Bp Corporation North America Inc.

Zhou Yu

Imtiaz Ahmed

Techniques to reduce noise in seismic data by receiving a set of seismic data comprising a plurality of input volumes each inclusive of positional data and at least one additional attribute related to the seismic data, selecting a first input volume of the plurality of input volumes having a first additional attribute related to the seismic data, and generating a pilot volume by selecting a range of input volumes of the plurality of input volumes and stacking input volumes of the range of input volumes with the first input volume. Additionally, generating a trained dictionary based upon transformation of the pilot volume, transforming the first input volume into transformed data, imposing a sparse condition on the transformed data utilizing the trained dictionary to generate sparsified data, and inverse transforming the sparsified data to generate an output data volume as a portion of a set of modified seismic data.

Non-linear solution to seismic data conditioning using trained dictionaries

There is provided herein a new system and method of local attribute match filtering which operates in the local attribute domain via the use of complex wavelet transform technology. This approach is adaptable to address various noise types in seismic data and, more particularly, is well suited to reduce the noise in geophone data as long as an associated hydrophone signal is relatively noise-free.

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Katy, TX, United States of America

Zhou Yu