Slowness adaptive f-k filtering of prestack seismic data

Geophysics ◽  
1994 ◽  
Vol 59 (1) ◽  
pp. 140-147 ◽  
Author(s):  
Guy Duncan ◽  
Greg Beresford
Keyword(s):  
Seismic Data ◽  
Seismic Signal ◽  
Signal Distortion ◽  
Coherent Noise ◽  
One Dimensional ◽  
Minimal Distortion ◽  
Velocity Filtering ◽  
High Pass ◽  
Attenuation Characteristics ◽  
Space Variant

Frequency‐wavenumber velocity filtering is often applied to prestack seismic data for the attenuation of coherent noise. Although the process often gives excellent results, it can sometimes result in signal smoothing and distortion and poor attenuation of coherent noise. A slowness adaptive f-k filter reduces signal distortion and improves the attenuation characteristics of the filter. The technique uses a time‐ and space‐variant narrow reject‐band f-k filter. Optionally, coherent noise is compressed before application of the filter. The apparent slowness of coherent noise events is estimated using local t-x slant stacks weighted by coherence. A two‐dimensional (2-D) window is moved across the shot record, and at each point on the record slant stacks are taken through the central sample of the window. The slowness value that produces the maximum stack is assigned to the central sample of the window. In this way, an instantaneous slowness image of the shot record is produced. A one‐dimensional (1-D), high‐pass, finite‐duration impulse‐response (FIR) filter is applied in a spatially and temporally varying way across the record on the basis of the instantaneous slowness values. Before filter application, trace‐to‐trace static and amplitude effects are estimated and removed from the data. This results in compression of coherent noise and improved attenuation after filtering. The filtering process has been applied to low‐fold prestack dynamite data from the Surat Basin, Australia. The results indicate that the technique has good attenuation characteristics and produces minimal distortion of seismic signal. The process, however, is computationally expensive.

A procedure for optimally removing localized coherent noise

Geophysics ◽  
1995 ◽  
Vol 60 (1) ◽  
pp. 191-203 ◽  
Author(s):  
A. Frank Linville ◽  
Robert A. Meek
Keyword(s):  
Seismic Data ◽  
Seismic Profile ◽  
Noise Attenuation ◽  
Signal Distortion ◽  
Coherent Noise ◽  
Vertical Seismic Profile ◽  
Seismic Records ◽  
Signal Distortions ◽  
Notch Filtering ◽  
Phase Differences

Primary reflections in seismic records are often obscured by coherent noise making processing and interpretation difficult. Trapped water modes, surface waves, scattered waves, air waves, and tube waves to name a few, must be removed early in the processing sequence to optimize subsequent processing and imaging. We have developed a noise canceling algorithm that effectively removes many of the commonly encountered noise trains in seismic data. All currently available techniques for coherent noise attenuation suffer from limitations that introduce unacceptable signal distortions and artifacts. Also, most of those techniques impose the dual stringent requirements of equal and fine spatial sampling in the field acquisition of seismic data. Our technique takes advantage of characteristics usually found in coherent noise such as being localized in time, highly aliased, nondispersive (or only mildly so), and exhibit a variety of moveout patterns across the seismic records. When coherent noise is localized in time, a window much like a surgical mute is drawn around the noise. The algorithm derives an estimate of the noise in the window, automatically correcting for amplitude and phase differences, and adaptively subtracts this noise from the window of data. This signal estimate is then placed back in the record. In a model and a land data example, the algorithm removes noise more effectively with less signal distortion than does f-k filtering or velocity notch filtering. Downgoing energy in a vertical seismic profile (VSP) with irregular receiver spacing is also removed.

Aliasing in the tau-p transform and the removal of spatially aliased coherent noise

Geophysics ◽  
1990 ◽  
Vol 55 (11) ◽  
pp. 1496-1503 ◽  
Author(s):  
Greg Turner
Keyword(s):  
Time Series ◽  
Radon Transform ◽  
Seismic Data ◽  
Graphical Method ◽  
Temporal Frequency ◽  
Coherent Noise ◽  
P Values ◽  
One Dimensional ◽  
Noise Rejection ◽  
P Domain

The tau-p transform is a discretized Radon transform. The choice of discretization parameters is a very important part of performing the transform. Insufficient sampling in the tau direction leads to aliasing problems equivalent to those encountered in any one‐dimensional time series. A simple graphical method illustrates that too coarse sampling in the p direction results in reconstructions containing data duplicated incorrectly at different spatial positions. The spacing of these duplications is dependent on the temporal frequency of the data. Insufficient spatial sampling of the original seismic data causes events to plot at multiple p values in tau-p domain, again dependent on temporal frequency. Therefore, to velocity filter spatially aliased noise efficiently, multiple p values must be filtered. The use of appropriate filters in the p-f domain can substantially improve the noise rejection capabilities of velocity filters on spatially aliased noise while having little effect on desired reflection signals.

