Noise model estimation in multimeasurement data

Patent metadata

Field	Value
Publication number	US-2016109591-A1
Application number	US-201514882903-A
Country	US
Kind code	A1
Filing date	Oct 14, 2015
Priority date	Oct 15, 2014
Publication date	Apr 21, 2016
Grant date	—

Abstract

Various implementations described herein are directed to methods for processing seismic data, including estimating a spectral noise power of multi-measurement seismic data received from a multi-dimensional seismic sensor array having multiple seismic sensors. The methods may include receiving a shot record of multi-measurement seismic data in time-domain, partitioning the shot record into overlapping time-space windows, and computing a frequency-domain spectrum for each time-space window. The methods may include computing a signal presence probability for each time-space window using the frequency-domain spectrum and prior probabilities of signal presence and absence for each time-space window. The methods may include iteratively updating a collective spectral noise power by recursively estimating the spectral noise power of a current time-space window based on the frequency spectrum for the current time-space window, the signal presence probability computed for the current time-space window, and a previously estimated spectral noise power of a previous time-space window.

First claim

What is claimed is:

1 . A method for estimating a spectral noise power of multi-measurement seismic data received from a multi-dimensional seismic sensor array having multiple seismic sensors, the method comprising:
receiving a shot record of the multi-measurement seismic data in time-domain; partitioning the shot record into a plurality of overlapping time-space windows; computing a frequency-domain spectrum for each time-space window; computing a signal presence probability for each time-space window using the frequency-domain spectrum and prior probabilities of signal presence and absence for each time-space window; and iteratively updating a collective spectral noise power by recursively estimating the spectral noise power of a current time-space window based on the frequency spectrum for the current time-space window, the signal presence probability computed for the current time-space window, and a previously estimated spectral noise power of a previous time-space window.

2 . The method of claim 1 , wherein the multi-measurement seismic data comprises stationary noise and non-stationary noise, and wherein estimating the spectral noise power comprises estimating the spectral noise power of the stationary noise and non-stationary noise in the multi-measurement seismic data.

3 . The method of claim 1 , wherein the multi-dimensional seismic sensor array is coupled to a streamer that is towed by a marine vessel, and wherein the multiple seismic sensors are coupled to the streamer at intervals along the streamer.

4 . The method of claim 1 , wherein the multiple seismic sensors comprise a hydrophone, a first accelerometer configured in an x-coordinate direction, a second accelerometer configured in a y-coordinate direction, and a third accelerometer configured in a z-coordinate direction.

5 . The method of claim 1 , wherein the shot record of the multi-measurement seismic data includes a combination of up-going and down-going wavefield data along with pressure noise data reflected from geological formations.

6 . The method of claim 1 , wherein partitioning the shot record into a plurality of overlapping time-space windows comprises partitioning the shot record into a plurality of columns, wherein each column comprises a columnar series of overlapping time-space windows, and wherein each column is offset from each other column by at least one time-space window.

7 . The method of claim 1 , wherein computing the frequency-domain spectrum for each time-space window comprises transforming each time-space window into a frequency-domain window using a Fast Fourier Transform (FFT).

8 . The method of claim 1 , wherein the prior probabilities of signal presence and absence for each time-space window refer to respective likelihood functions of the multi-measurement seismic data in case of signal presence and absence.

9 . The method of claim 1 , further comprising deriving an initial spectral noise power that is estimated from a time-space window having the least signal-to-noise ratio (SNR) when compared to other time-space windows, wherein the initial spectral noise power is used for recursively estimating the spectral noise power of the current time-space window.

10 . The method of claim 1 , further comprising estimating the spectral noise power of the previous time-space window based on the frequency spectrum for the previous time-space window and the signal presence probability computed for the previous time-space window.

11 . The method of claim 1 , wherein recursively estimating the spectral noise power comprises recursively estimating signal-to-noise ratio (SNR).

12 . The method of claim 1 , wherein computing the signal presence probability is based on a Bayesian framework for each time-space window using the frequency-domain spectrum and prior probabilities of signal presence and absence for each time-space window.

13 . A device for estimating a spectral noise power of multi-measurement seismic data received from a multi-dimensional seismic sensor array having multiple seismic sensors, the device comprising:
a processor; and memory having instructions stored thereon that, when executed by the processor, cause the processor to:
receive a shot record of the multi-measurement seismic data in time-domain;
partition the shot record into a plurality of overlapping time-space windows;
compute a frequency-domain spectrum for each time-space window;
compute a signal presence probability for each time-space window based on the frequency spectrum computed for each time-space window and previous information computed for a previous time-space window; and
estimate the spectral noise power of the current time-space window based on the frequency spectrum for the current time-space window, the signal presence probability computed for the current time-space window, and a previously estimated spectral noise power computed for the previous time-space window.

14 . The device of claim 13 , wherein the memory further comprises instructions that cause the processor to iteratively update the spectral noise power by recursively estimating the spectral noise power of one or more next time-space windows based on the frequency spectrum for the one or more next time-space windows, the signal presence probability computed for the one or more next time-space windows, and the previously estimated spectral noise power of the previous time-space window.

15 . The device of claim 13 , wherein the multi-measurement seismic data comprises stationary noise and non-stationary noise, and wherein estimating the spectral noise power comprises estimating the spectral noise power of the stationary noise and non-stationary noise in the multi-measurement seismic data.

16 . The device of claim 13 , wherein computing the frequency-domain spectrum for each time-space window comprises transforming each time-space window into a frequency-domain window using a Fast Fourier Transform (FFT).

17 . A non-transitory computer-readable medium having stored thereon a plurality of computer-executable instructions which, when executed by a computer, cause the computer to estimate a spectral noise power of multi-measurement seismic data received from a multi-dimensional seismic sensor array having multiple seismic sensors, wherein the computer-executable instructions that cause the computer to estimate the spectral noise power comprises computer-executable instructions that cause the computer to:
receive a shot record of the multi-measurement seismic data in time-domain; partition the shot record into a plurality of overlapping time-space windows; compute a frequency-domain spectrum for a current time-space window; compute a signal presence probability for the current time-space window based on the frequency-domain spectrum and prior probabilities of signal presence and absence for the current time-space window; estimate the spectral noise power of the current time-space window based on the frequency spectrum for the current time-space window and the signal presence probability computed for the current time-space window; and iteratively update a collective spectral noise power of the current time-space window by recursively estimating the spectral noise power of the current time-space window based on the frequency spectrum for the current time-space window, the signal presence probability computed for the current time-space window, and a previously estimated spectral noise power of a previous time-space window.

18 . The computer-readable medium of claim 17 , wherein the multi-dimen

Assignees

• Westerngeco Llc

Inventors

• Kamil Amin Yousif Izzeldin
• Loganathan Pradeep

Classifications

• G01V2210/34

  Noise estimation · CPC title

• G01V1/30 Primary

  Analysis (G01V1/50 takes precedence) · CPC title

• G01V1/3808

  Seismic data acquisition, e.g. survey design · CPC title

• G01V2210/144

  with functionally associated receivers, e.g. hydrophone and geophone pairs · CPC title

• G01V1/187

  Direction-sensitive hydrophones · CPC title