Quick Determination of the Earthquake Focal Mechanism from the Azimuthal Variation of the Initial P‐Wave Amplitude

2019 ◽  
Author(s):  
Stefania Tarantino ◽  
Simona Colombelli ◽  
Antonio Emolo ◽  
Aldo Zollo
Keyword(s):  
Focal Mechanism ◽  
Wave Amplitude ◽  
P Wave ◽  
Azimuthal Variation ◽  
Earthquake Focal Mechanism ◽  
Quick Determination

Combination of P and S data for the determination of earthquake focal mechanism

1971 ◽  
Vol 61 (6) ◽  
pp. 1655-1673 ◽  
Author(s):  
Umesh Chandra
Keyword(s):  
Focal Mechanism ◽  
P Wave ◽  
Wave Polarization ◽  
S Wave ◽  
Polarization Data ◽  
Wave Data ◽  
Earthquake Focal Mechanism ◽  
First Motion ◽  
Depth Ranges

abstract A method has been proposed for the combination of P-wave first-motion directions and S-wave polarization data for the numerical determination of earthquake focal mechanism. The method takes into account the influence of nearness of stations with inconsistent P-wave polarity observations, with respect to the assumed nodal planes. The mechanism solutions for six earthquakes selected from different geographic locations and depth ranges have been determined. Equal area projections of the nodal planes together with the P-wave first-motion and S-wave polarization data are presented for each earthquake. The quality of resolution of nodal plane determination on the basis of P-wave data, S-wave polarization, and the combination of P and S-wave data according to the present method, is discussed.

scholarly journals Simulation of the effect of stress-induced anisotropy on borehole compressional wave propagation

Geophysics ◽  
2014 ◽  
Vol 79 (4) ◽  
pp. D205-D216 ◽  
Author(s):  
Xinding Fang ◽  
Michael C. Fehler ◽  
Arthur Cheng
Keyword(s):  
Wave Propagation ◽  
Stress Concentration ◽  
Elastic Properties ◽  
Wave Velocity ◽  
Wave Amplitude ◽  
P Wave ◽  
Azimuthal Variation ◽  
P Wave Velocity ◽  
Velocity Region

Formation elastic properties near a borehole may be altered from their original state due to the stress concentration around the borehole. This can lead to an incorrect estimation of formation elastic properties measured from sonic logs. Previous work has focused on estimating the elastic properties of the formation surrounding a borehole under anisotropic stress loading. We studied the effect of borehole stress concentration on sonic logging in a moderately consolidated Berea sandstone using a two-step approach. First, we used an iterative approach, which combines a rock-physics model and a finite-element method, to calculate the stress-dependent elastic properties of the rock around a borehole subjected to an anisotropic stress loading. Second, we used the anisotropic elastic model obtained from the first step and a finite-difference method to simulate the acoustic response of the borehole. Although we neglected the effects of rock failure and stress-induced crack opening, our modeling results provided important insights into the characteristics of borehole P-wave propagation when anisotropic in situ stresses are present. Our simulation results were consistent with the published laboratory measurements, which indicate that azimuthal variation of the P-wave velocity around a borehole subjected to uniaxial loading is not a simple cosine function. However, on field scale, the azimuthal variation in P-wave velocity might not be apparent at conventional logging frequencies. We found that the low-velocity region along the wellbore acts as an acoustic focusing zone that substantially enhances the P-wave amplitude, whereas the high-velocity region caused by the stress concentration near the borehole results in a significantly reduced P-wave amplitude. This results in strong azimuthal variation of P-wave amplitude, which may be used to infer the in situ stress state.

S waves: Alaska and other earthquakes

1960 ◽  
Vol 50 (4) ◽  
pp. 581-597 ◽  
Author(s):  
William Stauder
Keyword(s):  
Focal Mechanism ◽  
Fault Plane ◽  
P Wave ◽  
Wave Analysis ◽  
Point Model ◽  
S Wave ◽  
S Waves ◽  
Wave Solutions ◽  
Single Force

ABSTRACT Techniques of S wave analysis are used to investigate the focal mechanism of four earthquakes. In all cases the results of the S wave analysis agree with previously determined P wave solutions and conform to a dipole with moment or single couple as the point model of the focus. Further, the data from S waves select one of the two nodal planes of P as the fault plane. Small errors in the determination of the angle of polarization of S are shown to result in scatter in the data of a peculiar character which might lead to misinterpretation. The same methods of analysis which in the present instances show excellent agreement with a dipole with moment source are the methods which in a previous paper required a single force type mechanism for a different group of earthquakes.

scholarly journals Real-time determination of earthquake focal mechanism via deep learning

2020 ◽  
Author(s):  
Wenhuan Kuang ◽  
Congcong Yuan ◽  
Jie Zhang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Focal Mechanism ◽  
Theoretical Data ◽  
Focal Mechanisms ◽  
Historical Earthquake ◽  
Advanced Technologies ◽  
Earthquake Focal Mechanism ◽  
Time Determination

Abstract An immediate report of the source focal mechanism with full automation after a destructive earthquake is crucial for timely characterizing the faulting geometry, evaluating the stress perturbation, and assessing the aftershock patterns. Advanced technologies such as Artificial Intelligence (AI) has been introduced to solve various problems in real-time seismology, but the real-time source focal mechanism is still a challenge. Here we propose a novel deep learning method namely Focal Mechanism Network (FMNet) to address this problem. The FMNet trained with 787,320 synthetic samples successfully estimates the focal mechanisms of four 2019 Ridgecrest earthquakes with magnitude larger than Mw 5.4. The network learns the global waveform characteristics from theoretical data, thereby allowing the extensive applications of the proposed method to regions of potential seismic hazards with or without historical earthquake data. After receiving data, the network takes less than two hundred milliseconds for predicting the source focal mechanism reliably on a single CPU.

