Joint hypocentral determination and the detection of low-velocity anomalies. An example from the Phlegraean Fields earthquakes

1990 ◽  
Vol 80 (1) ◽  
pp. 129-139 ◽  
Author(s):  
Jose Pujol ◽  
Richard Aster
Keyword(s):  
Earthquake Swarm ◽  
P Wave ◽  
S Wave ◽  
Time Data ◽  
Low Velocity ◽  
Data Set ◽  
Epicentral Area ◽  
Velocity Inversion ◽  
Phlegraean Fields ◽  
Velocity Anomalies

Abstract Arrival time data from the Phlegraean Fields (Italy) earthquake swarm recorded by the University of Wisconsin array in 1983 to 1984 were reanalyzed using a joint hypocentral determination (JHD) technique. The P- and S-wave station corrections computed as part of the JHD analysis show a circular pattern of central positive values surrounded by negative values whose magnitudes increase with distance from the center of the pattern. This center roughly coincides with the point of the maximum uplift (almost 2 m) associated with the swarm. Corrections range from −0.85 to 0.10 sec for P-wave arrivals and from −1.09 to 0.70 sec for S-wave arrivals. We interpret these patterns of corrections as caused by a localized low-velocity anomaly in the epicentral area, which agrees with the results of a previous 3-D velocity inversion of the same data set. The relocated (JHD) epicenters show less scatter than the epicenters obtained in the velocity inversion, and move more of the seismic activity to the vicinity of the only presently active fumarolic feature. The capability of the JHD technique to detect low-velocity anomalies and at the same time to give reliable locations, particularly epicenters, was verified using synthetic data generated for a 3-D velocity model roughly resembling the model obtained by velocity inversion.

On the relocation of earthquake clusters. A case history: The Arkansas earthquake swarm

1989 ◽  
Vol 79 (6) ◽  
pp. 1846-1862
Author(s):  
José Pujol ◽  
Jer-Ming Chiu ◽  
Arch Johnston ◽  
Byau-Heng Chin
Keyword(s):  
Seismic Activity ◽  
Earthquake Swarm ◽  
Singular Values ◽  
P Wave ◽  
Seismic Velocities ◽  
Low Velocity ◽  
Epicentral Area ◽  
Low Velocity Zone ◽  
Station Corrections ◽  
Velocity Zone

Abstract A portable digital network (the PANDA array) of 40 three-component stations with an aperture of about 35 km was deployed for 4 months in the Arkansas swarm area in 1987. Only 12 swarm events occurred during the deployment, in contrast to the intense seismic activity that characterized this region in 1982 to 1984. These events were relocated using a joint hypocentral determination technique (JHD). The JHD method used here allows for the simultaneous determination of P- and S-wave station corrections while providing information on the uniqueness of the solution based on the singular values of a matrix related to the station corrections. P-wave station corrections, determined when all nonzero singular values were used in the computations (or with the two smallest nonzero singular values deleted), show a circular pattern of positive values surrounded by negative values. The epicentral area is localized slightly displaced from the center of the pattern. Since positive and negative corrections correspond to velocities that are lower and higher, respectively, than the average, our results indicate that the swarm area is characterized by seismic velocities lower than those of its surroundings. Independent information on this region is afforded by reflection seismic lines recorded in the swarm area and its vicinity, which show that the hypocenters are located in a region where strong reflectors completely lose their coherence, indicating that this volume is anomalous when compared to surrounding crust. Additional support for a low-velocity zone comes from the results of a 3-D velocity inversion of the same PANDA data. A selected subset of data recorded digitally by the USGS in 1982 was also relocated. Comparison with the results from the PANDA data shows that the seismic activity did not migrate over a 5-yr period and that it is concentrated within a small volume between about 3 km and 6 km depth. While the results of this study do not determine the ultimate cause of the Arkansas swarm, the discovery of a pronounced localized low velocity zone is consistent with a previously proposed magmatic intrusion or a zone of highly fractured, fluid-filled crust.

