Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-8 and 13-20 are rejected under 35 U.S.C. 103 as being anticipated over Moseley et al. “Fast approximate simulation of seismic waves with deep learning” (2018) [herein “Moseley”] and SIAHKOOHI et al. “The importance of transfer learning in seismic modeling and imaging” (2019) [herein “SIAHKOOHI”].
Regarding Claim 1, Moseley teaches
A method, comprising:receiving a velocity model corresponding to at least one attribute of seismic data;
“We define the input velocity model to be a 1D profile of a horizontally layered Earth velocity model, with a depth of 3.2 km and a sample rate of 12.5 m.” (Pg. 5 Section II B).
“Our proof of concept deep neural network is trained using 50,000 synthetic examples of seismic waves propagating through different 2D horizontally layered velocity models.” (Abstract).
“We condition the network on the input 2D velocity model by concatenating the velocity model to the input of each convolutional layer.”. (Pg. 6 Section III B).
“We train the network to predict the wavefield evolution over time for different 2D horizontally layered velocity models and different starting wavefields as input.”. (Pg. 6 Section III A).
Moseley explicitly states a velocity model as an input with related seismic data.
receiving source wavelet data corresponding to the seismic data;
“We use an 8 Hz Ricker source emitted close to the surface and record the pressure response at 11 receiver locations placed symmetrically around the source, horizontally offset every 200 m (Fig. 1, top left).” (Pg. 5 Section II C).
“Training data is generated using the same workflow as Section IIC. For these simulations we also randomly vary the location of the source as well as the velocity model.”. (Pg. 7 Section III C).
A source wavelet i.e. ricker wavelet corresponding to seismic data is selected.
transmitting the velocity model, the source wavelet data … to a machine learning system; and
“FIG. 7. Full wavefield simulation over time using our deep convolutional network. We recursively predict the evolution of an initial wavefield (left-most frame) in our validation set using our deep convolutional network (middle), compared to the ground truth FD modelling (top). We show the prediction at t = 0.00, 0.08, 0.16 and 0.24 s (left to right) and its difference to ground truth (bottom). The input velocity model is also shown (bottom right).” (Pg. 7 Fig. 7).
“We use an 8 Hz Ricker source emitted close to the surface and record the pressure response at 11 receiver locations placed symmetrically around the source, horizontally offset every 200 m (Fig. 1, top left).” (Pg. 5 Section II C).
“We condition the network on the input 2D velocity model by concatenating the velocity model to the input of each convolutional layer.”. (Pg. 6 Section III B).
“Training data is generated using the same workflow as Section IIC. For these simulations we also randomly vary the location of the source as well as the velocity model.”. (Pg. 7 Section III C).
This shows transmitting the velocity model and the source wavelet data to a machine learning system.
training the machine learning system into a trained machine learning system using the velocity model, the source wavelet data ...
“We condition the network on the input 2D velocity model by concatenating the velocity model to the input of each convolutional layer.” (Pg. 6 Section III B).
“For each training example the network is used to recursively predict 11 time steps ahead, using the output prediction at each time step as the current wavefield input for the next time step. Our L2 loss function is then given by” (Pg. 7 III C).
“We use an 8 Hz Ricker source emitted close to the surface and record the pressure response at 11 receiver locations placed symmetrically around the source, horizontally offset every 200 m (Fig. 1, top left).” (Pg. 5 Section II C).
“Training data is generated using the same workflow as Section IIC. For these simulations we also randomly vary the location of the source as well as the velocity model.”. (Pg. 7 Section III C).
Moseley network is trained using a velocity model, concatenated at each convolution layer, on training data generated by the ricker source wavelet.
MOSELEY does not explicitly teach but SIAHKOOHI teaches
generating a guide image comprising an approximated wavefield derived from the velocity model;
“uconditioned = GθA A−1q (1) (2) (3) where dobserved represents observed data including the effects of the free surface. The symbol J denotes the low-fidelity Jacobian (linearized Born scattering operator), which acts on δd the data residual—i.e., observed data after removal of the direct wave. The matrix A corresponds to the low-fidelity discretized wave equation, and q to the source.”. (Pg. 2).
