Optimising the value of near-surface seismic reflection data

Lead Research Organisation: University of Southampton
Department Name: Sch of Ocean and Earth Science

Abstract

While seismic methods to investigate depths from several hundred m to km depth beneath the seabed have advanced significantly over the past 20 years, the techniques used to investigate the shallowest 100-200m largely remain very basic. In this project we will adapt techniques from conventional exploration to address the challenges of investigating the shallow subsurface at sub-metre resolution. We will apply a combination of prestack depth migration and post-stack and prestack waveform inversion to multi-offset high-resolution seismic data with three main aims: Deriving high quality structural images of the near surface; estimating physical properties of the near surface and interpreting those properties in terms of parameters that are relevant to engineering within the marine environment; determining error bounds on our estimates of the physical properties. We will do this using a combination of standard industry software such as ProMAX and custom codes to address particular aspects of the problem such as accounting for streamer geometry with sufficient accuracy, waveform inversion, and determining error bounds on the physical property estimates. We will work initially with existing datasets collected in a range of environments and water depths (from 10m to ~200m), although we will expect to collect at least one dedicated dataset with the involvement of the student, probably after ~2 years of the project.

Publications

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Studentship Projects

Project Reference Relationship Related To Start End Student Name
NE/R01051X/1 01/10/2017 31/05/2024
1942522 Studentship NE/R01051X/1 01/10/2017 31/12/2021 Samuel Clay
NE/W503150/1 01/04/2021 31/03/2022
1942522 Studentship NE/W503150/1 01/10/2017 31/12/2021 Samuel Clay
 
Description As part of the award one of the first milestones was to show that we could successfully provide accurate and precise offset values between the seismic source and each of the associated receivers/hydrophones within in a towed array in a marine environment. Using high frequency seismic sources, relying on GPS positions isn't robust enough to provide the necessary positional requirements for improved seismic processing, imaging and quantitative interpretation. Therefore we developed a methodology using the principals of inversion and machine learning to solve for positions and offsets of the receivers in a towed array relative to the source using the information stored within the seismic record which would adhere to the positional criteria for Ultra-High frequency seismic data processing.
Exploitation Route This research can be taken forward and used in the site investigation industry to allow the appropriate and effective quantifying of source to receiver offsets prior to geometry allocation during the processing workflow. Additionally, these findings associated with improved geometry allocation can be used to subsequently improve the velocity estimation during migration velocity analysis and overall image quality of the near surface for qualitative and quantitative interpretation purposes in both academic and industry settings.
Sectors Energy,Environment