Downhole Array Data Research Articles

Identification of Nonlinear Soil Properties from Downhole Array Data Using a Bayesian Model Updating Approach.

An accurate seismic response simulation of civil structures requires accounting for the nonlinear soil response behavior. This, in turn, requires understanding the nonlinear material behavior of in situ soils under earthquake excitations. System identification methods applied to data recorded during earthquakes provide an opportunity to identify the nonlinear material properties of in situ soils. In this study, we use a Bayesian inference framework for nonlinear model updating to estimate the nonlinear soil properties from recorded downhole array data. For this purpose, a one-dimensional finite element model of the geotechnical site with nonlinear soil material constitutive model is updated to estimate the parameters of the soil model as well as the input excitations, including incident, bedrock, or within motions. The seismic inversion method is first verified by using several synthetic case studies. It is then validated by using measurements from a centrifuge test and with data recorded at the Lotung experimental site in Taiwan. The site inversion method is then applied to the Benicia-Martinez geotechnical array in California, using the seismic data recorded during the 2014 South Napa earthquake. The results show the promising application of the proposed seismic inversion approach using Bayesian model updating to identify the nonlinear material parameters of in situ soil by using recorded downhole array data.

Site Characterization at Downhole Arrays by Joint Inversion of Dispersion Data and Acceleration Time Series

ABSTRACTWe present a sequential data assimilation algorithm based on the ensemble Kalman inversion to estimate the near-surface shear-wave velocity profile and damping; this is applicable when heterogeneous data and a priori information that can be represented in forms of (physical) equality and inequality constraints in the inverse problem are available. Although noninvasive methods, such as surface-wave testing, are efficient and cost-effective methods for inferring an VS profile, one should acknowledge that site characterization using inverse analyses can yield erroneous results associated with the lack of inverse problem uniqueness. One viable solution to alleviate the unsuitability of the inverse problem is to enrich the prior knowledge and/or the data space with complementary observations. In the case of noninvasive methods, the pertinent data are the dispersion curve of surface waves, typically resolved by means of active source methods at high frequencies and passive methods at low frequencies. To improve the inverse problem suitability, horizontal-to-vertical spectral ratio data are commonly used jointly with the dispersion data in the inversion. In this article, we show that the joint inversion of dispersion and strong-motion downhole array data can also reduce the margins of uncertainty in the VS profile estimation. This is because acceleration time series recorded at downhole arrays include both body and surface waves and therefore can enrich the observational data space in the inverse problem setting. We also show how the proposed algorithm can be modified to systematically incorporate physical constraints that further enhance its suitability. We use both synthetic and real data to examine the performance of the proposed framework in estimation of the VS profile and damping at the Garner Valley downhole array and compare them against the VS estimations in previous studies.

The Importance of Distinguishing Pseudoresonances and Outcrop Resonances in Downhole Array Data

ABSTRACTThis short note examines the downgoing wave effect and the appearance of pseudoresonances in downhole array data. It is demonstrated that pseudoresonances, distinct from the resonances associated with outcrop conditions, occur for sites with a shallow velocity contrast (VC) or with little to no VC. An approach is outlined to distinguish pseudoresonances from outcrop resonances using the theoretical 1D transfer functions for within and outcrop boundary conditions, as well as the horizontal-to-vertical spectral ratio. This approach is applied to hypothetical shear-wave velocity profiles, as well as three downhole array sites. We establish the importance of distinguishing pseudoresonances from outcrop resonances when using downhole array data to evaluate the accuracy of the 1D site response. For the example downhole array sites shown, the pseudoresonances are not captured well by 1D analysis, whereas the outcrop resonances are captured well. We propose that when evaluating the accuracy of 1D site-response analysis using downhole array data, the comparisons of the empirical and theoretical responses only consider the frequency range associated with outcrop resonances.

