Prediction of seismic p-wave velocity using machine learning.

Dumke, Ines and Berndt, Christian (Submitted) Prediction of seismic p-wave velocity using machine learning. Open Access Solid Earth Discussions . DOI 10.5194/se-2019-58.

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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 heatflow, are not taken into account. Here, we present a machine learning approach to predict 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 publically 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 improving geophysical models in areas lacking first-hand velocity data.

Document Type: Article
Research affiliation: OceanRep > GEOMAR > FB4 Dynamics of the Ocean Floor > FB4-GDY Marine Geodynamics
Refereed: No
Open Access Journal?: Yes
DOI etc.: 10.5194/se-2019-58
ISSN: 1869-9537
Date Deposited: 08 Apr 2019 13:22
Last Modified: 02 Jul 2019 13:45

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