Seismic data compression wavelet transform

What is a seismic wavelet?
The seismic wavelet is the link between seismic data (traces) on which interpretations are based and the geology (reflection coefficients) that is being interpreted, and it must be known to interpret the geology correctly.
However, it is typically unknown, and assumed to be both broad band and zero phase..
What is a wavelet in seismic?
What is a wavelet? From a seismic processor's point of view, a wavelet is one of the basic building blocks used to construct the seismic models on which seismic-processing methods are based.
In the various processing steps, the wavelets are removed from the seismic data to yield the final sections..
What is wavelet compression algorithm?
Wavelet compression offers an approach that allows one to reduce the size of the data while at the same time improving its quality through the removal of high-frequency noise components.
Data can easily be reduced below 1% of its original size..
Wavelets allow one to compress the image using less storage space with more details of the image.
The advantage of decomposing images to approximate and detail parts as in 3.3 is that it enables to isolate and manipulate the data with specific properties.

It has been observed that a much better compression is achieved by quantizing the decorrelated data than the original data. There are several transforms that

The seismic data compression algorithms usually require three stages: first a transformation, then a uniform or quasi-uniform quantization and finally a coding scheme. On the other hand, decompression algorithms perform the inverse process of each one of these steps.

The Wavelet transform is highly used to compress seismic data, due to the capabilities of the Wavelets on representing geophysical events in seismic data. We selected the lifting scheme to implement the Wavelet transform because it reduces both computational and storage resources.

Can a wavelet transform be used as a sparse transform domain?

Although the high-frequency noise is attenuated, some high-frequency information of the original data is also removed at the same time

Considering the instability of the recovery in the Fourier domain, we then use a wavelet transform as the sparse transform domain

How to compress seismic data?

Spanias et al

(1991) developed the transform coding method with the sub-band strategy and the uniform or ununiform magnitude and phase quantization rule to compress the seismic data, including event data and background noise data

However, current compression techniques implement compression of the discrete time series after initial sampling

Why is A curvelet a better representation of Multidimensional seismic data?

The amplitudes of the transform coefficients are sorted and depicted in descending order

Therefore, the faster the curve decreases, the sparser the representation the transform can perform

As is shown in the figure, the curvelet frame has the best performance in sparsely representing multidimensional seismic data

Seismic data compression wavelet transform

What is a seismic wavelet?

What is a wavelet in seismic?

What is wavelet compression algorithm?

Can a wavelet transform be used as a sparse transform domain?

How to compress seismic data?

Why is A curvelet a better representation of Multidimensional seismic data?