Source Quantification of Volcanic-Seismic Signals

2021 ◽  
pp. 425-467
Author(s):  
Hiroyuki Kumagai
Keyword(s):  
Seismic Signals

NOISE AND TARGET SIGNALS SIMULATION IN PASSIVE SEISMIC LOCATION SYSTEMS

2018 ◽  
pp. 73-78
Author(s):  
Yu. V. Morozov ◽  
M. A. Rajfeld ◽  
A. A. Spektor
Keyword(s):  
Pulse Sequence ◽  
Signal To Noise Ratio ◽  
Seismic Signal ◽  
Seismic Signals ◽  
Simulation Procedure ◽  
Proposed Model ◽  
Seismic Location ◽  
Passive Seismic ◽  
System Characteristics ◽  
Primary Pulse

The paper proposes the model of a person seismic signal with noise for the investigation of passive seismic location system characteristics. The known models based on Gabor and Berlage pulses have been analyzed. These models are not able wholly to consider statistical properties of seismic signals. The proposed model is based on almost cyclic character of seismic signals, Gauss character of fluctuations inside a pulse, random amplitude change from pulse to pulse and relatively small fluctuation of separate pulses positions. The simulation procedure consists of passing the white noise through a linear generating filter with characteristics formed by real steps of a person, and the primary pulse sequence modulation by Gauss functions. The model permits to control the signal-to-noise ratio after its reduction to unity and to vary pulse shifts with respect to person steps irregularity. It has been shown that the model of a person seismic signal with noise agrees with experimental data.

Reconstruction of seismic signals using S‐transform ridges

2021 ◽  
Author(s):  
Aleksander S. Serdyukov ◽  
Anton V. Azarov ◽  
Aleksander V. Yablokov ◽  
Tatiana V. Shilova ◽  
Valery D. Baranov
Keyword(s):  
Seismic Signals ◽  
S Transform

PRESSURE AMPLITUDES OF SEISMIC SIGNALS IN OFFSHORE LOUISIANA

Geophysics ◽  
1977 ◽  
Vol 42 (1) ◽  
pp. 3-16
Author(s):  
M. E. Arnold
Keyword(s):  
Field Tests ◽  
Signal Amplitude ◽  
Test Area ◽  
Seismic Signals ◽  
Code Generators ◽  
Single Detector ◽  
First Arrival ◽  
Reflected Signal ◽  
Refracted Waves ◽  
Seismic Lines

Pressure amplitudes were determined for various kinds of seismic signals observed on special test records obtained during field tests conducted along a 14,000-ft seismic lines in Eugene Island Block 184, offshore Louisiana. Vibrators attached to a Seismograph Service Corp. (SSC) boat generated swept‐frequency and monofrequency signals. Signals from detectors on a streamer cable towed by the boat were recorded by an SSC recording system. Signals from a vertical spread of detectors were recorded by a DFS/9000 recorder on the Transco 184 platform centrally located in the test area. Location of the boat was determined by analysis of time relations of signals from responders located at established positions some distance from the test area. Clock times from manually referenced timing code generators were recorded by both the SSC and DFS recorders to permit synchronization between separately recorded signals. The signals analyzed were separated into three classes: [Formula: see text] includes direct and refracted waves; [Formula: see text] consists of primary reflections; and [Formula: see text] includes signals diffracted from scatterers. The average level of first‐arrival signal [Formula: see text] and reflected signal [Formula: see text] for frequency sets 25, 40, 42.2, 50, and 70.4 Hz in the range of 1414 and 2143 ft, which encompasses streamer cable single‐detector groups, is 337 and 29.6 microbars, respectively. The amplitude of signals [Formula: see text], believed to be diffracted from the contact between key reflectors and a salt dome, ranges from 13 to 20 microbars and is 10 to 100 times the amplitudes of towing and ambient noise, respectively. The observed decay of first‐arrival signal amplitude is approximately proportional to the square root of range distance, or about 2 dB/1000 ft. The observed decay of reflected signal amplitude with range distance is approximately 1 dB/1000 ft.

Anomaly prediction in seismic signals using neural networks

2013 ◽  
Author(s):  
Aaron Waibel ◽  
Abdullah Ali Alshehri ◽  
Soundararajan Ezekiel
Keyword(s):  
Neural Networks ◽  
Seismic Signals

Local seismic attributes

Geophysics ◽  
2007 ◽  
Vol 72 (3) ◽  
pp. A29-A33 ◽  
Author(s):  
Sergey Fomel
Keyword(s):  
Image Registration ◽  
Seismic Signal ◽  
Seismic Attributes ◽  
Data Sets ◽  
Local Similarity ◽  
Seismic Signals ◽  
Practical Applications ◽  
Regularized Inversion ◽  
Signal Characteristics ◽  
Land Survey

Local seismic attributes measure seismic signal characteristics not instantaneously, at each signal point, and not globally, across a data window, but locally in the neighborhood of each point. I define local attributes with the help of regularized inversion and demonstrate their usefulness for measuring local frequencies of seismic signals and local similarity between different data sets. I use shaping regularization for controlling the locality and smoothness of local attributes. A multicomponent-image-registration example from a nine-component land survey illustrates practical applications of local attributes for measuring differences between registered images.

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