Nodal Seismograph Recordings of the 2019 Ridgecrest Earthquake Sequence

2020 ◽  
Vol 91 (6) ◽  
pp. 3622-3633
Author(s):  
Rufus D. Catchings ◽  
Mark R. Goldman ◽  
Jamison H. Steidl ◽  
Joanne H. Chan ◽  
Amir A. Allam ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Large Volume ◽  
Ambient Noise ◽  
Temporal Variations ◽  
Earthquake Sequence ◽  
S Wave ◽  
Data Set ◽  
Fault Structure ◽  
Nodal Data ◽  
Dense Arrays

Abstract The 2019 Ridgecrest, California, earthquake sequence included Mw 6.4 and 7.1 earthquakes that occurred on successive days beginning on 4 July 2019. These two largest earthquakes of the sequence occurred on orthogonal faults that ruptured the Earth’s surface. To better evaluate the 3D subsurface fault structure, (P- and S-wave) velocity, 3D and temporal variations in seismicity, and other important aspects of the earthquake sequence, we recorded aftershocks and ambient noise using up to 461 three-component nodal seismographs for about two months, beginning about one day after the Mw 7.1 mainshock. The ∼30,000Mw≥1 earthquakes that were recorded on the dense arrays provide an unusually large volume of data with which to evaluate the earthquake sequence. This report describes the recording arrays and is intended to provide metadata for researchers interested in evaluating various aspects of the 2019 Ridgecrest earthquake sequence using the nodal data set.

Geological variation in S-wave velocity structures in Northern Taiwan and implications for seismic hazards based on ambient noise analysis

2014 ◽  
Vol 96 ◽  
pp. 353-360
Author(s):  
Ya-Chuan Lai ◽  
Bor-Shouh Huang ◽  
Yu-Chih Huang ◽  
Huajian Yao ◽  
Ruey-Der Hwang ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Ambient Noise ◽  
Noise Analysis ◽  
Seismic Hazards ◽  
S Wave ◽  
Northern Taiwan

Shear‐wave velocity and density estimation from PS-wave AVO analysis: Application to an OBS dataset from the North Sea

Geophysics ◽  
2000 ◽  
Vol 65 (5) ◽  
pp. 1446-1454 ◽  
Author(s):  
Side Jin ◽  
G. Cambois ◽  
C. Vuillermoz
Keyword(s):  
Wave Velocity ◽  
North Sea ◽  
Random Noise ◽  
P Wave ◽  
S Wave ◽  
Data Set ◽  
Avo Analysis ◽  
The North ◽  
Avo Inversion ◽  
The North Sea

S-wave velocity and density information is crucial for hydrocarbon detection, because they help in the discrimination of pore filling fluids. Unfortunately, these two parameters cannot be accurately resolved from conventional P-wave marine data. Recent developments in ocean‐bottom seismic (OBS) technology make it possible to acquire high quality S-wave data in marine environments. The use of (S)-waves for amplitude variation with offset (AVO) analysis can give better estimates of S-wave velocity and density contrasts. Like P-wave AVO, S-wave AVO is sensitive to various types of noise. We investigate numerically and analytically the sensitivity of AVO inversion to random noise and errors in angles of incidence. Synthetic examples show that random noise and angle errors can strongly bias the parameter estimation. The use of singular value decomposition offers a simple stabilization scheme to solve for the elastic parameters. The AVO inversion is applied to an OBS data set from the North Sea. Special prestack processing techniques are required for the success of S-wave AVO inversion. The derived S-wave velocity and density contrasts help in detecting the fluid contacts and delineating the extent of the reservoir sand.

scholarly journals Investigating Near Surface S-Wave Velocity Properties Using Ambient Noise in Southwestern Taiwan

