# US 10,871,585 B2 · System for processing seismic data based upon linear optimization and related methods > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 10,871,585 B2 - Document ID: US10871585B2 - Status: Granted - Kind: B2 - Application number: 15/227,162 - Filed: 2016-08-03 - Published: 2018-02-08 - Granted: 2020-12-22 - Earliest claimed: 2016-08-03 - Assignees: Harris Corporation - Applicants: Harris Corporation - Inventors: Mark D. Rahmes; Jessica L. Ascough; George W. Lemieux; Raquel M. Cohen - Web page: https://patentgrep.com/patent/US10871585B2 ## CPC - G01V 1/306: {for determining physical properties of the subsurface, e.g. impedance, porosity or attenuation profiles} - G01V 1/303: {for determining velocity profiles or travel times} - G01V 2210/6222: Velocity; travel time - G01V 2210/6224: Density - G01V: GEOPHYSICS; GRAVITATIONAL MEASUREMENTS; DETECTING MASSES OR OBJECTS; TAGS (means for indicating the location of accidentally buried, e.g. snow-buried, persons A63B29/02) ## Abstract A system is for processing seismic data for a geologic formation generated by an array of acoustic transducers responsive to an acoustic source. The system may include a seismic data storage device, and a processor cooperating with the seismic data storage device to generate correlations of data from the array of acoustic transducers based upon a current estimate for at least one of density and velocity of the geologic formation, and perform a linear optimization based upon a plurality of different combinations of the correlations to determine a given subset of correlations having a highest objective function associated therewith. The processor may also update the current estimate for at least one of density and velocity of the geologic formation based upon the given subset of correlations. ## Claims ### Claim 1 A system for processing seismic data for a geologic formation generated by an array of acoustic transducers responsive to an acoustic source, the system comprising: a seismic data storage device; and a processor cooperating with the seismic data storage device to generate auto or cross-correlations of seismic P wave data from the array of acoustic transducers based upon a current estimate for velocity of the geologic formation and without measured and estimated seismic S wave data, perform a linear optimization based upon a plurality of different combinations of the correlations of seismic P wave data to determine a given subset of correlations having a highest objective function value associated therewith, update the current estimate for velocity of the geologic formation based upon the given subset of correlations, estimate density, composition, and location of various layers in the geologic formation including an oil payzone based upon the updated current estimate for velocity of the geologic formation so that a well is not drilled in a wrong location for the oil payzone. ### Claim 2 (depends on 1) The system of claim 1 wherein the processor performs the linear optimization based upon a genetic linear optimization. ### Claim 3 (depends on 1) The system of claim 1 wherein the processor further generates weights for the given subset of correlations, and updates the current estimate for velocity based upon the generated weights. ### Claim 4 (depends on 3) The system of claim 3 wherein the processor generates the weights using Q-learning. ### Claim 5 (depends on 1) The system of claim 1 wherein the processor further determines an error associated with the updated current estimate for velocity, and when the error is greater than a threshold, the processor repeats generating the correlations based upon the updated current estimate for velocity and performing the linear optimization. ### Claim 6 (depends on 1) The system of claim 1 wherein the plurality of different combinations includes at least one combination with less than all of the available correlations. ### Claim 7 (depends on 1) The system of claim 1 wherein the processor is configured to determine an anomaly based upon the updated current estimate for velocity of the geologic formation. ### Claim 8 (depends on 1) The system of claim 1 wherein the geologic formation comprises at least one of a land geologic formation and a subsea geologic formation. ### Claim 9 A system for processing seismic data for a geologic formation generated by an array of acoustic transducers responsive to an acoustic source, the system comprising: a seismic data storage device; and a processor cooperating with the seismic data storage device to generate auto or cross-correlations of seismic P wave data from the array of acoustic transducers based upon a current estimate for velocity of the geologic formation and without measured and estimated seismic S wave data, perform a genetic linear optimization based upon a plurality of different combinations of the correlations of seismic P wave data to determine a given subset of correlations having a highest objective function value associated therewith, generate weights for the given subset of correlations, update the current estimate for velocity of the geologic formation based upon the generated weights, store the updated current estimate for velocity of the geologic formation in the seismic data storage device, and estimate density, composition, and location of various layers in the geologic formation including an oil payzone based upon the updated current estimate for velocity of the geologic formation so that a well is not drilled in a wrong location for the oil payzone. ### Claim 10 (depends on 9) The system of claim 9 wherein the processor generates the weights using Q-learning. ### Claim 11 (depends on 9) The system of claim 9 wherein the processor further determines an error associated with the updated current estimate for velocity, and when the error is greater than a threshold, the processor repeats generating the correlations based upon the updated current estimate for velocity and performing the linear optimization. ### Claim 12 (depends on 9) The system of claim 9 wherein the plurality of different combinations includes at least one combination with less than all of the available correlations. ### Claim 13 (depends on 9) The system of claim 9 wherein the processor is configured to determine an anomaly based upon the updated current estimate for velocity of the geologic formation. ### Claim 14 (depends on 9) The system of claim 9 wherein the geologic formation comprises at least one of a land geologic formation and a subsea geologic formation. ### Claim 15 A method for processing seismic data for a geologic formation generated by an array of acoustic transducers responsive to an acoustic source, the method comprising: using a processor and an associated seismic model data storage device to generate auto or cross-correlations of seismic P wave data from the array of acoustic transducers based upon a current estimate for velocity of the geologic formation and without measured and estimated seismic S wave data, perform a linear optimization based upon a plurality of different combinations of the correlations at seismic P wave data to determine a given subset of correlations having a highest objective function associated value therewith, update the current estimate for velocity of the geologic formation based upon the given subset of correlations, and estimate density, composition, and location of various layers in the geologic formation including an oil payzone based upon the updated current estimate for velocity of the geologic formation so that a well is not drilled in a wrong location for the oil payzone. ### Claim 16 (depends on 15) The method of claim 15 wherein the linear optimization comprises a genetic linear optimization. ### Claim 17 (depends on 15) The method of claim 15 further comprising using the processor to generate weights for the given subset of correlations, and update the current estimate for at least one of density and velocity based upon the generated weights. ### Claim 18 (depends on 17) The method of claim 17 wherein the weights are generated based upon Q-learning. ### Claim 19 (depends on 15) The method of claim 15 further comprising using the processor to determine an error associated with the updated current estimate for at least one of density and velocity, and when the error is greater than a threshold, repeat generating the correlations based upon the updated current estimate for at least one of density and velocity and performing the linear optimization. ### Claim 20 (depends on 15) The method of claim 15 wherein the plurality of different combinations includes at least one combination with less than all of the available correlations.