scholarly journals A Two-Station Seismic Method to Localize Glacier Calving

2016 ◽  
Author(s):  
M. Jeffrey Mei ◽  
David M. Holland ◽  
Sridhar Anandakrishnan ◽  
Tiantian Zheng
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Seismic Wave ◽  
Seismic Velocity ◽  
S Wave ◽  
Arrival Times ◽  
Seismic Location ◽  
The Difference ◽  
Location Methods ◽  
Wave Arrival

Abstract. A method of determining glacier calving location using seismic wave arrival times from paired local seismic stations is presented. The difference in surface wave arrival times for each pair is used to define a locus (hyperbola) of possible origin. With multiple pairs, this can be used to triangulate for the origin of the seismic wave, which is interpreted as the calving location. This method is motivated by difficulties with traditional seismic location methods that fail due to the emergent nature of calving, which obscures the P and S- wave onsets, and the proximity of the seismometers, which combines body and surface waves into one arrival. Human observed calving events are used to calibrate the seismic velocity for the method, which is then applied to other calving events from August 2014 to August 2015. From this, a catalogue of calving locations is generated, which shows that calving preferentially happens at the northern end of Helheim Glacier.

3D Seismic Velocity Models for Alaska from Joint Tomographic Inversion of Body-Wave and Surface-Wave Data

2020 ◽  
Vol 91 (6) ◽  
pp. 3106-3119
Author(s):  
Avinash Nayak ◽  
Donna Eberhart-Phillips ◽  
Natalia A. Ruppert ◽  
Hongjian Fang ◽  
Melissa M. Moore ◽  
...  
Keyword(s):  
Surface Wave ◽  
High Velocity ◽  
Mantle Wedge ◽  
Seismic Velocity ◽  
Body Wave ◽  
Arrival Times ◽  
Velocity Models ◽  
Wave Data ◽  
Surface Wave Data ◽  
Wave Arrival

Abstract We present two new seismic velocity models for Alaska from joint inversions of body-wave and ambient-noise-derived surface-wave data, using two different methods. Our work takes advantage of data from many recent temporary seismic networks, including the Incorporated Research Institutions for Seismology Alaska Transportable Array, Southern Alaska Lithosphere and Mantle Observation Network, and onshore stations of the Alaska Amphibious Community Seismic Experiment. The first model primarily covers south-central Alaska and uses body-wave arrival times with Rayleigh-wave group-velocity maps accounting for their period-dependent lateral sensitivity. The second model results from direct inversion of body-wave arrival times and surface-wave phase travel times, and covers the entire state of Alaska. The two models provide 3D compressional- (VP) and shear-wave velocity (VS) information at depths ∼0–100  km. There are many similarities as well as differences between the two models. The first model provides a clear image of the high-velocity subducting plate and the low-velocity mantle wedge, in terms of the seismic velocities and the VP/VS ratio. The statewide model provides clearer images of many features such as sedimentary basins, a high-velocity anomaly in the mantle wedge under the Denali volcanic gap, low VP in the lower crust under Brooks Range, and low velocities at the eastern edge of Yakutat terrane under the Wrangell volcanic field. From simultaneously relocated earthquakes, we also find that the depth to the subducting Pacific plate beneath southern Alaska appears to be deeper than previous models.

scholarly journals Calving localization at Helheim Glacier using multiple local seismic stations

2017 ◽  
Vol 11 (1) ◽  
pp. 609-618 ◽  
Author(s):  
M. Jeffrey Mei ◽  
David M. Holland ◽  
Sridhar Anandakrishnan ◽  
Tiantian Zheng
Keyword(s):  
Surface Wave ◽  
Wave Velocity ◽  
Rayleigh Waves ◽  
Seismic Waves ◽  
Grid Search ◽  
Arrival Times ◽  
S Waves ◽  
Surface Wave Velocity ◽  
The Difference ◽  
Wave Arrival

Abstract. A multiple-station technique for localizing glacier calving events is applied to Helheim Glacier in southeastern Greenland. The difference in seismic-wave arrival times between each pairing of four local seismometers is used to generate a locus of possible event origins in the shape of a hyperbola. The intersection of the hyperbolas provides an estimate of the calving location. This method is used as the P and S waves are not distinguishable due to the proximity of the local seismometers to the event and the emergent nature of calving signals. We find that the seismic waves that arrive at the seismometers are dominated by surface (Rayleigh) waves. The surface-wave velocity for Helheim Glacier is estimated using a grid search with 11 calving events identified at Helheim from August 2014 to August 2015. From this, a catalogue of 11 calving locations is generated, showing that calving preferentially happens at the northern end of Helheim Glacier.

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.

