Synthetic subterranean source
This disclosure describes a system and method for generating images and location data of a subsurface object using existing infrastructure as a source. Many infrastructure objects (e.g., pipes, cables, conduits, wells, foundation structures) are constructed of rigid materials and have a known shape and location. Additionally these infrastructure objects can have exposed portions that are above or near the surface and readily accessible. A signal generator can be affixed to the exposed portion of the infrastructure object, which induces acoustic energy, or vibrations in the object. The object with affixed signal generator can then be used as a source in performing a subsurface imaging of subsurface objects, which are not exposed.
1. A method for imaging at least one subsurface object, comprising:
inducing an acoustic energy in a first object that extends into one or more subterranean formations from at, or near a terranean surface, the first object comprising an infrastructure object, the acoustic energy propagating from the first object to a second object, the second object comprising a construction infrastructure object and enclosed within the one or more subterranean formations;
recording, using an array of transducers, reflected acoustic energy that propagates from the second object to the array of transducers;
providing the recorded, reflected acoustic energy as an input to a machine learning algorithm to generate image data associated with the second object; and
generating, with the machine learning algorithm, a subsurface model that comprises the generated image data associated with the second object for presentation in a graphical user interface.
2. The method of claim 1 , wherein the first object comprises an underground pipe of a previously identified length and diameter.
3. The method of claim 1 , wherein the array of transducers comprises at least one accelerometer.
4. The method of claim 1 , wherein inducing acoustic energy in the first object comprises inducing vibrations of a predetermined frequency range in the first object.
5. The method of claim 1 , wherein additional seismic data is provided as an input to the machine learning algorithm.
6. The method of claim 1 , wherein the subsurface model comprises one or more faults or anomalies associated with the second object.
7. The method of claim 1 , wherein the second object is formed of at least one of plastic, wood, or ceramic.
8. The method of claim 1 , further comprising determining a condition of the second object based on the subsurface model.
9. The method of claim 8 , further comprising:
determining that the condition of the second object is poor; and
based on the determination, identifying an excavation location associated with the second object.
10. The method of claim 1 , wherein the acoustic energy comprises acoustic wave energy.
11. A non-transitory, computer readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
inducing an acoustic energy in a first object that extends into one or more subterranean formations from at, or near a terranean surface, the first object comprising an infrastructure object, the acoustic energy propagating from the first object to a second object, the second object comprising a construction infrastructure object and enclosed within the one or more subterranean formations;
recording, using an array of transducers, reflected acoustic energy that propagates from the second object to the array of transducers;
providing the recorded, reflected acoustic energy as an input to a machine learning algorithm to generate image data associated with the second object; and
generating, with the machine learning algorithm, a subsurface model that comprises the generated image data associated with the second object for presentation in a graphical user interface.
12. The medium of claim 11 , wherein the first object comprises an underground pipe of a previously identified length and diameter.
13. The medium of claim 11 , wherein the array of transducers comprises at least one accelerometer.
14. The medium of claim 11 , wherein inducing acoustic energy in the first object comprises inducing vibrations of a predetermined frequency range in the first object.
15. The medium of claim 11 , wherein additional seismic data is provided as an input to the machine learning algorithm.
16. A system for generating a global subsurface model, the system comprising:
one or more processors;
one or more tangible, non-transitory media operably connectable to the one or processors and storing instructions that, when executed, cause the one or more processors to perform operations comprising:
inducing an acoustic energy in a first object that extends into one or more subterranean formations from at, or near a terranean surface, the first object comprising a construction infrastructure object, the acoustic energy propagating from the first object to a second object, the second object comprising a construction infrastructure object and enclosed within the one or more subterranean formations;
recording, using an array of transducers, reflected acoustic energy that propagates from the second object to the array of transducers;
providing the recorded, reflected acoustic energy as an input to a machine learning algorithm to generate image data associated with the second object; and
generating, with the machine learning algorithm, a subsurface model that comprises the generated image data associated with the second object for presentation in a graphical user interface.