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 Crustal velocity structure of the Apennines (Italy) from P-wave travel time tomography

1996 ◽  
Vol 39 (6) ◽  
Author(s):  
C. Chiarabba ◽  
A. Amato
Keyword(s):  
Velocity Structure ◽  
P Wave ◽  
Depth Interval ◽  
Low Velocity ◽  
Data Set ◽  
Arrival Times ◽  
Velocity Anomaly ◽  
Minimum Number ◽  
Velocity Images ◽  
Lower Crustal

In this paper we provide P-wave velocity images of the crust underneath the Apennines (Italy), focusing on the lower crustal structure and the Moho topography. We inverted P-wave arrival times of earthquakes which occurred from 1986 to 1993 within the Apenninic area. To overcome inversion instabilities due to noisy data (we used bulletin data) we decided to resolve a minimum number of velocity parameters, inverting for only two layers in the crust and one in the uppermost mantle underneath the Moho. A partial inversion of only 55% of the overall dataset yields velocity images similar to those obtained with the whole data set, indicating that the depicted tomograms are stable and fairly insensitive to the number of data used. We find a low-velocity anomaly in the lower crust extending underneath the whole Apenninic belt. This feature is segmented by a relative high-velocity zone in correspondence with the Ortona-Roccamonfina line, that separates the northern from the southern Apenninic arcs. The Moho has a variable depth in the study area, and is deeper (more than 37 km) in the Adriatic side of the Northern Apennines with respect to the Tyrrhenian side, where it is found in the depth interval 22-34 km.

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.

Survey design for coal-scale 3D-PS seismic reflection

Geophysics ◽  
2016 ◽  
Vol 81 (6) ◽  
pp. P57-P70 ◽  
Author(s):  
Shaun Strong ◽  
Steve Hearn
Keyword(s):  
Survey Design ◽  
High Amplitude ◽  
Azimuthal Anisotropy ◽  
Mine Planning ◽  
P Wave ◽  
Depth Range ◽  
Low Velocity ◽  
Converted Wave ◽  
Data Set ◽  
Economic Considerations

Survey design for converted-wave (PS) reflection is more complicated than for standard P-wave surveys, due to raypath asymmetry and increased possibility of phase distortion. Coal-scale PS surveys (depth [Formula: see text]) require particular consideration, partly due to the particular physical properties of the target (low density and low velocity). Finite-difference modeling provides a pragmatic evaluation of the likely distortion due to inclusion of postcritical reflections. If the offset range is carefully chosen, then it may be possible to incorporate high-amplitude postcritical reflections without seriously degrading the resolution in the stack. Offsets of up to three times target depth may in some cases be usable, with appropriate quality control at the data-processing stage. This means that the PS survey design may need to handle raypaths that are highly asymmetrical and that are very sensitive to assumed velocities. A 3D-PS design was used for a particular coal survey with the target in the depth range of 85–140 m. The objectives were acceptable fold balance between bins and relatively smooth distribution of offset and azimuth within bins. These parameters are relatively robust for the P-wave design, but much more sensitive for the case of PS. Reduction of the source density is more acceptable than reduction of the receiver density, particularly in terms of the offset-azimuth distribution. This is a fortuitous observation in that it improves the economics of a dynamite source, which is desirable for high-resolution coal-mine planning. The final-survey design necessarily allows for logistical and economic considerations, which implies some technical compromise. However, good fold, offset, and azimuth distributions are achieved across the survey area, yielding a data set suitable for meaningful analysis of P and S azimuthal anisotropy.