Taxonomy for evaluating the site-specific applicability of one-dimensional ground response analysis

Because one-dimensional (1D) ground response analysis remains the state-of-practice for geotechnical earthquake engineering, one crucial task is to identify whether the response of a site can be modeled well by 1D analysis. In this study, ground response analyses incorporating 1D transfer functions (TF) and the H/V Spectral Ratio (HVSR) technique are carried out for 34 downhole array sites and used to develop a taxonomy that assesses the suitability of 1D analysis for a site. The empirical HVSR is used to identify the first-mode resonant frequency of the site, and the within and outcrop TF are used to distinguish true outcrop-resonances from pseudo-resonances. The taxonomy is focused on capturing outcrop-resonances rather than pseudo-resonances because pseudo-resonances are an artifact of downhole array sites and are not present in the outcropping analysis of non-downhole array sites. Using the proposed taxonomy, 69% of the sites are considered suitable for 1D analysis, which is a much larger percentage than found in other studies. This more favorable result is a direct result of considering only true-resonances in the assessment of 1D applicability. This taxonomy system is developed using downhole array data but it can be applied to any site (even non-downhole array sites) in which the empirical HVSR curve and shear wave velocity profile are measured, making it easily applied in engineering practice.

Insights into Modeling Small-Strain Site Response Derived from Downhole Array Data

AbstractThe small-strain damping ratio (Dmin) is a key parameter in site response models and using values from laboratory tests tends to overpredict the site response because laboratory tests canno...

Evaluation of One-Dimensional Multi-Directional Site Response Analyses Using Geotechnical Downhole Array Data in California and Japan

This study presents a comprehensive investigation of one-dimensional (1-D) site response analysis (SRA) to predict the dynamic response of soil deposits under earthquake loading utilizing the recordings at selected borehole arrays. Seven instrumented downhole arrays in California and Japan were studied using 41 recorded ground motions that cover a broad range of intensities. The arrays were initially assessed in terms of effectiveness of 1-D SRA using taxonomy screening. Furthermore, LS-DYNA, an advanced Finite Element (FE) program, was employed to develop the 1-D soil column models for the SRA of these arrays. The soil stress-strain behavior was characterized with three different models including one linear elastic and two nonlinear backbone curve formulations. All predictions were compared to the measured ground motions to quantify the model biases and uncertainties. Lastly, the practical limitations of 1-D SRA models considered herein are identified, and recommendations are provided to assess the usefulness of the predictions in engineering practice.

Critical Parameters Affecting Bias and Variability in Site-Response Analyses Using KiK-net Downhole Array Data

Because of the limited number of strong‐motion records that have measured ground response at large strains, any statistical analyses of seismic site‐response models subject to strong ground motions are severely limited by a small number of observations. Recent earthquakes in Japan, including the M w 9.0 Tohoku earthquake of March 2011, have substantially increased the observations of strong‐motion records that can be used to compare alternative site‐response models at large strains and can subsequently provide insight into the accuracy and precision of site‐response models. Using the Kiban‐Kyoshin network (KiK‐net) downhole array data in Japan, we analyze the accuracy (bias) and variability (precision) resulting from common site‐response modeling assumptions, and we identify critical parameters that significantly contribute to the uncertainty in site‐response analyses. We perform linear and equivalent‐linear site‐response analyses at 100 KiK‐net sites using 3720 ground motions ranging in amplitude from weak to strong; 204 of these records have peak ground accelerations greater than ![Graphic][1] at the ground surface. We find that the maximum shear strain in the soil profile, the observed peak ground acceleration at the ground surface, and the predominant spectral period of the surface ground motion are the best predictors of where the evaluated models become inaccurate and/or imprecise. The peak shear strains beyond which linear analyses become inaccurate in predicting surface pseudospectral accelerations (PSA; presumably as a result of nonlinear soil behavior) are a function of vibration period and are between 0.01% and 0.1% for periods 0.5 s do not display noticeable effects of nonlinear soil behavior. Online Material: Site‐specific information and model residuals at 100 KiK‐net stations. [1]: /embed/inline-graphic-1.gif

