Investigating the acoustic signs of different rock types based on the values of acoustic signal RMS

Recent years have seen a vast increase in the use of acoustic waves in real-time and non-destructive detection and monitoring applications in various industries such as mining. Acoustic signal processing methods can provide accurate and reliable estimates of the condition of a process or material in a highly cost-effective way without interrupting the ongoing operations. This paper investigated whether the class of a rock and its strength properties can be estimated based solely on acoustic signals captured during drilling operations. For this purpose, uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), the Schmidt rebound number (SRN), and longitudinal wave velocity (Pw) of a series of samples of sedimentary, metamorphic and igneous rocks were measured in a rock mechanics laboratory. The samples were then subjected to a drilling test and the acoustic signal propagating in the drilling medium was recorded by an acoustic sensor. After obtaining the time spectrum of the captured signals, their RMS values were calculated and compared with the mechanical properties of the corresponding rock samples. For the rocks tested in this study, the obtained RMS values were in the range of 800 to 1,600, and generally increased with an increase in strength and hardness. The RMS values obtained for each class of rocks had their own specific range. For sedimentary rocks, this range was 800 to 1000, for metamorphic rocks, it was 1000 to 1200, and for igneous rocks, it was 1400 to 1600. Given the differences in the range of RMS values obtained from the acoustic signals of drilling, these values can be used in the estimation of rock class and strength properties. These results show that there is significant potential for the future use of this approach in the indus try for field identification and classification of rocks, especially in deep drilling operations or when there is little informa tion about the characteristics of the rock being drilled.


Introduction
Acoustic emissions are the pressure waves generated in a material by the energy released due to deformation, fracture, or other types of failure (Khoshouei and Bagherpour, 2019). As these pressure waves propagate through the material, they can be captured by sensors and analysed to gain insight into the properties of the material (Hopwood and McGogney, 1987). This can be done by various types of sensors, such as ultrasonic sensors. Acoustic emission is known as a reliable means for detecting, predicting, and monitoring the behaviour of a physical process or phenomenon. This method has gained wide acceptance among industrial and scientific communities as a useful non-destructive testing and analysis approach (Yari and Bagherpour, 2018, a). Acoustic waves may appear and propagate in one, two, or three dimensions. The important characteristics of these waves include amplitude, wavelength, frequency, phase, wave energy, sound intensity, and sound pressure. When carefully measured and analysed, each of these characteristics can provide valuable information about the wave source and the propagation medium. As illustrated in Figure 1, the application of acoustic emission techniques in research can be classified into three categories: (i) predicting the state or properties of materials, (ii) monitoring the behaviour of components, processes, or materials, and (iii) detection of phenomena.
In mining and excavation projects, the prediction of factors such as drill bit wear rate, the rate of penetration (ROP), blasting performance (based on rock type and characteristics) and the feed rate of the mineral processing plants is of immense importance for decision making regarding the equipment to use in the operations (Yari and Bagherpour, 2018, b). This highlights the importance of having a method to make such predictions with sufficient precision, reliability, speed, and cost-effectiveness, as the project progresses. Over the years, researchers have developed various methods and criteria for predicting the physical and mechanical properties of rocks, each with their own advantages and drawbacks. In recent years, several industries have shown increasing interest in using low-cost and non-destructive variants of the aforementioned methods, including acoustic emission. The use of acoustic emission in the prediction and analysis of the physical and mechanical properties of rocks has been the subject of many studies in recent years. Vardhan  Acoustic emission can also be used in monitoring applications. Often, it is much more cost-effective to monitor the behaviour of equipment while they are being used than to let them wear out or break and then stop the operation for repair or replacement. In this area, Leššo et al. attempted to use vibro-acoustic signals to create an integrated information source for monitoring purposes Acoustic emission can also be used as a non-destructive means to quickly estimate the right operating mode, speed, and tools for a given operation and also the amount of energy to be consumed in that operation. Many studies have used acoustic emission for this purpose. For example, Williams and Hagan investigated the relationship between acoustic signals generated in rock and changes in rock cutting conditions (Williams and Hagan, 2006). Xing Zhu et al. observed the low-frequency acoustic signals generated by phenomena such as rockfall, rockslide, thunderstorm, wind turbulence, and erosion (Zhu et al., 2016).
Recent years have seen an increase in the use of acoustic emission techniques in various fields of engineering including geotechnics, drilling, mining engineering, and earth sciences. These techniques offer several advantages over the alternatives. Most importantly, assessment with this method can be done swiftly, easily and remotely without stopping the operations. Further, the tests of this method are perfectly non-destructive and can be completed and repeated very rapidly. The development and use of novel methods of acoustic signal processing can greatly contribute to the progress of geoscience and geotechnical experiments and operations in areas such as exploration and prospecting, drilling engineering, mineral processing, and rock mechanics tests.

