Abstract: A method, including: determining, with a computer, a reflectivity spectrum; generating, with a computer, a target spectrum that is a convolution of a roll-off function and the reflectivity spectrum; and filtering, with a computer, seismic data to cause the seismic data to have the target spectrum.

Abstract: A method, including: obtaining a seismic dataset that is separated into subsets according to predetermined subsurface reflection angle ranges; performing, with a computer, an acoustic full wavefield inversion process on each of the subsets, respectively, to invert for density and generate respective density models; generating acoustic impedances for each of the subsets, as a function of reflection angle, using the respective density models; and transforming, using a computer, the acoustic impedances for each of the subsets into reflectivity sections, wherein the transforming includes normalizing the reflectivity sections by their respective bandwidth.

Abstract: A method, including: obtaining a velocity model generated by an acoustic full wavefield inversion process; generating, with a computer, a variable Q model by applying pseudo-Q migration on processed seismic data of a subsurface region, wherein the velocity model is used as a guided constraint in the pseudo-Q migration; and generating, with a computer, a final subsurface velocity model that recovers amplitude attenuation caused by gas anomalies in the subsurface region by performing a visco-acoustic full wavefield inversion process, wherein the variable Q model is fixed in the visco-acoustic full wavefield inversion process.

Abstract: A method, including: determining, with a computer, a reflectivity spectrum; generating, with a computer, a target spectrum that is a convolution of a roll-off function and the reflectivity spectrum; and filtering, with a computer, seismic data to cause the seismic data to have the target spectrum.

Abstract: A method, including: obtaining a seismic dataset that is separated into subsets according to predetermined subsurface reflection angle ranges; performing, with a computer, an acoustic full wavefield inversion process on each of the subsets, respectively, to invert for density and generate respective density models; generating acoustic impedances for each of the subsets, as a function of reflection angle, using the respective density models; and transforming, using a computer, the acoustic impedances for each of the subsets into reflectivity sections, wherein the transforming includes normalizing the reflectivity sections by their respective bandwidth.

Abstract: A method, including: obtaining a velocity model generated by an acoustic full wavefield inversion process; generating, with a computer, a variable Q model by applying pseudo-Q migration on processed seismic data of a subsurface region, wherein the velocity model is used as a guided constraint in the pseudo-Q migration; and generating, with a computer, a final subsurface velocity model that recovers amplitude attenuation caused by gas anomalies in the subsurface region by performing a visco-acoustic full wavefield inversion process, wherein the variable Q model is fixed in the visco-acoustic full wavefield inversion process.

Abstract: Method for speeding up iterative inversion of seismic data (106) to obtain a subsurface model (102), using local cost function optimization. The frequency spectrum of the updated model at each iteration is controlled to match a known or estimated frequency spectrum for the subsurface region, preferably the average amplitude spectrum of the subsurface P-impedance. The controlling is done either by applying a spectral-shaping filter to the source wavelet (303) and to the data (302) or by applying the filter, which may vary with time, to the gradient of the cost function (403). The source wavelet's amplitude spectrum (before filtering) should satisfy D(f)=fIp(f)W(f), where f is frequency, D(f) is the average amplitude spectrum of the seismic data, and Ip(f) is the average amplitude spectrum for P-impedance in the subsurface region (306,402) or an approximation thereof.

Abstract: Wavelet estimation method, particularly advantageous for full wavefield inversion (“FWI”) of seismic data, that makes use of both the primary and multiple reflections in the data. The inventive method uses an FWI algorithm to generate a subsurface model from primary reflections (101) in a shallow layer before first arrival of multiple reflections (101). The model is then used to simulate multiples (102). The wavelet is subsequently modified (104) such that the simulated multiples closely match the true recorded multiples (103). The simulated multiples may then be subtracted from the measured data (105) thereby creating a deeper top layer of data substantially free of multiples, and the method may then be repeated to extend the subsurface model to a greater depth (106).

Abstract: Method for speeding up iterative inversion of seismic data (106) to obtain a subsurface model (102), using local cost function optimization. The frequency spectrum of the updated model at each iteration is controlled to match a known or estimated frequency spectrum for the subsurface region, preferably the average amplitude spectrum of the subsurface P-impedance. The controlling is done either by applying a spectral-shaping filter to the source wavelet (303) and to the data (302) or by applying the filter, which may vary with time, to the gradient of the cost function (403). The source wavelet's amplitude spectrum (before filtering) should satisfy D(f)=fIp(f)W(f), where f is frequency, D(f) is the average amplitude spectrum of the seismic data, and Ip(f) is the average amplitude spectrum for P-impedance in the subsurface region (306,402) or an approximation thereof.

Abstract: Method for speeding up iterative inversion of seismic data (106) to obtain a subsurface model (102), using local cost function optimization. The frequency spectrum of the updated model at each iteration is controlled to match a known or estimated frequency spectrum for the subsurface region, preferably the average amplitude spectrum of the subsurface P-impedance. The controlling is done either by applying a spectral-shaping filter to the source wavelet (303) and to the data (302) or by applying the filter, which may vary with time, to the gradient of the cost function (403). The source wavelet's amplitude spectrum (before filtering) should satisfy D(f)=fIp(f)W(f), where f is frequency, D(f) is the average amplitude spectrum of the seismic data, and Ip(f) is the average amplitude spectrum for P-impedance in the subsurface region (306,402) or an approximation thereof.

