Discrimination of seismicity distributed in different areas is essential for reliable seismic risk assessment in mines.Although machine learning has been widely applied in seismic data processing,feasibility and relia...Discrimination of seismicity distributed in different areas is essential for reliable seismic risk assessment in mines.Although machine learning has been widely applied in seismic data processing,feasibility and reliability of applying this technique to classify spatially clustered seismic events in underground mines are yet to be investigated.In this research,two groups of seismic events with a minimum local magnitude(ML) of-3 were observed in an underground coal mine.They were respectively located around a dyke and the longwall face.Additionally,two types of undesired signals were also recorded.Four machine learning methods,i.e.random forest(RF),support vector machine(SVM),deep convolutional neural network(DCNN),and residual neural network(ResNN),were used for classifying these signals.The results obtained based on a primary dataset showed that these seismic events could be classified with at least 91% accuracy.The DCNN using seismogram images as the inputs reached the best performance with more than 94% accuracy.As mining is a dynamic progress which could change the characteristics of seismic signals,the temporal variance in the prediction performance of DCNN was also investigated to assess the reliability of this classifier during mining.A cascaded workflow consisting of database update,model training,signal prediction,and results review was established.By progressively calibrating the DCNN model,it achieved up to 99% prediction accuracy.The results demonstrated that machine learning is a reliable tool for the automatic discrimination of spatially clustered seismicity in underground mining.展开更多
Seismic waveform clustering is a useful technique for lithologic identification and reservoir characterization.The current seismic waveform clustering algorithms are predominantly based on a fixed time window,which is...Seismic waveform clustering is a useful technique for lithologic identification and reservoir characterization.The current seismic waveform clustering algorithms are predominantly based on a fixed time window,which is applicable for layers of stable thickness.When a layer exhibits variable thickness in the seismic response,a fixed time window cannot provide comprehensive geologic information for the target interval.Therefore,we propose a novel approach for a waveform clustering workfl ow based on a variable time window to enable broader applications.The dynamic time warping(DTW)distance is fi rst introduced to effectively measure the similarities between seismic waveforms with various lengths.We develop a DTW distance-based clustering algorithm to extract centroids,and we then determine the class of all seismic traces according to the DTW distances from centroids.To greatly reduce the computational complexity in seismic data application,we propose a superpixel-based seismic data thinning approach.We further propose an integrated workfl ow that can be applied to practical seismic data by incorporating the DTW distance-based clustering and seismic data thinning algorithms.We evaluated the performance by applying the proposed workfl ow to synthetic seismograms and seismic survey data.Compared with the the traditional waveform clustering method,the synthetic seismogram results demonstrate the enhanced capability of the proposed workfl ow to detect boundaries of diff erent lithologies or lithologic associations with variable thickness.Results from a practical application show that the planar map of seismic waveform clustering obtained by the proposed workfl ow correlates well with the geological characteristics of wells in terms of reservoir thickness.展开更多
This paper outlines the results obtained from real time microseismic monitoring of an opencast coal mine in South India.The objective of the study is to investigate the stress changes within the rockmass along the slo...This paper outlines the results obtained from real time microseismic monitoring of an opencast coal mine in South India.The objective of the study is to investigate the stress changes within the rockmass along the slope due to underground mine development operation and their impact on the stability of the highwall slope.The installed microseismic systems recorded the seismic triggerings down toà2 moment magnitude.In general,most of the events recorded during the monitoring period are weak in seismic energy.The study adopts a simple and more reliable tool to characterize the seismically active zone for assessing the stability of the highwall in real time.The impact of underground working on the slope is studied on the basis of the seismic event impact contours and seismic clusters.During the monitoring period,it is observed that the intensity of the overall microseismic activity along the slope due to the mine development operations did not cause any adverse impact on the highwall stability.展开更多