Acoustic emission
One can extract a wide range of parameters from the acoustic signal spectrum. Acoustic signals can be analysed in two domains: time and frequency. The important parameters that can be extracted in the time domain include peak, mean spectrum, and RMS (Root Mean Square) of the spectrum. The most important parameter that can be extracted in the frequency domain is the base frequency. In this paper, evaluations were carried out on the time spectrum of signals and RMS.
RMS or root mean square represents the magnitude of the variations of the sound source. In fact, RMS is the ratio that determines the maximum possible quarter in frequency waves with possible variations. This parameter can be a good measure for examining changes in acoustic signals. RMS of an alternating wave can be calculated by Equation 1 (Yamaguchi et al., 2000). (1)

Where:
Xrms -is the RMS of signal, N -is the number of samples, Xi -is the signal amplitude at any given moment. Figure 2 shows the RMS and peak values of an acoustic signal.
drilling and harvesting of acoustic signals generated during drilling operations. The steps of the drilling test are such that, after preparing the samples in the shape and dimensions mentioned, the specimen is fixed by a special clamp. After this stage, the equipment for the recording of the acoustic and vibration signals generated by the drilling operation will be installed and ready, and after the drilling conditions such as the determination of the diameter of the drill bit, the trust force or Weights On the Bit (WOB), the drilling fluid, the speed of the drill bit which are shown in Table 1, the drilling tests begin at each rock sample and begin at the same time as the drilling starts, taking and recording acoustic and vibration signals. Signals are prepared after the completion of the drilling process for subsequent processing. The drilling machine with details, drill bit and rock samples are shown in Figure 3.

Laboratory scale Drilling Tests
Rock samples with different properties are used in the cubic form with dimensions 9×9×9 cm for drilling tests (Yari et al., 2019). A drill bit was used to perform the

Laboratory tests
Before conducting drilling operations on the rocks, their mechanical properties including uniaxial compressive strength (UCS), Brazilian tensile strength (BTS), Schmidt rebound number, and longitudinal wave velocity (Pw) were determined. The standards used for these measurements are listed in Table 2. Table 3 shows the type and properties of the rock samples.

Time Domain of Signals and RMS Values of Signals
During the drilling operation, the acoustic signals (sound pressure waves) propagating in the medium were recorded by an acoustic wave sensor. Figures 4 to 6 il-lustrate the spectrum of acoustic signals generated during the drilling of each rock sample.
The RMS value of each signal was calculated by Equation 1. The RMS values obtained for the measured acoustic signals are presented in Table 4.
As shown in Figure 7, the RMS values obtained for sedimentary and metamorphic rock samples are quite  similar. This can be attributed to the similarity of these two types of rocks in terms of textural properties. In comparison, igneous rocks have higher RMS values. According to the signals recorded in this study, sedimentary rocks have RMS values of 800 to 1000, metamorphic rocks have RMS values of 1000 to 1200, and igneous rocks have RMS values of 1400 to 1600. Naturally, to find a precise behavioural pattern, one has to conduct repeated tests on a large number of different rock samples, but the purpose of this article was only to examine the possibility of this approach.

