A STUDY OF CASED AND OPEN HOLES FOR DEEP‐HOLE SEISMIC DETECTION

Geophysics ◽  
1963 ◽  
Vol 28 (1) ◽  
pp. 8-13 ◽  
Author(s):  
D. R. Van Sandt ◽  
F. K. Levin
Keyword(s):  
Seismic Signal ◽  
High Amplitude ◽  
Borehole Wall ◽  
Detectable Effect ◽  
Seismic Signals ◽  
Deep Hole ◽  
Simultaneous Measurements ◽  
Seismic Detection

A study is made of the relative merits of recording seismic signals in cased and open boreholes. Simultaneous measurements of seismic signals in adjacent cased and open holes are compared. It is shown that, in general, the same natural earth noise is recorded in both holes, and the response to the high‐amplitude unidirectional signal is the same in both holes. The conclusion is that the casing in a borehole has no detectable effect upon a seismic signal if the casing is cemented to the borehole wall and wall‐coupled geophones are used.

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.

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.

Bayesian feature learning for seismic compressive sensing and denoising

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. O91-O104 ◽  
Author(s):  
Georgios Pilikos ◽  
A. C. Faul
Keyword(s):  
Compressive Sensing ◽  
State Of The Art ◽  
Feature Learning ◽  
Seismic Signal ◽  
Basis Functions ◽  
Sparse Signals ◽  
Seismic Signals ◽  
Process Factor ◽  
Learned Features ◽  
Fixed Basis

Extracting the maximum possible information from the available measurements is a challenging task but is required when sensing seismic signals in inaccessible locations. Compressive sensing (CS) is a framework that allows reconstruction of sparse signals from fewer measurements than conventional sampling rates. In seismic CS, the use of sparse transforms has some success; however, defining fixed basis functions is not trivial given the plethora of possibilities. Furthermore, the assumption that every instance of a seismic signal is sparse in any acquisition domain under the same transformation is limiting. We use beta process factor analysis (BPFA) to learn sparse transforms for seismic signals in the time slice and shot record domains from available data, and we use them as dictionaries for CS and denoising. Algorithms that use predefined basis functions are compared against BPFA, with BPFA obtaining state-of-the-art reconstructions, illustrating the importance of decomposing seismic signals into learned features.

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. 

scholarly journals Seismic monitoring of torrential and fluvial processes

2016 ◽  
Vol 4 (2) ◽  
pp. 285-307 ◽  
Author(s):  
Arnaud Burtin ◽  
Niels Hovius ◽  
Jens M. Turowski
Keyword(s):  
Seismic Noise ◽  
Seismic Waves ◽  
Seismic Signal ◽  
Seismic Monitoring ◽  
Surface Processes ◽  
Seismic Signals ◽  
Seismic Arrays ◽  
Time Frequency ◽  
Continuous Survey ◽  
Long Time

Abstract. In seismology, the signal is usually analysed for earthquake data, but earthquakes represent less than 1 % of continuous recording. The remaining data are considered as seismic noise and were for a long time ignored. Over the past decades, the analysis of seismic noise has constantly increased in popularity, and this has led to the development of new approaches and applications in geophysics. The study of continuous seismic records is now open to other disciplines, like geomorphology. The motion of mass at the Earth's surface generates seismic waves that are recorded by nearby seismometers and can be used to monitor mass transfer throughout the landscape. Surface processes vary in nature, mechanism, magnitude, space and time, and this variability can be observed in the seismic signals. This contribution gives an overview of the development and current opportunities for the seismic monitoring of geomorphic processes. We first describe the common principles of seismic signal monitoring and introduce time–frequency analysis for the purpose of identification and differentiation of surface processes. Second, we present techniques to detect, locate and quantify geomorphic events. Third, we review the diverse layout of seismic arrays and highlight their advantages and limitations for specific processes, like slope or channel activity. Finally, we illustrate all these characteristics with the analysis of seismic data acquired in a small debris-flow catchment where geomorphic events show interactions and feedbacks. Further developments must aim to fully understand the richness of the continuous seismic signals, to better quantify the geomorphic activity and to improve the performance of warning systems. Seismic monitoring may ultimately allow the continuous survey of erosion and transfer of sediments in the landscape on the scales of external forcing.

Effect of hydrophone arrays on offshore Texas seismic signals

Geophysics ◽  
1978 ◽  
Vol 43 (6) ◽  
pp. 1083-1098
Author(s):  
M. E. Arnold
Keyword(s):  
Dynamic Range ◽  
High Amplitude ◽  
Early Event ◽  
Seismic Signals ◽  
Multiple Reflections ◽  
Surface Reflection ◽  
Field Recording ◽  
First Arrival ◽  
Slope Line ◽  
Water Bottom