scholarly journals Coherent noise suppression by learning and analyzing the morphology of the data

Geophysics ◽  
2017 ◽  
Vol 82 (6) ◽  
pp. V397-V411 ◽  
Author(s):  
Pierre Turquais ◽  
Endrias G. Asgedom ◽  
Walter Söllner
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Morphological Diversity ◽  
Seismic Signal ◽  
Coherent Noise ◽  
Data Set ◽  
Redundant Dictionary ◽  
Manual Search ◽  
Mechanical Noise ◽  
Learned Dictionary

We have developed a method for suppressing coherent noise from seismic data by using the morphological differences between the noise and the signal. This method consists of three steps: First, we applied a dictionary learning method on the data to extract a redundant dictionary in which the morphological diversity of the data is stored. Such a dictionary is a set of unit vectors called atoms that represent elementary patterns that are redundant in the data. Because the dictionary is learned on data contaminated by coherent noise, it is a mix of atoms representing signal patterns and atoms representing noise patterns. In the second step, we separate the noise atoms from the signal atoms using a statistical classification. Hence, the learned dictionary is divided into two subdictionaries: one describing the morphology of the noise and the other one describing the morphology of the signal. Finally, we separate the seismic signal and the coherent noise via morphological component analysis (MCA); it uses sparsity with respect to the two subdictionaries to identify the signal and the noise contributions in the mixture. Hence, the proposed method does not use prior information about the signal and the noise morphologies, but it entirely adapts to the signal and the noise of the data. It does not require a manual search for adequate transforms that may sparsify the signal and the noise, in contrast to existing MCA-based methods. We develop an application of the proposed method for removing the mechanical noise from a marine seismic data set. For mechanical noise that is coherent in space and time, the results show that our method provides better denoising in comparison with the standard FX-Decon, FX-Cadzow, and the curvelet-based denoising methods.

4D-READY SEISMIC WITH Q-MARINE

2001 ◽  
Vol 41 (1) ◽  
pp. 671
Author(s):  
T. Brice ◽  
L. Larsen ◽  
S. Morice ◽  
M. Svendsun
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Processing System ◽  
Seismic Signal ◽  
Coherent Noise ◽  
Data Adaptive ◽  
A New Technique ◽  
Marine Seismic ◽  
Source Array ◽  
Array Geometry

A new concept for acquiring calibrated towed streamer seismic data is introduced through a new acquisition and processing system called ‘Q-Marine’. The specification of the new system has been defined by rigorous analysis of the factors that limit the sensitivity of seismic data in 4D studies and imaging. New sensor and streamer technology, new source technology and advances in positioning techniques and data processing have addressed these limitations.Sensitivity analysis revealed that the most significant perturbations to the seismic signal are swell noise and sensor sensitivity variations. Conventional analog groups of hydrophones are designed to suppress swell noise however a new technique for data-adaptive coherent noise attenuation delivers even greater noise suppression for densely spatially sampled single-sensor data.Although modern source controllers provide accurate airgun firing control, the signature of an airgun array may vary from shot to shot. This can be due to factors such as changes in the array geometry, air pressure variations, depth variations and wave action. A method for estimating the far-field signature of a source array is the Notional Source Method (proprietary to Schlumberger) which has been steadily refined since its first disclosure. A recent development compensates for variation in source array geometry by monitoring the position and azimuth of each subarray using GPS receivers mounted on the floats.New calibrated positioning and streamer control systems are part of the new acquisition system. Active vertical and lateral streamer control is achieved using steerable birds and positioning uncertainty is reduced through an in-built fully braced acoustic ranging system.Calibrated marine seismic data are achieved through quantifying the source output, the sensor responses and positioning uncertainty. The consequential improvements in seismic fidelity result in better imaging and more reliable 4D analysis.

A Single Bit Matters: Coherent Noise of Seismic Data Loggers

2015 ◽  
Vol 86 (3) ◽  
pp. 901-907 ◽  
Author(s):  
R. Takagi ◽  
K. Nishida ◽  
Y. Aoki ◽  
T. Maeda ◽  
K. Masuda ◽  
...  
Keyword(s):  
Seismic Data ◽  
Coherent Noise ◽  
Data Loggers

Coherent noise in marine seismic data

Geophysics ◽  
1983 ◽  
Vol 48 (7) ◽  
pp. 854-886 ◽  
Author(s):  
Ken Larner ◽  
Ron Chambers ◽  
Mai Yang ◽  
Walt Lynn ◽  
Willon Wai
Keyword(s):  
Seismic Data ◽  
Noise Suppression ◽  
Seismic Source ◽  
Coherent Noise ◽  
Critical Data ◽  
Predictive Deconvolution ◽  
Scattered Energy ◽  
Marine Seismic ◽  
The Many ◽  
Water Bottom

