning automated phase detection to robust source location.The methodology implementation comprises two principal components:(1)The MDNet employing Monte Carlo Dropout strategy enabling simultaneous phase detection/arrival picking and unce rtainty estimation;(2)an integrated hybrid-driven workflow with a traveltime-based inve rsion method for source location.Validation with field data demonstrates that MD-Net achieves superior performance under low signal-to-noise ratio conditions,maintaining detection accuracy exceeding 99%for both P-and S-waves.The phase arrival picking precision shows significant improvement,with a 40%reduction in standard deviation compared to the baseline model(P-S time difference decreasing from12.0 ms to 7.1 ms),while providing quantifiable uncertainty metrics for manual calibration.Source location results further reveal that our hybrid-driven workflow produces more physically plausible event distributions,with 100%of microseismic eve nts clustering along the primary fracture expanding direction.This performance surpasses traditional cross-correlation methods and single/multi-trace data-driven me thods in spatial rationality.This study establishes an inte rpretable,high-pre cision automated framework for HF-PMM applications,demonstrating potential for extension to diverse geological settings and monitoring configurations.展开更多
初至拾取是影响微震事件分析精度的重要因素之一。本文结合微震事件偏振特性和最小信息准则(akaike information criterion,AIC)函数特性,提出了一种利用偏振约束实现AIC初至拾取的改进方法:可以将偏振特征值拾取微震初至的应用扩展至...初至拾取是影响微震事件分析精度的重要因素之一。本文结合微震事件偏振特性和最小信息准则(akaike information criterion,AIC)函数特性,提出了一种利用偏振约束实现AIC初至拾取的改进方法:可以将偏振特征值拾取微震初至的应用扩展至单分量的微震数据,该方法将单分量微震数据视为三分量微震数据的一种特殊形式,利用三分量微震数据协方差矩阵的最大值序列对AIC方法进行约束,从而快速准确的拾取到微震数据的初至。文中应用该方法对不同信噪比的合成数据和实测数据进行了验证,同时与长短时平均(short time average/long time average,STA/LTA)、Maeda-AIC和偏振特征值方法进行了对比,结果显示该算法速度略低于上述3种方法,但精度和可靠性优于其他三种方法,同时与其他改进算法对比,不用设置阈值,并且选取时窗的长短对拾取结果几乎没有影响,可极大地提高算法的自动化程度。展开更多
文摘Accurate detection and picking of the P-phase onset time in noisy microseismic data from underground mines remains a big challenge. Reliable P-phase onset time picking is necessary for accurate source location needed for planning and rescue operations in the event of failures. In this paper, a new technique based on the discrete stationary wavelet transform （DSWT）and higher order statist!cs, is proposed for processing noisy data from underground mines. The objectives of this method are to （1） Improve manual detection and tPicking of P-phase onset; and （ii） provide an automatic means of detecting and picking P-phase onset me accurately. The DSWT is first used to filter the signal over several scales. The manual P-phase onset detection and picking are then obtained by computing the signal energy across selected scales with frequency bands that capture the signal of interest. The automatic P-phase onset, on the other hand, is achieved by using skewness- and kurtosis-based criterion applied to selected scales in a time-frequency domain. The method was tested using synthetic and field data from an underground limestone mine. Results were compared with results obtained by using the short-term to long-term average （STA/LTA） ratio and that by Reference Ge et al. （2009）. The results show that the me!hod provides a more reliable estimate of the P-phase onset arrival than the STA]LTA method when the signal to noise ratio is very low. Also, the results obtained from the field data matched accurately with the results from Reference Ge et al. （2009）.
基金sponsored by the National Key Research and Development Project(2018YFC1503202-01)the Emergency Management Project of the National Natural Science Foundation of China(41842042)
文摘In seismic data processing,picking of the P-wave first arrivals takes up plenty of time and labor,and its accuracy plays a key role in imaging seismic structures.Based on the convolution neural network(CNN),we propose a new method to pick up the P-wave first arrivals automatically.Emitted from MINI28 vibroseis in the Jingdezhen seismic experiment,the vertical component of seismic waveforms recorded by EPS 32-bit portable seismometers are used for manually picking up the first arrivals(a total of 7242).Based on these arrivals,we establish the training and testing sets,including 25,290 event samples and 710,616 noise samples(length of each sample:2 s).After 3,000 steps of training,we obtain a convergent CNN model,which can automatically classify seismic events and noise samples with high accuracy(>99%).With the trained CNN model,we scan continuous seismic records and take the maximum output(probability of a seismic event)as the P-wave first arrival time.Compared with STA/LTA(short time average/long time average),our method shows higher precision and stronger anti-noise ability,especially with the low SNR seismic data.This CNN method is of great significance for promoting the intellectualization of seismic data processing,improving the resolution of seismic imaging,and promoting the joint inversion of active and passive sources.
基金This work has been funded by the National Key Research and Development Project(2017YFC0307605,2017YFC0307702),National Science and Technology Major Project of China(No.2017ZX05008-008)and State Key Project(2016ZX05051004).The authors also thank Sinopec Oilfield Service Jianghan Corporation for providing the data and support and express their gratitude to the reviewers for their constructive comments.
