High‐Resolution Lithospheric Velocity Structure of Continental China by Double‐Difference Seismic Travel‐Time Tomography

2018 ◽  
Vol 90 (1) ◽  
pp. 229-241 ◽  
Author(s):  
Hailiang Xin ◽  
Haijiang Zhang ◽  
Min Kang ◽  
Rizheng He ◽  
Lei Gao ◽  
...  
Keyword(s):  
High Resolution ◽  
Travel Time ◽  
Velocity Structure ◽  
Double Difference ◽  
Travel Time Tomography

scholarly journals High resolution seismic velocity structure around the Yamasaki fault zone of southwest Japan as revealed from travel-time tomography

2013 ◽  
Vol 65 (8) ◽  
pp. 871-881 ◽  
Author(s):  
Andri Dian Nugraha ◽  
Shiro Ohmi ◽  
Jim Mori ◽  
Takuo Shibutani
Keyword(s):  
High Resolution ◽  
Travel Time ◽  
Fault Zone ◽  
Velocity Structure ◽  
Seismic Velocity ◽  
Southwest Japan ◽  
Seismic Velocity Structure ◽  
Travel Time Tomography

P and S velocity structure of the upper mantle beneath the Transantarctic Mountains, East Antarctic craton, and Ross Sea from travel time tomography

2006 ◽  
Vol 7 (7) ◽  
pp. n/a-n/a ◽  
Author(s):  
Timothy Watson ◽  
Andrew Nyblade ◽  
Douglas A. Wiens ◽  
Sridhar Anandakrishnan ◽  
Margaret Benoit ◽  
...  
Keyword(s):  
Travel Time ◽  
Upper Mantle ◽  
Velocity Structure ◽  
Ross Sea ◽  
Travel Time Tomography ◽  
Transantarctic Mountains

The South Fossa Magna, Japan, revealed by high-resolution P- and S-wave travel time tomography

1992 ◽  
Vol 208 (4) ◽  
pp. 377-396 ◽  
Author(s):  
Jonathan M. Lees ◽  
Motoo Ukawa
Keyword(s):  
High Resolution ◽  
Travel Time ◽  
The South ◽  
S Wave ◽  
Travel Time Tomography ◽  
Wave Travel

High resolution travel time tomography

2002 ◽  
Author(s):  
Yonadav Sudman ◽  
Dan Kosloff
Keyword(s):  
High Resolution ◽  
Travel Time ◽  
Travel Time Tomography

scholarly journals High-resolution seismic tomography of Long Beach, CA using machine learning

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Michael J. Bianco ◽  
Peter Gerstoft ◽  
Kim B. Olsen ◽  
Fan-Chi Lin
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Travel Time ◽  
Phase Speed ◽  
Small Scale ◽  
Long Beach ◽  
Travel Time Tomography ◽  
Tomography Method ◽  
Dense Sampling ◽  
Geophysical Features

Abstract We use a machine learning-based tomography method to obtain high-resolution subsurface geophysical structure in Long Beach, CA, from seismic noise recorded on a “large-N” array with 5204 geophones (~13.5 million travel times). This method, called locally sparse travel time tomography (LST) uses unsupervised machine learning to exploit the dense sampling obtained by ambient noise processing on large arrays. Dense sampling permits the LST method to learn directly from the data a dictionary of local, or small-scale, geophysical features. The features are the small scale patterns of Earth structure most relevant to the given tomographic imaging scenario. Using LST, we obtain a high-resolution 1 Hz Rayleigh wave phase speed map of Long Beach. Among the geophysical features shown in the map, the important Silverado aquifer is well isolated relative to previous surface wave tomography studies. Our results show promise for LST in obtaining detailed geophysical structure in travel time tomography studies.

scholarly journals Upper mantle velocity structure beneath the Tibetan Plateau from Pn travel time tomography

1997 ◽  
Vol 102 (B1) ◽  
pp. 493-505 ◽  
Author(s):  
D. E. McNamara ◽  
W. R. Walter ◽  
T. J. Owens ◽  
C. J. Ammon
Keyword(s):  
Tibetan Plateau ◽  
Travel Time ◽  
Upper Mantle ◽  
Velocity Structure ◽  
The Tibetan Plateau ◽  
Travel Time Tomography ◽  
Mantle Velocity

scholarly journals 3-D seismic travel-time tomography validation of a detailed subsurface model: a case study of the Záncara river basin (Cuenca, Spain)

Solid Earth ◽  
2019 ◽  
Vol 10 (1) ◽  
pp. 177-192
Author(s):  
David Marti ◽  
Ignacio Marzan ◽  
Jana Sachsenhausen ◽  
Joaquina Alvarez-Marrón ◽  
Mario Ruiz ◽  
...  
Keyword(s):  
High Resolution ◽  
Travel Time ◽  
River Basin ◽  
Seismic Velocity ◽  
Underground Structure ◽  
High Capacity ◽  
Seismic Source ◽  
P Wave ◽  
Shallow Subsurface ◽  
Travel Time Tomography

Abstract. A high-resolution seismic tomography survey was acquired to obtain a full 3-D P-wave seismic velocity image in the Záncara river basin (eastern Spain). The study area consists of lutites and gypsum from a Neogene sedimentary sequence. A regular and dense grid of 676 shots and 1200 receivers was used to image a 500 m×500 m area of the shallow subsurface. A 240-channel system and a seismic source, consisting of an accelerated weight drop, were used in the acquisition. Half a million travel-time picks were inverted to provide the 3-D seismic velocity distribution up to 120 m depth. The project also targeted the geometry of the underground structure with emphasis on defining the lithological contacts but also the presence of cavities and fault or fractures. An extensive drilling campaign provided uniquely tight constraints on the lithology; these included core samples and wireline geophysical measurements. The analysis of the well log data enabled the accurate definition of the lithological boundaries and provided an estimate of the seismic velocity ranges associated with each lithology. The final joint interpreted image reveals a wedge-shaped structure consisting of four different lithological units. This study features the necessary key elements to test the travel time tomographic inversion approach for the high-resolution characterization of the shallow subsurface. In this methodological validation test, travel-time tomography demonstrated to be a powerful tool with a relatively high capacity for imaging in detail the lithological contrasts of evaporitic sequences located at very shallow depths, when integrated with additional geological and geophysical data.

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