“… we consider two examples where we use a poor discretization (only second order) for the Laplacian. Because of this choice, our low-fidelity wave simulations are numerically dispersed…”. (Pg. 4).
“We generate from these models low- and high-fidelity wavefield pairs for various time-steps and source positions.”. (Pg. 5).
The previous wavefield frame helps predict the next which is akin to a “guide image” to predict the wavefield solution.
A-1q is an approximated wavefield generated from the velocity model (the wave equation A is parametrized by the velocity model) and supplied as the input image to the CNN i.e. a guide image comprising an approximated wavefield, consistent with the specification’s description of the guide image as an approximated of the true wavefield solution ([0060]).
… the guide image to a machine learning system; and
“Let Gθd , Gθg , and GθA be the CNNs used to condition the low-fidelity observed data, gradi ents, and wavefield simulations, parameterized by θd, θg, and θA, respectively. Mathematically, these conditionings take the following form: dconditioned = Gθd (dobserved), gconditioned = Gθg J (δd) , and uconditioned = GθA A−1q (1) (2) (3) where dobserved represents observed data including the effects of the free surface. The symbol J denotes the low-fidelity Jacobian (linearized Born scattering operator), which acts on δd the data residual—i.e., observed data after removal of the direct wave.”. (Pg. 2).
This shows transmitting the guide image to a machine learning system.
… and the guide image.
“The key idea now is to condition by training CNNs with pairs of low- and high-fidelity simulations.”. (Pg. 2).
“We train a CNN by minimizing objectives 4 for 5×401×11 = 22055 low- and high-fidelity wavefield snapshots simulated on these five training velocity models at 401 source locations and 11 randomly selected time snapshots.”. (Pg. 5).
SIAHKOOHI CNN is trained using low-fidelity approximated wavefield as the training input, paired with the high-fidelity target i.e. training that uses the guide image.
It would have been obvious to one skilled in the art before the effective filing date of the
claimed invention to incorporate the teachings of SIAHKOOHI’s approximated wavefield training input with MOSELEY’s machine learning system. The motivation for doing so would have been to “… map computationally cheap low-fidelity solutions to high-fidelity ones with a pre-trained Convolutional Neural Network (CNN).”. (Introduction). MOSELEY and SIAHKOOHI train convolution networks on wavefield image inputs for seismic simulations. The incorporation amounts to applying MOSELEY’s existing wavefield output with SIAHKOOHI’s low-fidelity simulation.
Regarding Claim 2, SIAHKOOHI does not explicitly teach but MOSELEY teaches
The method of claim 1, comprising generating, at the trained machine learning system, a wavefield solution corresponding to the velocity model.
“Training data is generated using the same workflow as Section II C. For these simulations we also randomly vary the location of the source as well as the velocity model. We generate 5000 simulations and from each simulation extract 8 training examples. Each training example contains the previous wavefield, the current wavefield and 11 future wavefields, over different starting time steps.” (Section III C).“Our training loss converges and we assess the performance of our trained network using a validation set of 200 unseen examples. The full wavefield prediction over multiple time steps for 1 randomly selected example in the validation set is shown in Fig. 7. For the example shown the trained convolutional network is able to approximate the update equation given by Eq. 4. The predicted wavefield expands outward and reflections occur at velocity boundaries. The speed and shape of the wavefront also changes when entering different velocity layers, as expected.” (Section III D).
“Full wavefield simulation over time using our deep convolutional network. We recursively predict the evolution of an initial wavefield (left-most frame) in our validation set using our deep convolutional network (middle), compared to the ground truth FD modelling (top). We show the prediction at t = 0.00, 0.08, 0.16 and 0.24 s (left to right) and its difference to ground truth (bottom). The input velocity model is also shown (bottom right).”. (Fig. 7).
Moseley teaches generating a wavefield solution corresponding the velocity model by prediction.
Regarding Claim 3, SIAHKOOHI does not explicitly teach but MOSELEY teaches
The method of claim 2, comprising applying the wavefield solution in a migration operation to characterize a reservoir in a subsurface region of Earth.
