Mapping full seismic waveforms to vertical velocity profiles by deep learning

نویسندگان

چکیده

Building realistic and reliable models of the subsurface is primary goal seismic imaging. We have constructed an ensemble convolutional neural networks (CNNs) to build velocity directly from data. Most other approaches attempt map full data into 2D labels. exploit regularity acquisition train CNNs gathers neighboring common midpoints (CMPs) vertical 1D logs. This allows us integrate well-log inversion, simplify mapping by using labels, accommodate larger dips relative single CMP inputs. dynamically generate training in parallel with CNNs, which reduces overfitting. Data generation more computationally expensive than conventional full-waveform inversion (FWI). However, once network trained, sets similar parameters can be inverted much faster FWI. The multiCMP CNN tested on multiple synthetic models, performs well, was combined FWI for even better performance.

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ژورنال

عنوان ژورنال: Geophysics

سال: 2021

ISSN: ['0016-8033', '1942-2156']

DOI: https://doi.org/10.1190/geo2019-0473.1