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 Imaging of the Upper Mantle Beneath Southeast Asia: Constrained by Teleseismic P-Wave Tomography

2020 ◽  
Vol 12 (18) ◽  
pp. 2975
Author(s):  
Huiyan Shi ◽  
Tonglin Li ◽  
Rongzhe Zhang ◽  
Gongcheng Zhang ◽  
Hetian Yang
Keyword(s):  
South China Sea ◽  
Southeast Asia ◽  
Travel Time ◽  
South China ◽  
High Velocity ◽  
Velocity Model ◽  
P Wave ◽  
Deep Part ◽  
Low Velocity ◽  
Velocity Anomalies

It is of great significance to construct a three-dimensional underground velocity model for the study of geodynamics and tectonic evolution. Southeast Asia has attracted much attention due to its complex structural features. In this paper, we collected relative travel time residuals data for 394 stations distributed in Southeast Asia from 2006 to 2019, and 14,011 seismic events were obtained. Then, teleseismic tomography was applied by using relative travel time residuals data to invert the velocity where the fast marching method (FMM) and subspace method were used for every iteration. A novel 3D P-wave velocity model beneath Southeast Asia down to 720 km was obtained using this approach. The tomographic results suggest that the southeastern Tibetan Plateau, the Philippines, Sumatra, and Java, and the deep part of Borneo exhibit high velocity anomalies, while low velocity anomalies were found in the deep part of the South China Sea (SCS) basin and in the shallow part of Borneo and areas near the subduction zone. High velocity anomalies can be correlated to subduction plates and stable land masses, while low velocity anomalies can be correlated to island arcs and upwelling of mantle material caused by subduction plates. We found a southward subducting high velocity body in the Nansha Trough, which was presumed to be a remnant of the subduction of the Dangerous Grounds into Borneo. It is further inferred that the Nansha Trough and the Dangerous Grounds belong to the same tectonic unit. According to the tomographic images, a high velocity body is located in the deep underground of Indochina–Natuna Island–Borneo–Palawan, depth range from 240 km to 660 km. The location of the high velocity body is consistent with the distribution range of the ophiolite belt, so we speculate that the high velocity body is the remnant of thee Proto-South China Sea (PSCS) and Paleo-Tethys. This paper conjectures that the PSCS was the southern branch of Paleo-Tethys and the gateway between Paleo-Tethys and the Paleo-Pacific Ocean. Due to the squeeze of the Australian plate, PSCS closed from west to east in a scissor style, and was eventually extinct under Borneo.

Near-surface scattering phenomena and implications for tunnel detection

2015 ◽  
Vol 3 (1) ◽  
pp. SF43-SF54 ◽  
Author(s):  
Shelby L. Peterie ◽  
Richard D. Miller
Keyword(s):  
Synthetic Data ◽  
P Wave ◽  
S Wave ◽  
Seismic Line ◽  
Data Set ◽  
Near Surface ◽  
Tunnel Detection ◽  
Impedance Contrast ◽  
Diffraction Imaging ◽  
Imaging Algorithms

Tunnel locations are accurately interpreted from diffraction sections of focused mode converted P- to S-wave diffractions from a perpendicular tunnel and P-wave diffractions from a nonperpendicular (oblique) tunnel. Near-surface tunnels are ideal candidates for diffraction imaging due to their small size relative to the seismic wavelength and large acoustic impedance contrast at the tunnel interface. Diffraction imaging algorithms generally assume that the velocities of the primary wave and the diffracted wave are approximately equal, and that the diffraction apex is recorded directly above the scatterpoint. Scattering phenomena from shallow tunnels with kinematic properties that violate these assumptions were observed in one field data set and one synthetic data set. We developed the traveltime equations for mode-converted and oblique diffractions and demonstrated a diffraction imaging algorithm designed for the roll-along style of acquisition. Potential processing and interpretation pitfalls specific to these diffraction types were identified. Based on our observations, recommendations were made to recognize and image mode-converted and oblique diffractions and accurately interpret tunnel depth, horizontal location, and azimuth with respect to the seismic line.

