Quantitative fracture prediction from seismic data

Discrimination and Enhancement of Fracture Signals on Surface Seismic Data

pore pressure prediction from seismic reflection data in an oil field

pore pressure is an important parameter, in exploration and production of hydrocarbon resources. lack of accurate knowledge about pore pressure, before drilling phase, leads to serious dangers in drilling process. accurate knowledge about distribution of pore pressure in a field, leads to reduce risks in drilling, improve well planning and mud weight calculations. during burial, normally pressu...

Seismic Data Forecasting: A Sequence Prediction or a Sequence Recognition Task

In this paper, we have tried to predict earthquake events in a cluster of seismic data on pacific ring of fire, using multivariate adaptive regression splines (MARS). The model is employed as either a predictor for a sequence prediction task, or a binary classifier for a sequence recognition problem, which could alternatively help to predict an event. Here, we explain that sequence prediction/r...

Lossless Seismic Data Compression using Adaptive Linear Prediction

This paper presents a comparison of adaptive linear pre-dictors as applied to the area of lossless compression of seismic waveform data. Three methods are explored: the normalize least-mean square (NLMS) algorithm, the gradient adaptive lattice (GAL) algorithm, and the recursive least squares lattice (RLSL) algorithm. When compared to standard linear prediction techniques, all three of these me...

Improved seismic fragility modeling from empirical data

The improved empirical fragility model addresses statistical dependence among observations of seismic performances, which arises from common but unknown factors influencing the observations. The proposed model accounts for this dependence by explicitly including common variables in the formulation of the limit state for individual components. Additionally, the fact that observations of the same...