Investigation of the relationship between the RMS signal value and the mechanical properties of rocks
This section examines the relationship between the mechanical properties of the tested rock samples and the RMS value obtained by the time spectrum analysis of acoustic signals generated during drilling. The results of this investigation for the relationship between the me- The above diagrams suggest that depending on their mechanical properties, rock samples generate unique acoustic signals during drilling. The RMS value obtained for the signal of each rock seems to be specific Rudarsko-geološko-naftni zbornik i autori (The Mining-Geology-Petroleum Engineering Bulletin and the authors) ©, 2020, pp. 29-38, DOI: 10.17794/rgn.2020.3.3 Figure 11: The relationship between Pw and RMS value enough to be used in the identification of rock type in drilling operations. Considering the UCS, BTS and SRN values illustrated in Figures 8 to 11, there appears to be a significant difference between the three classes of rocks in terms of RMS value based on the 9 tested rock samples. With the physical and mechanical properties of the rocks and the amount of the signals RMS from the sensor embedded on the drilling machine as the input data of the method, the rocks can be classified. One of the limitations of this method is having an accurate acoustic sensor and placing it in a suitable place where the acoustic signals generated from drilling can be accurately measured. These results are promising for the future use of this approach in the industry for the field identification and classification of rock types. The importance of this approach lies in its ability to estimate the strength and class of a rock in a non-destructive method only based on the RMS values obtained from unavoidable drilling operations. This method can be used for realtime determination of operating parameters such as drill bit ROP and drilling fluid flow rate without needing to stop the operation and sample the rock.

Conclusion
Recent years have seen a dramatic increase in the use of acoustic waves as a means for the real-time and nondestructive detection and monitoring of physical phenomena in various industries. Indeed, there has always been a demand for detection and monitoring methods with the ability to perform their duty accurately, reliably, and cost-effectively without interrupting the monitored operation. This paper examined the possibility of estimating the class and strength properties of a rock being drilled based on the acoustic signals recorded during the drilling operation. For this purpose, 9 samples of sedimentary, metamorphic and igneous rocks were collected and tested in a rock mechanics laboratory to determine their mechanical properties including uniaxial compres-sive strength (UCS), Brazilian tensile strength (BTS), the Schmidt rebound number (SRN) and longitudinal wave velocity (Pw). After these measurements, samples were prepared for drilling tests. During the drilling tests, an acoustic wave sensor was used to record the acoustic signals propagating in the medium or, in other words, the sound pressure level of the entire drilling process. After obtaining the time spectrum of the signal collected from each rock, the RMS value of the signal was calculated and compared with the properties of that rock. The analysis of the acoustic signals captured during the drilling tests showed that the RMS values of these signals fall in the range of 8 to 16. In general, stronger and harder rocks had higher RMS values, indicating that drilling sound pressure propagates better in harder rocks. Based on the rock samples tested in this paper, in sedimentary rocks, RMS was in the range of 800 to 1000, in metamorphic rocks, it was in the range of 1000 to 1200, and in igneous rocks, which had a higher strength and hardness, it was in the range of 1400 to 1600. Based on the 9 rock samples tested in 3 main groups, the results showed that the RMS values of acoustic signals recorded in the drilling process are specific enough to be used in the identification of the class of the drilled rock and the estimation of its strength properties. These promising results demonstrate that there is significant potential for the use of this method in the industry under field conditions but, in order to increase the reliability of this method and provide a comprehensive model, in the future more rock samples with extensive physical and mechanical properties will be tested and evaluated. In projects involving deep drilling or where there is little information about the characteristics of the area, this method can be used to identify the rock and estimate its properties in real-time based on the acoustic signals recorded by a sensor installed on the rock surface. One of the most important advantages of this method is its simplicity, as it does not need heavy computation nor the addition of expensive and sophisticated equipment and sensors to the operation. The purpose of this study was to introduce the approach described above and determine whether RMS values of acoustic signals can be used for rock identification purposes. However, to find a definitive model for determining the type and class of rocks based on RMS values, it is necessary to perform extensive testing on a large number of rock samples.