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Seismic Characterization of Subsurface Structures at Rock Valley, Nevada.

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Title: Seismic Characterization of Subsurface Structures at Rock Valley, Nevada.
Authors: Li, David1, Kai Gao1 kaigao@lanl.gov, Ting Chen1, Lianjie Huang1, Swanson, Erika1, Snelson, Catherine1, Bodmer, Miles2, Zeiler, Cleat2, Turley, Reagan3
Source: Bulletin of the Seismological Society of America. Oct2024, Vol. 114 Issue 5, p2654-2667. 14p.
Abstract: The Source Physics Experiment (SPE) aims at improving explosion monitoring techniques by investigating source characteristics of chemical explosions in geologic formations. One of the critical tasks in Rock Valley Direct Comparison (RV/DC), SPE phase III, is to prepare for the main experiment by characterizing the subsurface structures at the test site. Based on the seismic data acquired during an accelerated-weight-drop (AWD) seismic survey at Rock Valley, we first pick the P-wave first-arrival travel times and derive a P-wave velocity model using the adjoint-state first-arrival travel-time tomography. We then apply reverse-time migration to the processed seismic data and obtain high-resolution images of the subsurface structures along the two main survey lines. Our migration results show several reflectors corresponding to major geologic formation boundaries. We employ a multitask machine learning model to enhance the reverse-time migration images and identify faults from these images. We find that our automatically picked faults correlate well with the locations of known faults in the region in addition to many geologically undetected faults. Our subsurface characterization results refine our understanding of the geology in this region and provide valuable velocity and structural information for RV/DC geologic model building and fault identification. [ABSTRACT FROM AUTHOR]
Copyright of Bulletin of the Seismological Society of America is the property of Seismological Society of America and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Seismic Characterization of Subsurface Structures at Rock Valley, Nevada.
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  Data: <searchLink fieldCode="AR" term="%22Li%2C+David%22">Li, David</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Kai+Gao%22">Kai Gao</searchLink><relatesTo>1</relatesTo><i> kaigao@lanl.gov</i><br /><searchLink fieldCode="AR" term="%22Ting+Chen%22">Ting Chen</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Lianjie+Huang%22">Lianjie Huang</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Swanson%2C+Erika%22">Swanson, Erika</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Snelson%2C+Catherine%22">Snelson, Catherine</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Bodmer%2C+Miles%22">Bodmer, Miles</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Zeiler%2C+Cleat%22">Zeiler, Cleat</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Turley%2C+Reagan%22">Turley, Reagan</searchLink><relatesTo>3</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Bulletin+of+the+Seismological+Society+of+America%22">Bulletin of the Seismological Society of America</searchLink>. Oct2024, Vol. 114 Issue 5, p2654-2667. 14p.
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: The Source Physics Experiment (SPE) aims at improving explosion monitoring techniques by investigating source characteristics of chemical explosions in geologic formations. One of the critical tasks in Rock Valley Direct Comparison (RV/DC), SPE phase III, is to prepare for the main experiment by characterizing the subsurface structures at the test site. Based on the seismic data acquired during an accelerated-weight-drop (AWD) seismic survey at Rock Valley, we first pick the P-wave first-arrival travel times and derive a P-wave velocity model using the adjoint-state first-arrival travel-time tomography. We then apply reverse-time migration to the processed seismic data and obtain high-resolution images of the subsurface structures along the two main survey lines. Our migration results show several reflectors corresponding to major geologic formation boundaries. We employ a multitask machine learning model to enhance the reverse-time migration images and identify faults from these images. We find that our automatically picked faults correlate well with the locations of known faults in the region in addition to many geologically undetected faults. Our subsurface characterization results refine our understanding of the geology in this region and provide valuable velocity and structural information for RV/DC geologic model building and fault identification. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Bulletin of the Seismological Society of America is the property of Seismological Society of America and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1785/0120230272
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        Text: English
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              Text: Oct2024
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