Prediction of seismic P-wave velocity using machine learning

• Published in: Solid earth (Göttingen) Vol. 10; no. 6; pp. 1989 - 2000
• Main Authors: Dumke, Ines, Berndt, Christian
• Format: Journal Article
• Language: English
• Published: Gottingen Copernicus GmbH 15-11-2019; Copernicus Publications
• Subjects: Artificial intelligence; Boreholes; Datasets; Depth; Earth science; Forests; Geology; Geophysics; Heat flow; Heat transmission; Learning algorithms; Machine learning; Oceans; P waves; Prediction models; Seismic data; Seismic velocities; Seismic waves; Training; Velocity; Wave propagation; Wave velocity
• ISSN: 1869-9529; 1869-9510; 1869-9529
• DOI: 10.5194/se-10-1989-2019

Measurements of seismic velocity as a function of depth
are generally restricted to borehole locations and are therefore sparse in
the world's oceans. Consequently, in the absence of measurements or suitable
seismic data, studies requiring knowledge of seismic velocities often obtain
these from simple empirical relationships. However, empirically derived
velocities may be inaccurate, as they are typically limited to certain
geological settings, and other parameters potentially influencing seismic
velocities, such as depth to basement, crustal age, or heat flow, are not
taken into account. Here, we present a machine learning approach to predict
the overall trend of seismic P-wave velocity (vp) as a function of
depth (z) for any marine location. Based on a training dataset consisting of
vp(z) data from 333 boreholes and 38 geological and spatial predictors
obtained from publicly available global datasets, a prediction model was
created using the random forests method. In 60 % of the tested locations,
the predicted seismic velocities were superior to those calculated
empirically. The results indicate a promising potential for global
prediction of vp(z) data, which will allow the improvement of geophysical
models in areas lacking first-hand velocity data.