PROCESSING OF MARINE SEISMIC DATA FROM AREAS WITH LARGE AND RAPID VARIATION IN WATER DEPTH

1971 ◽  
Vol 11 (1) ◽  
pp. 95
Author(s):  
Al Sabitay
Keyword(s):  
Seismic Data ◽  
Bottom Topography ◽  
Computer Programs ◽  
Bottom Surface ◽  
Band Pass Filter ◽  
Process Data ◽  
Rapid Variation ◽  
Pass Filter ◽  
Marine Seismic ◽  
Water Bottom

The offshore search for oil and gas is progressively moving further out to sea as near-shore structures are delineated and drilled. Prospects that overlap the edge of the continental shelf and slope will more than likely present problems in the processing of marine seismic data because of large and rapid variation in water depth.Magellan Petroleum encountered such difficulties in the digital computer processing of its East Gippsland Basin Prospect which is located some 50 miles southeast of the Victoria coastline.A series of problems developed when an integrated computer program sequence or "package" was applied to the data. It was found that first break suppression schedules, deconvolution design gates, band-pass filter application gates and velocity functions could not be changed often enough due to program restrictions.Where the water bottom topography was rough, the restriction of submitting only three or four water depths to vary the velocity function and subsequent calculation of normal move-out corrections resulted in questionable accuracy for the corrected results.Sometimes, water bottom variations required individual trace static corrections which were not available in this particular "package" processing.Water bottom multiple periods vary as rapidly as the surface that generates them. A meticulous selection of the parameters of deconvolution programs is necessary to attenuate multiples under such conditions. Also close examination of the purposes and consequently methods of deconvolution computer programs is necessary to maximize the effectiveness of this powerful processing tool.Diffractions are frequently generated at points on an irregular sea bottom surface. Such detractions mask true water bottom reflections in deeper water and thus decrease the geophysicist's ability to process data accurately where computer programs require true water bottom depth.Presentation of record sections which illustrate problems and their probable solutions comprise a major part of this paper.

PC‐based acquisition and processing of high‐resolution marine seismic data

Geophysics ◽  
1996 ◽  
Vol 61 (6) ◽  
pp. 1804-1812 ◽  
Author(s):  
Ho‐Young Lee ◽  
Byung‐Koo Hyun ◽  
Young‐Sae Kong
Keyword(s):  
High Resolution ◽  
Seismic Data ◽  
Spectral Characteristics ◽  
Processing System ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Optical Disk Drive ◽  
Air Gun ◽  
Marine Seismic

We have improved the quality of high‐resolution marine seismic data using a simple PC‐based acquisition and processing system. The system consists of a PC, an A/D converter, and a magneto‐optical disk drive. The system has been designed to acquire single‐channel data at up to 60,000 samples per second and to perform data processing of seismic data by a simple procedure. Test surveys have been carried out off Pohang, southern East Sea of Korea. The seismic systems used for the test were an air gun and a 3.5 kHz sub‐bottom profiling system. Spectral characteristics of the sources were analyzed. Simple digital signal processes which include gain recovery, deconvolution, band‐pass filter, and swell filter were performed. The quality of seismic sections produced by the system is greatly enhanced in comparison to analog sections. The PC‐based system for acquisition and processing of high‐resolution marine seismic data is economical and versatile.

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 1D time-varying median filter for seismic random, spike-like noise elimination

Geophysics ◽  
2009 ◽  
Vol 74 (1) ◽  
pp. V17-V24 ◽  
Author(s):  
Yang Liu ◽  
Cai Liu ◽  
Dian Wang
Keyword(s):  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Median Filter ◽  
Random Noise ◽  
Threshold Value ◽  
Band Pass Filter ◽  
Time Varying ◽  
Noise Elimination ◽  
Pass Filter ◽  
Median Filters

Random noise in seismic data affects the signal-to-noise ratio, obscures details, and complicates identification of useful information. We have developed a new method for reducing random, spike-like noise in seismic data. The method is based on a 1D stationary median filter (MF) — the 1D time-varying median filter (TVMF). We design a threshold value that controls the filter window according to characteristics of signal and random, spike-like noise. In view of the relationship between seismic data and the threshold value, we chose median filters with different time-varying filter windows to eliminate random, spike-like noise. When comparing our method with other common methods, e.g., the band-pass filter and stationary MF, we found that the TVMF strikes a balance between eliminating random noise and protecting useful information. We tested the feasibility of our method in reducing seismic random, spike-like noise, on a synthetic dataset. Results of applying the method to seismic land data from Texas demonstrated that the TVMF method is effective in practice.

