Methods and apparatus for data collection

Patent metadata

Field	Value
Publication number	US-9268777-B2
Application number	US-201213557633-A
Country	US
Kind code	B2
Filing date	Jul 25, 2012
Priority date	Jun 25, 2012
Publication date	Feb 23, 2016
Grant date	Feb 23, 2016

Abstract

Systems and techniques for directing data collection. Upon an initial data collection, the uncertainty of all or of a portion or portions of the collected data is evaluated. The collected data may be associated with a region, with portions of the collected data associated with subregions. Further data collection, including changes to or refinement of collection techniques, is undertaken based on evaluations of the uncertainty. Further data collection may be undertaken only for portions of the data for which uncertainty exceeds a threshold. Uncertainty evaluation may be performed at least in part using a model. The model may be an initial hypothesis model, and the model may be optimized as further data is collected, and the optimized model may be used to guide further data collection techniques, with iterations of data collection and model optimization being carried out concurrently.

First claim

We claim:

1. An apparatus comprising:
at least one processor;
memory storing computer program code;
wherein the memory storing the computer program code is configured to, with the at least one processor, configure the apparatus to at least:
create a model using a set of collected data evaluate uncertainty associated with at least one subset of the set of collected data;
if the at least one subset of the set of collected data is characterized by uncertainty exceeding a specified threshold, directing additional data collection to refine the at least one subset of the set of data;
evaluate the model to determine if the model requires refinement; and
iteratively refine the model using the additional data and directing additional data collection and refining the at least one subset of the set of data using the refined model.

2. The apparatus of claim 1 , wherein the set of collected data comprises a result of an initial collection and is characterized by a relatively low level of detail.

3. The apparatus of claim 1 , wherein evaluating uncertainty comprises performing quality and error distribution analysis for at least one subset of the set of collected data.

4. The apparatus of claim 3 , wherein the set of collected data comprises data collected over a larger region and each of the subsets of collected data comprises data collected over a subregion of the larger region.

5. The apparatus of claim 1 , wherein directing additional data collection comprises directing specified mechanisms of data collection.

6. The apparatus of claim 5 , wherein at least one of the specified mechanisms of data collection comprises a higher density of data collection relative to the initial data collection.

7. The apparatus of claim 1 , wherein evaluating uncertainty comprises:
generating processed data using a model to process the collected data;
computing an error for the processed data; and
performing sensitivity analysis and error minimization for the processed data.

8. The apparatus of claim 7 , wherein evaluating uncertainty further comprises iteratively optimizing the model as further data is collected.

9. The apparatus of claim 1 , wherein evaluating uncertainty comprises comparing an error value against a threshold, and wherein a subset of data is designated as uncertain if its error value exceeds a threshold.

10. The apparatus of claim 9 , wherein the error value is an average error value computed over a subset of data defined by a moving window.

11. An apparatus comprising:
at least one processor:
memory storing computer program code:
wherein the memory storing the computer program code is configured to, with the at least one processor, cause the apparatus to at least:
perform a modeling process using stored data;
perform at least one of model quality analysis, error distribution analysis, and sensitivity analysis;
determine, based at least in part on the at least one of the model quality analysis, error distribution analysis, and sensitivity analysis, if the model requires refinement; and
iteratively refine the model, wherein refining the model comprises collecting and storing additional data, and wherein collection and storage of additional data is also iteratively performed, with collection and storage of additional data being refined as the model is refined, with additional and refined data being used to refine the model and with the refined model being used to direct collection of additional data and to refine the data.

12. The apparatus of claim 11 , wherein refining the model continues until a specified level goal is met.

13. The method of claim 11 , wherein refining the model continues so long as improvements can be achieved at a desired level of efficiency.

14. The method of claim 11 , wherein the modeling process is a full waveform immersion process.

15. A non-transitory computer readable medium storing a program of instructions, execution of which by a processor configures an apparatus to at least:
create a model using a set of collected data
evaluate uncertainty associated with at least one subset of the set of collected data;
if the at least one subset of the set of collected data is characterized by uncertainty exceeding a specified threshold, directing additional data collection to refine the at least one subset of the set of data;
evaluate the model to determine if the model requires refinement; and
iteratively refine the model using the additional data and directing additional data collection and refining the at least one subset of the set of data using the refined model.

16. The non-transitory computer readable medium of claim 15 , wherein the set of collected data comprises a result of an initial collection and is characterized by a relatively low level of detail.

17. The non-transitory computer readable medium of claim 15 , wherein evaluating uncertainty comprises performing quality and error distribution analysis for at least one subset of the set of collected data.

18. The non-transitory computer readable medium of claim 17 , wherein the set of collected data comprises data collected over a larger region and each of the subsets of collected data comprises data collected over a subregion of the larger region.

19. The non-transitory computer readable medium of claim 15 , wherein directing additional data collection comprises directing specified mechanisms of data collection.

20. The non-transitory computer readable medium of claim 18 , wherein at least one of the specified mechanisms of data collection comprises a higher density of data collection relative to the initial data collection.

Assignees

• Lu Ligang
• Perrone Michael Peter
• IBM

Inventors

• Lu Ligang
• Perrone Michael Peter

Classifications

• G01V2200/14

  Quality control · CPC title

• G06F19/3437

  Physics · mapped topic

• G06F17/30595

  Physics · mapped topic

• G06F17/30286

  Physics · mapped topic

• G01V1/3808

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