Abstract

Non-invasive site characterization techniques have the potential to rapidly evaluate large subsurface volumes to guide subsequent invasive geotechnical site investigation. Among these methods, seismic full waveform inversion (FWI) stands out for its potential to recover detailed two-dimensional (2D) images of the subsurface. However, FWI’s need for substantial computational resources and sensitivity to the initial starting model has limited its utilization as a generalpurpose geotechnical site characterization tool. Addressing this, prior studies have shown data-driven methods can predict 2D subsurface structures composed of soil over rock. In the present study, we aim to generalize these findings to all nearsurface conditions. We propose a novel model generation framework that utilizes techniques from geostatistics to generate complex 2D subsurface models. The generated models include dipping soil and rock layers, soil lenses, boulders, and underground utilities; none of which have been considered previously. We use our model generation framework to simulate 100,000 2D subsurface models. We simulate field data acquisition along these 100,000 synthetic models, by numerically solving the elastic wave equation using an impulse source at the model’s center surrounded by 24 receivers (12 on either side). The data-driven predictive model, trained on 90% of the simulated data, achieved a mean absolute percent error on the testing set of 19%. Furthermore, these predictions are made within fractions of a second circumventing the computational and starting-model-related challenges associated with traditional 2D FWI. These results demonstrate that data-driven methods can predict complex images of the subsurface to enable rapid subsurface imaging for geotechnical applications.

Abstract

Non-invasive site characterization techniques have the potential to rapidly evaluate large subsurface volumes to guide subsequent invasive geotechnical site investigation. Among these methods, seismic full waveform inversion (FWI) stands out for its potential to recover detailed two-dimensional (2D) images of the subsurface. However, FWI’s need for substantial computational resources and sensitivity to the initial starting model has limited its utilization as a generalpurpose geotechnical site characterization tool. Addressing this, prior studies have shown data-driven methods can predict 2D subsurface structures composed of soil over rock. In the present study, we aim to generalize these findings to all nearsurface conditions. We propose a novel model generation framework that utilizes techniques from geostatistics to generate complex 2D subsurface models. The generated models include dipping soil and rock layers, soil lenses, boulders, and underground utilities; none of which have been considered previously. We use our model generation framework to simulate 100,000 2D subsurface models. We simulate field data acquisition along these 100,000 synthetic models, by numerically solving the elastic wave equation using an impulse source at the model’s center surrounded by 24 receivers (12 on either side). The data-driven predictive model, trained on 90% of the simulated data, achieved a mean absolute percent error on the testing set of 19%. Furthermore, these predictions are made within fractions of a second circumventing the computational and starting-model-related challenges associated with traditional 2D FWI. These results demonstrate that data-driven methods can predict complex images of the subsurface to enable rapid subsurface imaging for geotechnical applications.

Abstract

The multi-channel analysis of surface waves (MASW) is a widely employed surface wave method. The fundamental mode inversion is the usual scheme in MASW, because separating the fundamental mode from other high modes is feasible with an adequately long array. However, the extracted fundamental mode represents only part of the dynamic characteristics, it may deviate from the theoretical fundamental mode due to truncation effect, and part of it may correspond to another higher mode or leaky mode, resulting in mode misidentification. Conversely, the spectral analysis of surface waves (SASW) method features a more rigorous inversion scheme by matching the effective mode, but it may suffer from the tricky phase-unwrapping in the dispersion data reduction. This study introduces an improved dynamic response solution for elastic-layered media subjected to vertical loads. The proposed dynamic response solution is fast and accurate, facilitating the full wavefield inversion in terms of the frequency-velocity spectrum. The MASW frequencyvelocity spectrum inversion considers testing configuration and comprehensively models all wave phenomena, including near-field effect, truncation effect, and leaky waves. The MASW frequency-velocity spectrum inversion merges the convenience of MASW dispersion analysis with the rigorous inversion scheme adopted by SASW. The new MASW frequency-velocity spectrum inversion is compared with the SASW effective mode inversion and MASW fundamental mode inversion. The results show that both SASW effective mode inversion and MASW frequency-velocity spectrum inversion produce better inverted results than the MASW fundamental mode inversion, while the process of MASW frequency-velocity spectrum inversion is more convenient and robust. Finally, a field example is used to demonstrate the applicability of frequency-velocity spectrum inversion.

Abstract

The presence of karstification processes in the terrain poses a threat to the safety of the foundations of structures, which in the case of gypsum materials becomes more dangerous due to the rate at which they can progress. This work describes the case of a Spanish high-speed railway bridge whose route runs over a Tertiary gypsum formation belonging to the lower Miocene unit of the Madrid Basin. These are very firm materials in a healthy state, that allow the direct support of shallow foundations, which stand out for their high heterogeneity, with the presence of massive gypsum banks as well as alternating levels of gypsum and clay. However, these materials can be affected by karst processes of dissolution, weathering, and replacement by clays, in which case there is a marked degradation of their mechanical properties, and the eventual local appearance of cavities. In these circumstances, it is necessary to carry out soil improvement treatments, such as compaction grouting, or resort to deep foundations with piles that transmit the loads of the structure to a lower substrate in a non-disturbed state. By its nature, the detection of this type of processes by drilling rotary boreholes is not trivial, and there is often uncertainty associated with its spatial variability. This communication presents the results obtained after the execution of a geotechnical research campaign, consisting of the realization of seismic tomography borehole-profiles under each support of the viaduct, supported by the execution of conventional rotary boreholes. The results of this exploration have made it possible to understand and define the spatial extent of the materials degraded by karstification and the selection of robust foundation typologies that favour the sustainable and resilient nature of the infrastructure.