Data-driven seismic prestack velocity inversion via combining residual network with convolutional autoencoder

Geobody estimation by Bhattacharyya method utilizing nonlinear inverse modeling of magnetic data in Baba-Ali iron deposit, NW Iran

“…In 2019, Giraud et al [ 46 ] approach enabled the probabilistic geological modeling integration to calculate petrophysical constraints without petrophysical information. It employed uncertainty-guided inversion in uncertain regions of the study area by updating the physical property model and optimizing the inversion problem [ 24 , [46] , [47] , [48] ]. Fu et al [ 32 ] qualified a mixed geophysical survey optimized method for taking single-point, continuous measurements (e.g., the transient electromagnetic (TEM), the magnetotelluric (MT), and the high-precision ground magnetic survey (HPGMS) methods).…”

Estimation of Depth, Physical Parameters, and Location using Nonlinear Inverse Modeling of Magnetic Data by Bhattacharya Method in a field of NW Iran

“…In 2019, Giraud et al's approach enables the integration of probabilistic geological modeling in geophysical inversion in the absence of petrophysical information su cient to calculate petrophysical constraints. It uses geophysical measurements to optimize the inverse problem by updating the physical property model, preferably in geologically uncertain parts of the studied area during what called uncertainty-guided inversion (Giraud et al, 2019;Liu et al, 2022). An optimized combination of geophysical survey methods was capable by Fu et al (2020) for taking single-point, continuous measurements (e.g., the high-precision ground magnetic survey (HPGMS), the transient electromagnetic (TEM), and the magnetotelluric (MT) methods) can be employed to accurately determine the anomalous planar spatial locations, anomalous pro le morphologies, and burial depths of concealed iron ore bodies such as banded iron formation (BIF)-type (Florio et al, 2022;Fu et al, 2020), and the survey helped to characterize subsurface geology better (Madani et al, 2019).…”

Geobody Estimation by Bhattacharyya Method Utilizing Nonlinear Inverse Modeling of Magnetic Data in Baba-Ali Iron Deposit, NW Iran

“…In 2019, Giraud et al's approach enabled the integration of probabilistic geological modeling in geophysical inversion without petrophysical information sufficient to calculate petrophysical constraints. It uses geophysical measurements to optimize the inverse problem by updating the physical property model, preferably in geologically uncertain parts of the studied area during what is called uncertainty-guided inversion (Biswas et al, 2022;Giraud et al, 2019;Kianoush et al, 2023;Kianoush et al, 2022;Liu et al, 2022). An optimized combination of geophysical survey methods was capable by Fu et al (2020) for taking single-point, continuous measurements (e.g., the high-precision ground magnetic survey (HPGMS), the transient electromagnetic (TEM), and the magnetotelluric (MT) methods) can be employed to accurately determine the anomalous planar spatial locations, anomalous profile morphologies, and burial depths of concealed iron ore bodies such as banded iron formation (BIF)-type (Florio et al, 2022;Fu et al, 2020), and the survey helped to characterize subsurface geology better (Madani et al, 2019).…”