“Seismic simulations are invaluable in many areas of geophysics. In earthquake monitoring, they are a key tool for quantifying the ground motion of potential earthquakes [1]. In oil and gas prospecting, they are used to understand the seismic response of hydrocarbon reservoirs [2, 3]. In geophysical surveying, they show how the subsurface is illuminated by different survey designs [4]. In global geophysics, seismic simulations are invaluable for obtaining snapshots of the Earth’s interior dynamics [5] and for deciphering source or path effects from individual seismograms [6].” (Section I Para. 1).
Regarding Claim 4, SIAHKOOHI does not explicitly teach but MOSELEY teaches
The method of claim 2, comprising receiving a second velocity model and transmitting the second velocity model to the trained machine learning system.
“During training both the test loss and the training loss converge to similar values, suggesting the network is able to generalise over different input velocity models.” (Pg. 5 Section II E).
“Similar to Section II, we only predict the 2D acoustic pressure response (Eq. 1) and keep the density and the size of the Earth model fixed. We train the network to predict the wavefield evolution over time for different 2D horizontally layered velocity models and different starting wavefields as input.” (Pg. 6 Section III A).
“We expect the network to generalise well over unseen velocity models.” (Pg. 4 Section II A).
Moseley teaches being able to generally use different velocity models i.e. “second model” to produce corresponding wavefield results.
Regarding Claim 5, SIAHKOOHI does not explicitly teach but MOSELEY teaches
The method of claim 4, comprising generating, at the trained machine learning system, a second wavefield solution corresponding to the second velocity model.
“We expect the network to generalise well over unseen velocity models.” (Pg. 4 Section II A).
Moseley teaches being able to generally use different velocity models i.e. “second model” to produce corresponding wavefield results.
Regarding Claim 6, SIAHKOOHI does not explicitly teach but MOSELEY teaches
The method of claim 5, comprising applying the second wavefield solution in a migration operation to characterize a reservoir in a subsurface region of Earth.
“Seismic simulations are invaluable in many areas of geophysics. In earthquake monitoring, they are a key tool for quantifying the ground motion of potential earthquakes [1]. In oil and gas prospecting, they are used to understand the seismic response of hydrocarbon reservoirs [2, 3]. In geophysical surveying, they show how the subsurface is illuminated by different survey designs [4]. In global geophysics, seismic simulations are invaluable for obtaining snapshots of the Earth’s interior dynamics [5] and for deciphering source or path effects from individual seismograms [6].” (Pg.1 Section I).
“In Full Waveform Inversion (FWI), a strategy quickly becoming widespread in the field of seismic imaging, forward simulations are used thousands of times to iteratively estimate a medium’s elastic properties [8].” (Pg.1 Section I).
Moseley teaches iteratively applying generated wavefield solutions to characterize a reservoir subsurface.
Regarding Claim 7, SIAHKOOHI does not explicitly teach but MOSELEY teaches
The method of claim 5, wherein generating the second wavefield solution comprises utilizing the guide image at the trained machine learning system.
“The input to the network is the current and previous wavefield frames concatenated together and the output is a prediction of the wavefield at the next time step.” (Pg. 6 Section B).
“We train the network to predict the wavefield evolution over time for different 2D horizontally layered velocity models and different starting wavefields as input.” (Pg. 6 Section A).
Moseley teaches utilizing a “guide image” (frames) for the second wavefield solution as the design allows for different wavefields and different velocity models.
Regarding Claim 8, SIAHKOOHI does not explicitly teach but MOSELEY teaches
The method of claim 5, wherein generating the second wavefield solution comprises utilizing second source wavelet data corresponding to the seismic data at the trained machine learning system.
“For these simulations we also randomly vary the location of the source as well as the velocity model. We generate 5000 simulations and from each simulation extract 8 training examples. Each training example contains the previous wavefield, the current wavefield and 11 future wavefields, over different starting time steps.” (Pg. 7 Section III C).
“Our training loss converges and we assess the performance of our trained network using a validation set of 200 unseen examples.” (Pg. 7 Section III D).
Moseley shows that its system allows for a second source wavelet as it can use different source locations for source data for each velocity model that is evaluated.
Claim 13 recites substantially the same limitations as a combination of claims 1-2 except these claims are directed to a “A tangible and non-transitory machine readable medium, comprising instructions to cause a machine learning system to:” Therefore, these claims are rejected under the same rationale as addressed above.