文摘Microseismic phase detection and arrival picking are critical steps in the processing of hydraulic fracturing microseismic monitoring data.As the signal-to-noise ratios of P-and S-waves diff er because of the infl uence of focal mechanisms,geometry,and background noise,diffi culties are introduced in the eff ective discrimination of seismic phases and the accurate acquisition of arrivals in conventional processing methods.In this paper,we propose a method for identifying microseismic phase arrival time by comprehensively analyzing the variation of moveout curves and combining the intra-event waveform similarities with the energy ratio of multitrace signals.First,a curve-fi tting formula is constructed with perforation arrivals,and event detection is achieved by adopting an energy-weighted similarity coeffi cient that seeks plausible fi tting curves with a sliding time window in continuous microseismic recordings.Then,the P-and S-waves are separated by the fitting parameters.The known arrival time trend of the microseismic phase is employed to calculate residual time corrections.Finally,the accurate arrival results of the microseismic phases can be obtained by picking the arrivals of stacked traces.The reliability and eff ectiveness of the proposed method for microseismic phase detection and arrival picking were determined through tests using field data.Arrival results indicate that the proposed method can improve accuracy compared with the traditional energy ratio method.
基金supported by the National Major Scientific and Technological Special Project during the 13th Five-year Plan Period(No.2016ZX05045003-005)
文摘Accurately detecting the arrival time of a channel wave in a coal seam is very important for in-seam seismic data processing. The arrival time greatly affects the accuracy of the channel wave inversion and the computed tomography （CT） result. However, because the signal-to-noise ratio of in-seam seismic data is reduced by the long wavelength and strong frequency dispersion, accurately timing the arrival of channel waves is extremely difficult. For this purpose, we propose a method that automatically picks up the arrival time of channel waves based on multi-channel constraints. We first estimate the Jaccard similarity coefficient of two ray paths, then apply it as a weight coefficient for stacking the multi- channel dispersion spectra. The reasonableness and effectiveness of the proposed method is verified in an actual data application. Most importantly, the method increases the degree of automation and the pickup precision of the channel-wave arrival time.
文摘基于微机电系统(Micro-Electro-Mechanical System,MEMS)技术研制的MEMS强震仪具有易集成、维护成本低和低功耗等优点,在地震监测领域应用广泛.然而,MEMS强震仪集成的软、硬件资源有限,并且受仪器自身噪声等因素干扰较大,地震信号测量结果质量较低,对嵌入算法要求更高.针对这一问题,本文提出一种更适用于MEMS强震仪的改进长短时窗均值比(Short Term Average/Long Term Average,STA/LTA)算法.首先,通过构建抗干扰(Anti-interference,AR)特征函数抑制基线漂移和低频噪声的干扰,提高STA/LTA算法拾取地震事件的抗干扰能力;其次,提出采用“延时长窗”的方式,提高STA/LTA算法的计算效率和拾取精度,减少STA/LTA算法对MEMS集成资源的占用;最后,结合时窗位置进一步探究不同时窗大小对STA/LTA算法拾取效率的影响.实际地震资料处理结果表明,本文提出的改进STA/LTA算法计算效率更高,实时性和抗干扰能力更强,更适用于集成资源有限的MEMS强震仪.
基金funded by the Deep Earth Probe and Mineral Resources Exploration-National Science and Technology Major Project(Grant No.2024ZD1002503)。
文摘Passive microseismic monitoring(PMM)serves as a fundamental technology for assessing hydraulic fracturing(HF)effectiveness,with a key focus on accurate and efficient phase detection/arrival picking and source location.In PMM data processing,the data-driven paradigm(deep learning based)outperforms the model-driven paradigm in characteristic extraction but lacks quality control and uncertainty quantification.Monte Carlo Dropout,a Bayesian uncertainty quantification technique,performs stochastic neuron deactivation through multiple forward propagation samplings.Therefore,this study proposes a deep learning neural network incorporating uncertainty quantification with manual quality control integration,establishing an optimized workflow spanning automated phase detection to robust source location.The methodology implementation comprises two principal components:(1)The MDNet employing Monte Carlo Dropout strategy enabling simultaneous phase detection/arrival picking and unce rtainty estimation;(2)an integrated hybrid-driven workflow with a traveltime-based inve rsion method for source location.Validation with field data demonstrates that MD-Net achieves superior performance under low signal-to-noise ratio conditions,maintaining detection accuracy exceeding 99%for both P-and S-waves.The phase arrival picking precision shows significant improvement,with a 40%reduction in standard deviation compared to the baseline model(P-S time difference decreasing from12.0 ms to 7.1 ms),while providing quantifiable uncertainty metrics for manual calibration.Source location results further reveal that our hybrid-driven workflow produces more physically plausible event distributions,with 100%of microseismic eve nts clustering along the primary fracture expanding direction.This performance surpasses traditional cross-correlation methods and single/multi-trace data-driven me thods in spatial rationality.This study establishes an inte rpretable,high-pre cision automated framework for HF-PMM applications,demonstrating potential for extension to diverse geological settings and monitoring configurations.
文摘初至拾取是影响微震事件分析精度的重要因素之一。本文结合微震事件偏振特性和最小信息准则(akaike information criterion,AIC)函数特性,提出了一种利用偏振约束实现AIC初至拾取的改进方法:可以将偏振特征值拾取微震初至的应用扩展至单分量的微震数据,该方法将单分量微震数据视为三分量微震数据的一种特殊形式,利用三分量微震数据协方差矩阵的最大值序列对AIC方法进行约束,从而快速准确的拾取到微震数据的初至。文中应用该方法对不同信噪比的合成数据和实测数据进行了验证,同时与长短时平均(short time average/long time average,STA/LTA)、Maeda-AIC和偏振特征值方法进行了对比,结果显示该算法速度略低于上述3种方法,但精度和可靠性优于其他三种方法,同时与其他改进算法对比,不用设置阈值,并且选取时窗的长短对拾取结果几乎没有影响,可极大地提高算法的自动化程度。