Prediction of strain energy-based liquefaction resistance of sand–silt mixtures: An evolutionary approach

Liquefaction is a catastrophic type of ground failure, which usually occurs in loose saturated soil deposits under earthquake excitations. A new predictive model is presented in this study to estimate the amount of strain energy density, which is required for the liquefaction triggering of sand–silt mixtures. A wide-ranging database containing the results of cyclic tests on sand–silt mixtures was first gathered from previously published studies. Input variables of the model were chosen from the available understandings evolved from the previous studies on the strain energy-based liquefaction potential assessment. In order to avoid overtraining, two sets of validation data were employed and a particular monitoring was made on the behavior of the evolved models. Results of a comprehensive parametric study on the proposed model are in accord with the previously published experimental observations. Accordingly, the amount of strain energy required for liquefaction onset increases with increase in initial effective overburden pressure, relative density, and mean grain size. The effect of nonplastic fines on strain energy-based liquefaction resistance shows a more complicated behavior. Accordingly, liquefaction resistance increases with increase in fines up to about 10–15% and then starts to decline for a higher increase in fines content. Further verifications of the model were carried out using the valuable results of some downhole array data as well as centrifuge model tests. These verifications confirm that the proposed model, which was derived from laboratory data, can be successfully utilized under field conditions.

A Wavelet-based Seismogram Inversion Algorithm for the In Situ Characterization of Nonlinear Soil Behavior

We present a full waveform inversion algorithm of downhole array seismogram recordings that can be used to estimate the inelastic soil behavior in situ during earthquake ground motion. For this purpose, we first develop a new hysteretic scheme that improves upon existing nonlinear site response models by allowing adjustment of the width and length of the hysteresis loop for a relatively small number of soil parameters. The constitutive law is formulated to approximate the response of saturated cohesive materials, and does not account for volumetric changes due to shear leading to pore pressure development and potential liquefaction. We implement the soil model in the forward operator of the inversion, and evaluate the constitutive parameters that maximize the cross-correlation between site response predictions and observations on ground surface. The objective function is defined in the wavelet domain, which allows equal weight to be assigned across all frequency bands of the non-stationary signal. We evaluate the convergence rate and robustness of the proposed scheme for noise-free and noise-contaminated data, and illustrate good performance of the inversion for signal-to-noise ratios as low as 3. We finally employ the proposed scheme to downhole array data, and show that results compare very well with published data on generic soil conditions and previous geotechnical investigation studies at the array site. By assuming a realistic hysteretic model and estimating the constitutive soil parameters, the proposed inversion accounts for the instantaneous adjustment of soil response to the level and strain and load path during transient loading, and allows results to be used in predictions of nonlinear site effects during future events.

A novel framework integrating downhole array data and site response analysis to extract dynamic soil behavior

Seismic site response analysis is commonly used to predict ground response due to local soil effects. An increasing number of downhole arrays are deployed to measure motions at the ground surface and within the soil profile and to provide a check on the accuracy of site response analysis models. Site response analysis models, however, cannot be readily calibrated to match field measurements. A novel inverse analysis framework, self-learning simulations (SelfSim), to integrate site response analysis and field measurements is introduced. This framework uses downhole array measurements to extract the underlying soil behavior and develops a neural network-based constitutive model of the soil. The resulting soil model, used in a site response analysis, provides correct ground response. The extracted cyclic soil behavior can be further enhanced using multiple earthquake events. The performance of the algorithm is successfully demonstrated using synthetically generated downhole array recordings.