Claims 14-18 recite substantially the same limitations as claims 1-8 except these claims are directed to a “The tangible and non-transitory machine readable medium of claim [X], comprising instructions to cause the machine learning system to”. Therefore, these claims are rejected under the same rationale as addressed above.
Regarding Claim 19, Moseley teaches
A device, comprising:
an input that when in operation receives a velocity model corresponding to at least one attribute of seismic data,
“We define the input velocity model to be a 1D profile of a horizontally layered Earth velocity model, with a depth of 3.2 km and a sample rate of 12.5 m.” (Pg. 5 Section II B).
“Our proof of concept deep neural network is trained using 50,000 synthetic examples of seismic waves propagating through different 2D horizontally layered velocity models.” (Abstract).
“We condition the network on the input 2D velocity model by concatenating the velocity model to the input of each convolutional layer.”. (Pg. 6 Section III B).
“We train the network to predict the wavefield evolution over time for different 2D horizontally layered velocity models and different starting wavefields as input.”. (Pg. 6 Section III A).
The network’s input layer, which receives the concatenated velocity model in operation, is an input that when in operation receives a velocity model corresponding to at least one attribute of seismic data.
source wavelet data corresponding to the seismic data,
“We use an 8 Hz Ricker source emitted close to the surface and record the pressure response at 11 receiver locations placed symmetrically around the source, horizontally offset every 200 m (Fig. 1, top left).” (Pg. 5 Section II C).
“Training data is generated using the same workflow as Section IIC. For these simulations we also randomly vary the location of the source as well as the velocity model.”. (Pg. 7 Section III C).
A source wavelet i.e. ricker wavelet corresponding to seismic data is selected.
a machine learning system that the velocity model, the source wavelet data … to generate a wavefield solution corresponding to the velocity model.
“Training data is generated using the same workflow as Section II C. For these simulations we also randomly vary the location of the source as well as the velocity model. We generate 5000 simulations and from each simulation extract 8 training examples. Each training example contains the previous wavefield, the current wavefield and 11 future wavefields, over different starting time steps.” (Section III C).“Our training loss converges and we assess the performance of our trained network using a validation set of 200 unseen examples. The full wavefield prediction over multiple time steps for 1 randomly selected example in the validation set is shown in Fig. 7. For the example shown the trained convolutional network is able to approximate the update equation given by Eq. 4. The predicted wavefield expands outward and reflections occur at velocity boundaries. The speed and shape of the wavefront also changes when entering different velocity layers, as expected.” (Section III D).
“Full wavefield simulation over time using our deep convolutional network. We recursively predict the evolution of an initial wavefield (left-most frame) in our validation set using our deep convolutional network (middle), compared to the ground truth FD modelling (top). We show the prediction at t = 0.00, 0.08, 0.16 and 0.24 s (left to right) and its difference to ground truth (bottom). The input velocity model is also shown (bottom right).”. (Fig. 7).
“The input to the network is the current and previous wavefield frames concatenated together and the output is a prediction of the wavefield at the next time step.” (Pg. 6).
“We condition the network on the input 2D velocity model by concatenating the velocity model to the input of each convolutional layer.”. (Pg. 6).
“We use an 8 Hz Ricker source emitted close to the surface and record the pressure response at 11 receiver locations placed symmetrically around the source…”. (Pg. 5).
Moseley’s convolutional network is a machine learning system that is trained using the velocity model, concatenated at each convolutional layer, on training data generated by the source wavelet, and once trained generates wavefield solutions corresponding to the input velocity model.
MOSELEY does not explicitly teach but SIAHKOOHI teachesand a guide image based upon at least one attribute of the velocity model; and
“uconditioned = GθA A−1q (1) (2) (3) where dobserved represents observed data including the effects of the free surface. The symbol J denotes the low-fidelity Jacobian (linearized Born scattering operator), which acts on δd the data residual—i.e., observed data after removal of the direct wave. The matrix A corresponds to the low-fidelity discretized wave equation, and q to the source.”. (Pg. 2).
