Evaluation of different methods for deriving geotechnical parameters from electric and seismic streamer data

Vagnon, Federico; Comina, Cesare; Arato, A.

doi:10.1016/j.enggeo.2022.106670

Cited by 9 publications

(6 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lithological characterization and geomechanical parameters assessment are required to deal with uncertainty in the infrastructure planning phase (Svensson & Friberg, 2018) or to help in the diagnosis of infrastructure damages (Vagnon et al., 2022) and even in the evaluation risks affecting infrastructures (Holmes et al., 2021). First, lithological characterization allows for assessing the variance and inhomogeneity of the subsoil conditions.…”

Section: Introductionmentioning

confidence: 99%

Geomechanical parameters assessment and geological characterization using fuzzy C means clustering of electrical resistivity and seismic data

Benjumea

Gabàs

Macau

et al. 2023

Near Surface Geophysics

View full text Add to dashboard Cite

A broad understanding of the subsoil characteristics is required to deal with uncertainty in the infrastructure planning phase or even in the diagnosis of infrastructure damage. In this work, electrical resistivity and seismic methods have been employed for lithological characterization and geomechanical parameters assessment. The study area is characterized by high variability in geotechnical characteristics due to complex lithology. For lithological discrimination, we have used a soft clustering method to combine the independently electrical‐ and seismic‐derived models. The outputs of this integration process are zonal models that help to decrease interpretation uncertainties. From single seismic profiles, both Vp and Vs information are extracted allowing geomechanical parameters to be estimated. All this information is critical for assessing the variance and inhomogeneity of the subsoil characteristics. The methodology shown in this work can be used in the infrastructure planning phase as well as to ascertain the causes of detected damage on built infrastructure.

show abstract

Section: Introductionmentioning

confidence: 99%

Geomechanical parameters assessment and geological characterization using fuzzy C means clustering of electrical resistivity and seismic data

Benjumea

Gabàs

Macau

et al. 2023

Near Surface Geophysics

View full text Add to dashboard Cite

show abstract

“…Integration of more than one geophysical technique is often useful to constrain the geophysical interpretation and improve subsurface characterization [12,[20][21][22][23][24]. The geophysical methods that are often employed include, but are not limited to, electrical resistivity tomography (ERT), induced polarization (IP), self-potential (SP), electromagnetic (EM), multichannel analysis of surface waves (MASW), seismic shear (S)-wave reflection, seismic compressional (P)-wave refraction, and ground penetrating radar (GPR).…”

Section: Introductionmentioning

confidence: 99%

Geophysical and Geochemical Pilot Study to Characterize the Dam Foundation Rock and Source of Seepage in Part of Pensacola Dam in Oklahoma

Sanuade,

Ismail

2023

Water

View full text Add to dashboard Cite

Pensacola Dam, operated by the Grand River Dam Authority (GRDA), is a multiple-arch buttress dam constructed in 1940. The dam has little or no existing geophysical reports on the integrity of the dam foundation rock and even less knowledge at depth. Visual inspection indicated evidence of seepage at some arches of the dam. As a pilot study, we conducted a suite of geophysical surveys inside two arches (Arch-16 and Arch-17) and a part of the downstream berm to characterize the dam foundation rock, delineate seepage zones, and identify the most appropriate geophysical methods for temporal monitoring of the dam’s conditions. The geophysical methods included electrical resistivity tomography (ERT), self-potential (SP), multichannel analysis of surface waves (MASW), compressional (P)-wave refraction, and shear (S)-wave reflection. Water samples were collected for geochemical analysis to investigate the source of the seepage flow inside Arch-16. The geophysical results characterized the dam foundation rock into an unsaturated limestone and chert overlying a water-saturated limestone and chert. The ERT profiles indicated that groundwater is rising inside the arches and significantly dropping under the downstream berm, which can be due to the uplift pressure beneath the dam base. Zones of high seepage potential were detected near the buttress walls of the two surveyed arches, which may be related to previous blasting, excavation of the dam foundation, concrete placement, or improper grouting. The geochemical analysis of water samples taken from the artesian wells inside Arch-16 and the Grand Lake revealed different chemical compositions, suggesting that the source of water could be a mixture of groundwater and lake water or lake water interacting with rock and reaching the surface through fractures; however, more sampling and further analysis are required to ascertain the source of the seeps. This study showed that the ERT, SP, and S-wave reflection methods have effectively characterized the dam foundation rock and seepage zones beneath the arches. The study provided a better understanding of the conditions of the dam foundation rock, evaluated the utilized geophysical methods, and determined the optimum geophysical methods that can be used for the characterization and monitoring of the subsurface conditions along the entire length of the dam. In this study, we have demonstrated that the integration of effective geophysical surveys and geochemical analysis yielded optimum results in solving a complex dam safety problem. This strategy promotes the best practice for dam safety investigation.

show abstract

“…Hashash and Groholski (2010) reviewed comprehensively the non-linear site response analysis and its engineering practices with affecting parameters. Vagnon et al (2022) and Benjumea et al (2023) used electrical resistivity and seismic methods for the lithological characterization of a region and geomechanical parameter characterization.…”

Section: Introductionmentioning

confidence: 99%

“…Vagnon et al. (2022) and Benjumea et al. (2023) used electrical resistivity and seismic methods for the lithological characterization of a region and geomechanical parameter characterization.…”

Section: Introductionmentioning

confidence: 99%

Influence of input motion and surface layer properties on seismic site response: A stochastic simulation method–based MLR model

Yıldız

2023

Near Surface Geophysics

View full text Add to dashboard Cite

Seismic site response analyses are simulations in which the effects of geological conditions on seismic waves are examined. The uncertainties that make these analyses crucial are defined as the source of motion, the travel path of seismic waves, and geological conditions. In this study, a series of non-linear seismic response analyses were performed using data from site investigation studies. The results of non-linear analyses performed under earthquakes with different characteristics based on real soil data were investigated in terms of acceleration time histories and response spectra. The relationship between site response with the surface layer properties and input motion properties used in the simulations was examined in a parametrical manner. Based on the results obtained, Monte Carlo simulation, which is a stochastic data simulation method, was performed. Additionally, multiple regression and variance analysis (ANOVA) was performed on the dataset created by both site response analysis and stochastic simulations. The MLR model displayed highly accurate results with a coefficient of determination, R 2 of 0.9763, and a standard error of 0.109. The efficiency level of the independent variables used as input parameters in the simulations on the dependent variable, AF was examined. It was revealed that the coefficient of lateral earth pressure at rest (K o ), earthquake motion (i.e., PGA of input motion) and surface layer thickness (d surface ) from the soil properties had the highest effect on the amplification factor.

show abstract

Evaluation of different methods for deriving geotechnical parameters from electric and seismic streamer data

Cited by 9 publications

References 23 publications

Geomechanical parameters assessment and geological characterization using fuzzy C means clustering of electrical resistivity and seismic data

Geomechanical parameters assessment and geological characterization using fuzzy C means clustering of electrical resistivity and seismic data

Geophysical and Geochemical Pilot Study to Characterize the Dam Foundation Rock and Source of Seepage in Part of Pensacola Dam in Oklahoma

Influence of input motion and surface layer properties on seismic site response: A stochastic simulation method–based MLR model

Contact Info

Product

Resources

About