Performance evaluation of CNN for seismic resolution enhancement (a) Validation accuracy (b) Validation loss (c) Predicted values vs residuals (d) Results of different evaluation metrics.

Performance evaluation of CNN for seismic resolution enhancement (a) Validation accuracy (b) Validation loss (c) Predicted values vs residuals (d) Results of different evaluation metrics.

<p>Performance evaluation of CNN for seismic resolution enhancement (a) Validation accuracy (b) Validation loss (c) Predicted values vs residuals (d) Results of different evaluation metrics.</p>

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Detalles Bibliográficos
Autor principal:	Irshad Ali (5562320) (author)
Otros Autores:	Wakeel Ahmad (17000850) (author), Syed M. Adnan (22290842) (author)
Publicado:	2025
Materias:	Genetics Biotechnology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified Information Systems not elsewhere classified synthetic seismogram generation refined subsurface characterization reducing exploration risks quantitative subsurface properties primarily acoustic impedance highest r2 score geologically complex areas conventional cnn architectures calculating acoustic impedance transforms seismic data seismic data reconstruction real seismic data 052 ), along 0031 ), rmse reduced geological risk improve seismic resolution oversimplified subsurface models log data 0557 ), reduced effectiveness low resolution thereby establishing study addresses specifically lenet rock boundaries robust benchmark reflection coefficients process enables often resulting noise sensitivity models evaluated lowest mse lithological variations hydrocarbon reservoirs geophysical technique feature extraction 993 ).
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