A geology-based 3D velocity model of the Amatrice Basin (Central Italy)

Livani, Michele; Scrocca, Davide; Gaudiosi, Iolanda; Mancini, Marco; Cavinato, Gian Paolo; Franco, R. de; Caielli, Grazia; Vignaroli, Gianluca; Romi, Alessandro; Moscatelli, Massimiliano

doi:10.1016/j.enggeo.2022.106741

Cited by 3 publications

(4 citation statements)

References 59 publications

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“…As an example, once populated with the values of seismic velocity, the 3D geological model can find several applications in seismological studies. It can be used to improve the procedure and reduce the uncertainties during earthquake location, contribute to the calculation of more accurate focal mechanisms and perform wavepropagation and ground-motion simulations (e.g., Magistrale et al, 1996;Süss et al, 2001;Molinari et al, 2015;Livani et al, 2022). The 3D model also represents a starting model in perturbation studies, such as linearized inversions of travel times for crustal velocities (e.g., Magistrale and Day, 1999) or for studies related to the seismic waveforms for crustal structure and, moreover, it can be used to derive densities and compare them to gravity observations (Roy and Clayton, 1999).…”

Section: Discussionmentioning

confidence: 99%

“…To this end, the definition of the physical and geometrical attributes of the outcropping and subsurface geological units provides fundamental knowledge for several scientific topics and many engineering plans and operations. As an example, the integration of physical parameters of subsurface geological units into a well-defined 3D model can be applied to (i) to reduce the uncertainties for earthquake location, contributing to the calculation of more accurate focal mechanisms and performing wavepropagation and ground-motion simulations (e.g., Magistrale et al, 1996;Süss et al, 2001;Molinari et al, 2015;Livani et al, 2022), and (ii) understand, simulate, and predict the response of the geological body to subsurface natural and anthropic processes. The latter is the case of 3D geomechanical numerical models, which represent effective tools to evaluate and predict the possible effects -both at the surface and in the subsurface -of geofluid extraction and storage, to guarantee a safe management of such activities as well as to quantify and better understand the ongoing geological processes (e.g., tectonic deformation, natural subsidence, etc.…”

Section: Introductionmentioning

confidence: 99%

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Subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy)

Livani

Petracchini

Benetatos

et al. 2023

Preprint

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Abstract. The Po Plain (Italy) is one of the most densely populated and productive regions of Europe, characterized by a flourishing economy (also linked to strategic subsurface resources) and several world cultural and natural heritage sites. The coupling of social-economic interests with geological hazards (i.e., seismic, subsidence and flooding hazards) in this area requires accurate knowledge of the subsurface geology, active geological processes, and impact of human activities on natural environments to mitigate the potential natural and anthropic risks. Most data unveiling the subsurface geology of this region were produced by the hydrocarbon exploration industry. Po Plain hosts indeed many hydrocarbon fields that were discovered since the early 1950s giving rise to the subsurface exploration through extensive seismic reflection surveys and drilling of numerous deep wells. In this work, geological-geophysical data from 160 deep wells drilled for hydrocarbon exploration/exploitation purposes in the Po Plain and in the facing northern Adriatic Sea have been collected and digitized along with several published geological cross-sections and maps. These data have been used to reconstruct the overall subsurface 3D architecture and to extract the physical properties of the subsurface geological units. The digitized data are suitable to be imported into geo-software environments so to derive the geophysical-mechanical properties of the geological units for a wealth of applied and scientific studies such as geomechanical, geophysical and seismological studies. The integrated dataset may represent a useful tool in defining strategies to ensure the safety of the urbanized areas and human activities and to reduce natural and anthropic risks that may affect this crucial region of Europe. Nowadays, such issues are particularly relevant for the underground industry development related to the increasing interest on possible CO2 and hydrogen underground storage, which can play a fundamental role in the energy transition process towards the decarbonisation goals.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy)

Livani

Petracchini

Benetatos

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…It can be used to improve the procedure and to reduce the uncertainties during earthquake location, it can contribute to the calculation of more accurate focal mechanisms, and it can perform wave propagation and ground motion sim-ulations (e.g. Magistrale et al, 1999;Süss et al, 2001;Molinari et al, 2015;Livani et al, 2022). The 3D model also represents a starting model in perturbation studies, such as linearized inversions of travel times for crustal velocities (e.g.…”