3D elastic full-waveform inversion of surface waves in the presence of irregular topography using an envelope-based misfit function

Geophysics ◽  
2018 ◽  
Vol 83 (1) ◽  
pp. R1-R11 ◽  
Author(s):  
Dmitry Borisov ◽  
Ryan Modrak ◽  
Fuchun Gao ◽  
Jeroen Tromp
Keyword(s):  
Surface Wave ◽  
Objective Function ◽  
Surface Waves ◽  
Large Scale ◽  
Body Wave ◽  
Waveform Inversion ◽  
Full Waveform Inversion ◽  
S Wave ◽  
Near Surface ◽  
Full Waveform

Full-waveform inversion (FWI) is a powerful method for estimating the earth’s material properties. We demonstrate that surface-wave-driven FWI is well-suited to recovering near-surface structures and effective at providing S-wave speed starting models for use in conventional body-wave FWI. Using a synthetic example based on the SEG Advanced Modeling phase II foothills model, we started with an envelope-based objective function to invert for shallow large-scale heterogeneities. Then we used a waveform-difference objective function to obtain a higher-resolution model. To accurately model surface waves in the presence of complex tomography, we used a spectral-element wave-propagation solver. Envelope misfit functions are found to be effective at minimizing cycle-skipping issues in surface-wave inversions, and surface waves themselves are found to be useful for constraining complex near-surface features.

Joint microseismic event location and anisotropic velocity inversion with the cross double-difference method using downhole microseismic data

Geophysics ◽  
2020 ◽  
Vol 85 (3) ◽  
pp. KS63-KS73
Author(s):  
Yangyang Ma ◽  
Congcong Yuan ◽  
Jie Zhang
Keyword(s):  
Arrival Time ◽  
Velocity Model ◽  
P Wave ◽  
Double Difference ◽  
S Wave ◽  
Arrival Times ◽  
Monitoring Well ◽  
The Cross ◽  
Microseismic Event ◽  
Wave Arrival

We have applied the cross double-difference (CDD) method to simultaneously determine the microseismic event locations and five Thomsen parameters in vertically layered transversely isotropic media using data from a single vertical monitoring well. Different from the double-difference (DD) method, the CDD method uses the cross-traveltime difference between the S-wave arrival time of one event and the P-wave arrival time of another event. The CDD method can improve the accuracy of the absolute locations and maintain the accuracy of the relative locations because it contains more absolute information than the DD method. We calculate the arrival times of the qP, qSV, and SH waves with a horizontal slowness shooting algorithm. The sensitivities of the arrival times with respect to the five Thomsen parameters are derived using the slowness components. The derivations are analytical, without any weak anisotropic approximation. The input data include the cross-differential traveltimes and absolute arrival times, providing better constraints on the anisotropic parameters and event locations. The synthetic example indicates that the method can produce better event locations and anisotropic velocity model. We apply this method to the field data set acquired from a single vertical monitoring well during a hydraulic fracturing process. We further validate the anisotropic velocity model and microseismic event locations by comparing the modeled and observed waveforms. The observed S-wave splitting also supports the inverted anisotropic results.

Multi-Window Weighted Stacking of Surface-Wave Dispersion

Geophysics ◽  
2020 ◽  
pp. 1-53
Author(s):  
Sylvain Pasquet ◽  
Wei Wang ◽  
Po Chen ◽  
Brady A. Flinchum
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Real Data ◽  
Wave Dispersion ◽  
Lateral Resolution ◽  
Data Sets ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Shallow Subsurface ◽  
Depth Of Investigation

Surface wave (SW) methods are classically used to characterize shear (S-) wave velocities ( VS) of the shallow subsurface through the inversion of dispersion curves. When targeting 2D shallow structures with sharp lateral heterogeneity, windowing and stacking techniques can be implemented to provide a better description of VS lateral variations. These techniques, however, suffer from the trade-off between lateral resolution and depth of investigation, well-known when using multichannel analysis of surface waves (MASW). We propose a novel methodology aimed at enhancing both lateral resolution and depth of investigation of MASW results through the use of multi-window weighted stacking of surface waves (MW-WSSW). MW-WSSW consists in stacking dispersion images obtained from data segments of different sizes, with a wavelength-based weight that depends on the aperture of the data selection window. In that sense, MW-WSSW provides additional weight to short wavelengths in smaller windows so as to better inform shallow parts of the subsurface, and vice versa for deeper velocities. Using multiple windows improves the depth of investigation, while applying wavelength-based weights enhances shallow lateral resolution. MW-WSSW was implemented within the open-source package SWIP, and applied to the processing of synthetic and real data sets. In both cases we compared it with standard windowing and stacking procedures that are already implemented in SWIP. MW-WSSW provided convincing results with optimized lateral extent, improved shallow resolution, and increased depth of investigation.