Despite significant advances in marine streamer design, seismic data are often plagued by coherent noise having approximately linear moveout across stacked sections. With an understanding of the characteristics that distinguish such noise from signal, we can decide which noise‐suppression techniques to use and at what stages to apply them in acquisition and processing. Three general mechanisms that might produce such noise patterns on stacked sections are examined: direct and trapped waves that propagate outward from the seismic source, cable motion caused by the tugging action of the boat and tail buoy, and scattered energy from irregularities in the water bottom and sub‐bottom. Depending upon the mechanism, entirely different noise patterns can be observed on shot profiles and common‐midpoint (CMP) gathers; these patterns can be diagnostic of the dominant mechanism in a given set of data. Field data from Canada and Alaska suggest that the dominant noise is from waves scattered within the shallow sub‐buttom. This type of noise, while not obvious on the shot records, is actually enhanced by CMP stacking. Moreover, this noise is not confined to marine data; it can be as strong as surface wave noise on stacked land seismic data as well. Of the many processing tools available, moveout filtering is best for suppressing the noise while preserving signal. Since the scattered noise does not exhibit a linear moveout pattern on CMP‐sorted gathers, moveout filtering must be applied either to traces within shot records and common‐receiver gathers or to stacked traces. Our data example demonstrates that although it is more costly, moveout filtering of the unstacked data is particularly effective because it conditions the data for the critical data‐dependent processing steps of predictive deconvolution and velocity analysis.

A method of ground‐roll elimination

Geophysics ◽  
1988 ◽  
Vol 53 (7) ◽  
pp. 894-902 ◽  
Author(s):  
Ruhi Saatçilar ◽  
Nezihi Canitez
Keyword(s):  
Seismic Data ◽  
Seismic Reflection ◽  
Wave Motion ◽  
Frequency Interval ◽  
Vertical Resolution ◽  
Matched Filters ◽  
One Dimensional ◽  
Frequency Bands ◽  
Ground Roll ◽  
Seismic Reflections

Amplitude‐ and frequency‐modulated wave motion constitute the ground‐roll noise in seismic reflection prospecting. Hence, it is possible to eliminate ground roll by applying one‐dimensional, linear frequency‐modulated matched filters. These filters effectively attenuate the ground‐roll energy without damaging the signal wavelet inside or outside the ground roll’s frequency interval. When the frequency bands of seismic reflections and ground roll overlap, the new filters eliminate the ground roll more effectively than conventional frequency and multichannel filters without affecting the vertical resolution of the 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 Monitoring and analysis of seismic data during the 2018 sunda strait tsunami

2021 ◽  
Vol 331 ◽  
pp. 07006
Author(s):  
Wahyu Kurniawan ◽  
Daryono ◽  
IDK Kerta ◽  
Bayu Pranata ◽  
Tri Winugroho
Keyword(s):  
Seismic Data ◽  
Warning System ◽  
Seismic Signal ◽  
The Body ◽  
Volcanic Earthquake ◽  
Time Zone ◽  
Ground Shaking ◽  
Tsunami Early Warning ◽  
Seismograph Station ◽  
Tsunami Signal

The tsunami of Sunda Strait occurred on December 22, 2018, at 21:03 West Indonesia Time (zone). An eruption of Mount Anak Krakatau caused an eruption that triggered a landslide on the slopes of Mount Anak Krakatau covering an area of 64 hectares that hit the coastal area of western Banten and southern Lampung and resulted in 437 deaths, 14.059 people were injured, and 33.721 people were displaced. Before the tsunami, signal transmissions (gaps) at the Lava seismograph station installed on the body of Mount Anak Krakatau experienced broken so that Mount Anak Krakatau Observation Post could not record volcanic earthquake signals since December 22, 2018, at 21.03 West Indonesia Time (zone). Given these facts, proper monitoring and analysis were required to monitor and analyze the source of ground vibrations originating from the eruption of Mount Anak Krakatau. Therefore, this study aims to confirm the eruptive activity of Mount Anak Krakatau based on seismic monitoring and analysis sourced from the BMKG's seismic sensor network. The method the author uses is by monitoring the seismic signal recorded by the seismometer and analyzing the seismic signal using the Seiscomp3 software. By the results of monitoring and analysis of seismic data, it was found that the location of the center of the ground shaking was on Mount Anak Krakatau with a magnitude of 3.4, and a depth of 1 km. To anticipate similar tsunami events in the future, it is very necessary to have a tsunami early warning system originating from volcanic activity and volcanic body avalanches.

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