scholarly journals Shear wave travel time, amplitude, and waveform analysis for earthquakes in the kurile slab: Constraints on deep slab structure and mantle heterogeneity

1991 ◽  
Vol 96 (B9) ◽  
pp. 14445-14460 ◽  
Author(s):  
Susan Y. Schwartz ◽  
Thorne Lay ◽  
Susan L. Beck
1978 ◽  
Vol 68 (4) ◽  
pp. 973-985
Author(s):  
Robert S. Hart ◽  
Rhett Butler

abstract The wave-form correlation technique (Hart, 1975) for determining precise teleseismic shear-wave travel times is extended to two large earthquakes with well-constrained source mechanisms, the 1968 Borrego Mountain, California earthquake and the 1973 Hawaii earthquake. A total of 87 SH travel times in the distance range of 30° to 92° were obtained through analysis of WWSSN and Canadian Network seismograms from these two events. Major features of the travel-time data include a trend toward faster travel times at a distance of about 40° (previously noted by Ibrahim and Nuttli, 1967; Hart, 1975); another somewhat less pronounced trend toward faster times at about 75°; a +6 sec base line shift, with respect to the Jeffreys-Bullen Table, for the Borrego Mountain data; and large azimuthally-dependent scatter for the Hawaiian data, probably reflecting dramatic lateral variations in the near-source region. When azimuthal variations in the Hawaii data are removed, the travel times from both events show very low scatter. The correlations were also used to investigate SH amplitudes for possible distance dependence in the data and variations in tβ*. The Borrego Mountain data show very low scatter and no discernible distance dependence. All of the data are compatible with a value of tβ* = 5.2 ± 0.5. The amplitudes from the Hawaii earthquake show the same azimuthal variations found in the travel-time data. When those effects are removed, the Hawaii data satisfies a value of tβ* equal to 4.0 ± 0.5 and, as with the other data set, no distance dependence is apparent.


2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Shanshan Liu ◽  
Yipeng Zhao ◽  
Zhiming Wang

The existing artificial intelligence model uses single-point logging data as the eigenvalue to predict shear wave travel times (DTS), which does not consider the longitudinal continuity of logging data along the reservoir and lacks the multiwell data processing method. Low prediction accuracy of shear wave travel time affects the accuracy of elastic parameters and results in inaccurate sand production prediction. This paper establishes the shear wave prediction model based on the standardization, normalization, and depth correction of conventional logging data with five artificial intelligence methods (linear regression, random forest, support vector regression, XGBoost, and ANN). The adjacent data points in depth are used as machine learning eigenvalues to improve the practicability of interwell and the accuracy of single-well prediction. The results show that the model built with XGBoost using five points outperforms other models in predicting. The R2 of 0.994 and 0.964 are obtained for the training set and testing set, respectively. Every model considering reservoir vertical geological continuity predicts test set DTS with higher accuracy than single-point prediction. The developed model provides a tool to determine geomechanical parameters and give a preliminary suggestion on the possibility of sand production where shear wave travel times are not available. The implementation of the model provides an economic and reliable alternative for the oil and gas industry.


1972 ◽  
Vol 101 (1) ◽  
pp. 74-89
Author(s):  
S. K. Arora ◽  
C. A. Krishnan
Keyword(s):  

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