Critical signature sound when rocks crack

Date:
March 30, 2022
Source:
Texas A&M University
Summary:
Finding the specific sound a rock makes when it cracks and breaks
seems impossible when surrounded by other subsurface noises. But
researchers have now discovered a way to hear and validate that
sound.



FULL STORY ========================================================================== Finding the specific sound a rock makes when it cracks and breaks seems impossible when surrounded by other subsurface noises. But Texas A&M
University researcher Dr. Siddharth Misra, the Ted H. Smith, Jr. '75
and Max R. Vordenbaum '73 DVG Associate Professor in the Harold Vance Department of Petroleum Engineering, discovered a way to hear and validate
that sound in a project funded by the Basic Energy Sciences program of
the Department of Energy (DOE).


==========================================================================
"The DOE calls sounds of specific events the 'signs of signature,'"
said Misra.

"In this case, the signature identified the break or mechanical
discontinuity of a rock in the earth's subsurface, especially as the
breaks continued to grow or propagate into fractures." Misra and his
doctoral candidate Rui Liu published their preliminary findings in the
May 2022 issue of the Systems and Signals Processingjournal.

Why does Basic Energy Sciences want this signature identified? Sounds are
often important clues to environmental and security changes. Threatening noises, such as underground explosions, are hard to mistake. But the
small sounds of a high- rise building foundation cracking and failing
are just as threatening. So, the fundamental sound of rock undergoing mechanical failure is a basic and critical clue worth finding.

"This research goes to the heart of identifying something specific
within a massive data set," said Misra. "An example is credit card transactions. You cannot monitor the whole data set for fraud because the transactions are so varied. You must find some indicative sign, such as
a credit card charge in one city to book an airline flight immediately
after that same card pays for an Uber in another city. That discrepancy
is a signature." Previous attempts to pinpoint underground mechanical
failures never brought reliable success, but Misra found that an unusual combination of three research methods -- supervised machine learning,
causal discovery and rapid simulations -- could tackle the problem.



==========================================================================
The supervised machine learning began with lab experiments in which
a multipoint sensor system was placed on the surface of a rock and
recorded sound wave-transmission measurements through the material as it cracked and finally failed. Computers monitored the information and were
taught which data signatures meant initial, intermediate and end-stage
damage. One tell-tale signature that repeatedly traveled up and down
across the zero point between positive and negative measurements caught
the computer's attention, once it knew what to look for.

"I can only see the color or shape of something with my eyes," said
Liu. "But machine learning can pick out so many more characteristics
from the data. It picked out those positive and negative turnings, and
we used that sign to get further results." Misra and Liu searched for
the causation of each of these turnings to confirm their source. They
couldn't rely on the computer to complete this step because machine
learning is not the best interpreter.

"During the heat of the summer, ice cream sales increase and drowning
deaths increase," said Misra. "If you use machine learning or simple statistical methods, they might say people are drowning because people
eat ice cream.

That's a correlation. Though they are both related to the summer heat,
they are not connected to each other. They each have a different cause. We
are looking for causation for these turnings because that's when they
become meaningful." Misra and Liu created a workflow that could generate scenarios of various fracture propagations and measured waveforms. Then,
they increased the workflow's speed to rapidly run up to 20,000 different simulations of possibilities for each event. This allowed the researchers
to discover the best cause-and-effect explanations.

"We didn't control how the discontinuity propagated, so there's a lot
of randomness," said Misra. "Yet, as the fractures grew, despite the differences in direction or length, results showed a similar increase in amplifications or positive and negative turnings across the zero point in
the waveforms. So, this is a definite signature of rock failure, which,
to the best of my knowledge, was not known prior to this research."
While the signature discovery is exciting, the project still has several
months to go. Misra intends to explore the limits of the data-driven simulations and causal discovery approach. He will also test other
methods to see if similar or different results occur.

"What we need to do as scientists, as engineers, is to find causality,
find causation," said Misra. "We tried a lot of different techniques to discover this signature and its causal relationships. A lot of approaches didn't work, but one did. Now we need to find the limits of what it
can do."

========================================================================== Story Source: Materials provided by Texas_A&M_University. Original
written by Nancy Luedke.

Note: Content may be edited for style and length.


========================================================================== Journal Reference:
1. Rui Liu, Siddharth Misra. Monitoring the propagation of mechanical
discontinuity using data-driven causal discovery and supervised
learning.

Mechanical Systems and Signal Processing, 2022; 170: 108791 DOI:
10.1016/ j.ymssp.2021.108791 ==========================================================================

Link to news story: https://www.sciencedaily.com/releases/2022/03/220330164536.htm

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