3D Methodology for OBC Pre-Processing

Soudani, Mohamed; Boëlle, Jean-Luc; Hugonnet, P.; Grandi, A.

doi:10.3997/2214-4609.201402225

Cited by 4 publications

(4 citation statements)

References 2 publications

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“…This demonstrates the advantage of the elastic decomposition procedure. Note, however, that the power of the acoustic scheme lies in the fact that the knowledge of the seafloor elastic properties is not required (the reflectivity r can be obtained by comparing the P w up and P w down wavefields; Soudani et al 2006). Note also that Amundsen et al (2001) have described in detail a water multiple removal technique, based on the use of the acoustic upgoing–downgoing wavefields, that is valid for any order, but which is computationally more expensive.…”

Section: Rf Decomposition Equationsmentioning

confidence: 99%

“…However, these methods suffer from the fact that there are many events at a constant offset that arrive at different angles, and hence the approximation breaks down. Recently, the acoustic decomposition procedure has been implemented in the τ -p domain (τ being the intercept time, p the horizontal slowness) and is therefore valid for all incidence angles (Lokshtanov 1993(Lokshtanov , 1995(Lokshtanov , 2000Soudani et al 2005Soudani et al , 2006.…”

Section: Introductionmentioning

confidence: 99%

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Receiver function decomposition of OBC data: theory

Edme

Singh²

2009

Geophysical Journal International

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S U M M A R YThis paper deals with theoretical aspects of wavefield decomposition of Ocean Bottom Cable (OBC) data in the τ -p domain, considering a horizontally layered medium. We present both the acoustic decomposition and elastic decomposition procedures in a simple and compatible way. Acoustic decomposition aims at estimating the primary upgoing P wavefield just above the ocean-bottom, whereas elastic decomposition aims at estimating the primary upgoing P and S wavefields just below the ocean-bottom. Specific issues due to the interference phenomena at the receiver level are considered. Our motivation is to introduce the two-step decomposition scheme called 'receiver function' (RF) decomposition that aims at determining the primary upgoing P and S wavefields (RF P and RF S , free of any water layer multiples). We show that elastic decomposition is a necessary step (acting as pre-conditioning) before applying the multiple removal step by predictive deconvolution. We show the applicability of our algorithm on a synthetic data example.

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Section: Rf Decomposition Equationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Receiver function decomposition of OBC data: theory

Edme

Singh²

2009

Geophysical Journal International

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“…Such a technology using either ocean bottom node (OBN) or ocean bottom cable (OBC) acquisition typically records multicomponent data, which is composed of pressure and particle velocity/acceleration components measured by a hydrophone and geophone/accelerometer sensors, respectively. The four‐component (4C) recording system provides measurements of converted wavefields, which complement the conventional compressional wavefields (Fjellanger et al ., 2006; Jenkinson et al ., 2010) by offering valuable insights to PP/PS imaging and amplitude versus angle as well as assisting in the elimination of both water‐layer multiples and receiver‐side ghosts by summing the hydrophone and vertical component geophone (Barr and Sanders, 1989; Soubaras, 1996; Osen et al ., 1999; Amunsen et al ., 2000; Xia et al ., 2006; Soudani et al ., 2006; Vaxelaire et al ., 2007; Wang et al . 2010).…”

Section: Introductionmentioning

confidence: 99%

Quality control for the geophone reorientation of ocean bottom seismic data using k‐means clustering

Jeong

Almubarak

Tsingas

2021

Geophysical Prospecting

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During ocean bottom seismic acquisition, seafloor multicomponent geophones located in rugose and sloping water bottom can be affected by skewed energy distribution, such as leaked shear energy on the vertical geophones and leaked compressional energy on the horizontal geophones. To correct for the tilted energy distribution, which is one of most effective preprocessing steps, a geophone reorientation step is applied. This is a simple and straightforward process that applies a 3‐dimensional rotation matrix with respect to the orientation angles. Since the reorientation process highly affects the outcome of the entire data processing workflow, it has to be accompanied by a careful quality control process to verify its validity for the whole survey area. In this study, we propose a quality control workflow for the geophone reorientation by using unsupervised machine learning. A correlation analysis is employed to compare numerical versus analytical solutions of both the azimuth and the incidence angles for the direct arrivals. A comparison of both solutions aims to generate correlation coefficients that are indicative of the accuracy of geophone orientation. The correlation coefficients are subsequently investigated by the k‐means clustering algorithm to differentiate and identify normally and abnormally deployed/reoriented geophones. Numerical experiments on a field ocean bottom seismic data set confirm that the proposed workflow effectively provides reliable labels for normally and abnormally deployed/reoriented geophones. The labelling assigned by the proposed quality control workflow is a suitable indicator for abnormalities in the geophone reorientation step and will be helpful for further investigation, such as re‐correction or removal of abnormally reoriented geophones.

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“…This method based on the criterion that the proper scale factors are those that best whiten the summed data [9]. Hoffe (2000) described the wave field characteristics of OBC dual-sensor seismic data and revealed the physical nature of dual-sensor data summing [10]; Soudani (2006) proposed a three-dimensional OBC combining processing techniques [11]; Hugonnet (2011) presented a three-step OBC dual-sensor combining processing technique by using the cross ghost wave operator [12]. studied the method of OBC seismic data with low SNR [13].…”

Section: Introductionmentioning

confidence: 99%

A Processing Workflow for Ocean Bottom Cable Dual-Sensor Acquisition Data: A Case of 3D Seismic Data in Bohai Bay

Yang¹,

Wang²

2019

OJG

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The technique of ocean bottom cable (OBC) dual-sensor acquisition is an effective method to suppress the ghost wave and the reverberation at the receiver. With the advent of this technique, the processing method has become the key to the effective use of the OBC dual-sensor data. This paper has developed a new set of processing workflow based on the principle of combining the hydrophone and geophone data. This new process was applied to the OBC data acquired in Bohai area. The actual processing results show that the ghost and the reverberation are attenuated effectively. The frequency energy of the first notch point of the hydrophone data increased from −22 dB to −13 dB, and the frequency energy of the first notch point of the geophone data increased from −18 dB to −10 dB. The spectral characteristics of the dual-sensor data are more reasonable. The frequency spectrum is broadened and richer, and the resolution of the stack profile is improved greatly.

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3D Methodology for OBC Pre-Processing

Cited by 4 publications

References 2 publications

Receiver function decomposition of OBC data: theory

Receiver function decomposition of OBC data: theory

Quality control for the geophone reorientation of ocean bottom seismic data using k‐means clustering

A Processing Workflow for Ocean Bottom Cable Dual-Sensor Acquisition Data: A Case of 3D Seismic Data in Bohai Bay

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