The Main Central Thrust (MCT) in Himalaya is seismically active in segments. In recent times, strain release within these active segments produce five spatial clusters (A to E;Figure 1). The seismicity within the clus...The Main Central Thrust (MCT) in Himalaya is seismically active in segments. In recent times, strain release within these active segments produce five spatial clusters (A to E;Figure 1). The seismicity within the cluster zones occurs in two depth bands;corresponding to the base of upper and lower crust. Depth sections across the clusters illustrate gently dipping subducted Indian Plate, overriding Tibetan Plate and compressed Sedimentary Wedge in between, with mid crustal ramping of MCT. Several presumptions / hypotheses have been put forward to decipher the causes of clustering along MCT. These are segmental activation of MCT, cross fault interactions, zones of arc parallel and arc perpendicular compressions, pore pressure perturbations, low heat flow zones etc. But these hypotheses need to be evaluated in the future after more ground level data are available. The maximum size of seismic threat that MCT can produce is inferred to be around Mw 7.0 in those clusters.展开更多
According to the seismic and geological differences among every oil measures in mid-deep layers at west slope in Qikou Sag, varieties of new techniques on geophysics and geochemistry were introduced , such as seismic ...According to the seismic and geological differences among every oil measures in mid-deep layers at west slope in Qikou Sag, varieties of new techniques on geophysics and geochemistry were introduced , such as seismic pro-cess of target and CWS, neural network seismic microfacies cluster analysis, 3D interval velocity analysis. Taking advan-tage of the series of new techniques aims at predicting synthetically and quantitatively the deep gravity flow channel sandbody. Furthermore, considering the structural setting data, potential structural-lithologic traps were specified. As a re-sult, the geological and drilling effect is obviously promoted.展开更多
The Cluster and Hamming methods are used in this paper for a comprehensive study on geology,geomorphology,geophysical field,crustal deformation,active faults,regional stress axes and their relation in Hebei region.Fou...The Cluster and Hamming methods are used in this paper for a comprehensive study on geology,geomorphology,geophysical field,crustal deformation,active faults,regional stress axes and their relation in Hebei region.Fourteen potential seismic zones in which shocks with M≥6 may happen have been identified.Shocks with M≥6 have occurred in seven of them,and the others have been considered as a future strong earthquake areas.Both the K value and testing of deleting nodes show the stability of results obtained in this paper.The potential seismic zones identified in the paper fall into the areas of marked risk areas within 10 years in North China,but the scale of the identified zones is smaller.The Datong-Yanggao earthquake with M-6.1 occurred in October 1989 precisely in the 14th potential seismic zone mentioned above.展开更多
By using the SLC(Single-Link Cluster)method,this study worked in three respects:(a)set up three-dimensional(3-D)SLC software that can deal with a large catalogue of earthquakes and analyze the characteristics of earth...By using the SLC(Single-Link Cluster)method,this study worked in three respects:(a)set up three-dimensional(3-D)SLC software that can deal with a large catalogue of earthquakes and analyze the characteristics of earthquakes’ clustering and scattering in time-space:(b)defined several parameters to describe the distinguishing feature for the SLC frame and developed a technique to calculate the 3-D SLC frames and these parameters with gradual time-sliding,and inspected their variations with time,especially before large events; and(c)by using these means,treated the earthquake catalogue in the top area of the Kunlun-Altun-Arc as well as some valuable results that had been obtained.展开更多
基金the Australia Coal Association Research Program(ACARP)(Grant Nos.C26006 and C26053)Supports from CSIRO。
文摘Discrimination of seismicity distributed in different areas is essential for reliable seismic risk assessment in mines.Although machine learning has been widely applied in seismic data processing,feasibility and reliability of applying this technique to classify spatially clustered seismic events in underground mines are yet to be investigated.In this research,two groups of seismic events with a minimum local magnitude(ML) of-3 were observed in an underground coal mine.They were respectively located around a dyke and the longwall face.Additionally,two types of undesired signals were also recorded.Four machine learning methods,i.e.random forest(RF),support vector machine(SVM),deep convolutional neural network(DCNN),and residual neural network(ResNN),were used for classifying these signals.The results obtained based on a primary dataset showed that these seismic events could be classified with at least 91% accuracy.The DCNN using seismogram images as the inputs reached the best performance with more than 94% accuracy.As mining is a dynamic progress which could change the characteristics of seismic signals,the temporal variance in the prediction performance of DCNN was also investigated to assess the reliability of this classifier during mining.A cascaded workflow consisting of database update,model training,signal prediction,and results review was established.By progressively calibrating the DCNN model,it achieved up to 99% prediction accuracy.The results demonstrated that machine learning is a reliable tool for the automatic discrimination of spatially clustered seismicity in underground mining.
