A deep learning network for estimation of seismic local slopes

Abstract

The local slopes contain rich information of the reflection geometry, which can be used to facilitate many subsequent procedures such as seismic velocities picking, normal move out correction, time-domain imaging and structural interpretation. Generally the slope estimation is achieved by manually picking or scanning the seismic profile along various slopes. We present here a deep learning-based technique to automatically estimate the local slope map from the seismic data. In the presented technique, three convolution layers are used to extract structural features in a local window and three fully connected layers serve as a classifier to predict the slope of the central point of the local window based on the extracted features. The deep learning network is trained using only synthetic seismic data, it can however accurately estimate local slopes within real seismic data. We examine its feasibility using simulated and real-seismic data. The estimated local slope maps demonstrate the successful performance of the synthetically-trained network.

Application of High Density Resistivity Method in Karst Exploration: A Case Study

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Multi-Task Deep Learning Seismic Impedance Inversion Optimization Based on Homoscedastic Uncertainty

“…More recently, with the successes of deep learning in the computer vision community, time series forecasting [2], and natural language processing, researchers have developed various data-driven seismic inversion techniques. The amount of available seismic data is growing exponentially and the deep learning methods are becoming integral components of geophysical exploration workflows [3], such as P-wave detection [4], seismic fault detection [5][6][7][8], seismic data noise attenuation [9,10], seismic data interpolation [11][12][13][14][15], and seismic slope estimation [16].…”