Blended‐acquisition design of irregular geometries towards faster, cheaper, safer and better seismic surveying

Abstract

The application of blended acquisition has drawn considerable attention owing to its ability to improve the operational efficiency as well as the data quality and health, safety and environment performance. Furthermore, the acquisition of less data contributes to the business aspect, while the desired data density is still realizable via subsequent data reconstruction. The use of fewer detectors and sources also minimizes operational risks in the field. Therefore, a combined implementation of these technologies potentially enhances the value of a seismic survey further. One way to encourage this is to minimize any imperfection in deblending and data reconstruction during processing. In addition, one may derive survey parameters that enable a further improvement in these processes as introduced in this study. The proposed survey design workflow iteratively performs the following steps to derive the survey parameters responsible for source blending as well as the spatial sampling of detectors and sources. The first step is the application of blending and sampling operators to unblended and well‐sampled data. We then apply closed‐loop deblending and data reconstruction. The residue for a given design from this step is evaluated and subsequently used by genetic algorithms to simultaneously update the survey parameters related to both blending and spatial sampling. The updated parameters are fed into the next iteration until they satisfy the given termination criteria. We also propose a repeated encoding sequence to form a parameter sequence in genetic algorithms, making the size of problem space manageable. The results of the proposed workflow are outlined using blended dispersed source array data incorporating different scenarios that represent acquisition in marine, transition zone and land environments. Clear differences attributed solely to the parameter design are easily recognizable. Additionally, a comparison among different optimization schemes illustrates the ability of genetic algorithms along with a repeated encoding sequence to find better solutions within a computationally affordable time. The optimized parameters yield a notable enhancement in the deblending and data reconstruction quality and consequently provide optimal acquisition scenarios.

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

“…Finally, the estimated deblended data are updated: where α i is a scale factor to orient the residual to the minimum; a step length in a steepest decent algorithm in our case (Nakayama et al . ). This update is the input for the next iteration.…”

Acquisition design for direct reflectivity and velocity estimation from blended and irregularly sampled data

“…To manage the large problem space, we presented a survey‐design scheme utilizing a genetic algorithm (GA) (Nakayama et al . ). Together with a constraint on the parameter space, a so‐called repeated encoding sequence, this scheme aimed at deriving survey parameters allowing for optimum deblending and data reconstruction quality.…”

“…Designing survey parameters in an irregular fashion requires extensive efforts as it inherently makes the parameter selection problem huge, unlike a conventional survey that generally employs regularly positioned detectors and sources along with a spatially uniform source signature. To manage the large problem space, we presented a survey-design scheme utilizing a genetic algorithm (GA) (Nakayama et al 2019). Together with a constraint on the parameter space, a so-called repeated encoding sequence, this scheme aimed at deriving survey parameters allowing for optimum deblending and data reconstruction quality.…”

Seismic Data Acquisition and Processing

“…Digitization of this vast amount of data (wavefield) has traditionally been done honoring the Nyquist Sampling Criterion, which requires a uniform sampling rate of more than twice the highest frequencyor wavenumberdepending upon the domain (time/space)present in the data. Sampling below the Nyquist rate produces artifacts (called aliases) in the digitized data at lower frequencies (or wavenumbers) and can eventually interfere with real signals that may be present theresee Claerbout (1985a) or Menke (1989) for details.…”

“…There are many good resources available even for the narrower field of Reflection Seismic Data Acquisition and (Signal) Processing, for example, Vermeer (2002), Yilmaz (2001), Menke (1989), Liner (2004, and Sheriff and Geldart (1995); the last one also contains some historical background and material over refraction seismics. Recently, some resources have also been made available for downloading on the Internet, for example, Claerbout (1985a, b).…”