2015 ◽  
Vol 26 (2-2) ◽  
pp. 205 ◽  
Author(s):  
Chun-Hsiang Kuo ◽  
Kuo-Liang Wen ◽  
Che-Min Lin ◽  
Strong Wen ◽  
Jyun-Yan Huang
Keyword(s):  
Wave Velocity ◽  
Ambient Noise ◽  
S Wave ◽  
Near Surface ◽  
Southwestern Taiwan

Ground roll: A potential tool for constraining shallow shear‐wave structure

Geophysics ◽  
1993 ◽  
Vol 58 (5) ◽  
pp. 713-719 ◽  
Author(s):  
Ghassan I. Al‐Eqabi ◽  
Robert B. Herrmann
Keyword(s):  
Surface Wave ◽  
Phase Velocity ◽  
Wave Velocity ◽  
Wave Structure ◽  
S Wave ◽  
Data Set ◽  
Velocity Inversion ◽  
Ground Roll ◽  
Lateral Variations ◽  
Wave Arrival

The objective of this study is to demonstrate that a laterally varying shallow S‐wave structure, derived from the dispersion of the ground roll, can explain observed lateral variations in the direct S‐wave arrival. The data set consists of multichannel seismic refraction data from a USGS-GSC survey in the state of Maine and the province of Quebec. These data exhibit significant lateral changes in the moveout of the ground‐roll as well as the S‐wave first arrivals. A sequence of surface‐wave processing steps are used to obtain a final laterally varying S‐wave velocity model. These steps include visual examination of the data, stacking, waveform inversion of selected traces, phase velocity adjustment by crosscorrelation, and phase velocity inversion. These models are used to predict the S‐wave first arrivals by using two‐dimensional (2D) ray tracing techniques. Observed and calculated S‐wave arrivals match well over 30 km long data paths, where lateral variations in the S‐wave velocity in the upper 1–2 km are as much as ±8 percent. The modeled correlation between the lateral variations in the ground‐roll and S‐wave arrival demonstrates that a laterally varying structure can be constrained by using surface‐wave data. The application of this technique to data from shorter spreads and shallower depths is discussed.

The 1998 Valhall microseismic data set: An integrated study of relocated sources, seismic multiplets, and S-wave splitting

Geophysics ◽  
2009 ◽  
Vol 74 (5) ◽  
pp. B183-B195 ◽  
Author(s):  
K. De Meersman ◽  
J.-M. Kendall ◽  
M. van der Baan
Keyword(s):  
Seismic Anisotropy ◽  
Amplitude Ratio ◽  
Temporal Variations ◽  
Oil Field ◽  
P Wave ◽  
Wave Form ◽  
S Wave ◽  
Data Set ◽  
Wave Splitting ◽  
Source Mechanisms

We relocate 303 microseismic events recorded in 1998 by sensors in a single borehole in the North Sea Valhall oil field. A semiautomated array analysis method repicks the P- and S-wave arrival times and P-wave polarizations, which are needed to locate these events. The relocated sources are confined predominantly to a [Formula: see text]-thick zone just above the reservoir, and location uncertainties are half those of previous efforts. Multiplet analysis identifies 40 multiplet groups, which include 208 of the 303 events. The largest group contains 24 events, and five groups contain 10 or more events. Within each multiplet group, we further improve arrival-time picking through crosscorrelation, which enhances the relative accuracy of the relocated events and reveals that more than 99% of the seismic activity lies spatially in three distinct clusters. The spatial distribution of events and wave-form similarities reveal two faultlike structures that match well with north-northwest–south-southeast-trending fault planes interpreted from 3D surface seismic data. Most waveform differences between multiplet groups located on these faults can be attributed to S-wave phase content and polarity or P-to-S amplitude ratio. The range in P-to-S amplitude ratios observed on the faults is explained best in terms of varying source mechanisms. We also find a correlation between multiplet groups and temporal variations in seismic anisotropy, as revealed by S-wave splitting analysis. We explain these findings in the context of a cyclic recharge and dissipation of cap-rock stresses in response to production-driven compaction of the underlying oil reservoir. The cyclic nature of this mechanism drives the short-term variations in seismic anisotropy and the reactivation of microseismic source mechanisms over time.