Real-time monitoring of seismic moment and radiated energy

2020 ◽  
Author(s):  
Davide Scafidi ◽  
Daniele Spallarossa ◽  
Matteo Picozzi ◽  
Dino Bindi
Keyword(s):  
Real Time ◽  
Seismic Moment ◽  
P Wave ◽  
Calibration Data ◽  
S Wave ◽  
Data Set ◽  
Local Networks ◽  
Radiated Energy ◽  
The Impact ◽  
Large Earthquakes

<p>Understanding the dynamics of faulting is a crucial target in earthquake source physics (Yoo et al., 2010). To study earthquake dynamics it is indeed necessary to look at the source complexity from different perspectives; in this regard, useful information is provided by the seismic moment (M0), which is a static measure of the earthquake size, and the seismic radiated energy (ER), which is connected to the rupture kinematics and dynamics (e.g. Bormann & Di Giacomo 2011a). Studying spatial and temporal evolution of scaling relations between scaled energy (i.e., e = ER/M0) versus the static measure of source dimension (M0) can provide valuable indications for understanding the earthquake generation processes, single out precursors of stress concentrations, foreshocks and the nucleation of large earthquakes (Picozzi et al., 2019). In the last ten years, seismology has undergone a terrific development. Evolution in data telemetry opened the new research field of real-time seismology (Kanamori 2005), which targets are the rapid determination of earthquake location and size, the timely implementation of emergency plans and, under favourable conditions, earthquake early warning. On the other hand, the availability of denser and high quality seismic networks deployed near faults made possible to observe very large numbers of micro-to-small earthquakes, which is pushing the seismological community to look for novel big data analysis strategies. Large earthquakes in Italy have the peculiar characteristic of being followed within seconds to months by large aftershocks of magnitude similar to the initial quake or even larger, demonstrating the complexity of the Apennines’ faults system (Gentili and Giovanbattista, 2017). Picozzi et al. (2017) estimated the radiated seismic energy and seismic moment from P-wave signals for almost forty earthquakes with the largest magnitude of the 2016-2017 Central Italy seismic sequence. Focusing on S-wave signals recorded by local networks, Bindi et al. (2018) analysed more than 1400 earthquakes in the magnitude ranges 2.5 ≤ Mw ≤ 6.5 of the same region occurred from 2008 to 2017 and estimated both ER and M0, from which were derived the energy magnitude (Me) and Mw for investigating the impact of different magnitude scales on the aleatory variability associated with ground motion prediction equations. In this work, exploiting first steps made in this direction by Picozzi et al. (2017) and Bindi et al. (2018), we derived a novel approach for the real-time, robust estimation of seismic moment and radiated energy of small to large magnitude earthquakes recorded at local scales. In the first part of the work, we describe the procedure for extracting from the S-wave signals robust estimates of the peak displacement (PDS) and the cumulative squared velocity (IV2S). Then, exploiting a calibration data set of about 6000 earthquakes for which well-constrained M0 and theoretical ER values were available, we describe the calibration of empirical attenuation models. The coefficients and parameters obtained by calibration were then used for determining ER and M0 of a testing dataset</p>

The impact of common‐offset migration on porosity estimation by AVO inversion

Geophysics ◽  
2001 ◽  
Vol 66 (3) ◽  
pp. 755-762 ◽  
Author(s):  
Arild Buland ◽  
Martin Landrø
Keyword(s):  
P Wave ◽  
High Porosity ◽  
S Wave ◽  
Seismic Line ◽  
Data Set ◽  
The North ◽  
Avo Inversion ◽  
Time Migration ◽  
The Impact ◽  
Porosity Estimation

The impact of prestack time migration on porosity estimation has been tested on a 2-D seismic line from the Valhall/Hod area in the North Sea. Porosity is estimated in the Cretaceous chalk section in a two‐step procedure. First, P-wave and S-wave velocity and density are estimated by amplitude variation with offset (AVO) inversion. These parameters are then linked to porosity through a petrophysical rock data base based on core plug analysis. The porosity is estimated both from unmigrated and prestack migrated seismic data. For the migrated data set, a standard prestack Kirchhoff time migration is used, followed by simple angle and amplitude corrections. Compared to modern high‐cost, true amplitude migration methods, this approach is faster and more practical. The test line is structurally fairly simple, with a maximum dip of 5°; but the results differ significantly, depending on whether migration is applied prior to the inversion. The maximum difference in estimated porosity is of the order of 10% (about 50% relative change). High‐porosity zones estimated from the unmigrated data were not present on the porosity section estimated from the migrated data.

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