The local slopes contain rich information of the reflection geometry,which can be used to facilitate many subsequent procedures such as seismic velocities picking,normal move out correction,time-domain imaging and str...The local slopes contain rich information of the reflection geometry,which can be used to facilitate many subsequent procedures such as seismic velocities picking,normal move out correction,time-domain imaging and structural interpretation.Generally the slope estimation is achieved by manually picking or scanning the seismic profile along various slopes.We present here a deep learning-based technique to automatically estimate the local slope map from the seismic data.In the presented technique,three convolution layers are used to extract structural features in a local window and three fully connected layers serve as a classifier to predict the slope of the central point of the local window based on the extracted features.The deep learning network is trained using only synthetic seismic data,it can however accurately estimate local slopes within real seismic data.We examine its feasibility using simulated and real-seismic data.The estimated local slope maps demonstrate the succes sful performance of the synthetically-trained network.展开更多
The occurrence of microseismic is not random but is related to the physical properties of the underground medium.Due to the low intensity and the influence of noise,microseismic eventually lead to poor signal-to-noise...The occurrence of microseismic is not random but is related to the physical properties of the underground medium.Due to the low intensity and the influence of noise,microseismic eventually lead to poor signal-to-noise ratio.We proposed a method for automatic detection of microseismic events by adoption of multiscale top-hat transformation.The method is based on the difference between the signal and noise in the multiscale top-hat transform section and achieves the detection on a specific section.The microseismic data are decomposed into different scales by multiscale morphology top-hat transformation firstly.Then the potential microseismic events could be detected by picking up the peak value in the multiscale top-hat section,and the characteristic profile obtains the start point with a specific threshold value.Finally,the synthetic data experiences demonstrate the advantages of this method under strong and weak noisy conditions,and the filed data example also shows its reliability and adaptability.展开更多
基金supported by the Science Foundation of China University of Petroleum,Beijing under Grant No.:2462018YJRC020 and 2462020YXZZ006the National Natural Science Foundation of China under grant no.:41904098+1 种基金the Young Elite Scientists Sponsorship Program by CAST(YESS)under Grant No.:2018QNRC001partly supported by the National Natural Science Foundation of China under Grant No.:41874156 and 42074167。
文摘The local slopes contain rich information of the reflection geometry,which can be used to facilitate many subsequent procedures such as seismic velocities picking,normal move out correction,time-domain imaging and structural interpretation.Generally the slope estimation is achieved by manually picking or scanning the seismic profile along various slopes.We present here a deep learning-based technique to automatically estimate the local slope map from the seismic data.In the presented technique,three convolution layers are used to extract structural features in a local window and three fully connected layers serve as a classifier to predict the slope of the central point of the local window based on the extracted features.The deep learning network is trained using only synthetic seismic data,it can however accurately estimate local slopes within real seismic data.We examine its feasibility using simulated and real-seismic data.The estimated local slope maps demonstrate the succes sful performance of the synthetically-trained network.
基金supported in part by the National Natural Science Foundation of China under Grant 41904098Fundamental Research Funds for the Central Universities,under Grant 2462018YJRC020 and Grant 2462020YXZZ006。
文摘The occurrence of microseismic is not random but is related to the physical properties of the underground medium.Due to the low intensity and the influence of noise,microseismic eventually lead to poor signal-to-noise ratio.We proposed a method for automatic detection of microseismic events by adoption of multiscale top-hat transformation.The method is based on the difference between the signal and noise in the multiscale top-hat transform section and achieves the detection on a specific section.The microseismic data are decomposed into different scales by multiscale morphology top-hat transformation firstly.Then the potential microseismic events could be detected by picking up the peak value in the multiscale top-hat section,and the characteristic profile obtains the start point with a specific threshold value.Finally,the synthetic data experiences demonstrate the advantages of this method under strong and weak noisy conditions,and the filed data example also shows its reliability and adaptability.