基金supported by the National Science and Technology Major Project (No. 2017ZX05001-003)。
文摘Seismic waveform clustering is a useful technique for lithologic identification and reservoir characterization.The current seismic waveform clustering algorithms are predominantly based on a fixed time window,which is applicable for layers of stable thickness.When a layer exhibits variable thickness in the seismic response,a fixed time window cannot provide comprehensive geologic information for the target interval.Therefore,we propose a novel approach for a waveform clustering workfl ow based on a variable time window to enable broader applications.The dynamic time warping(DTW)distance is fi rst introduced to effectively measure the similarities between seismic waveforms with various lengths.We develop a DTW distance-based clustering algorithm to extract centroids,and we then determine the class of all seismic traces according to the DTW distances from centroids.To greatly reduce the computational complexity in seismic data application,we propose a superpixel-based seismic data thinning approach.We further propose an integrated workfl ow that can be applied to practical seismic data by incorporating the DTW distance-based clustering and seismic data thinning algorithms.We evaluated the performance by applying the proposed workfl ow to synthetic seismograms and seismic survey data.Compared with the the traditional waveform clustering method,the synthetic seismogram results demonstrate the enhanced capability of the proposed workfl ow to detect boundaries of diff erent lithologies or lithologic associations with variable thickness.Results from a practical application show that the planar map of seismic waveform clustering obtained by the proposed workfl ow correlates well with the geological characteristics of wells in terms of reservoir thickness.
基金the S&T project ‘‘High resolution microseismic monitoring for early detection and analysis of slope failure in opencast mines’’ funded by inistry of Coal,Government of IndiaThe Singareni Collieries Co Ltd (SCCL),Andhra Pradesh
文摘This paper outlines the results obtained from real time microseismic monitoring of an opencast coal mine in South India.The objective of the study is to investigate the stress changes within the rockmass along the slope due to underground mine development operation and their impact on the stability of the highwall slope.The installed microseismic systems recorded the seismic triggerings down toà2 moment magnitude.In general,most of the events recorded during the monitoring period are weak in seismic energy.The study adopts a simple and more reliable tool to characterize the seismically active zone for assessing the stability of the highwall in real time.The impact of underground working on the slope is studied on the basis of the seismic event impact contours and seismic clusters.During the monitoring period,it is observed that the intensity of the overall microseismic activity along the slope due to the mine development operations did not cause any adverse impact on the highwall stability.
文摘The Main Central Thrust (MCT) in Himalaya is seismically active in segments. In recent times, strain release within these active segments produce five spatial clusters (A to E;Figure 1). The seismicity within the cluster zones occurs in two depth bands;corresponding to the base of upper and lower crust. Depth sections across the clusters illustrate gently dipping subducted Indian Plate, overriding Tibetan Plate and compressed Sedimentary Wedge in between, with mid crustal ramping of MCT. Several presumptions / hypotheses have been put forward to decipher the causes of clustering along MCT. These are segmental activation of MCT, cross fault interactions, zones of arc parallel and arc perpendicular compressions, pore pressure perturbations, low heat flow zones etc. But these hypotheses need to be evaluated in the future after more ground level data are available. The maximum size of seismic threat that MCT can produce is inferred to be around Mw 7.0 in those clusters.
文摘According to the seismic and geological differences among every oil measures in mid-deep layers at west slope in Qikou Sag, varieties of new techniques on geophysics and geochemistry were introduced , such as seismic pro-cess of target and CWS, neural network seismic microfacies cluster analysis, 3D interval velocity analysis. Taking advan-tage of the series of new techniques aims at predicting synthetically and quantitatively the deep gravity flow channel sandbody. Furthermore, considering the structural setting data, potential structural-lithologic traps were specified. As a re-sult, the geological and drilling effect is obviously promoted.
文摘The Cluster and Hamming methods are used in this paper for a comprehensive study on geology,geomorphology,geophysical field,crustal deformation,active faults,regional stress axes and their relation in Hebei region.Fourteen potential seismic zones in which shocks with M≥6 may happen have been identified.Shocks with M≥6 have occurred in seven of them,and the others have been considered as a future strong earthquake areas.Both the K value and testing of deleting nodes show the stability of results obtained in this paper.The potential seismic zones identified in the paper fall into the areas of marked risk areas within 10 years in North China,but the scale of the identified zones is smaller.The Datong-Yanggao earthquake with M-6.1 occurred in October 1989 precisely in the 14th potential seismic zone mentioned above.
基金This project was sponsored by the United Earthquake Science Foundation (93068), China
文摘By using the SLC(Single-Link Cluster)method,this study worked in three respects:(a)set up three-dimensional(3-D)SLC software that can deal with a large catalogue of earthquakes and analyze the characteristics of earthquakes’ clustering and scattering in time-space:(b)defined several parameters to describe the distinguishing feature for the SLC frame and developed a technique to calculate the 3-D SLC frames and these parameters with gradual time-sliding,and inspected their variations with time,especially before large events; and(c)by using these means,treated the earthquake catalogue in the top area of the Kunlun-Altun-Arc as well as some valuable results that had been obtained.
基金Projects(42277175,41672252,41972316)supported by the National Natural Science Foundation of ChinaProject(2023JJ30657)supported by the Hunan Provincial Natural Science Foundation of China。