17. The system of claim 16 , wherein the first object comprises an underground pipe of a previously identified length and diameter.
18. The system of claim 16 , wherein the array of transducers comprises at least one accelerometer.
19. The system of claim 16 , wherein inducing acoustic energy in the first object comprises inducing vibrations of a predetermined frequency range in the first object.
20. A method for imaging at least one subsurface object, comprising:
inducing an acoustic energy in a first object that extends into one or more subterranean formations from at, or near a terranean surface, the first object comprising an infrastructure object, the acoustic energy propagating from the first object to a second object, the second object enclosed within the one or more subterranean formations;
recording, using an array of transducers, reflected acoustic energy that propagates from the second object to the array of transducers;
providing the recorded, reflected acoustic energy as an input to a machine learning algorithm to generate image data associated with the second object;
generating, with the machine learning algorithm, a subsurface model that comprises the generated image data associated with the second object for presentation in a graphical user interface;
determining that a condition of the second object is poor based on the subsurface model; and
based on the determination, identifying an excavation location associated with the second object.
21. The method of claim 20 , wherein the first object comprises an underground pipe of a previously identified length and diameter.
22. The method of claim 20 , wherein the array of transducers comprises at least one accelerometer.
23. The method of claim 20 , wherein inducing acoustic energy in the first object comprises inducing vibrations of a predetermined frequency range in the first object.
24. The method of claim 20 , wherein additional seismic data is provided as an input to the machine learning algorithm.
25. The method of claim 20 , wherein the subsurface model comprises one or more faults or anomalies associated with the second object.
26. The method of claim 20 , wherein the second object is formed of at least one of plastic, wood, or ceramic.
27. A non-transitory, computer readable storage medium storing instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:
inducing an acoustic energy in a first object that extends into one or more subterranean formations from at, or near a terranean surface, the first object comprising an infrastructure object, the acoustic energy propagating from the first object to a second object, the second object enclosed within the one or more subterranean formations;
recording, using an array of transducers, reflected acoustic energy that propagates from the second object to the array of transducers;
providing the recorded, reflected acoustic energy as an input to a machine learning algorithm to generate image data associated with the second object;
generating, with the machine learning algorithm, a subsurface model that comprises the generated image data associated with the second object for presentation in a graphical user interface;
determining that a condition of the second object is poor based on the subsurface model; and
based on the determination, identifying an excavation location associated with the second object.
28. The medium of claim 27 , wherein the first object comprises an underground pipe of a previously identified length and diameter.
29. The medium of claim 27 , wherein the array of transducers comprises at least one accelerometer.
30. The medium of claim 27 , wherein inducing acoustic energy in the first object comprises inducing vibrations of a predetermined frequency range in the first object.
31. The medium of claim 27 , wherein additional seismic data is provided as an input to the machine learning algorithm.
32. A system for generating a global subsurface model, the system comprising:
one or more processors;
one or more tangible, non-transitory media operably connectable to the one or more processor and storing instructions that, when executed, cause the one or more processors to perform operations comprising:
inducing an acoustic energy in a first object that extends into one or more subterranean formations from at, or near a terranean surface, the first object comprising a construction infrastructure object, the acoustic energy propagating from the first object to a second object, the second object enclosed within the one or more subterranean formations;
recording, using an array of transducers, reflected acoustic energy that propagates from the second object to the array of transducers;
providing the recorded, reflected acoustic energy as an input to a machine learning algorithm to generate image data associated with the second object;
generating, with the machine learning algorithm, a subsurface model that comprises the generated image data associated with the second object for presentation in a graphical user interface;
determining that a condition of the second object is poor based on the subsurface model; and
based on the determination, identifying an excavation location associated with the second object.
33. The system of claim 32 , wherein the first object comprises an underground pipe of a previously identified length and diameter.
34. The system of claim 32 , wherein the array of transducers comprises at least one accelerometer.
35. The system of claim 32 , wherein inducing acoustic energy in the first object comprises inducing vibrations of a predetermined frequency range in the first object.