Seafloor Erosion induced by overbanking flow derived by a 2.5D high resolution sparker seismic dataset, uppermost Kaoping Submarine Canyon, southwestern offshore Taiwan

2020 ◽  
Author(s):  
Wei-Chung Hsiao ◽  
Yi-Ching Yeh ◽  
Yen-Yu Cho ◽  
Shu-Kun Hsu
Keyword(s):  
River Mouth ◽  
Seismic Source ◽  
Submarine Canyon ◽  
Point Of View ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Match Filter ◽  
The North ◽  
Manila Trench ◽  
Marine Seismic

<p>The Kaoping submarine canyon (KPSC) originates from Kaoping River, southwestern Taiwan that extends about 250 kilometers long from the Kaoping River mouth down to the Manila Trench. It can be divided into three major sections: upper reach (meandering), middle reach (NW-SE trending and V-shaped canyon) and lower reach (meandering). Based on recent a swath bathymetric data in the uppermost KPSC, an obvious seafloor depression can be observed in the eastern bank of the canyon. The eastern bank of the canyon reveals about 30-50 meters in average lower than western bank. The mechanism is blurred. In this study, to investigate fine sedimentary structures in 3D point of view, we used marine sparker seismic method. The seismic source frequency varies from 100 to 1200 Hz which can provide about 0.6 meters vertical resolution (i.e. central frequency 600 Hz and 1,600 m/s Vp). We have collected 75 in-lines across the canyon and 3 cross-lines perpendicular to the in-line. The data went through conventional marine seismic data processing procedures such as bad trace kill, band-pass filter, 2D geometry settings, NMO stacking, swell correction, match filter and predictive deconvolution. The 2D dataset was reformatted by applying 3D geometry settings to create a 3D seismic cube. The result shows that a wide incision channel was first found in the north of Xiaoliuchiu islet. Through depth, this channel becomes two narrower channels divided by a mud diapir. This down cutting can be traced down to transgressive sequence in prior to LGM (Last Glacial Maximum). In addition, a deep-towed sub-bottom profiler shows an obvious down-lapping structures heading off canyon that indicates over banking flow may be a key role to cause this erosional event.</p>

Noise reduction in seismic data using Fourier correction coefficient filtering

Geophysics ◽  
1997 ◽  
Vol 62 (5) ◽  
pp. 1617-1627 ◽  
Author(s):  
Douglas Alsdorf
Keyword(s):  
Noise Reduction ◽  
Correlation Coefficient ◽  
Seismic Data ◽  
Wave Number ◽  
Correction Coefficient ◽  
Band Pass Filter ◽  
Data Set ◽  
Pass Filter ◽  
Phase Angle Difference ◽  
Reduction Methods

The correlation coefficient between two frequency (or two wave number) componets equals the cosine of their phase‐angle difference. This relation can be exploited to build a filter that separates noise from signal in seismic data in either the F‐X or F-K domain (termed “correlation coefficient filtering”). To implement this filter, seismic data are first divided to form two subsets that are then compared using the cosine function. Signal is defined as the correlative frequencies (or wavelengths) while noncorrelative energy is attributed to noise. Depending on the application, appropriate subsets may consist of (1) groups of adjacent traces or (2) low‐fold stacks created from differing shot gathers. When comparing adjacent traces [i.e., (1)], the correlation coefficient filter combines both phase and dip information and assumes that reflections advance relatively little in time across traces and less than the noise. Correlation coefficient filtering of low‐fold stacks [i.e., (2)] does not depend on dip. Reflections are assumed to be present in both subsets whereas the noise is found only in one data set. Hence, the reflections are correlative and the noise is noncorrelative. In either case, the filter reduces linearly dipping coherent energy, ground roll, and randomly occurring noise bursts while generally maintaining signal integrity. A primary advantage of this filter is its simplicity. It is implemented much like a simple band‐pass filter, thus requiring much less parameterization than alternative noise‐reduction methods.

