The investigations on the structure and temporal variations of the continent crust have been the long-lasting topics of seismology. The artificial sources play the most important role in these studies for their well-k...The investigations on the structure and temporal variations of the continent crust have been the long-lasting topics of seismology. The artificial sources play the most important role in these studies for their well-known locations and occur times among all the sources producing elastic waves,so that precise results are obtained. In past decades,we have successfully excited the airgun sources in on-land water bodies such as reservoirs,expanding their applications in marine seismology. Large volume airguns have been used to produce low frequency signals which can travel hundreds of kilometers after stacking,providing a source connecting the exploration seismology and traditional seismology at regional scale. In this review,we introduce the changes through which we turn the airgun into scientific instruments by experiments. We also present the advances on imaging the continental crust structure and monitoring the velocity variations using the highly repeatable signals emitted by airgun sources. The potential applications such as imaging the subsurface of urban areas and high resolution illuminating of mines are proposed and the challenges for further investigations are also discussed.展开更多
Reservoir earthquake characteristics such as small magnitude and large quantity may result in low monitoring efficiency when using traditional methods.However,methods based on deep learning can discriminate the seismi...Reservoir earthquake characteristics such as small magnitude and large quantity may result in low monitoring efficiency when using traditional methods.However,methods based on deep learning can discriminate the seismic phases of small earthquakes in a reservoir and ensure rapid processing of arrival time picking.The present study establishes a deep learning network model combining a convolutional neural network(CNN) and recurrent neural network(RNN).The neural network training uses the waveforms of 60 000 small earthquakes within a magnitude range of 0.8-1.2 recorded by 73 stations near the Dagangshan Reservoir in Sichuan Province as well as the data of the manually picked P-wave arrival time.The neural network automatically picks the P-wave arrival time,providing a strong constraint for small earthquake positioning.The model is shown to achieve an accuracy rate of 90.7 % in picking P waves of microseisms in the reservoir area,with a recall rate reaching 92.6% and an error rate lower than 2%.The results indicate that the relevant network structure has high accuracy for picking the P-wave arrival times of small earthquakes,thus providing new technical measures for subsequent microseismic monitoring in the reservoir area.展开更多
The 2018 Palu M_W7.5 earthquake and tsunami attracted geophysicists' attention for its strike slip focal mechanism and magnitude. We inspected the details of this disaster and discussed its particularity and possi...The 2018 Palu M_W7.5 earthquake and tsunami attracted geophysicists' attention for its strike slip focal mechanism and magnitude. We inspected the details of this disaster and discussed its particularity and possible causations. The submarine landslide and special terrain conditions could have contributed to the unusual size of the tsunami. The early warning system and the post disaster response is also reviewed. Efficient social warnings and broadcast systems along with good maintenance is essential. We also found that enhancing publics scientific literacy is the most important way to reduce disaster damage and casualties. Moreover, social conditions and rebuilding difficulties post tsunami are related as reference resources for future disaster management strategies.展开更多
基金the National Natural Science Foundation of China(41674061,41790463 and 41674058)
文摘The investigations on the structure and temporal variations of the continent crust have been the long-lasting topics of seismology. The artificial sources play the most important role in these studies for their well-known locations and occur times among all the sources producing elastic waves,so that precise results are obtained. In past decades,we have successfully excited the airgun sources in on-land water bodies such as reservoirs,expanding their applications in marine seismology. Large volume airguns have been used to produce low frequency signals which can travel hundreds of kilometers after stacking,providing a source connecting the exploration seismology and traditional seismology at regional scale. In this review,we introduce the changes through which we turn the airgun into scientific instruments by experiments. We also present the advances on imaging the continental crust structure and monitoring the velocity variations using the highly repeatable signals emitted by airgun sources. The potential applications such as imaging the subsurface of urban areas and high resolution illuminating of mines are proposed and the challenges for further investigations are also discussed.
基金supported by the National Key R&D Program of China(2018YFC1503200)the National Natural Science Foundation of China(41790463,41804063,42074060)the Scientific Research InstitutesBasic Research and Development Operations Special Fund of the Institute of Geophysics,China Earthquake Administration(DQJB19B29,DQJB20B27)。
文摘Reservoir earthquake characteristics such as small magnitude and large quantity may result in low monitoring efficiency when using traditional methods.However,methods based on deep learning can discriminate the seismic phases of small earthquakes in a reservoir and ensure rapid processing of arrival time picking.The present study establishes a deep learning network model combining a convolutional neural network(CNN) and recurrent neural network(RNN).The neural network training uses the waveforms of 60 000 small earthquakes within a magnitude range of 0.8-1.2 recorded by 73 stations near the Dagangshan Reservoir in Sichuan Province as well as the data of the manually picked P-wave arrival time.The neural network automatically picks the P-wave arrival time,providing a strong constraint for small earthquake positioning.The model is shown to achieve an accuracy rate of 90.7 % in picking P waves of microseisms in the reservoir area,with a recall rate reaching 92.6% and an error rate lower than 2%.The results indicate that the relevant network structure has high accuracy for picking the P-wave arrival times of small earthquakes,thus providing new technical measures for subsequent microseismic monitoring in the reservoir area.
基金sponsored by the Scientific Research Institutes' Basic Research and Development Operations Special Fund of the Institute of Geophysics,China Earthquake Administration(Grant No.DQJB18B08)the National Natural Science Foundation of China(Grant No.41674058 and 41804063)
文摘The 2018 Palu M_W7.5 earthquake and tsunami attracted geophysicists' attention for its strike slip focal mechanism and magnitude. We inspected the details of this disaster and discussed its particularity and possible causations. The submarine landslide and special terrain conditions could have contributed to the unusual size of the tsunami. The early warning system and the post disaster response is also reviewed. Efficient social warnings and broadcast systems along with good maintenance is essential. We also found that enhancing publics scientific literacy is the most important way to reduce disaster damage and casualties. Moreover, social conditions and rebuilding difficulties post tsunami are related as reference resources for future disaster management strategies.