Abstract: Method for speeding up iterative inversion of seismic data (106) to obtain a subsurface model (102), using local cost function optimization. The frequency spectrum of the updated model at each iteration is controlled to match a known or estimated frequency spectrum for the subsurface region, preferably the average amplitude spectrum of the subsurface P-impedance. The controlling is done either by applying a spectral-shaping filter to the source wavelet (303) and to the data (302) or by applying the filter, which may vary with time, to the gradient of the cost function (403). The source wavelet's amplitude spectrum (before filtering) should satisfy D(f)=fIp(f)W(f), where f is frequency, D(f) is the average amplitude spectrum of the seismic data, and Ip(f) is the average amplitude spectrum for P-impedance in the subsurface region (306,402) or an approximation thereof.

Abstract: Wavelet estimation method, particularly advantageous for full wavefield inversion (“FWI”) of seismic data, that makes use of both the primary and multiple reflections in the data. The inventive method uses an FWI algorithm to generate a subsurface model from primary reflections (101) in a shallow layer before first arrival of multiple reflections (101). The model is then used to simulate multiples (102). The wavelet is subsequently modified (104) such that the simulated multiples closely match the true recorded multiples (103). The simulated multiples may then be subtracted from the measured data (105) thereby creating a deeper top layer of data substantially free of multiples, and the method may then be repeated to extend the subsurface model to a greater depth (106).

Abstract: A method for assessing the suitability of seismic data for quantitative amplitude analysis, where the concern is excessive residual normal moveout (RNMO). The invention uses a near offset stack and a far offset stack, the time difference between the two, a mute pattern, a reflection shape assumption for the RNMO, and a waveform and frequency for the far stack traces to generate a formula that estimates far stack amplitude error caused by RNMO. The formula can be used to compensate the far stack amplitude where the error is not so great as to require reprocessing of the data. The method can also be applied to interpreted amplitude maps.

Abstract: A method for spectral balancing of near- and far-offset seismic data, whereby velocity and offsets are determined for the seismic data. First, an NMO stretch compensation filter is created by the following steps. A frequency spectrum of the near-offset seismic data is estimated and a stretch factor &bgr; is calculated for the frequency spectrum, using the velocity and the offsets. A stretched frequency spectrum of near-offset seismic data is calculated, based on the stretch factor &bgr;. The frequency spectrum of near-offset seismic data is divided by the stretched frequency spectrum of near-offset seismic data, generating a first frequency response filter. High frequency gain is limited in the first frequency response filter, generating a first gain-limited filter. The first gain-limited filter is convolved with a low pass filter, generating the NMO stretch compensation filter. Then the NMO stretch compensation filter is applied to the seismic data.

Abstract: A method for moveout-correcting CDP gathers of seismic data that takes nonhyperbolic moveout at large offsets into account. The entire range of offsets is divided into smaller ranges. Within each smaller range, a curve is fitted to the reflection time vs. offset data, and these separate curves are made to be continuous at range boundaries. The curves may be the hyperbolic or quartic options available in commercial seismic data processing packages. Additional computation time required due to lack of a single velocity function can be compensated for by interpolation techniques. The method has application to, inter alia, AVO analysis.

Abstract: A method for spectral balancing of near- and far-offset seismic data, whereby velocity and offsets are determined for the seismic data. First, an NMO stretch compensation filter is created by the following steps. A frequency spectrum of the near-offset seismic data is estimated and a stretch factor &bgr; is calculated for the frequency spectrum, using the velocity and the offsets. A stretched frequency spectrum of near-offset seismic data is calculated, based on the stretch factor &bgr;. The frequency spectrum of near-offset seismic data is divided by the stretched frequency spectrum of near-offset seismic data, generating a first frequency response filter. High frequency gain is limited in the first frequency response filter, generating a first gain-limited filter. The first gain-limited filter is convolved with a low pass filter, generating the NMO stretch compensation filter. Then the NMO stretch compensation filter is applied to the seismic data.

Abstract: A method of evaluating a subsurface region by separating/enhancing a certain type of seismic event data of interest from an overall set of seismic event data which includes other, different types of seismic event data is disclosed herein. In accordance with one feature, a particular type of gather is generated from the seismic event data such that the gather includes at least a portion of the data which is of interest and at least a portion of the other data. A series of data discrimination lines are incorporated into the gather at positions and directions which are established in the gather in a predetermined way. Using the data discrimination lines, the data of interest which is present in the gather is separated/enhanced with respect to the other data within the gather. The separated data may be used for example in producing a map of the particular subterranean region.

Abstract: A method for producing high vertical resolution seismic images from crosswell data is disclosed. In accordance with one aspect of the disclosure, a set of vertically spaced, generally horizontally extending continuous layers and associated nodes are defined within a region between two boreholes. The specific number of nodes is selected such that the value of a particular characteristic of the subterranean region at each of the nodes is one which can be determined from the seismic data. Once values are established at the nodes, values of the particular characteristic are assigned to positions between the node points of each layer based on the values at node within that layer and without regard to the values at node points within any other layer. A seismic map is produced using the node values and the assigned values therebetween. In accordance with another aspect of the disclosure, an approximate model of the region is established using direct arrival traveltime data.