A 184 million traces per km<sup>2</sup> seismic survey with nodes – acquisition and processing

Ourabah, Amine; Crosby, Alistair

doi:10.1190/segam2020-3426358.1

Cited by 4 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We apply the new method to a land dataset recorded by a dense array and compare the results with those obtained using the traditional method. The dataset consists of raw shot records taken from a sub-area of a nodal land seismic survey that was conducted in a desert environment [31]. This dataset offers ultra-high trace density with over 180 million traces per km 2 on a 12.5 m × 12.5 m receiver grid and a 100 m ×12.5 m source grid.…”

Section: Introductionmentioning

confidence: 99%

Surface wave dispersion inversion using an energy likelihood function

Zhang,

Zheng,

Curtis

2022

Preprint

View full text Add to dashboard Cite

Seismic surface wave dispersion inversion is used widely to study the subsurface structure of the Earth. The dispersion property is usually measured by using frequency-phase velocity (f-c) analysis and by picking phase velocities from the obtained f-c spectrum. However, because of potential contamination the f-c spectrum often has multimodalities at each frequency for each mode. These introduce uncertainty and errors in the picked phase velocities, and consequently the obtained shear velocity structure is biased. To overcome this issue, in this study we introduce a new method which directly uses the spectrum as data. We achieve this by solving the inverse problem in a Bayesian framework and define a new likelihood function, the energy likelihood function, which uses the spectrum energy to define data fit. We apply the new method to a land dataset recorded by a dense receiver array, and compare the results to those obtained using the traditional method. The results show that the new method produces more accurate results since they better match independent data from refraction tomography. This real-data application also shows that it can be applied efficiently since it removes the need to pick phase velocities, and with relatively little adjustment to current practice since it uses standard f-c panels to define the likelihood. We therefore recommend using the energy likelihood function rather than explicitly picking phase velocities in surface wave dispersion inversion.

show abstract

Section: Introductionmentioning

confidence: 99%

Surface wave dispersion inversion using an energy likelihood function

Zhang,

Zheng,

Curtis

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…taken from a sub-area of a nodal land seismic survey that was conducted in a desert environment (Ourabah & Crosby 2020). This dataset offers ultra-high trace density with over 180 million traces per km 2 on a 12.5 m × 12.5 m receiver grid and a 100 m ×12.5 m source grid.…”

mentioning

confidence: 99%

Surface wave dispersion inversion using an energy likelihood function

Zhang

Zheng

Curtis

2022

Geophysical Journal International

View full text Add to dashboard Cite

Summary Seismic surface wave dispersion inversion is used widely to study the subsurface structure of the Earth. The dispersion property is usually measured by using frequency-phase velocity (f-c) analysis of data recorded on a local array of receivers. The apparent phase velocity at each frequency of the surface waves travelling across the array is that at which the f-c spectrum has maximum amplitude. However, because of potential contamination by other wave arrivals or due to complexities in the velocity structure the f-c spectrum often has multiple maxima at each frequency for each mode. These introduce errors and ambiguity in the picked phase velocities, and consequently the estimated shear velocity structure can be biased, or may not account for the full uncertainty in the data. To overcome this issue we introduce a new method which directly uses the spectrum as the data to be inverted. We achieve this by solving the inverse problem in a Bayesian framework and define a new likelihood function, the energy likelihood function, which uses the spectrum energy to define data fit. We apply the new method to a land dataset recorded by a dense receiver array, and compare the results to those obtained using the traditional method. The results show that the new method produces more accurate results since they better match independent data from refraction tomography. This real-data application also shows that it can be applied with relatively little adjustment to current practice since it uses standard f-c panels to define the likelihood, and efficiently since it removes the need to pick phase velocities. We therefore conclude that the energy likelihood function can be a valuable tool for surface wave dispersion inversion in practice.

show abstract

Unlocking ultra-high-density seismic for CCUS applications by combining nimble nodes and agile source technologies

Ourabah¹,

Chatenay²

2022

The Leading Edge

View full text Add to dashboard Cite

In the quest for denser, nimbler, and lower-cost seismic surveys, the industry is seeing a revolution in the miniaturization of seismic equipment, with autonomous nodes approaching the size of a geophone and sources becoming portable by crews on foot. This has created a paradigm shift in the way seismic is acquired in difficult terrains, making zero-environmental-footprint surveys a reality while reducing cost and health, safety, and environmental risk. The simplification of survey operation and the new entry price of seismic surveys unlocked by these technologies are already benefiting industries beyond oil and gas exploration. High trace density seismic has become accessible to industries playing a key role in the net-zero era, such as geothermal and carbon capture, utilization, and storage (CCUS), to which a good understanding of the subsurface geology is crucial to their success. We describe these benefits as observed during an ultra-high-density seismic survey acquired in June 2020 through a partnership between STRYDE, Explor, and Carbon Management Canada over the Containment and Monitoring Institute site. The smallest and lightest source and receiver equipment in the industry were used to achieve a trace density of 257 million traces/km2 over this test site dedicated to CCUS studies. We discuss the operational efficiency of the seismic acquisition, innovative techniques for data transfer and surveying, and preliminary results of the seismic data processing with a focus on the near-surface model and fast-track time migration.

show abstract

A 184 million traces per km² seismic survey with nodes – acquisition and processing

Cited by 4 publications

References 6 publications

Surface wave dispersion inversion using an energy likelihood function

Surface wave dispersion inversion using an energy likelihood function

Surface wave dispersion inversion using an energy likelihood function

Unlocking ultra-high-density seismic for CCUS applications by combining nimble nodes and agile source technologies

Contact Info

Product

Resources

About

A 184 million traces per km2 seismic survey with nodes – acquisition and processing

Cited by 4 publications

References 6 publications

Surface wave dispersion inversion using an energy likelihood function

Surface wave dispersion inversion using an energy likelihood function

Surface wave dispersion inversion using an energy likelihood function

Unlocking ultra-high-density seismic for CCUS applications by combining nimble nodes and agile source technologies

Contact Info

Product

Resources

About

A 184 million traces per km² seismic survey with nodes – acquisition and processing