scholarly journals High-resolution Rayleigh-wave velocity maps of central Europe from a dense ambient-noise data set

2012 ◽  
Vol 188 (3) ◽  
pp. 1173-1187 ◽  
Author(s):  
J. Verbeke ◽  
L. Boschi ◽  
L. Stehly ◽  
E. Kissling ◽  
A. Michelini
Keyword(s):  
High Resolution ◽  
Rayleigh Wave ◽  
Central Europe ◽  
Wave Velocity ◽  
Ambient Noise ◽  
Data Set ◽  
Rayleigh Wave Velocity ◽  
Noise Data

Full-waveform matching of VP and VS models from surface waves

2019 ◽  
Vol 218 (3) ◽  
pp. 1873-1891 ◽  
Author(s):  
Farbod Khosro Anjom ◽  
Daniela Teodor ◽  
Cesare Comina ◽  
Romain Brossier ◽  
Jean Virieux ◽  
...  
Keyword(s):  
Surface Waves ◽  
Wave Velocity ◽  
Real Data ◽  
Test Site ◽  
P Wave ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Data Set ◽  
Near Surface ◽  
Velocity Models

SUMMARY The analysis of surface wave dispersion curves (DCs) is widely used for near-surface S-wave velocity (VS) reconstruction. However, a comprehensive characterization of the near-surface requires also the estimation of P-wave velocity (VP). We focus on the estimation of both VS and VP models from surface waves using a direct data transform approach. We estimate a relationship between the wavelength of the fundamental mode of surface waves and the investigation depth and we use it to directly transform the DCs into VS and VP models in laterally varying sites. We apply the workflow to a real data set acquired on a known test site. The accuracy of such reconstruction is validated by a waveform comparison between field data and synthetic data obtained by performing elastic numerical simulations on the estimated VP and VS models. The uncertainties on the estimated velocity models are also computed.

Measurement of the shear slowness of slow formations from monopole logging-while-drilling sonic logs

Geophysics ◽  
2019 ◽  
Vol 85 (1) ◽  
pp. D45-D52
Author(s):  
Yuanda Su ◽  
Xinding Fang ◽  
Xiaoming Tang
Keyword(s):  
Numerical Modeling ◽  
Wave Velocity ◽  
Fluid Velocity ◽  
S Wave ◽  
Data Set ◽  
Acoustic Logging ◽  
Wave Velocities ◽  
Logging While Drilling ◽  
Sonic Logs ◽  
Refracted Waves

Acoustic logging-while-drilling (LWD) is used to measure formation velocity/slowness during drilling. In a fast formation, in which the S-wave velocity is higher than the borehole-fluid velocity, monopole logging can be used to obtain P- and S-wave velocities by measuring the corresponding refracted waves. In a slow formation, in which the S-wave velocity is less than the borehole-fluid velocity, because the fully refracted S-wave is missing, quadrupole logging has been developed and used for S-wave slowness measurement. A recent study based on numerical modeling implies that monopole LWD can generate a detectable transmitted S-wave in a slow formation. This nondispersive transmitted S-wave propagates at the formation S-wave velocity and thus can be used for measuring the S-wave slowness of a slow formation. We evaluate a field example to demonstrate the applicability of monopole LWD in determining the S-wave slowness of slow formations. We compare the S-wave slowness extracted from a monopole LWD data set acquired in a slow formation and the result derived from the quadrupole data recorded in the same logging run. The results indicated that the S-wave slowness can be reliably determined from monopole LWD sonic data in fairly slow formations. However, we found that the monopole approach is not applicable to very slow formations because the transmitted S-wave becomes too weak to detect when the formation S-wave slowness is much higher than the borehole-fluid slowness.

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