Auditory discrimination of seismic signals from earthquakes and explosions

1965 ◽  
Vol 55 (1) ◽  
pp. 1-25
Author(s):  
G. E. Frantti ◽  
L. A. Levereault
Keyword(s):  
Auditory System ◽  
Seismic Data ◽  
Magnetic Tape ◽  
Path Length ◽  
World Wide ◽  
Propagation Path ◽  
Seismic Signals ◽  
Chance Performance ◽  
Short Period ◽  
Seismic Frequencies

Abstract Magnetic tape recordings of short-period seismic signals from approximately 200 earthquakes and explosions were time-compressed by a factor of up to 512 to shift seismic frequencies to the audible range. These seismic data include the inhomogeneities introduced by substantial variations in the locations of sources and receivers (world-wide), propagation path length (32 to 7000 km), and source magnitude (M = 0.5 to M = 6.5). Subjects were trained with a representative set of the “seismic sounds.” Auditory experiments were conducted to determine the ability of the human auditory system to distinguish between seismic signals from earthquakes and explosions. The results of the experiments suggest that a trained listener can identify approximately two-thirds of the seismic sounds presented, where one half corresponds to chance performance.

Spectral characteristics of central Nevada microearthquakes

1970 ◽  
Vol 60 (5) ◽  
pp. 1547-1559 ◽  
Author(s):  
Bruce M. Douglas ◽  
Alan Ryall ◽  
Ray Williams
Keyword(s):  
Spectral Characteristics ◽  
Propagation Path ◽  
Seismic Signals ◽  
Fourier Amplitude ◽  
Recording Site ◽  
Focal Distance ◽  
Amplitude Spectra ◽  
Spectral Frequency ◽  
Group Characteristics ◽  
Event Magnitude

Abstract Fourier amplitude spectra were computed for 40 central Nevada microearthquakes, selected to consider, independently, effects of azimuth and distance from known sources. Spectra were averaged for groups of events to eliminate peculiarities of individual records and emphasize group characteristics. Spectral characteristics did not behave systematically as a function of azimuth from the recording site to the source, but peak spectral frequency was found to correlate strongly with event magnitude and to some degree also with focal distance. These preliminary results suggest that recordings of small earthquakes and microearthquakes can be used to provide detailed information on the character of seismic signals related to properties of the source and propagation path.

LONG-PERIOD SURFACE-RELATED MULTIPLE SUPPRESSION IN 2D MARINE SEISMIC DATA USING PREDICTIVE DECONVOLUTION AND COMBINATION OF SURFACE-RELATED MULTIPLE ELIMINATION AND PARABOLIC RADON FILTERING

2021 ◽  
Author(s):  
Pimpawee Sittipan ◽  
Pisanu Wongpornchai
Keyword(s):  
Seismic Data ◽  
Seismic Reflection ◽  
Petroleum Reservoirs ◽  
The United States ◽  
Multiple Reflections ◽  
Long Period ◽  
Predictive Deconvolution ◽  
Short Period ◽  
Marine Seismic ◽  
Multiple Elimination

Some of the important petroleum reservoirs accumulate beneath the seas and oceans. Marine seismic reflection method is the most efficient method and is widely used in the petroleum industry to map and interpret the potential of petroleum reservoirs. Multiple reflections are a particular problem in marine seismic reflection investigation, as they often obscure the target reflectors in seismic profiles. Multiple reflections can be categorized by considering the shallowest interface on which the bounces take place into two types: internal multiples and surface-related multiples. Besides, the multiples can be categorized on the interfaces where the bounces take place, a difference between long-period and short-period multiples can be considered. The long-period surface-related multiples on 2D marine seismic data of the East Coast of the United States-Southern Atlantic Margin were focused on this research. The seismic profile demonstrates the effectiveness of the results from predictive deconvolution and the combination of surface-related multiple eliminations (SRME) and parabolic Radon filtering. First, predictive deconvolution applied on conventional processing is the method of multiple suppression. The other, SRME is a model-based and data-driven surface-related multiple elimination method which does not need any assumptions. And the last, parabolic Radon filtering is a moveout-based method for residual multiple reflections based on velocity discrimination between primary and multiple reflections, thus velocity model and normal-moveout correction are required for this method. The predictive deconvolution is ineffective for long-period surface-related multiple removals. However, the combination of SRME and parabolic Radon filtering can attenuate almost long-period surface-related multiple reflections and provide a high-quality seismic images of marine seismic data.