A method of correcting P-wave magnitudes for the effect of earthquake focal mechanism

1971 ◽  
Vol 61 (6) ◽  
pp. 1811-1826 ◽  
Author(s):  
Atiq A. Syed ◽  
Otto W. Nuttli
Keyword(s):  
Atlantic Ocean ◽  
Focal Mechanism ◽  
Present Method ◽  
Body Wave ◽  
Geographic Region ◽  
P Wave ◽  
Aleutian Island ◽  
Transform Fault ◽  
Routine Manner ◽  
Earthquake Focal Mechanism

abstract This paper presents a methodology for correcting body-wave magnitudes for the effect of focal mechanism in a routine manner. The method requires a knowledge of the prevailing or dominant mechanism for a geographic region, from which tables are constructed which enable one to make the necessary correction. Included in the paper are tables for Aleutian Island, Kamchatka and mid-Atlantic Ocean earthquakes. From a study of seven earthquakes, it is concluded that the present method gives essentially the same average magnitude with the same standard deviation as a more exact method of correcting for the focal mechanism. The latter method uses the focal-mechanism parameters of the earthquake, which must be determined independently for each earthquake. The existing distribution of seismograph stations is such that transform-fault earthquakes of the mid-Atlantic Ocean will consistently have their P-wave magnitudes underestimated by about 0.2 magnitude units, if no correction is made for the focal mechanism. On the other hand, P-wave magnitudes of earthquakes in Kamchatka and south of the axis of the Aleutian Trench will be overestimated by about 0.2 units.

scholarly journals The Waiotapu earthquake of 1983, December 14

1984 ◽  
Vol 17 (4) ◽  
pp. 272-279
Author(s):  
Euan G. C. Smith ◽  
Brad J. Scott ◽  
John H. Latter
Keyword(s):  
Focal Mechanism ◽  
Earthquake Swarm ◽  
Geodetic Data ◽  
Shallow Depth ◽  
P Wave ◽  
Valley Area ◽  
Swarm Activity ◽  
Epicentral Region ◽  
East West

The continual earthquake swarm activity in the Waiotapu-Waikite Valley area that commenced in April 1982, reached a climax on 14 December 1983 with the occurrence of a magnitude 5.1 shock at shallow depth, on or close to the Ngapouri fault, near Waiotapu. It was the largest event in this area for more than 40 years. Felt intensities reached MM VII and possibly MM VIII in the epicentral region and resulted in claims for $29,000 worth of damage. Although inadequate for the determination of a focal mechanism, first P-wave motions indicate that the earthquake produced east-west extension. On the assumption that the shock occurred on the Ngapouri fault (strike N55°E, northwest side down), this implies sinistral movement with a lesser dip slip component. Geodetic data are consistent with extention at N110°E in the region, although the magnitude of the strain is technically too small to be statistically significant.

The S wave project for focal mechanism studies earthquakes of 1962

1964 ◽  
Vol 54 (6B) ◽  
pp. 2199-2208 ◽  
Author(s):  
William Stauder ◽  
G. A. Bollinger
Keyword(s):  
Focal Mechanism ◽  
P Wave ◽  
S Wave ◽  
Polarization Data ◽  
Saint Louis ◽  
Saint Louis University ◽  
S Waves ◽  
Focal Mechanism Solutions ◽  
First Motion

Abstract The Department of Geophysics of Saint Louis University has instituted a routine program for the determination of the focal mechanism of the larger earthquakes of each year using methods developed for the use of S waves in focal mechanism studies. Suites of records from selected stations are assembled from the WWSS microfilm file for each earthquake of interest. A combination of P-wave first motion and S-wave polarization data is then used to determine graphically the mechanism of the earthquakes. Thirty-six earthquakes of 1962 were selected for study. The focal mechanism solutions are presented for twenty-three of these shocks. There is evidence of patterns characteristic of the focal mechanism of earthquakes occurring in Kamchatka, the Aleutian Islands and South America. A complete presentation of all the data and of all the solutions is available in a more lengthy report.

scholarly journals Real-time determination of earthquake focal mechanism via deep learning

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Wenhuan Kuang ◽  
Congcong Yuan ◽  
Jie Zhang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Focal Mechanism ◽  
Theoretical Data ◽  
Focal Mechanisms ◽  
Historical Earthquake ◽  
Advanced Technologies ◽  
Earthquake Focal Mechanism ◽  
Time Determination

AbstractAn immediate report of the source focal mechanism with full automation after a destructive earthquake is crucial for timely characterizing the faulting geometry, evaluating the stress perturbation, and assessing the aftershock patterns. Advanced technologies such as Artificial Intelligence (AI) has been introduced to solve various problems in real-time seismology, but the real-time source focal mechanism is still a challenge. Here we propose a novel deep learning method namely Focal Mechanism Network (FMNet) to address this problem. The FMNet trained with 787,320 synthetic samples successfully estimates the focal mechanisms of four 2019 Ridgecrest earthquakes with magnitude larger than Mw 5.4. The network learns the global waveform characteristics from theoretical data, thereby allowing the extensive applications of the proposed method to regions of potential seismic hazards with or without historical earthquake data. After receiving data, the network takes less than two hundred milliseconds for predicting the source focal mechanism reliably on a single CPU.

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