A RECURSIVE TIME‐VARYING BAND‐PASS FILTER

Geophysics ◽  
1975 ◽  
Vol 40 (3) ◽  
pp. 520-526 ◽  
Author(s):  
Z. J. Nikolic
Keyword(s):  
Data Processing ◽  
Seismic Data ◽  
Processing Time ◽  
Dominant Frequency ◽  
Band Pass Filter ◽  
Time Varying ◽  
Seismic Data Processing ◽  
Pass Filter ◽  
Band Pass

Time‐varying, digital band‐pass filters are extensively used in seismic data processing, since the dominant frequency of reflected signals usually becomes lower and their bandwidth narrower with the passage of time. In routine seismic data processing, time‐varying, digital band‐pass filtering is stepwise.

scholarly journals PENERAPAN METODE F-K DEMULTIPLE DALAM KASUS ATENUASI WATER-BOTTOM MULTIPLE

2016 ◽  
Vol 11 (1) ◽  
pp. 33
Author(s):  
Subarsyah Subarsyah ◽  
Sahudin Sahudin
Keyword(s):  
Seismic Data ◽  
Final Section ◽  
Signal To Noise Ratio ◽  
Multiple Reflections ◽  
Seismic Section ◽  
Multiple Attenuation ◽  
Seismic Acquisition ◽  
Marine Seismic ◽  
Second Category ◽  
Water Bottom

Keberadaan water-bottom multiple merupakan hal yang tidak bisa dihindari dalam akuisisi data seismik laut, tentu saja hal ini akan menurunkan tingkat perbandingan sinyal dan noise. Beberapa metode atenuasi telah dikembangkan dalam menekan noise ini. Metode atenuasi multiple diklasifikasikan dalam tiga kelompok meliputi metode dekonvolusi yang mengidentifikasi multiple berdasarkan periodisitasnya, metode filtering yang memisahkan refleksi primer dan multiple dalam domain tertentu (F-K,Tau-P dan Radon domain) serta metode prediksi medan gelombang. Penerapan metode F-K demultiple yang masuk kategori kedua akan diterapkan terhadap data seismik PPPGL tahun 2010 di perairan Teluk Tomini. Atenuasi terhadap water-bottom multiple berhasil dilakukan akan tetapi pada beberapa bagian multiple masih terlihat dengan amplitude relatif lebih kecil. F-K demultiple tidak efektif dalam mereduksi multiple pada offset yang pendek dan multiple pada zona ini yang memberikan kontribusi terhadap keberadaan multiple pada penampang akhir. Kata kunci : F-K demultiple, multiple, atenuasi The presence of water-bottom multiple is unavoidable in marine seismic acquisition, of course, this will reduce signal to noise ratio. Several attenuation methods have been developed to suppress this noise. Multiple attenuation methods are classified into three groups first deconvolution method based on periodicity, second filtering method that separates the primary and multiple reflections in certain domains (FK, Tau-P and the Radon domain) ang the third method based on wavefield prediction. Application of F-K demultiple incoming second category will be applied to the seismic data in 2010 PPPGL at Tomini Gulf waters. Attenuation of the water-bottom multiple successful in reduce multiple but in some parts of seismic section multiple still visible with relatively smaller amplitude. FK demultiple not effective in reducing multiple at near offset and multiple in this zone contribute to the existence of multiple in final section. Key words : F-K demultiple, multiple, attenuation

scholarly journals ACOUSTIC DETECTION AND CHARACTERIZATION OF MARINE SEDIMENT WITH SHALLOW SEISMIC TECHNOLOGY IN RAMBAT WATERS, BANGKA BELITUNG