The effect of hydrophone arrays in the recording of seismic signals during offshore Texas seismic marine experiments is judged by comparing traces of spatially tapered hydrophone array signals with traces that are combinations of simultaneously recorded wavetest hydrophone signals. Each spatially tapered hydrophone group array consists of 26 hydrophones nonuniformly spaced over 212 ft. The wavetest streamer section consists of 36 groups of two hydrophones, each pair connected in parallel and with hydrophones back‐to‐back for acceleration cancellation, with 5-ft spacing between groups. Reflection from deep subsurface interfaces are negligibly affected by hydrophone arrays except for very long arrays and/or long‐range distances. Consequently, the report is primarily concerned with the effects of simulated and real hydrophone arrays on first‐arrival signal and early subbottom reflections. Comparison of theoretical and actual seismic traces from an Aquapulse source for near range distances (835 ft) used in normal operations indicates that (1) near‐simultaneous arrival of the direct wave and surface reflection result in their virtual cancellation, (2) the early event with largest amplitude is associated with constructive interference between source and receiver ghost reflections, and (3) the “pseudo‐bubble” period effectively fixed the predominant frequency of all seismic events at values near 28 Hz. At medium range distances (4755 ft), such comparisons indicate that (1) first arrivals are refracted waves traveling in subbottom layers; (2) the water‐bottom reflection is beyond critical angle and is, therefore, complex; (3) the early events with largest amplitude are multiple reflections; and (4) at least two orders of water‐bottom multiples are identified. The attenuation of the high‐amplitude, first‐arrival signal that includes the water‐bottom reflection permits greater dynamic range in field recording and higher levels of “true” amplitude for later reflections without overload distortion of early events on playback. However, if improved resolution of reflection from moderate depths (∼4000 ft) is important, then arrays of length studied in this report (∼200 ft) should not be used to record signals at range distances greater than about 2000 ft because frequencies above 50 Hz are attenuated severely. Spectral analysis of wavetest records in the absence of signals shows that the wavenumber distribution of the noise is located along a slope line equivalent to 5000 ft/sec between wavenumbers that imply a spectral distribution of 30 to 100 Hz. Theoretical array response studies show that both the 36‐element Chebyshev array and the 26‐element spatially tapered array are superior to a 36‐element uniformly weighted array in rejection of seismic noise in the spectral range of 30 to 100 Hz.

scholarly journals A Novel Method of Seismic Signal Detection Using Waveform Features

2020 ◽  
Vol 10 (8) ◽  
pp. 2919
Author(s):  
Jian Li ◽  
Mengmin He ◽  
Gaofeng Cui ◽  
Xiaoming Wang ◽  
Weidong Wang ◽  
...  
Keyword(s):  
Signal Detection ◽  
Probability Model ◽  
Joint Probability ◽  
Seismic Signal ◽  
Detection Performance ◽  
Feature Model ◽  
Location Information ◽  
Seismic Signals ◽  
Historical Events ◽  
Joint Probability Model

The detection of seismic signals is vital in seismic data processing and analysis. Many algorithms have been proposed to resolve this issue, such as the ratio of short-term and long-term power averages (STA/LTA), F detector, Generalize F, and etc. However, the detection performance will be affected by the noise signals severely. In this paper, we propose a novel seismic signal detection method based on the historical waveform features to improve the seismic signals detection performance and reduce the affection from the noise signals. We use the historical events location information in a specific area and waveform features information to build the joint probability model. For the new signal from this area, we can determine whether it is the seismic signal according to the value of the joint probability. The waveform features used to construct the model include the average spectral energy on a specific frequency band, the energy of the component obtained by decomposing the signal through empirical mode decomposition (EMD), and the peak and the ratio of STA/LTA trace. We use the Gaussian process (GP) to build each feature model and finally get a multi-features joint probability model. The historical events location information is used as the kernel of the GP, and the historical waveform features are used to train the hyperparameters of GP. The beamforming data of the seismic array KSRS of International Monitoring System are used to train and test the model. The testing results show the effectiveness of the proposed method.

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.  

On the separation of co- and post-seismic signals due to large earthquakes from GRACE observations

2020 ◽  
Author(s):  
Jin Li ◽  
Jianli Chen ◽  
Song-Yun Wang ◽  
Lu Tang ◽  
Xiaogong Hu
Keyword(s):  
Time Series ◽  
Seismic Signal ◽  
Seismic Signals ◽  
Fitting Method ◽  
Short Period ◽  
Gravity Recovery ◽  
Andaman Earthquake ◽  
Large Earthquakes ◽  
Temporal Gravity

<p>Satellite gravimetry observations from GRACE (Gravity Recovery and Climate Experiment) and GRACE Follow-On are widely used to study the co-seismic and post-seismic deformations caused by large earthquakes. Temporal gravity changes from GRACE provide good constraints to investigate the fault slips of large earthquakes especially for oceanic areas. However, reliable retrieval of seismic signals is still challenging due to large uncertainties and limited spatial and temporal resolutions of GRACE observations. To extract the co- and post-seismic signals from GRACE, the time series fitting method based on least squares is commonly used. In the time series fitting, the earthquake occurrence time parameter (t0) is usually set at the mid-month point, since most available GRACE time-variable data are monthly solutions. Nevertheless, a lot of large earthquakes did not occur exactly at mid-month. By simulative tests, we demonstrate that the commonly used mid-month approximation for the fitting parameter t0 can cause noticeable bias for the seismic signal extraction. The several-days deviation in the parameter t0 leads to obvious difference for the time series fitting of seismic signals, since the post-seismic changes are rapid and significant within a short period after the earthquake. With the case study of the 2004 Mw9.1 Sumatra-Andaman earthquake (which occurred on December 26), we indicate that the bias due to the commonly used mid-month t0 approximation reaches above 10 percent amplitude of the extracted co-seismic signals. Thus the exact date for the fitting parameter t0 should be used for more reliable separation of the co- and post-seismic signals from GRACE observations.</p>

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