Applicability of artificial neural networks for obtaining velocity models from synthetic seismic data |
| |
Authors: | C Baronian M A Riahi C Lucas |
| |
Institution: | (1) Institute of Geophysics, University of Tehran, Tehran, Iran;(2) Electrical and Computer Engineering Department, University of Tehran, Tehran, Iran |
| |
Abstract: | Seismic velocity analysis is a crucial part of seismic data processing and interpretation which has been practiced using different
methods. In contrast to time consuming and complicated numerical methods, artificial neural networks (ANNs) are found to be
of potential applicability. ANN ability to establish a relationship between an input and output space is considered to be
appropriate for mapping seismic velocity corresponding to travel times picked from seismograms. Accordingly a preliminary
attempt is made to evaluate the applicability of ANNs to determine velocity and dips of dipping layered earth models corresponding
to travel time data. The study is based on synthetic data generated using inverse modeling approach for three earth models.
The models include a three-layer structure with same dips and same directions, a three-layer model with different dips and
same directions, as well as a two-layer model with different dips and directions. An ANN structure is designed in three layers,
namely, input, output, and hidden ones. The training and testing process of the ANN is successfully accomplished using the
synthetic data. The evaluation of the applicability of the trained ANN to unknown data sets indicates that the ANN can satisfactorily
compute velocity and dips corresponding to travel times. The error intervals between the desired and calculated velocity and
dips are shown to be acceptably small in all cases. The applicability of the trained ANN in extrapolating is also evaluated
using a number of data outside of the range already known to ANN. The results indicate that the trained ANN acceptably approximates
the velocity and dips. Furthermore, the trained ANN is also evaluated in terms of capability of handling deficiency in input
data where acceptable results were also achieved in velocity and dip calculations. Generally, this study shows that velocity
analysis using ANNs can promisingly tackle the challenge of retrieving an initial velocity model from the travel time hyperbolas
of seismic data. |
| |
Keywords: | Artificial neural networks (ANN) Seismic velocity analysis Synthetic seismograms Dipping structures |
本文献已被 SpringerLink 等数据库收录! |
|