Horizon Detection in Seismic Data: An Application of Linked Feature Detection from Multiple Time Series

Robert G Aykroyd,Fathi M O Hamed

doi:10.1155/2014/548070

Robert G Aykroyd, Fathi M O Hamed

Open Access

https://doi.org/10.1155/2014/548070

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Abstract

Seismic studies are a key stage in the search for large scale underground features such as water reserves, gas pockets, or oil fields. Sound waves, generated on the earth’s surface, travel through the ground before being partially reflected at interfaces between regions with high contrast in acoustic properties such as between liquid and solid. After returning to the surface, the reflected signals are recorded by acoustic sensors. Importantly, reflections from different depths return at different times, and hence the data contain depth information as well as position. A strong reflecting interface, called a horizon, indicates a stratigraphic boundary between two different regions, and it is the location of these horizons which is of key importance. This paper proposes a simple approach for the automatic identification of horizons, which avoids computationally complex and time consuming 3D reconstruction. The new approach combines nonparametric smoothing and classification techniques which are applied directly to the seismic data, with novel graphical representations of the intermediate steps introduced. For each sensor position, potential horizon locations are identified along the corresponding time-series traces. These candidate locations are then examined across all traces and when consistent patterns occur the points are linked together to form coherent horizons.

Highlights

There are many applications with multiple time-series data recorded at high frequency, such as environmental science, geophysics, financial trading, and internet marketing
A seismic study will form motivation for the proposed approach, but the method could be applied to other situations where the aim is to identify coherent features across multiple time series
The model of recorded seismic data is completed by the convolution of the reflection coefficient series with a transfer function which is often taken as the Ricker wavelet [12]

Summary

Introduction

There are many applications with multiple time-series data recorded at high frequency, such as environmental science, geophysics, financial trading, and internet marketing. Even in 2D the reconstruction process is usually mathematically and computationally challenging, and performing a full 3D reconstruction for a large scale seismic problem (possibly involving terabytes of data) may be impractical and ineffective—this is in contrast to other geophysical data collection methods (see, e.g., the review paper of [7]). Lendzionowski et al [8] and van der Baan and Paul [9] use pattern recognition methods in seismology They compare the traces against a library of patterns from known features with the aim of identifying similar features. This model will be used to generate synthetic data, but it will not be used for data analysis.

A Model for Seismic Data

Method of Analysis

Conclusions