Section: Discussionmentioning

confidence: 99%

“…As an example, the integration of physical parameters of subsurface geological units into a well-defined 3D model can be applied to (i) reduce the uncertainties for earthquake location, contributing to the calculation of more accurate focal mechanisms and performing wave propagation and ground motion simulations (e.g. Magistrale et al, 1996;Süss et al, 2001;Molinari et al, 2015;Livani et al, 2022); and (ii) understand, simulate, and predict the response of the geological body to subsurface natural and anthropic processes. The latter is the case for 3D geomechanical numerical models, which represent effective tools for the evaluation and prediction of the possible effects -both at the surface and in the subsurface -of geofluid extraction and storage to guarantee the safe management of such activities, as well as to quan-tify and better understand the ongoing geological processes (e.g.…”

Section: Introductionmentioning

confidence: 99%

Subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy)

Livani,

Petracchini,

Benetatos

et al. 2023

Earth Syst. Sci. Data

Self Cite

View full text Add to dashboard Cite

Abstract. The Po Plain (Italy) is one of the most densely populated and productive regions of Europe, characterized by a flourishing economy (also linked to strategic subsurface resources) and several world cultural and natural heritage sites. The coupling of socio-economic interests with geological hazards (i.e. seismic, subsidence, and flooding hazards) in this area requires accurate knowledge of the subsurface geology, the active geological processes, and the impact of human activities on natural environments to mitigate the potential natural and anthropic risks. Most data unveiling the subsurface geology of this region were produced by the hydrocarbon exploration industry. Indeed, the Po Plain hosts many hydrocarbon fields that have been discovered since the early 1950s, giving rise to the subsurface exploration through extensive seismic reflection surveys and drilling of numerous deep wells. In this work, geological and geophysical data from 160 deep wells drilled for hydrocarbon exploration and/or exploitation purposes in the Po Plain and in the facing northern Adriatic Sea have been collected and digitized along with several published geological cross-sections and maps. These data have been used to reconstruct the overall subsurface 3D architecture and to extract the physical properties of the subsurface geological units. The digitized data are suitable to be imported into geo-software environments so as to derive the geophysical and mechanical properties of the geological units for a wealth of applied and scientific studies such as geomechanical, geophysical, and seismological studies. The integrated dataset may represent a useful tool in defining regional first-order strategies to ensure the safety of the urbanized areas and human activities and to reduce natural and anthropic risks that may affect this crucial region of Europe. In particular, the data collected would be useful to highlight sensible areas where data collection and more detailed studies are needed. Nowadays, such issues are particularly relevant for the underground industry development related to the increasing interest in possible CO2 and hydrogen underground storage, which can play a fundamental role in the energy transition process towards decarbonization goals. The full dataset is available at the following link: https://doi.org/10.5281/zenodo.8126519 (Livani et al., 2023).

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GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data

Guo,

Xu,

Wang

et al. 2024

Geosci. Model Dev.

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Abstract. Borehole data are essential for conducting precise urban geological surveys and large-scale geological investigations. Traditionally, explicit modelling and implicit modelling have been the primary methods for visualizing borehole data and constructing 3D geological models. However, explicit modelling requires substantial manual labour, while implicit modelling faces problems related to uncertainty analysis. Recently, machine learning approaches have emerged as effective solutions for addressing these issues in 3D geological modelling. Nevertheless, the use of machine learning methods for constructing 3D geological models is often limited by insufficient training data. In this paper, we propose the semi-supervised deep learning using pseudo-labels (SDLP) algorithm to overcome the issue of insufficient training data. Specifically, we construct the pseudo-labels in the training dataset using the triangular irregular network (TIN) method. A 3D geological model is constructed using borehole data obtained from a real building engineering project in Shenyang, Liaoning Province, NE China. Then, we compare the results of the 3D geological model constructed based on SDLP with those constructed by a support vector machine (SVM) method and an implicit Hermite radial basis function (HRBF) modelling method. Compared to the 3D geological models constructed using the HRBF algorithm and the SVM algorithm, the 3D geological model constructed based on the SDLP algorithm better conforms to the sedimentation patterns of the region. The findings demonstrate that our proposed method effectively resolves the issues of insufficient training data when using machine learning methods and the inability to perform uncertainty analysis when using the implicit method. In conclusion, the semi-supervised deep learning method with pseudo-labelling proposed in this paper provides a solution for 3D geological modelling in engineering project areas with borehole data.

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A geology-based 3D velocity model of the Amatrice Basin (Central Italy)

Cited by 3 publications

References 59 publications

Subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy)

Subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy)

Subsurface geological and geophysical data from the Po Plain and the northern Adriatic Sea (north Italy)

GeoPDNN 1.0: a semi-supervised deep learning neural network using pseudo-labels for three-dimensional shallow strata modelling and uncertainty analysis in urban areas from borehole data

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