Rock Properties and Seismic Attenuation: Neural Network Analysis

Abstract

Using laboratory data, the influence of rock parameters on seismic attenuation has been analyzed using artificial neural networks and regression models. The predictive capabilities of the neural networks and multiple linear regresssion were compared. The neural network outperforms the multiple linear regression in predicting attenuation values, given a set of input of rock parameters. The neural network can make complex decision mappings and this capability is exploited to examine the influence of various rock parameters on the overall seismic attenuation. The results indicate that the most influential rock parameter on the overall attenuation is the clay content, closely followed by porosity. Though grain size contribution is of lower importance than clay content and porosity, its value of 16 percent is sufficiently significant to be considered in the modeling and interpretation of attenuation data.

Quantifying bedrock-fracture patterns within the shallow subsurface: Implications for rock mass strength, bedrock landslides, and erodibility

“…Seismic velocity through a rock mass is greatly reduced by bedrock fractures [ Sjogren et al , 1979; Hudson , 1980; El‐Naqa , 1996; Boadu , 1997; Kahraman , 2001; Leucci and De Giorgi , 2006; Fratta and Santamarina , 2002; Cha et al , 2009]. Because the p‐wave velocity gives an integrated measure of all material within the rock mass, however, material that fills fracture spaces will affect the velocity magnitude [ Fratta and Santamarina , 2002; Jaeger et al , 2007; Cha et al , 2009].…”

“…Consequently, we assess fracture‐induced velocity reductions by calculating the difference between the intact‐bedrock velocities and the model‐derived velocity profiles. The magnitude of the velocity reduction, at any given depth, is dependent on the density of bedrock fractures, such that large reductions correspond to higher fracture densities, and vice versa [ Sjogren et al , 1979; Hudson , 1980, 1981; El‐Naqa , 1996; Boadu , 1997; Kahraman , 2001; Fratta and Santamarina , 2002; Leucci and De Giorgi , 2006; Cha et al , 2009]. Average velocity reductions were calculated from the average single‐ and multilayer velocity profiles relative to the average intact bedrock velocity for both regions (Figure 7 and Table 1).…”

Estimation of monthly evaporative loss using relevance vector machine, extreme learning machine and multivariate adaptive regression spline models

“…Thus, there is no thumb rule for data division between the training and testing phases. For example, Kurup and Dudani (2002) used 63 % of the available data used for training; Boadu (1997) used 80 % of the available data used for training; Pal (2006) used 69 % of the available data used for training. Following an earlier works [e.g.…”

Source rock characterization of the Albian Kazhdumi formation by integrating well logs and geochemical data in the Azadegan oilfield, Abadan plain, SW Iran

“…6. The aforementioned three-layered NN was trained by using the Levenberg Marquardt training algorithm (TrainLM), which details of their computation process and training can be found in Bishop (1995) and Boadu (1997Boadu ( , 1998. The performance of the model was measured by using mean squared error (MSE).…”