2014 ◽  
Vol 5 (2) ◽  
Author(s):  
Haqqu Ramdhani ◽  
Henry M. Manik ◽  
Susilohadi Susilohadi
Keyword(s):  
Gain Control ◽  
Data Interpretation ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Reflection Seismic ◽  
Illegal Mining ◽  
Predictive Deconvolution ◽  
Band Pass ◽  
Marine Seismic ◽  
Seafloor Sediment

<p>High resolution of marine seismic reflection seismic were used to detect the layers of seafloor sediment and to interpret the seismic data geologically. The objectives of this study weres to detect and to characterize the seafloor sediment in the Rambat area, West Bangka, Bangka Belitung. Acquisition data was held on 10-24  August  2012 located between 105.1°00'00" - 105.5°00'00 " N and 1.7°00'00"-1.9° 00'00" W. Several methods used to process the data were geometry processing, band pass filter, predictive deconvolution,  and Autocoralation Gain Control (AGC)  in order to reduce the multiple noise and to  ease the data interpretation. Seismic cross section found in Cross Rambat (CRMBT) line 11 exhibited  sedimentation process of the sea floor which rocky substrates. The process was assumed to be occurred due to legal and illegal mining activities for long period of time.</p> <p>Keywords: seismic, acoustic, sediment, band pass filter, deconvolution, noise</p>

scholarly journals WATER SURFACE RECONSTRUCTION IN AIRBORNE LASER BATHYMETRY FROM REDUNDANT BED OBSERVATIONS

2017 ◽  
Vol IV-2/W4 ◽  
pp. 123-130 ◽  
Author(s):  
G. Mandlburger ◽  
N. Pfeifer ◽  
U. Soergel
Keyword(s):  
Water Level ◽  
Water Surface ◽  
Specular Reflection ◽  
Bottom Topography ◽  
Point Clouds ◽  
Bottom Surface ◽  
Airborne Laser ◽  
Water Surfaces ◽  
Surface Deviations ◽  
Water Bottom

In airborne laser bathymetry knowledge of exact water level heights is a precondition for applying run-time and refraction correction of the raw laser beam travel path in the medium water. However, due to specular reflection especially at very smooth water surfaces often no echoes from the water surface itself are recorded (drop outs). In this paper, we first discuss the feasibility of reconstructing the water surface from redundant observations of the water bottom in theory. Furthermore, we provide a first practical approach for solving this problem, suitable for static and locally planar water surfaces. It minimizes the bottom surface deviations of point clouds from individual flight strips after refraction correction. Both theoretical estimations and practical results confirm the potential of the presented method to reconstruct water level heights in dm precision. Achieving good results requires enough morphological details in the scene and that the water bottom topography is captured from different directions.

scholarly journals ACOUSTIC DETECTION AND CHARACTERIZATION OF MARINE SEDIMENT WITH SHALLOW SEISMIC TECHNOLOGY IN RAMBAT WATERS, BANGKA BELITUNG

2014 ◽  
Vol 5 (2) ◽  
Author(s):  
Haqqu Ramdhani ◽  
Henry M. Manik ◽  
Susilohadi Susilohadi
Keyword(s):  
Gain Control ◽  
Data Interpretation ◽  
Band Pass Filter ◽  
Pass Filter ◽  
Reflection Seismic ◽  
Illegal Mining ◽  
Predictive Deconvolution ◽  
Band Pass ◽  
Marine Seismic ◽  
Seafloor Sediment

High resolution of marine seismic reflection seismic were used to detect the layers of seafloor sediment and to interpret the seismic data geologically. The objectives of this study weres to detect and to characterize the seafloor sediment in the Rambat area, West Bangka, Bangka Belitung. Acquisition data was held on 10-24  August  2012 located between 105.1°00'00" - 105.5°00'00 " N and 1.7°00'00"-1.9° 00'00" W. Several methods used to process the data were geometry processing, band pass filter, predictive deconvolution,  and Autocoralation Gain Control (AGC)  in order to reduce the multiple noise and to  ease the data interpretation. Seismic cross section found in Cross Rambat (CRMBT) line 11 exhibited  sedimentation process of the sea floor which rocky substrates. The process was assumed to be occurred due to legal and illegal mining activities for long period of time. Keywords: seismic, acoustic, sediment, band pass filter, deconvolution, noise

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