scholarly journals Dynamics of the Askja caldera July 2014 landslide, Iceland, from seismic signal analysis: precursor, motion and aftermath

2017 ◽  
Author(s):  
Anne Schöpa ◽  
Wei-An Chao ◽  
Bradley Lipovsky ◽  
Niels Hovius ◽  
Robert S. White ◽  
...  
Keyword(s):  
Seismic Data ◽  
Signal Analysis ◽  
Slope Failure ◽  
Stable State ◽  
Seismic Signal ◽  
Early Warning Systems ◽  
High Energy ◽  
Stick Slip ◽  
Short Period ◽  
Large Landslide

Abstract. Using data from a network of 58 seismic stations, we characterise a large landslide that occurred at the southeastern corner of the Askja caldera, Iceland, on 21 July 2014, including its precursory tremor and mass wasting aftermath. Our study is motivated by the need for deeper generic understanding of the processes operating not only at the time of catastrophic slope failure, but also in the preparatory phase and during the transient into the subsequent stable state. In addition, it is prompted by the high hazard potential of the steep caldera lake walls at Askja as tsunami waves created by the landslide reached famous tourist spots 60 m above the lake level. Since direct observations of the event are lacking, the seismic data give valuable details on the dynamics of this landslide episode. The excellent seismic data quality and coverage of the stations of the Askja network made it possible to jointly analyse the long- and short-period signals of the landslide to obtain information about the triggering, initiation, timing, and propagation of the slide. The seismic signal analysis and a landslide force history inversion of the long-period seismic signals showed that the Askja landslide was a single, large event starting at the SE corner of the caldera lake at 23:24:05 UTC and propagating to the NW in the following 2 min. The bulk sliding mass was 7–16 × 1010 kg, equivalent to a collapsed volume of 35–80 × 106 m3, and the centre of mass was displaced horizontally downslope by 1260 ± 250 m during landsliding. The seismic records of stations up to 30 km away from the landslide source area show a tremor signal that started 30 min before the main landslide failure. It is harmonic, with a fundamental frequency of 2.5 Hz and shows time-dependent changes of its frequency content. We attribute the complex tremor signal to accelerating and decelerating stick-slip motion on failure planes at the base and the sides of the landslide body. The accelerating motion culminated in aseismic slip of the landslide visible as a drop in the seismic amplitudes down to the background noise level 2 min before the landslide high-energy signal begins. We propose that the seismic signal of the precursory tremor may be developed as an indicator for landslide early-warning systems. The 8 hours after the main landslide failure are characterised by smaller slope failures originating from the destabilised caldera wall decaying in frequency and magnitude. We introduce the term afterslides for this subsequent, declining slope activity after a large landslide.

scholarly journals Combined Multiple Attenuation Methods and Geological Interpretation : Seram Sea Case Study 2D Marine Seismic Data

2019 ◽  
Vol 34 (1) ◽  
Author(s):  
Tumpal Bernhard Nainggolan ◽  
Said Muhammad Rasidin ◽  
Imam Setiadi
Keyword(s):  
Satisfactory Result ◽  
Radon Transform ◽  
Seismic Data ◽  
Thrust Belt ◽  
Geological Interpretation ◽  
Structural Style ◽  
Predictive Deconvolution ◽  
Fold And Thrust Belt ◽  
Short Period ◽  
Marine Seismic

