Abstract
Abstract. 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.
Highlights
Seismic P-wave velocities and velocity–depth profiles are needed in many marine–geophysical applications, e.g. for seismic data processing, for time–depth conversions, or to estimate hydrate concentrations in gas hydrate modelling
Direct measurements of seismic velocities, are sparse and limited to borehole locations such as those drilled by the Deep Sea Drilling Project (DSDP), the Ocean Drilling Program (ODP), and the International Ocean Discovery Program (IODP)
Predictions of prediction score 3, which were characterized by lower root mean square error (RMSE) and mean absolute error (MAE) values and a higher R2 than the five empirical functions, often exhibited a good fit to the true vp(z) curve (Fig. 2a–d)
Summary
Seismic P-wave velocities (vp) and velocity–depth profiles are needed in many marine–geophysical applications, e.g. for seismic data processing, for time–depth conversions, or to estimate hydrate concentrations in gas hydrate modelling. In the absence of measurements and refraction seismic data, constant velocities are often used for time–depth conversions Brune et al, 2010) or processing of reflection seismic data (Crutchley et al, 2010, 2011, 2013; Netzeband et al, 2010; Krabbenhoeft et al, 2013; Dumke et al, 2014), even though a constant velocity–depth profile is generally unrealistic and will lead to inaccurate results In the absence of measurements and refraction seismic data, constant velocities are often used for time–depth conversions (e.g. Brune et al, 2010) or processing of reflection seismic data (Crutchley et al, 2010, 2011, 2013; Netzeband et al, 2010; Krabbenhoeft et al, 2013; Dumke et al, 2014), even though a constant velocity–depth profile is generally unrealistic and will lead to inaccurate results
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