Preliminary determination of crustal structure in the Katmai National Monument, Alaska

1967 ◽  
Vol 57 (6) ◽  
pp. 1367-1392
Author(s):  
Eduard Berg ◽  
Susumu Kubota ◽  
Jurgen Kienle
Keyword(s):  
Crustal Structure ◽  
Seismic Data ◽  
Bouguer Anomaly ◽  
Volcanic Area ◽  
Average Depth ◽  
S Wave ◽  
Contour Map ◽  
Alaska Peninsula ◽  
National Monument

Abstract Seismic and gravity observations were carried out in the active volcanic area of Katmai in the summer of 1965. A determination of hypocenters has been aftempted using S and P arrivals at a station located at Kodiak and two stations located in the Monument. However, in most cases, deviations of travel times from the Jeffreys-Bullen tables were rather large. Therefore hypocenters are not well located. A method based on P- and S-wave arrivals yields a Poisson's ratio of 0.3 for the upper part of the mantle under Katmai. This higher value is probably due to the magma formation. The average depth to the Moho from seismic data in the same area is 38 km and 32 km under Kodiak. Using Woollard's relation between Bouguer anomaly and depth to the Moho, a small mountain root under the volcanoes with a depth of 34 km was found dipping gently up to 31 km on the NW side. The active volcanic cones are located along an uplift block. This block is associated with a 35 mgal Bouguer anomaly. The Bouguer anomaly contour map for the Alaska Peninsula is given and an interpretation attempted.

Determination of the crustal structure and seismicity of the Linfen rift with S-wave velocity mapping

2020 ◽  
Vol 14 (3) ◽  
pp. 647-659
Author(s):  
Zigen Wei ◽  
Risheng Chu ◽  
Meiqin Song ◽  
Xiaolin Yang ◽  
Shanshan Wu ◽  
...  
Keyword(s):  
Wave Velocity ◽  
Crustal Structure ◽  
S Wave ◽  
Velocity Mapping

Optimal Density Determination of Bouguer Anomaly Using Fractal Analysis (Case of study: Charak area,IRAN)

2005 ◽  
Vol 1 (1) ◽  
pp. 21-24
Author(s):  
Hamid Reza Samadi
Keyword(s):  
Surface Roughness ◽  
Fractal Dimension ◽  
Fractal Analysis ◽  
Fractal Geometry ◽  
Host Rock ◽  
Bouguer Anomaly ◽  
Research Area ◽  
Density Difference ◽  
Optimal Density

In exploration geophysics the main and initial aim is to determine density of under-research goals which have certain density difference with the host rock. Therefore, we state a method in this paper to determine the density of bouguer plate, the so-called variogram method based on fractal geometry. This method is based on minimizing surface roughness of bouguer anomaly. The fractal dimension of surface has been used as surface roughness of bouguer anomaly. Using this method, the optimal density of Charak area insouth of Hormozgan province can be determined which is 2/7 g/cfor the under-research area. This determined density has been used to correct and investigate its results about the isostasy of the studied area and results well-coincided with the geology of the area and dug exploratory holes in the text area

Exploring the Deeper Crustal Structure Beneath the Tatun Volcanic Area, Taiwan

2021 ◽  
Author(s):  
Chien-Min Su ◽  
Wei-Jhe Wu ◽  
Strong Wen ◽  
Yi-Heng Li ◽  
Yen-Che Liao ◽  
...  
Keyword(s):  
Crustal Structure ◽  
Volcanic Area

scholarly journals Responses of dipole-source reflected shear waves in acoustically slow formations

2019 ◽  
Author(s):  
Hao Wang ◽  
Ning Li ◽  
Caizhi Wang ◽  
Hongliang Wu ◽  
Peng Liu ◽  
...  
Keyword(s):  
Finite Difference Time Domain ◽  
Dipole Source ◽  
Sh Wave ◽  
S Wave ◽  
High Fracture ◽  
S Waves ◽  
Angle Fracture ◽  
Dip Angle ◽  
Difference Time

Abstract In the process of dipole-source acoustic far-detection logging, the azimuth of the fracture outside the borehole can be determined with the assumption that the SH–SH wave is stronger than the SV–SV wave. However, in slow formations, the considerable borehole modulation highly complicates the dipole-source radiation of SH and SV waves. A 3D finite-difference time-domain method is used to investigate the responses of the dipole-source reflected shear wave (S–S) in slow formations and explain the relationships between the azimuth characteristics of the S–S wave and the source–receiver offset and the dip angle of the fracture outside the borehole. Results indicate that the SH–SH and SV–SV waves cannot be effectively distinguished by amplitude at some offset ranges under low- and high-fracture dip angle conditions, and the offset ranges are related to formation properties and fracture dip angle. In these cases, the fracture azimuth determined by the amplitude of the S–S wave not only has a $180^\circ $ uncertainty but may also have a $90^\circ $ difference from the actual value. Under these situations, the P–P, S–P and S–S waves can be combined to solve the problem of the $90^\circ $ difference in the azimuth determination of fractures outside the borehole, especially for a low-dip-angle fracture.

scholarly journals Deep Crustal Structure of the Capricorn Orogen from Gravity and Seismic Data