Multiple often and always appear in marine seismic data due to very high acoustic impedance contrasts. These events have undergone more than one reflection. This causes the signal to arrive back at the receiver at an erroneous time, which, in turn, causes false results and can result in data misinterpretation. Several types of multiple suppression have been studied in literature. Methods that attenuate multiples can be classified into three broad categories: deconvolution methods; filtering methods and wavefield prediction subtraction methods. The study area is situated on Seram Sea in between 131°15’E – 132°45’E and 3°0’S – 4°0’S, Seram Trough which is located beneath Seram Sea at northern part of the Banda-Arc – Australian collision zone and currently the site of contraction between Bird’s Head and Seram. This research uses predictive deconvolution and FK-filter to attenuate short period multiple from their move out, then continued by SRME method to predict multiple that cannot be attenuated from previous method, then followed by Radon transform to attenuate multiple that still left and cannot be attenuated by SRME method. The result of each method then compared to each other to see how well multiple attenuated. Predictive deconvolution and F-K filter could not give satisfactory result especially complex area where multiple in dipping event is not periodic, SRME method successfully attenuate multiple especially in near offset multiple without need subsurface information, while SRME method fails to attenuate long offset multiple, combination of SRME method and Radon transform can give satisfactory result with careful selection of the Radon transform parameters because it can obscure some primary reflectors. Based on geological interpretation, Seram Trough is built by dominant structural style of deposited fold and thrust belt. The deposited fold and thrust belt has a complexly fault geometry from western zone until eastern of seismic line.

Machine Learning analysis of seismic signals recorded at Stromboli Volcano

2021 ◽  
Author(s):  
Darius Fenner ◽  
Georg Rümpker ◽  
Horst Stöcker ◽  
Megha Chakraborty ◽  
Wei Li ◽  
...  
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Noise Level ◽  
Volcanic Eruptions ◽  
Time Varying ◽  
Seismic Signals ◽  
Machine Learning Model ◽  
Automatic Adaptation ◽  
Consistent Manner ◽  
Recent Developments

<p>At Stromboli, minor volcanic eruptions occur at time intervals of approximately five minutes on average, making it one of the most active volcanoes worldwide. In addition to these mostly harmless events, there are also stronger eruptions and paroxysms which pose a serious threat to residents and tourists. In light of recent developments in Machine Learning, this study attempts to apply these new tools for the analysis of the time-varying volcanic eruptions at Stromboli. As input for the Machine-Learning approach, we use continuous recordings of seismic signals from two seismometers on the island. The data is available from IRIS  and includes records starting in 2012 up to the present. </p><p>One primary challenge is to label and classify the data, i.e., to discriminate events of interest from noise. The variety of signal-appearance in the recorded data is wide, in some periods the events are clearly distinguishable from noise whereas, in other cases relevant events are obscured by the high noise level. To enable the event-detection in all cases, we developed the following algorithm: in the first step, the seismic data is pre-processed with an STA/LTA-Filter, which allows detection of events based on a prominence threshold. However, due to the diversity of signal patterns, a fixed set of hyperparameters (STA- and LTA-window length, prominence threshold, correlation coefficient) fails to reliably extract the relevant events in a consistent manner. Therefore, the (time-varying) noise level of the recordings is used as an additional key indicator. After this, the hyperparameters are optimized. The automatic adaptation is then used for labeling the continuous seismic data.</p><p>After extracting the events based on this approach, a machine learning model is trained to analyze the recordings for possible patterns in the interval times and the event amplitudes. This study is expected to provide constraints on the possibility to detect complex time-dependent patterns of the eruption history at Stromboli.</p>

scholarly journals Applicataion of ormsby wavelet for generation of synthetic seismic signals

2018 ◽  
Vol 7 (2.7) ◽  
pp. 794
Author(s):  
E Sai Sumanth ◽  
V Joseph ◽  
Dr K S Ramesh ◽  
Dr S Koteswara Rao
Keyword(s):  
Wavelet Transform ◽  
Seismic Data ◽  
Wavelet Transforms ◽  
Seismic Signal ◽  
Core Structure ◽  
Seismic Signals ◽  
Seismic Reflections ◽  
Input Wave

