The magma system of Changbaishan-Tianchi Volcanic region is studied with three-dimensional deep seismic sounding (DSS) technique. The results show that the magma system of Changbaishan-Tianchi volcanic region, mainly ...The magma system of Changbaishan-Tianchi Volcanic region is studied with three-dimensional deep seismic sounding (DSS) technique. The results show that the magma system of Changbaishan-Tianchi volcanic region, mainly characterized by low velocity of P wave, can be divided into three parts in terms of depth. At the depth range of 9-15 km, the distribution of the magma system is characterized by extensiveness, large scale and near-SN orientation. This layer is the major place for magma storage. From the depth of 15 km down to the lower crust, it is characterized by small lateral scale, which indicates the 'trace' of magma intrusion from the upper mantle into the crust and also implies that the magma system most probably extends to the upper mantle, or even deeper.(less than 8-9 km deep), the range of magma distribution is even smaller, centering on an SN-oriented area just north of the Tianchi crater. If low velocity of P wave is related to the magma system, it then reflects that the magma here is still in a state of relatively high temperature. In this sense, the magma system of Changbaishan-Tianchi volcanic region is at least not 'remains', in other words, it is in an 'active' state.展开更多
Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficul...Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficulty in selecting parameters,and the low computational efficiency.Inspired by deep learning,we suggest a deep learning-based workflow for seismic time-frequency analysis.The sparse S transform network(SSTNet)is first built to map the relationship between synthetic traces and sparse S transform spectra,which can be easily pre-trained by using synthetic traces and training labels.Next,we introduce knowledge distillation(KD)based transfer learning to re-train SSTNet by using a field data set without training labels,which is named the sparse S transform network with knowledge distillation(KD-SSTNet).In this way,we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels.To test the availability of the suggested KD-SSTNet,we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.展开更多
Deployments of seismic stations in Antarctica are an ambitious project to improve the spatial resolution of the Antarctic Plate and surrounding regions. Several international programs had been conducted in wide area o...Deployments of seismic stations in Antarctica are an ambitious project to improve the spatial resolution of the Antarctic Plate and surrounding regions. Several international programs had been conducted in wide area of the Antarctic continent during the International Polar Year (IPY 2007-2008). The “Antarctica’s GAmburtsev Province (AGAP)”, the “GAmburtsev Mountain SEISmic experiment (GAMSEIS)” as a part of AGAP, and the “Polar Earth Observing Network (POLENET)” were major contributions to the IPY. The AGAP/GAMSEIS was an internationally coordinated deployments of more than few tens of broadband seismographs over the wide area of East Antarctica. Detailed information on crustal thickness and mantle structure provides key constraints on an origin of the Gamburtsev Mountains;and more broad structure and evolution of the East Antarctic craton and sub-glacial environment. From POLENET data obtained, local and regional signals associated with ice movements were recorded together with a significant number of teleseismic events. Moreover, seismic deployments have been carried out in the Lützow-Holm Bay (LHB), East Antarctica, by Japanese activities. The recorded teleseismic and local events are of sufficient quality to image the structure and dynamics of the crust and mantle, such as the studies by receiver functions suggesting a heterogeneous upper mantle. In addition to studies on the shallow part of the Earth, we place emphasis on these seismic deployments’ ability to image the Earth’s deep interior, as viewed from Antarctica, as a large aperture array in the southern high latitude.展开更多
基金Key project of the Ninth Five-Year plan from China Seismological Bureau (95-11-02-01).Contribution No. RCEG200107, Research Ce
文摘The magma system of Changbaishan-Tianchi Volcanic region is studied with three-dimensional deep seismic sounding (DSS) technique. The results show that the magma system of Changbaishan-Tianchi volcanic region, mainly characterized by low velocity of P wave, can be divided into three parts in terms of depth. At the depth range of 9-15 km, the distribution of the magma system is characterized by extensiveness, large scale and near-SN orientation. This layer is the major place for magma storage. From the depth of 15 km down to the lower crust, it is characterized by small lateral scale, which indicates the 'trace' of magma intrusion from the upper mantle into the crust and also implies that the magma system most probably extends to the upper mantle, or even deeper.(less than 8-9 km deep), the range of magma distribution is even smaller, centering on an SN-oriented area just north of the Tianchi crater. If low velocity of P wave is related to the magma system, it then reflects that the magma here is still in a state of relatively high temperature. In this sense, the magma system of Changbaishan-Tianchi volcanic region is at least not 'remains', in other words, it is in an 'active' state.
基金supported by the National Natural Science Foundation of China (42274144,42304122,and 41974155)the Key Research and Development Program of Shaanxi (2023-YBGY-076)+1 种基金the National Key R&D Program of China (2020YFA0713404)the China Uranium Industry and East China University of Technology Joint Innovation Fund (NRE202107)。
文摘Time-frequency analysis is a successfully used tool for analyzing the local features of seismic data.However,it suffers from several inevitable limitations,such as the restricted time-frequency resolution,the difficulty in selecting parameters,and the low computational efficiency.Inspired by deep learning,we suggest a deep learning-based workflow for seismic time-frequency analysis.The sparse S transform network(SSTNet)is first built to map the relationship between synthetic traces and sparse S transform spectra,which can be easily pre-trained by using synthetic traces and training labels.Next,we introduce knowledge distillation(KD)based transfer learning to re-train SSTNet by using a field data set without training labels,which is named the sparse S transform network with knowledge distillation(KD-SSTNet).In this way,we can effectively calculate the sparse time-frequency spectra of field data and avoid the use of field training labels.To test the availability of the suggested KD-SSTNet,we apply it to field data to estimate seismic attenuation for reservoir characterization and make detailed comparisons with the traditional time-frequency analysis methods.
文摘Deployments of seismic stations in Antarctica are an ambitious project to improve the spatial resolution of the Antarctic Plate and surrounding regions. Several international programs had been conducted in wide area of the Antarctic continent during the International Polar Year (IPY 2007-2008). The “Antarctica’s GAmburtsev Province (AGAP)”, the “GAmburtsev Mountain SEISmic experiment (GAMSEIS)” as a part of AGAP, and the “Polar Earth Observing Network (POLENET)” were major contributions to the IPY. The AGAP/GAMSEIS was an internationally coordinated deployments of more than few tens of broadband seismographs over the wide area of East Antarctica. Detailed information on crustal thickness and mantle structure provides key constraints on an origin of the Gamburtsev Mountains;and more broad structure and evolution of the East Antarctic craton and sub-glacial environment. From POLENET data obtained, local and regional signals associated with ice movements were recorded together with a significant number of teleseismic events. Moreover, seismic deployments have been carried out in the Lützow-Holm Bay (LHB), East Antarctica, by Japanese activities. The recorded teleseismic and local events are of sufficient quality to image the structure and dynamics of the crust and mantle, such as the studies by receiver functions suggesting a heterogeneous upper mantle. In addition to studies on the shallow part of the Earth, we place emphasis on these seismic deployments’ ability to image the Earth’s deep interior, as viewed from Antarctica, as a large aperture array in the southern high latitude.