2015 ◽  
Vol 2015 (1) ◽  
pp. 1-3
Author(s):  
Abdulrhman H. Alghamdi ◽  
Alan R.A. Aitken ◽  
Michael C. Dentith
Keyword(s):  
Crustal Structure ◽  
Seismic Data ◽  
Deep Crustal Structure

THE DETERMINATION OF DIGITAL WIENER FILTERS BY MEANS OF GRADIENT METHODS

Geophysics ◽  
1973 ◽  
Vol 38 (2) ◽  
pp. 310-326 ◽  
Author(s):  
R. J. Wang ◽  
S. Treitel
Keyword(s):  
Seismic Data ◽  
High Speed ◽  
Gradient Methods ◽  
Wiener Filter ◽  
Gradient Algorithm ◽  
Rapid Convergence ◽  
True Solution ◽  
Peripheral Device ◽  
Wiener Filters

The normal equations for the discrete Wiener filter are conventionally solved with Levinson’s algorithm. The resultant solutions are exact except for numerical roundoff. In many instances, approximate rather than exact solutions satisfy seismologists’ requirements. The so‐called “gradient” or “steepest descent” iteration techniques can be used to produce approximate filters at computing speeds significantly higher than those achievable with Levinson’s method. Moreover, gradient schemes are well suited for implementation on a digital computer provided with a floating‐point array processor (i.e., a high‐speed peripheral device designed to carry out a specific set of multiply‐and‐add operations). Levinson’s method (1947) cannot be programmed efficiently for such special‐purpose hardware, and this consideration renders the use of gradient schemes even more attractive. It is, of course, advisable to utilize a gradient algorithm which generally provides rapid convergence to the true solution. The “conjugate‐gradient” method of Hestenes (1956) is one of a family of algorithms having this property. Experimental calculations performed with real seismic data indicate that adequate filter approximations are obtainable at a fraction of the computer cost required for use of Levinson’s algorithm.

Geometrical spreading in a layered transversely isotropic medium with vertical symmetry axis

Geophysics ◽  
2003 ◽  
Vol 68 (6) ◽  
pp. 2082-2091 ◽  
Author(s):  
Bjørn Ursin ◽  
Ketil Hokstad
Keyword(s):  
Ray Tracing ◽  
Seismic Data ◽  
Symmetry Axis ◽  
Transversely Isotropic ◽  
Analytical Approximation ◽  
Geometrical Spreading ◽  
S Wave ◽  
Weak Anisotropy ◽  
P Waves ◽  
Amplitude Versus

Compensation for geometrical spreading is important in prestack Kirchhoff migration and in amplitude versus offset/amplitude versus angle (AVO/AVA) analysis of seismic data. We present equations for the relative geometrical spreading of reflected and transmitted P‐ and S‐wave in horizontally layered transversely isotropic media with vertical symmetry axis (VTI). We show that relatively simple expressions are obtained when the geometrical spreading is expressed in terms of group velocities. In weakly anisotropic media, we obtain simple expressions also in terms of phase velocities. Also, we derive analytical equations for geometrical spreading based on the nonhyperbolic traveltime formula of Tsvankin and Thomsen, such that the geometrical spreading can be expressed in terms of the parameters used in time processing of seismic data. Comparison with numerical ray tracing demonstrates that the weak anisotropy approximation to geometrical spreading is accurate for P‐waves. It is less accurate for SV‐waves, but has qualitatively the correct form. For P waves, the nonhyperbolic equation for geometrical spreading compares favorably with ray‐tracing results for offset‐depth ratios less than five. For SV‐waves, the analytical approximation is accurate only at small offsets, and breaks down at offset‐depth ratios less than unity. The numerical results are in agreement with the range of validity for the nonhyperbolic traveltime equations.

Automatic phase identification of earthquake based on the UBDN deep network

2021 ◽  
pp. 1-10
Author(s):  
Jianxian Cai ◽  
Xun Dai ◽  
Zhitao Gao ◽  
Yan Shi
Keyword(s):  
Seismic Data ◽  
Short Term Memory ◽  
Long Term Memory ◽  
Phase Identification ◽  
S Wave ◽  
Traditional Methods ◽  
Term Memory ◽  
Seismic Stations ◽  
2008 Wenchuan Earthquake ◽  
Deep Network

Seismic data obtained from seismic stations are the major source of the information used to forecast earthquakes. With the growth in the number of seismic stations, the size of the dataset has also increased. Traditionally, STA/LTA and AIC method have been applied to process seismic data. However, the enormous size of the dataset reduces accuracy and increases the rate of missed detection of the P and S wave phase when using these traditional methods. To tackle these issues, we introduce the novel U-net-Bidirectional Long-Term Memory Deep Network (UBDN) which can automatically and accurately identify the P and S wave phases from seismic data. The U-net based UBDN strongly maintains the U-net’s high accuracy in edge detection for extracting seismic phase features. Meanwhile, it also reduces the missed detection rate by applying the Bidirectional Long Short-Term Memory (Bi-LSTM) mode that processes timing signals to establish the relationship between seismic phase features. Experimental results using the Stanford University seismic dataset and data from the 2008 Wenchuan earthquake aftershock confirm that the proposed UBDN method is very accurate and has a lower rate of missed phase detection, outperforming solutions that adapt traditional methods by an order of magnitude in terms of error percentage.

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