“… we consider two examples where we use a poor discretization (only second order) for the Laplacian. Because of this choice, our low-fidelity wave simulations are numerically dispersed…”. (Pg. 4).
“We generate from these models low- and high-fidelity wavefield pairs for various time-steps and source positions.”. (Pg. 5).
The previous wavefield frame helps predict the next which is akin to a “guide image” to predict the wavefield solution.
A-1q is an approximated wavefield generated from the velocity model (the wave equation A is parametrized by the velocity model) and supplied as the input image to the CNN i.e. a guide image comprising an approximated wavefield, consistent with the specification’s description of the guide image as an approximated of the true wavefield solution ([0060]).
a machine learning system that … the guide image to generate a wavefield solution corresponding to the velocity model.
“The key idea now is to condition by training CNNs with pairs of low- and high-fidelity simulations.”. (Pg. 2).
“We train a CNN by minimizing objectives 4 for 5×401×11 = 22055 low- and high-fidelity wavefield snapshots simulated on these five training velocity models at 401 source locations and 11 randomly selected time snapshots.”. (Pg. 5).
“With these trained CNNs, we map low-fidelity data, gradients, and simulations to high-fidelity ones.”. (Pg. 2).
SIAHKOOHI’s CNN is a machine learning system that is trained using the low-fidelity approximated wavefield as its training input, paired with the high-fidelity target, and generates the corrected wavefield solution i.e. a machine learning system trained using the guide image to generate a wavefield solution.
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to incorporate the teachings of SIAHKOOHI’s approximated wavefield training input with MOSELEY’s machine learning system. The motivation for doing so would have been to “… map computationally cheap low-fidelity solutions to high-fidelity ones with a pre-trained Convolutional Neural Network (CNN).” (Pg. 2). Both MOSELEY and SIAHKOOHI train convolutional networks on wavefield image inputs for seismic simulation. The incorporation amounts to supplying MOSELEY’s existing wavefield input with SIAHKOOHI’s low-fidelity simulation, with a reasonable expectation of success.
Regarding Claim 20, SIAHKOOHI does not explicitly teach but MOSELEY teaches
The device of claim 19, comprising an output that when in operation transmits the wavefield solution for use in a migration operation to characterize a reservoir in a subsurface region of Earth.
“Seismic simulations are invaluable in many areas of geophysics. In earthquake monitoring, they are a key tool for quantifying the ground motion of potential earthquakes [1]. In oil and gas prospecting, they are used to understand the seismic response of hydrocarbon reservoirs [2, 3]. In geophysical surveying, they show how the subsurface is illuminated by different survey designs [4]. In global geophysics, seismic simulations are invaluable for obtaining snapshots of the Earth’s interior dynamics [5] and for deciphering source or path effects from individual seismograms [6].” (Section I Para. 1).
Moseley explains the purpose of wavefield solutions in pursuit of characterizing subsurface regions of the earth.
Claims 9-11 are rejected under 35 U.S.C. 103 as being unpatentable over Moseley et al. “Fast approximate simulation of seismic waves with deep learning” (2018) [herein “Moseley”], SIAHKOOHI et al. “The importance of transfer learning in seismic modeling and imaging” (2019) [herein “SIAHKOOHI”], and “WAVEFIELD RECONSTRUCTION INVERSION VIA PHYSICS-INFORMED NEURAL NETWORKS” (2021) by Song et al [herein “Song”].
Regarding Claim 9, MOSELEY and SIAHKOOHI do not explicitly teach but SONG teaches
The method of claim 1, wherein the approximated wavefield is determined based on a travel time of a wave of the velocity model.
“We use the scattered wavefield, δu = u − u0, as an alternative solution to get the wavefield. The Lippmann Schwinger form of the acoustic wave equation is shown as [65]:” (Pg. 3). (Please refer to Equations 4-7).
“For 3D isotropic case, the analytical solution for constant velocity and a point source located at xs is given by:” (Pg. 3). (Please refer to Equations 4-7).
Song shows wavefield u0 is determined by the analytical solution for constant velocity, whose phase is a function of the travel time of the wave from the source to each point of the velocity model. i.e. an approximated wavefield determined based on a travel time of a wave of the velocity model.