Inverse analysis of weak and strong motion downhole array data from the Mw7.0 Sanriku-Minami earthquake

A hybrid optimization scheme, comprising a genetic algorithm in series with a local least-squares fit operator, is used for the inversion of weak and strong motion downhole array data obtained by the Kik-Net Strong Motion Network during the M w7.0 Sanriku-Minami Earthquake. Inversion of low-amplitude waveforms is first employed for the estimation of low-strain dynamic soil properties at five stations. Successively, the frequency-dependent equivalent linear algorithm is used to predict the mainshock site response at these stations, by subjecting the best-fit elastic profiles to the downhole-recorded strong motion. Finally, inversion of the mainshock empirical site response is employed to extract the equivalent linear dynamic soil properties at the same locations. The inversion algorithm is shown to provide robust estimates of the linear and equivalent linear impedance profiles, while the attenuation structures are strongly affected by scattering effects in the near-surficial heterogeneous layers. The forward and inversely estimated equivalent linear shear wave velocity structures are found to be in very good agreement, illustrating that inversion of strong motion site response data may be used for the approximate assessment of nonlinear effects experienced by soil formations during strong motion events.

Site response and vertical seismic arrays

Vertical downhole arrays are deployed to record seismic soil (and overall site) behavior. Such arrays have already recorded a large body of benchmark earthquake case histories worldwide. These valuable records document: • mechanisms of vertical wave propagation and site resonance, • characteristics of site amplification for soft and stiff soil formations, and • cyclic soil behavior during liquefaction. At present, downhole array data offer a most effective means for calibration and verification of our predictive computational capabilities. This review is mainly focused on seven vertical-array sites that have been widely studied in the last few years. A number of techniques developed for analyses of downhole seismic records are presented. Relevance of each downhole site, characteristics of the installed array, available seismic records, and lessons learned to date are discussed. Finally, general conclusions are drawn based on the soil/site dynamic properties identified from these downhole records.

Modeling of wave propagation in northern Los Angeles basin

AbstractThe Los Angeles basin is characterized by deep structural complexity and is the site of numerous earthquakes. We analyze the amplitude and waveform data of earthquakes recorded in the Los Angeles basin in order to identify the structurally controlled path effects. This has been done in order to determine which features of the structure affect the observations and to generate improved models in different parts of the basin increasing our capabilities of predicting seismic hazard. Several structural models of the L.A. basin that are based on regional subsurface studies, exposed sections, scattered oil wells, and other limited geophysical data have recently become available. In this study, we have used seismic modeling techniques together with geologic models to attempt to explain site variations in some important data bases. We have modeled waveforms of (1) the 19 January 1989 Malibu earthquake data recorded in Pasadena, (2) an M = 2.8 earthquake recorded by the USC downhole array in the Baldwin Hills, southern California, and (3) two aftershocks of the Whittier Narrows earthquake recorded at five stations along a profile.The modeling of the Malibu earthquake results in a layered one-dimensional model representing 6.5 km of layered sediment underlain by a high-velocity basement. Direct arrivals, basin reflected phases, and multiple reverberations are modeled fairly well by this one-dimensional model. The downhole array data in the Baldwin Hills show a significant near-surface amplification that can be modeled by very low-velocity near-surface materials. A series of aftershocks of the Whittier Narrows earthquake recorded at close distances at temporary recording sites shows a wide variation in peak accelerations due both to radiation pattern and propagation effects. The effect of lateral heterogeneity is evident in the travel time, where stations within the basin show later arrival times than those away from the basin for the same epicentral distance. Seismograms recorded at different sites show variation in waveforms due to multi-pathing of rays. We use such triplicated arrivals to constrain the structure of different interfaces. Using these criteria and the synthetic seismograms calculated by ray theory and finite difference methods, we have been able to model the tangential seismograms due to two of these events recorded at five stations along a two-dimensional profile.All three modeling exercises helped us understand wave propagation in the basin environment. A basin-reflected phase was identified in the seismogram from the Malibu earthquake, which was used to derive an effective depth of the basement. Near-surface amplification of the waves observed in the downhole data could be explained by constructive interference of the multiply reflected waves through the thin near-surface low-velocity layer. Similarly, site-dependent variation of waveform and travel time shown by the seismograms from the aftershocks of the Whittier Narrows, California, earthquake could be accounted for when a laterally varying structure was used.