Evidence for serpentinization of the Ionian upper mantle from simultaneous inversion of P- and S-wave arrival times

2016 ◽  
Vol 102 ◽  
pp. 115-120 ◽  
Author(s):  
Antonino D’Alessandro ◽  
Giorgio Mangano ◽  
Giuseppe D’Anna ◽  
Salvatore Scudero
Keyword(s):  
Upper Mantle ◽  
S Wave ◽  
Arrival Times ◽  
Simultaneous Inversion ◽  
Wave Arrival

Crustal structure of the Iceland region from dispersion of surface waves

1962 ◽  
Vol 52 (2) ◽  
pp. 359-388
Author(s):  
Eysteinn Tryggvason
Keyword(s):  
Surface Layer ◽  
Surface Wave ◽  
Surface Waves ◽  
Wave Velocity ◽  
Crustal Structure ◽  
Wave Dispersion ◽  
P Wave ◽  
S Wave ◽  
Surface Wave Dispersion ◽  
Mid Atlantic Ridge

ABSTRACT A number of Icelandic records of earthquakes originating in the Mid-Atlantic Seismic Belt between 52° and 70° N. lat. have been investigated. The surface waves on these records are chiefly in the period interval 3–10 sec, and are first mode Love-waves and Rayleigh-waves. The surface wave dispersion can be explained by a three-layered crustal structure as follows. A surface layer of S-wave velocity about 2.7 km/sec covering the whole region studied, a second layer of S-wave velocity about 3.6 km/sec covering Iceland and extending several hundred kilometers off the coasts and a third layer of S-wave velocity about 4.3 km/sec and P-wave velocity about 7.4 km/sec underlying the whole region. The thickness of the surface layer appears to be about 4 km on the Mid-Atlantic Ridge south of Iceland and in western Iceland, 3 km in central Iceland and 7 km northwest of Iceland. The second layer is apparently of similar thickness than the surface layer, while the third layer is thick; and the surface wave dispersion does not indicate any layer of higher wave velocity. This 7.4-layer is supposed to belong to the mantle, although its wave velocity is significantly lower than usually found in the upper mantle

Gravity–capillary jet-like surface waves generated by an underwater bubble

2019 ◽  
Vol 866 ◽  
pp. 841-864 ◽  
Author(s):  
Youn J. Kang ◽  
Yeunwoo Cho
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Wave Pattern ◽  
Capillary Waves ◽  
Maximum Height ◽  
Gravitational Acceleration ◽  
Poisson Problem ◽  
Conservation Of Momentum ◽  
Proportional Relationship ◽  
The Difference

Jet-like surface waves generated by an electric-spark-generated underwater bubble are experimentally studied. Three different motions of jet-like surface waves are observed depending on the inception position of the bubble ($d$: 0.28–7 mm) below the free surface and the maximum radius of the bubble ($R_{m}$: 1.5–3.6 mm). When $d/R_{m}>1.3$, the surface wave shows a simple smooth hump (case 1). When $0.82<d/R_{m}<1.3$, a single droplet or multiple droplets are pinched off sequentially or simultaneously at the tip or from some points of the jet-like surface wave (case 2). Finally, when $d/R_{m}<0.82$, a series of squirting and jetting phenomena are observed at the top of the jet-like surface wave (case 3). For case 1, a proportional relationship is found between $\unicode[STIX]{x1D70C}gh/\unicode[STIX]{x0394}p$ and $(d/R_{m})^{-4.4}$, where $\unicode[STIX]{x1D70C}$ is the density of the fluid, $g$ is the gravitational acceleration and $\unicode[STIX]{x0394}p$ is the difference between the reference atmospheric pressure and the vapour pressure inside a bubble. This proportional relationship is explained semi-analytically using a scaling argument and conservation of momentum and energy, with the help of the Kelvin impulse theory. In addition, we solve the relevant axisymmetric Cauchy–Poisson problem where the initial condition is a jet-like surface wave near its maximum height. By comparing the analytical wave solution with the observed surface wave pattern, it is found that the resultant surface waves are indeed gravity–capillary waves where both the gravity and the surface tension are equally important.

A comprehensive comparison between the refraction microtremor and seismic interferometry methods for phase-velocity estimation

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. EN99-EN108 ◽  
Author(s):  
Zongbo Xu ◽  
T. Dylan Mikesell ◽  
Jianghai Xia ◽  
Feng Cheng
Keyword(s):  
Surface Wave ◽  
Surface Waves ◽  
Wave Velocity ◽  
Velocity Estimation ◽  
Seismic Interferometry ◽  
S Wave ◽  
Noise Sources ◽  
Surface Wave Dispersion ◽  
Near Surface ◽  
Refraction Microtremor

Passive-source seismic-noise-based surface-wave methods are now routinely used to investigate the near-surface geology in urban environments. These methods estimate the S-wave velocity of the near surface, and two methods that use linear recording arrays are seismic interferometry (SI) and refraction microtremor (ReMi). These two methods process noise data differently and thus can yield different estimates of the surface-wave dispersion, the data used to estimate the S-wave velocity. We have systematically compared these two methods using synthetic data with different noise source distributions. We arrange sensors in a linear survey grid, which is conveniently used in urban investigations (e.g., along roads). We find that both methods fail to correctly determine the low-frequency dispersion characteristics when outline noise sources become stronger than inline noise sources. We also identify an artifact in the ReMi method and theoretically explain the origin of this artifact. We determine that SI combined with array-based analysis of surface waves is the more accurate method to estimate surface-wave phase velocities because SI separates surface waves propagating in different directions. Finally, we find a solution to eliminate the ReMi artifact that involves the combination of SI and the [Formula: see text]-[Formula: see text] transform, the array processing method that underlies the ReMi method.

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