It would have been obvious to one skilled in the art before the effective filing date of the
claimed invention to incorporate the teachings of Song’s travel time-based determination of a wave for wavefield estimation with Moseley-Siahkoohi’s machine learning system. The motivation for doing so would have been to use “… the underlying physical laws as loss functions to train the neural network (NN), and it has shown its effectiveness in solving the Helmholtz equation and generating Green’s functions, specifically for the scattered wavefield.”. (Abstract).
Regarding Claim 10, Moseley and Siahkoohi do not explicitly teach but Song teaches
The method of claim 9, comprising determining the approximated wavefield of the velocity model based on a straight line travel time of a wave of the velocity model.
“We use the scattered wavefield, δu = u − u0, as an alternative solution to get the wavefield. The Lippmann Schwinger form of the acoustic wave equation is shown as [65]:” (Section 2.2).
(Please refer to Equations 4 - 7).
“For 3D isotropic case, the analytical solution for constant velocity and a point source located at xs is given by:”. (Section 2.2). (Please refer to Equations 4 - 7).
Song’s analytical background solution is a straight-line travel time of a wave, on which the approximated wavefield u0 is determined.
Regarding Claim 11, Moseley and Siahkoohi do not explicitly teach but Song teaches
The method of claim 9, comprising determining the approximated wavefield of the velocity model based on a travel time of a diagonal or another chosen direction of a wave of the velocity model.
“For 3D isotropic case, the analytical solution for constant velocity and a point source located at xs is given by:”. (Section 2.2). (Please refer to Equations 4 - 7).
Song’s analytical solution is a function of the source to point vector x, xs and is therefore evaluated for any chosen direction of wave propagation, including diagonal directions, so the approximated wavefield is determined based on a travel time along a diagonal or another chosen direction.
Claim 12 is rejected under 35 U.S.C. 103 as being unpatentable over Moseley et al.
“Fast approximate simulation of seismic waves with deep learning” (2018) [herein “Moseley”], SIAHKOOHI et al. “The importance of transfer learning in seismic modeling and imaging” (2019) [herein “SIAHKOOHI”], and US 20170038490 A1 (2017) by HU et al [herein “HU”].
Regarding Claim 12, Moseley and Siahkoohi do not explicitly teach but Hu teaches
The method of claim 9, comprising determining the approximated wavefield of the velocity model based on a stretched wavefield travel time of a wave of the velocity model.
“This, and other aspects, can include one or more of the following features. The ray-equation method is implemented for the Kirchhoff integral method. In some instances, a multi-parameter Green's function can be computed based on raytracing in either depth or converted/stretched time domain. In some implementations, the ray-equation based Kirchhoff integral method for composite velocity model is used to image both free surface multiples and primary reflections of VSP data simultaneously. In some instances, ray parameters can be computed based on gradients of travel time fields computed based on a velocity model for the VSP data geometry... “. (005).
Hu’s green function computed by raytracing in the converted/stretched time domain, from travel time fields computed based on velocity model, determines the approximated wavefield based on a stretched wavefield travel time of a wave of the velocity model.
It would have been obvious to one skilled in the art before the effective filing date of the claimed invention to incorporate the teachings of HU’s stretched travel time of a green’s function calculation for wavefield estimation with Moseley-Siahkoohi’s machine learning system. The motivation for doing so would have been to “… analyze location and geology of reservoirs that contain hydrocarbons and can be used to design a drilling process for placing wellbores in the earth to maximize oil or gas production.”. (0036).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
US 2022/0413172 A1 teaches a method may include obtaining a P-wave velocity model and velocity ratio data regarding a geological region of interest. The method may further include generating, based on the P-wave velocity model and the velocity ratio data, an initial S-wave velocity model regarding the geological region of interest.
US 2019/0064389 A1 teaches a method including: storing, in a computer memory, geophysical data obtained from a survey of a subsurface region; and extracting, with a computer, a subsurface physical property model by processing the geophysical data with one or more convolutional neural networks, which are trained to relate the geophysical data to at least one subsurface physical property consistent with geological prior information.
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/N.E.M./Examiner, Art Unit 2189
/REHANA PERVEEN/Supervisory Patent Examiner, Art Unit 2189
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