The acquisition and processing of dithered slip‐sweep vibroseis data

We built a five‐component (5C) land seismic sensor that measures both the three‐component (3C) particle acceleration and two vertical gradients of the horizontal wavefield through a pair of 3C microelectromechanical accelerometers. The sensor is a small cylindrical device planted vertically just below the earth's surface. We show that seismic acquisition and processing 5C sensor data has the potential to replace conventional seismic acquisition with analogue geophone groups by single 5C sensors placed at the same station interval when combined with a suitable aliased ground roll attenuation algorithm. The 5C sensor, therefore, allows for sparser, more efficient, data acquisition. The accuracy of the 5C sensor wavefield gradients depends on the 3C accelerometers, their sensitivity, self‐noise and their separation. These sensor component specifications are derived from various modelling studies. The design principles of the 5C sensor are validated using test data from purpose‐built prototypes. The final prototype was constructed with a pair of 3C accelerometers separated by 20 cm and with a self‐noise of 35 ng Hz−1/2. Results from a two‐dimensional seismic line show that the seismic image of 5C sensor data with ground roll attenuated using 5C sensor gradient data was comparable to simulated analogue group data as is the standard in the industry. This field example shows that up to three times aliased ground roll was attenuated. The 5C sensor also allows for correcting vertical component accelerometer data for sensor tilt. It is shown that a vertical component sensor that is misaligned with the vertical direction by 10° introduces an error in the seismic data of around –20 dB with respect to the seismic signal, which can be fully corrected. Advances in sensor specifications and processing algorithms are expected to lead to even more effective ground roll attenuation, enabling a reduction in the receiver density resulting in a smaller number of sensors that must be deployed and, therefore, improving the operational efficiency while maintaining image quality.

Section: Discussionmentioning

confidence: 99%

A five component land seismic sensor for measuring lateral gradients of the wavefield

Muyzert

Allouche

Edme

et al. 2018

“…There are plenty of examples containing some concepts of blended acquisition. See, for example, simultaneous sources in land acquisition (Bagaini, 2006), simultaneous sources in marine (Beasely et al., 1998; Beasely, 2008), coding and decording (Ikelle, 2007), distance‐separated simultaneous sweeping or shooting (DSSS, Bouska, 2010; Ishiyama et al., 2012), distance‐separated simultaneous slip sweep (DSSSS, Bouska, 2010) and managed sources and spread (MSS, Bagaini et al., 2012). These conventional methods often require certain constraints in the encoding and operations, such as large distance separation among shot locations and large time shifts among shot times in the blended‐source array, so that the shot‐generated wavefields do not overlap spatially and temporally with each other at least around the offset‐time window of interest.…”

Section: Concept and Methodsmentioning

confidence: 99%

Blended acquisition with temporally signatured/modulated and spatially dispersed source array: Concept and method

Ishiyama

2023

We introduce a blended‐acquisition method: temporally signatured and/or modulated and spatially dispersed source array. The former, signatured and dispersed source array, has much less constraints in the encoding with operational flexibility, allowing non‐uniform sampling and non‐patterned shooting both in the space and time dimension. The latter, modulated and dispersed source array, allows straightforward deblending by filtering and physically separating frequency channels in the frequency domain. We demonstrate our method by synthesizing the blended acquisition followed by deblended‐data‐reconstruction processing in order to discuss the virtues. The examples show that this method could make the blended‐acquisition encoding and operations indeed simple and robust; the same is true for the deblending processing.This article is protected by copyright. All rights reserved

“…The blending and deblending, or simultaneous sourcing methodology is a leading‐edge technology that is becoming common wisdom in the industry today, for example slip‐sweep (Rozemond ); distance‐separated simultaneous sweeping or shooting (Bouska ; Ishiyama, Mercado and Belaid ); independent simultaneous sourcing (Howe et al . ); managed sources and spread (Bagaini, Daly and Moore ). There are plenty of examples using some concepts of this methodology; however, these are under certain constraints such as large distance separation among shot locations and large time shifts among shot times, thereby so far not fully enjoy the benefits of this methodology.…”

Section: Introductionmentioning

confidence: 99%

Research note: deblended‐data reconstruction using generalized blending and deblending models

Ishiyama

Ali

Ishikawa

et al. 2019

We introduce a concept of generalized blending and deblending, develop its models and accordingly establish a method of deblended‐data reconstruction using these models. The generalized models can handle real situations by including random encoding into the generalized operators both in the space and time domain, and both at the source and receiver side. We consider an iterative optimization scheme using a closed‐loop approach with the generalized blending and deblending models, in which the former works for the forward modelling and the latter for the inverse modelling in the closed loop. We applied our method to existing real data acquired in Abu Dhabi. The results show that our method succeeded to fully reconstruct deblended data even from the fully generalized, thus quite complicated blended data. We discuss the complexity of blending properties on the deblending performance. In addition, we discuss the applicability to time‐lapse seismic monitoring as it ensures high repeatability of the surveys. Conclusively, we should acquire blended data and reconstruct deblended data without serious problems but with the benefit of blended acquisition.