Quantitative imaging of complex structures from dense wide-aperture seismic data by multiscale traveltime and waveform inversions: a case study

Geophysical prospecting, v. 52, n. 6, p. 625-651, 2004International audienc

Multiscale imaging of complex structures from multi-fold wide-aperture seismic data by frequency-domain full-waveform inversion: application to a thrust belt

An application of full-waveform tomography to dense onshore wide-aperture seismic data recorded in a complex geological setting (thrust belt) is presented. The waveform modelling and tomography are implemented in the frequency domain. The modelling part is solved with a finite-difference method applied to the visco-acoustic wave equation. The inversion is based on a local gradient method. Only the P-wave velocity is involved in the inversion. The inversion is applied iteratively to discrete frequency components by proceeding from low to high frequencies. This defines a multiscale imaging in the sense that high wavenumbers are progressively incorporated in images. The linearized waveform tomography requires an accurate starting velocity model that has been developed by first-arrival traveltime tomography. After specific pre-processing of the data, 16 frequency components ranging between 5.4 and 20 Hz were inverted. Ten iterations were computed per frequency component leading to 160 tomographic models. The waveform tomography has successfully imaged southwest dipping structures previously identified from other geophysical data as being associated with high-resistivity bodies. The relevance of the tomographic images is locally demonstrated by comparison of a velocity–depth function extracted from the waveform tomography models with a coincident vertical seismic profiling (VSP) log available on the profile. Moreover, comparison between observed and synthetic seismograms computed in the (starting) traveltime and waveform tomography models demonstrates unambiguously that the waveform tomography successfully predicts for wide-angle reflections from southwest-dipping geological structures. This study demonstrates that the combination of first-arrival traveltime and frequency domain full-waveform tomographies applied to dense wide-aperture seismic data is a promising approach to quantitative imaging of complex geological structures. Indeed, wide-aperture acquisition geometries offer the opportunity to develop an accurate background velocity model for the subsequent waveform tomography. This is critical, because the building of the macromodel remains an open question when only near-vertical reflection data are considered.Published1032-1056ope

Quantitative imaging of complex structures from multi-fold wide-aperture seismic data: a case study

An integrated multi-scale seismic imaging flow including first-arrival traveltime tomography and waveform inversion is applied to dense onshore wide-aperture seimsic data recorded in a complex geological setting (southern Apennines thrust belt, Italy

Multiscale imaging of complex structures from multi-fold wide-aperture seismic data by frequency-domain full-waveform inversion: application to a thrust belt

An application of full-waveform tomography to dense onshore wide-aperture seismic data recorded in a complex geological setting (thrust belt) is presented. The waveform modelling and tomography are implemented in the frequency domain. The modelling part is solved with a finite-difference method applied to the visco-acoustic wave equation. The inversion is based on a local gradient method. Only the P-wave velocity is involved in the inversion. The inversion is applied iteratively to discrete frequency components by proceeding from low to high frequencies. This defines a multiscale imaging in the sense that high wavenumbers are progressively incorporated in images. The linearized waveform tomography requires an accurate starting velocity model that has been developed by first-arrival traveltime tomography. After specific pre-processing of the data, 16 frequency components ranging between 5.4 and 20 Hz were inverted. Ten iterations were computed per frequency component leading to 160 tomographic models. The waveform tomography has successfully imaged southwest dipping structures previously identified from other geophysical data as being associated with high-resistivity bodies. The relevance of the tomographic images is locally demonstrated by comparison of a velocity–depth function extracted from the waveform tomography models with a coincident vertical seismic profiling (VSP) log available on the profile. Moreover, comparison between observed and synthetic seismograms computed in the (starting) traveltime and waveform tomography models demonstrates unambiguously that the waveform tomography successfully predicts for wide-angle reflections from southwest-dipping geological structures. This study demonstrates that the combination of first-arrival traveltime and frequency domain full-waveform tomographies applied to dense wide-aperture seismic data is a promising approach to quantitative imaging of complex geological structures. Indeed, wide-aperture acquisition geometries offer the opportunity to develop an accurate background velocity model for the subsequent waveform tomography. This is critical, because the building of the macromodel remains an open question when only near-vertical reflection data are considered

Systeme de mesures physiques et microphysiques embarque

SIGLEAvailable from CEN Saclay, Service de Documentation, 91191 Gif-sur-Yvette Cedex (France) / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Predicting hospitalisations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study

Objective: To predict older adults’ risk of avoidable hospitalisation related to ambulatory care sensitive conditions (ACSC) using machine learning applied to administrative health data of Ontario, Canada. Design, setting and participants: A retrospective cohort study was conducted on a large cohort of all residents covered under a single-payer system in Ontario, Canada over the period of 10 years (2008– 2017). The study included 1.85 million Ontario residents between 65 and 74 years old at any time throughout the study period. Data sources: Administrative health data from Ontario, Canada obtained from the (ICES formerly known as the Institute for Clinical Evaluative Sciences Data Repository. Main outcome measures: Risk of hospitalisations due to ACSCs 1 year after the observation period. Results: The study used a total of 1 854 116 patients, split into train, validation and test sets. The ACSC incidence rates among the data points were 1.1% for all sets. The final XGBoost model achieved an area under the receiver operating curve of 80.5% and an area under precision–recall curve of 0.093 on the test set, and the predictions were well calibrated, including in key subgroups. When ranking the model predictions, those at the top 5% of risk as predicted by the model captured 37.4% of those presented with an ACSC-related hospitalisation. A variety of features such as the previous number of ambulatory care visits, presence of ACSC-related hospitalisations during the observation window, age, rural residence and prescription of certain medications were contributors to the prediction. Our model was also able to capture the geospatial heterogeneity of ACSC risk in Ontario, and especially the elevated risk in rural and marginalised regions. Conclusions: This study aimed to predict the 1-year risk of hospitalisation from ambulatory-care sensitive conditions in seniors aged 65–74 years old with a single, large-scale machine learning model. The model shows the potential to inform population health planning and interventions to reduce the burden of ACSC-related hospitalisations.Published versionThis study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care (MLTC). This work was supported by the New Frontiers in Research Fund (NFRFE2018-00662), a Canada Research Chair in Population Health Analytics (950- 230702) (LR), Ontario Graduate Scholarship (number N/A) (VH), Canadian Institutes of Health Research Banting and Best Canada Graduate Scholarship Master’s and Doctoral awards (numbers N/A) (VH), and Vector Institute Post-graduate Fellowship (number N/A) (VH)