Introduction

Using conventional reflectivity data alone to determine geologic features from seismic is a long drawn out process which requires careful interpretation of several surfaces. In order to help make the process more efficient, attribute volumes and RGB (Red, Green, Blue) blends of frequency bandpass volumes can be used to reveal the geology before interpretation begins. Understanding the geology before beginning the interpretation process allows the interpreter to make more informed decisions.

The data in this study is multi-client data presented with permission from Geophysical Pursuit, Inc. The 3D seismic was shot in Dawson County, Texas; in the Midland Basin. The stratigraphy of interest will be the San Andres formation down to the Ellenburger (Wright, 2008).

Method

Frequency data reveals another dimension of information within seismic data. Besides geologic features, frequency data can also tell us about the thickness of events (Paton and McArdle, 2014). Thicker homogeneous events tend to be lower frequency than thinner events, this is easily highlighted on the RGB color blends by reds (lower frequency) and blues (higher frequencies). Within a volume variation in lithology also affects frequency, as harder more compacted units are usually higher frequency than unconsolidated units.

Theory

Frequency decomposition is a method of filtering the seismic into the frequency bandpass volumes and recombining 3 select volumes into red, green and blue channels for the low, mid and high frequencies respectively. This interplay of colors allows for more geologic detail to been seen than when using amplitude alone. There are several types of frequency decomposition available; this study will focus on continuous wavelet transform (CWT) and matching pursuit algorithms (Lowell et al, 2014) to achieve the desired outputs.

In a CWT frequency decomposition there is a constant relationship between the bandwidth and frequency of the bandpass volumes. A low frequency bandpass volume will have a narrow bandwidth, where as a high frequency volume will have wide bandwidth. The wide bandwidth of the higher frequency volumes means that more of the frequency spectrum is contributing to the result.

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