Assessing soil amplifications in Groningen, the Netherlands

Geophysical Journal International

Rodríguez-Marek

Edwards

et al. 2022

Summary Seismic damping of near-surface deposits is an important input to site-response analysis for seismic hazard assessment. In Groningen, the Netherlands, gas production from a reservoir at 3 km depth causes seismicity. Above the gas field, an 800 m thick layer of unconsolidated sediments exist, which consists of a mixture of sand, gravel, clay and peat strata. Shear waves induced at 3 km depth experience most of their anelastic attenuation in these loose sediments. A good estimate of damping is therefore crucial for modeling realistic ground-motion levels. In Groningen, we take advantage of a large network of 200 m deep vertical arrays to estimate damping from recordings of the induced events. As a first step, we apply seismic interferometry by deconvolution to estimate local transfer functions over these vertical arrays. Subsequently, two different methods are employed. The first is the ’up-going’ method, where the amplitude decay of the retrieved up-going wave is used. The second is the ’up-down’ method, where the amplitude difference between retrieved up- and down-going waves is utilized. For the up-going method, the amplitude of the up-going direct wave is affected by both elastic and anelastic effects. In order to estimate the anelastic attenuation it is necessary to remove the elastic amplification first. Despite the fact that elastic compensation could be determined quite accurately, non-physical damping values were estimated for a number of boreholes. Likely, the underlying cause was small differences in effective response functions of geophones at different depths. It was found that the up-down method is more robust. With this method, elastic propagation corrections are not needed. In addition, small differences in in situ geophone response are irrelevant because the up- and down-going waves retrieved at the same geophone, are used. For the 1D case we showed that for estimating the local transfer function, the complex reverberations need to be included in the interferometric process. Only when this is done, the transfer function does not contain elastic transmission loss and Q estimation can be made without knowing the soil profile in detail. Uncertainty in the estimated damping was found from the signal-to-noise ratio of the estimated transfer function. The Q profiles estimated with the up-down method were used to derive a damping model for the top 200 m of the entire Groningen field. A scaling relation was derived by comparing estimated Q profiles with low-strain damping profiles that were constructed using published models for low-strain damping linked to soil properties. This scaling relation, together with the soil-properties based damping model, allowed up-scaling of the model to each grid-cell in the Groningen field. For depths below 200 m, damping was derived from the attenuation of the microseism over Groningen. The mean damping model, over a frequency band between 2 and 20 Hz, was estimated to be 2.0% (0-50 m depth), 1.3% (50-100 m), 0.66% (100-150 m), 0.57% (150-200 m) and 0.5% (200-580 m).

Section: Up-down Methodsmentioning

confidence: 87%

Derivation of a near-surface damping model for the Groningen gas field

Geophysical Journal International

Rodríguez-Marek

Edwards

et al. 2022

“…The installation of a large dense network of borehole geophones (Dost et al, 2017) enabled intensive research activity. Seismic measurements on multiple depth levels were used to estimate shallow 1D velocity and attenuation profiles (Hofman et al, 2017;Ruigrok et al, 2022) and to estimate soil amplifications (Van Ginkel et al, 2019), while the large azimuthal coverage of the network was used to test different quality assessment parameters for passive image interferometry (Fokker & Ruigrok, 2019). The great amount of geological and geophysical models, provided by previous studies, and the presence of the large seismic network make Groningen an ideal region to test our approach of physics-based pore pressure monitoring.…”

Section: Groningen Setting Data and Modelsmentioning

confidence: 99%

4D Physics-Based Pore Pressure Monitoring Using Passive Image Interferometry

Fokker

Hawkins

et al. 2023

Preprint

This study introduces a technique for four-dimensional pore pressure monitoring using passive image interferometry. Surface-wave velocity changes as a function of frequency are directly linked to depth variations of pore pressure changes through sensitivity kernels. We demonstrate that these kernels can be used to invert time-lapse seismic velocity changes, retrieved with passive image interferometry, for hydrological pore pressure variations as a function of time, depth and region. This new approach is applied in the Groningen region of the Netherlands. We show good recovery of pore pressure variations in the upper 200 m of the subsurface from passive seismic velocity observations. This depth range is primarily limited by the reliable frequency range of the seismic data.

“…The installation of a large dense network of borehole geophones (Dost et al., 2017) enabled intensive research activity. Seismic measurements on multiple depth levels were used to estimate shallow 1D velocity and attenuation profiles (Hofman et al., 2017; Ruigrok et al., 2022) and to estimate soil amplifications (Van Ginkel et al., 2019), while the large azimuthal coverage of the network was used to test different quality assessment parameters for passive image interferometry (Fokker & Ruigrok, 2019). The great amount of geological and geophysical models, provided by previous studies, and the presence of the large seismic network make Groningen an ideal region to test our approach of physics‐based pore pressure monitoring.…”

Section: Groningen Setting Data and Modelsmentioning

confidence: 99%

4D Physics‐Based Pore Pressure Monitoring Using Passive Image Interferometry

Fokker

Geophysical Research Letters

Hawkins

et al. 2023

Self Cite

This study introduces a technique for four‐dimensional pore pressure monitoring using passive image interferometry. Surface‐wave velocity changes as a function of frequency are directly linked to depth variations of pore pressure changes through sensitivity kernels. We demonstrate that these kernels can be used to invert time‐lapse seismic velocity changes, retrieved with passive image interferometry, for hydrological pore pressure variations as a function of time, depth, and region. This new approach is applied in the Groningen region of the Netherlands. We show good recovery of pore pressure variations in the upper 200 m of the subsurface from passive seismic velocity observations. This depth range is primarily limited by the reliable frequency range of the seismic data.