Seismic acquisition method and apparatus
The presently disclosed seismic acquisition technique employs a receiver array and a processing methodology that are designed to attenuate the naturally occurring seismic background noise recorded along with the seismic data during the acquisition. The approach leverages the knowledge that naturally occurring seismic background noise moves with a slower phase velocity than the seismic signals used for imaging and inversion and, in some embodiments, may arrive from particular preferred directions. The disclosed technique comprises two steps: 1) determining from the naturally occurring seismic background noise in the preliminary seismic data a range of phase velocities and amplitudes that contain primarily noise and the degree to which that noise needs to be attenuated, and 2) designing an acquisition and processing method to attenuate that noise relative to the desired signal.
1. A computer-implemented method for use in geophysical exploration, comprising:
accessing a set of source time functions, a set of measured receiver array data, and a subsurface attribute model, the subsurface attribute model defining a modeling grid;
forward modeling the source time functions in the subsurface attribute model, including:
generating a set of modeled receiver array data from modeled receiver arrays gathered from the modeling grid, wherein generating the set of modeled receiver array data includes:
propagating energy from a source through the subsurface attribute model to elements of the modeling grid during forward wavefield extrapolation to generate a modeled source wavefield at each receiver element in the modeled receiver array;
gathering the elements of the modeling grid into the modeled receiver arrays;
weighting each element of each modeled receiver array; and
summing each modeled receiver array over the weighted elements to produce the modeled receiver array data; and
differencing the measured receiver array data from the modeled receiver array data to obtain a data residual for each gathered and modeled receiver array;
back propagating the data residual into the subsurface attribute model to produce a modeled residual wavefield; and
updating the subsurface attribute model using the modeled residual wavefield; and
iterating updates to the surface attribute model to convergence.
2. The computer-implemented method of claim 1 , wherein back propagating the data residual into the subsurface attribute model includes:
multiplying the data residual from each modeled receiver array by each of the array weights in turn; and
backward propagating each data residual into the subsurface attribute model to produce the modeled residual wavefield.
3. The computer-implemented method of claim 1 , wherein back propagating the data residual into the subsurface attribute includes:
multiplying the data residual from each modeled receiver array by each of the array weights in turn; and
backward propagating each data residual into the subsurface attribute model to produce the modeled residual wavefield.
4. The computer-implemented method of claim 1 , wherein the subsurface attribute model is a velocity model.
5. A computing apparatus programmed to perform an operation for use in geophysical exploration, the operation comprising:
accessing a set of source time functions, a set of measured receiver array data, and a subsurface attribute model, the subsurface attribute model defining a modeling grid;
forward modeling the source time functions in the subsurface attribute model, including:
generating a set of modeled receiver array data from modeled receiver arrays gathered from the modeling grid, wherein generating the set of modeled receiver array data includes:
propagating energy from a source through the subsurface attribute model to elements of the modeling grid during forward wavefield extrapolation to generate a modeled source wavefield at each receiver element in the modeled receiver array;
gathering the elements of the modeling grid into the modeled receiver arrays;
weighting each element of each modeled receiver array; and
summing each modeled receiver array over the weighted elements to produce the modeled receiver array data; and
differencing the measured receiver array data from the modeled receiver array data to obtain a data residual for each gathered and modeled receiver array;
back propagating the data residual into the subsurface attribute model to produce a modeled residual wavefield; and
updating the subsurface attribute model using the modeled residual wavefield; and
iterating updates to the surface attribute model to convergence.
6. The computing apparatus of claim 5 , wherein back propagating the data residual into the subsurface attribute model includes:
multiplying the data residual from each modeled receiver array by each of the array weights in turn; and
backward propagating each data residual into the subsurface attribute model to produce the modeled residual wavefield.
7. The computing apparatus of claim 5 , wherein back propagating the data residual into the subsurface attribute includes:
multiplying the data residual from each modeled receiver array by each of the array weights in turn; and
backward propagating each data residual into the subsurface attribute model to produce the modeled residual wavefield.
8. The computing apparatus of claim 5 , wherein the subsurface attribute model is a velocity model.
9. A non-transitory program storage medium encoded with computing instructions for use in geophysical exploration, the computing instructions comprising:
accessing a set of source time functions, a set of measured receiver array data, and a subsurface attribute model, the subsurface attribute model defining a modeling grid;
forward modeling the source time functions in the subsurface attribute model, including:
generating a set of modeled receiver array data from modeled receiver arrays gathered from the modeling grid, wherein generating the set of modeled receiver array data includes:
propagating energy from a source through the subsurface attribute model to elements of the modeling grid during forward wavefield extrapolation to generate a modeled source wavefield at each receiver element in the modeled receiver array;
gathering the elements of the modeling grid into the modeled receiver arrays;
weighting each element of each modeled receiver array; and
summing each modeled receiver array over the weighted elements to produce the modeled receiver array data; and
differencing the measured receiver array data from the modeled receiver array data to obtain a data residual for each gathered and modeled receiver array;
back propagating the data residual into the subsurface attribute model to produce a modeled residual wavefield; and
updating the subsurface attribute model using the modeled residual wavefield; and
iterating updates to the surface attribute model to convergence.
10. The non-transitory program storage medium of claim 9 , wherein back propagating the data residual into the subsurface attribute model includes:
multiplying the data residual from each modeled receiver array by each of the array weights in turn; and
backward propagating each data residual into the subsurface attribute model to produce the modeled residual wavefield.
11. The non-transitory program storage medium of claim 9 , wherein back propagating the data residual into the subsurface attribute includes:
multiplying the data residual from each modeled receiver array by each of the array weights in turn; and
backward propagating each data residual into the subsurface attribute model to produce the modeled residual wavefield.
12. The non-transitory program storage medium of claim 9 , wherein the subsurface attribute model is a velocity model.