Joint inversion of shear wave travel time residuals and geoid and depth anomalies for long-wavelength variations in upper mantle temperature and composition along the Mid-Atlantic Ridge

1991 ◽  
Vol 96 (B12) ◽  
pp. 19981-20009 ◽  
Author(s):  
Anne F. Sheehan ◽  
Sean C. Solomon
Keyword(s):  
Travel Time ◽  
Shear Wave ◽  
Upper Mantle ◽  
Joint Inversion ◽  
Long Wavelength ◽  
Wave Travel ◽  
Mid Atlantic Ridge ◽  
Mantle Temperature

scholarly journals A common deep source for upper-mantle upwellings below the Ibero-western Maghreb region from teleseismic P-wave travel-time tomography

2018 ◽  
Vol 499 ◽  
pp. 157-172 ◽  
Author(s):  
Chiara Civiero ◽  
Vincent Strak ◽  
Susana Custódio ◽  
Graça Silveira ◽  
Nicholas Rawlinson ◽  
...  
Keyword(s):  
Travel Time ◽  
Upper Mantle ◽  
P Wave ◽  
Travel Time Tomography ◽  
Wave Travel ◽  
Deep Source

scholarly journals Shear-wave travel times and amplitudes for two well-constrained earthquakes

1978 ◽  
Vol 68 (4) ◽  
pp. 973-985
Author(s):  
Robert S. Hart ◽  
Rhett Butler
Keyword(s):  
Travel Time ◽  
Shear Wave ◽  
Correlation Technique ◽  
Travel Times ◽  
Time Data ◽  
Data Set ◽  
A Value ◽  
Travel Time Data ◽  
Distance Dependence ◽  
Wave Travel

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.

Teleseismic P-wave travel time tomography of the Alpine upper mantle using AlpArray seismic network data

2020 ◽  
Author(s):  
Marcel Paffrath ◽  
Wolfgang Friederich ◽  
Keyword(s):  
Travel Time ◽  
Upper Mantle ◽  
High Velocity ◽  
Eastern Alps ◽  
Alpine Region ◽  
Seismic Network ◽  
P Wave ◽  
Travel Times ◽  
Travel Time Tomography ◽  
Wave Travel

<p>We perform a teleseismic P-wave travel time tomography to examine geometry and slab structure of the upper mantle beneath the Alpine orogen. Vertical component data of the extraordinary dense seismic network AlpArray are used which were recorded at over 600 temporary and permanent broadband stations deployed by 24 different European institutions in the greater Alpine region, reaching from the Massif Central to the Pannonian Basin and from the Po plain to the river Main. Mantle phases of 347 teleseismic events between 2015 and 2019 of magnitude 5.5 and higher are evaluated automatically for direct and core diffracted P arrivals using a combination of higher-order statistics picking algorithms and signal cross correlation. The resulting database contains over 170.000 highly accurate absolute P picks that were manually revised for each event. The travel time residuals exhibit very consistent and reproducible spatial patterns, already pointing at high velocity slabs in the mantle.</p><p>For predicting P-wave travel times, we consider a large computational box encompassing the Alpine region up to a depth of 600 km within which we allow 3D-variations of P-wave velocity. Outside this box we assume a spherically symmetric earth and apply the Tau-P method to calculate travel times and ray paths. These are injected at the boundaries of the regional box and continued using the fast marching method. We invert differences between observed and predicted travel times for P-wave velocities inside the box. Velocity is discretized on a regular grid with an average spacing of about 25 km. The misfit reduction reaches values of up to 75% depending on damping and smoothing parameters.</p><p>The resulting model shows several steeply dipping high velocity anomalies following the Alpine arc. The most prominent structure stretches from the western Alps into the Apennines mountain range reaching depths of over 500 km. Two further anomalies extending down to a depth of 300 km are located below the central and eastern Alps, separated by a clear gap below the western part of the Tauern window. Further to the east the model indicates a possible high-velocity connection between the eastern Alps and the Dinarides. Regarding the lateral position of the central and eastern Alpine slabs, our results confirm previous studies. However, there are differences regarding depth extent, dip angles and dip directions. Both structures dip very steeply with a tendency towards northward dipping. We perform various general, as well as purpose-built resolution tests, to verify the capabilities of our setup to resolve slab gaps as well as different possible slab dipping directions.</p>

Teleseismic P-wave travel time tomography of the Alpine upper mantle using AlpArray seismic network data

2021 ◽  
Author(s):  
Marcel Paffrath ◽  
Wolfgang Friederich ◽  
Keyword(s):  
Travel Time ◽  
Upper Mantle ◽  
High Velocity ◽  
Alpine Region ◽  
Seismic Network ◽  
P Wave ◽  
Travel Times ◽  
Massif Central ◽  
Travel Time Tomography ◽  
Wave Travel

<p>We perform a teleseismic P-wave travel time tomography to examine geometry and slab structure of the upper mantle beneath the Alpine orogen. Vertical component data of the extraordinary dense seismic network AlpArray are used which were recorded at over 600 temporary and permanent broadband stations deployed by 24 different European institutions in the greater Alpine region, reaching from the Massif Central to the Pannonian Basin and from the Po plain to the river Main. Mantle phases of 370 teleseismic events between 2015 and 2019 of magnitude 5.5 and higher are evaluated automatically for direct and core diffracted P arrivals using a combination of higher-order statistics picking algorithms and signal cross correlation. The resulting database contains over 170.000 highly accurate absolute P picks that were manually revised for each event. The travel time residuals exhibit very consistent and reproducible spatial patterns, already pointing at high velocity slabs in the mantle.</p><p>For predicting P-wave travel times we consider a large computational box encompassing the Alpine region up to a depth of 600 km within which we allow 3D-variations of P-wave velocity. To account for influences of the strongly heterogeneous crust that cannot be resolved with teleseismic data, we integrate a complex three-dimensional crustal model directly into our model. Outside the box we assume a spherically symmetric earth and apply the Tau-P method to calculate travel times and ray paths. These are injected at the boundaries of the regional box and continued using the fast marching method (Rawlinson et al. 2005). We invert differences between observed and predicted traveltimes for P-wave velocities inside the box. Velocity is discretized on a regular grid with a spacing of about 25x25x15 km. The misfit reduction reaches values of over 80% depending on damping and smoothing parameters.</p><p>The resulting model shows several steeply dipping high velocity anomalies following the Alpine arc. The most prominent structure stretches from the western Alps into the Apennines mountain range reaching depths of over 500 km. Two further anomalies of high complexity extending down to a depth of 300 km are located below the central and eastern Alps, both being detached from the lithosphere and separated by a clear gap below the western part of the Tauern window. The central anomaly shows mainly southwards dipping, whereas the eastern anomaly is mainly dipping to the northeast. We compare our results to former studies, confirming lateral positions of the anomalies. However, the new results can benefit from the superior resolution capabilities of the dense AlpArray seismic network, providing more accurate insights into depth extent, dip angle and directions. We perform various general, as well as purpose-built resolution tests, to verify the capabilities of our setup to resolve slab gaps as well as different possible slab dipping directions.</p>

scholarly journals Artificial Intelligence Method for Shear Wave Travel Time Prediction considering Reservoir Geological Continuity

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Shanshan Liu ◽  
Yipeng Zhao ◽  
Zhiming Wang
Keyword(s):  
Artificial Intelligence ◽  
Travel Time ◽  
Shear Wave ◽  
Single Point ◽  
Oil And Gas Industry ◽  
Support Vector ◽  
Travel Times ◽  
Sand Production ◽  
Travel Time Prediction ◽  
Wave Travel

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.

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