Investigation of signals reflected from earth’s surface and its crust helps in understanding its core structure. Wavelet transforms is one of the sophisticated tools for analyzing the seismic reflections. In the present work a synthetic seismic signal contaminated with noise is synthesized  and analyzed using Ormsby wavelet[1]. The wavelet transform has efficiently extracted the spectra of the synthetic seismic signal as it smoothens the noise present in the data and upgrades the flag quality of the seismic data due to termers. Ormsby wavelet gives the most redefined spectrum of the input wave so it could be used for the analysis of the seismic reflections. 

Lunar rock Q in 3000-5000 range achieved in laboratory

1977 ◽  
Vol 285 (1327) ◽  
pp. 475-479 ◽  
Keyword(s):  
Seismic Data ◽  
Room Temperature ◽  
Low Frequency ◽  
Thin Layers ◽  
Lunar Sample ◽  
Lunar Rock ◽  
Seismic Frequencies ◽  
Different Gases ◽  
Frequency Apparatus

Q measurements carried out by the vibrating bar technique on lunar sample 70215,85 have yielded Q values as high as 4800 at room temperature. The strong outgassing procedures necessary to raise the Q to these high values from Q =- 60 (when received) and studies of the effect on Q by a variety of different gases shows that the removal of thin layers of adsorbed H 2 O are responsible for the dramatic increase in Q . Experiments carried out with a low frequency apparatus on a terrestrial analogue at 50 Hz suggest similar increases in Q with outgassing, thus providing evidence that dramatic effects on Q can be expected to occur down to seismic frequencies. These results, in part, explain the contrast between seismic data in the lunar and terrestrial crust in terms of the absence and presence respectively of adsorbed H 2 O.

scholarly journals Application of zero phase wavelet on synthetic seismic signals with noise

2018 ◽  
Vol 7 (2.7) ◽  
pp. 906
Author(s):  
Sai Srinivas Goli ◽  
Sireesha Papanaboyina ◽  
Satya Ramesh Kanchumarthi ◽  
Koteswara Rao Sanagapallea
Keyword(s):  
Event Detection ◽  
Seismic Data ◽  
Seismic Reflection ◽  
Seismic Signal ◽  
Accurate Information ◽  
Seismic Signals ◽  
Reflected Wave ◽  
Zero Phase

Detection of time of occurrences of different phases and frequencies is of highest importance in seismic reflected signals. Seismic reflection analysis gives us accurate information about the event detection, source acquisition of triggered seismic data and its mechanisms. In the present work an attempt is made to generate a synthetic seismic with noise generally present in the seismograph using. The synthetic seismic signal is extracted by zero phase wavelet. Crews software is used in this extraction. The zero phase wavelet could efficiently extract the seismic signal present in the reflected wave.  

Physical Wavelet Frame Denoising

Geophysics ◽  
2003 ◽  
Vol 68 (1) ◽  
pp. 225-231 ◽  
Author(s):  
Rongfeng Zhang ◽  
Tadeusz J. Ulrych
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Real Data ◽  
Wavelet Denoising ◽  
Seismic Signals ◽  
Wavelet Frame ◽  
New Approach ◽  
Design And Implementation ◽  
Ground Roll ◽  
Acoustic Processing

This paper deals with the design and implementation of a new wavelet frame for noise suppression based on the character of seismic data. In general, wavelet denoising methods widely used in image and acoustic processing use well‐known conventional wavelets which, although versatile, are often not optimal for seismic data. The new approach, physical wavelet frame denoising uses a wavelet frame that takes into account the characteristics of seismic data both in time and space. Synthetic and real data tests show that the approach is effective even for seismic signals contaminated by strong noise which may be random or coherent, such as ground roll or air waves.

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