Estimating picking errors in near‐surface seismic data to enable their time‐lapse interpretation of hydrosystems

Dangeard, Marine; Bodet, Ludovic; Pasquet, Sylvain; Thiesson, Julien; Guérin, Roger; Jougnot, Damien; Longuevergne, Laurent

doi:10.1002/nsg.12019

Cited by 15 publications

(25 citation statements)

References 59 publications

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“…Identifying consistent, repeatable first arrivals in SRT data is a recognised challenge with no universally accepted solution. Attempts include using automatic picking algorithms (e.g., Khalaf et al, 2018) and using statistical approaches (e.g., Dangeard et al, 2018) to minimize absolute and relative errors introduced by operators picking time-lapse SRT data. In this study, reciprocal errors between inverse source-receiver configurations are used to identify 'bad' picks that display an unacceptable differential in reciprocal travel-time.…”

Section: Data Quality and Processingmentioning

confidence: 99%

Landslide monitoring using seismic refraction tomography – The importance of incorporating topographic variations

Whiteley

Chambers

Uhlemann

et al. 2020

Engineering Geology

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Seismic refraction tomography provides images of the elastic properties of subsurface materials in landslide settings. Seismic velocities are sensitive to changes in moisture content, which is a triggering factor in the initiation of many landslides. However, the application of the method to long-term monitoring of landslides is rarely used, given the challenges in undertaking repeat surveys and in handling and minimizing the errors arising from processing time-lapse surveys. Using the results of a recent, novel, long-term seismic refraction monitoring campaign at an active landslide in the UK, a simple method for producing a reliable time-series of inverted seismic velocity cross-sections is presented in a workflow. Potential sources of error include those arising from inaccurate and inconsistent determination of first-arrival times, inaccurate receiver positioning, and selection of inappropriate inversion starting models. At our site, a comparative analysis of variations in seismic velocity to real-world variations in topography over time shows that topographic error alone can account for changes in seismic velocity of greater than ±10% in a significant proportion (23%) of the data acquired. The seismic velocity variations arising from real material property changes at the near-surface of the landslide, linked to other sources of environmental data, are demonstrated to be of a similar magnitude. Over the monitoring period we observe subtle variations in the bulk seismic velocity of the sliding layer that are demonstrably related to variations in moisture content. This highlights the need to incorporate accurate topographic information for each time-step in the monitoring time-series. The goal of the proposed workflow is to minimize the sources of potential errors, and to preserve the changes observed by real variations in the subsurface. Following the workflow produces spatially comparable, time-lapse velocity cross-sections formulated from disparate, discretely-acquired datasets. These practicable steps aim to aid the use of the seismic refraction tomography method for the long-term monitoring of landslides prone to hydrological destabilization.

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Section: Data Quality and Processingmentioning

confidence: 99%

Landslide monitoring using seismic refraction tomography – The importance of incorporating topographic variations

Whiteley

Chambers

Uhlemann

et al. 2020

Engineering Geology

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“…Field measurements indicate that surface-wave dispersion in unconsolidated granular media is sensitive to changes in the saturation profile (e.g., Bodet, 2019;Dangeard et al, 2018;Z. ;Lu, 2014;Pasquet, Bodet, et al, 2016;West & Menke, 2000).…”

Section: Discussionmentioning

confidence: 99%

Surface‐Wave Dispersion in Partially Saturated Soils: The Role of Capillary Forces

et al. 2021

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The critical zone is a region of the shallow subsurface that ranges from the top of the vegetation canopy to the base of superficial aquifers. It comprises rocks, soils, water, air, and living organisms; contains the vast majority of life-sustaining resources; and regulates the interaction between the atmosphere and aquifers (e.g., Binley et al., 2015;Parsekian et al., 2015). The combined use of geophysical multiscale probing and imaging techniques along with the integration of hydrological, hydrogeological, and geochemical data is widely practiced for the observation of the partially saturated region of the critical zone, that is, the vadose zone (e.g., Parsekian et al., 2015). This approach to geophysical subsurface characterization, referred to as hydrogeophysics (e.g., Hubbard & Linde, 2011;Rubin & Hubbard, 2006), is dominated by electrical and electromagnetic methods due to their strong sensitivity with regard to water content and salinity (e.g., Friedman, 2005). However, given that seismic waves are inherently sensitive to key hydraulic properties of porous media, such as, porosity, permeability,

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“…Tomographic inversions based on grid-ray tracing are associated with the length of the seismic pattern (maximum offset) and the signal-to-noise ratio (SNR) of first breaks (Jing et al, 2006;Sheng et al, 2006;Sawasdee et al, 2007;Sun and Zhang, 2016;Dangeard et al, 2018;Park et al, 2018). Both real seismic acquisition and field topography will yield an irregularity among CSGs or CRGs, like asymmetrical folds of subsurface grids and uneven intervals of surface grids (Sun and Zhang, 2016;Park et al, 2018).…”

Section: Residual Long-wavelength Statics On Common Offset Gathermentioning

confidence: 99%

An integrated long‐wavelength statics method applied to seismic processing of Tibetan permafrost

Liu

Feng

et al. 2022

Near Surface Geophysics

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Permafrost in high‐altitude regions complicates near‐surface modelling and exacerbates static effects during seismic processing and interpretation, especially in the form of long‐wavelength issues in cases where it extends laterally beyond the length of the seismic wavelength. The long‐wavelength component of static corrections (statics) for permafrost when solved using a single conventional method is prone to seismic imaging artefacts due to limited field layouts and unmet preconditions. The nonlinearity between the velocities and the thickness from low‐velocity near surface to high‐velocity bedrocks ulteriorly complicates near‐surface modelling for long‐wavelength statics by first‐arrival inversion. Combining the advantages of as much long‐wavelength characteristics as possible associated with the permafrost and rugged topography, we estimate an integrated long‐wavelength statics method including tomographic inversion and refraction residual statics. The integrated method can approximate linearized near‐surface modelling through multi‐step processing. An example from the Qiangtang Basin (central Qinghai–Tibet Plateau) suggests that the integrated method is capable of gradual refining complex near‐surface modelling and long‐wavelength statics and eliminates upward‐stretching abnormalities caused by Tibetan permafrost.

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Estimating picking errors in near‐surface seismic data to enable their time‐lapse interpretation of hydrosystems

Cited by 15 publications

References 59 publications

Landslide monitoring using seismic refraction tomography – The importance of incorporating topographic variations

Landslide monitoring using seismic refraction tomography – The importance of incorporating topographic variations

Surface‐Wave Dispersion in Partially Saturated Soils: The Role of Capillary Forces

An integrated long‐wavelength statics method applied to seismic processing of Tibetan permafrost

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