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Microseismic technology has been widely used in geological hazard monitoring. However, the effective identification and classification of microseismic events in mines have always been a challenge in hazard monitoring. In this paper, a new method combining the empirical mode decomposition (EMD) algorithm and artificial neural network is proposed for the classification and identification of microseismic waveforms. This is the first study to explore such a model in the microseismic monitoring of coal mines. The data for this study were collected from a coal mine in northern China. A total of 2768 microseismic events and 2435 non-microseismic events were manually selected from a large amount of data. Firstly, the microseismic waveform reconstruction was performed using the 4th to 8th Intrinsic Mode Function (IMF) obtained by the EMD algorithm. Secondly, the peak factor, clearance factor, impulse factor, kurtosis, skewness, and waveform factor of each reconstructed microseismic waveform were used as features. Subsequently, the convolutional neural network (CNN) was used to classify microseismic events. Among them, data from 3600 microseismic events were used as the training set, and data from 1603 microseismic events were used as the test set. Finally, the original waveform data were used as input and compared with the classification results processed with the EMD algorithm. The classification methods of the BP neural network and Radial Basis Function (RBF) neural network were used for verification. The results of the processed features showed a significant increase in accuracy. The classification accuracy of CNN, BP neural network, and RBF neural network are 97.37%, 91.99%, and 94.23%, respectively. The results show that the utilization of the feature processing technique and CNN algorithm in this study demonstrates superior efficacy in microseismic event classification, which can be used in practice. • The EMD algorithm and ANN was constructed to classify microseismic waveforms. • Six features of the reconstructed waveform were selected as the classification basis. • The EMD algorithm was used to make the six waveform characteristics more significant. [ABSTRACT FROM AUTHOR]

The Laojunshan ore cluster region is situated at the north Song Chay dome, which is the junction of the Yangtze, the Cathaysia, and the Indo-China Blocks. The north Song Chay dome characterized by the Laojunshan metamorphic core complex is an important ore-forming region for W-Sn-Pb-Zn-In elements related to the Late Cretaceous granitic magmatism. In this paper, the Bi-dimensional empirical mode decomposition (BEMD) is applied to separate gravity components from the original gravity data, combined with the known geological and mineralized information, to explore the relationships between the gravity components and various ore-controlling factors at different depths. Four intrinsic mode functions (IMFs) and a residue are obtained. They may imply the spatial distribution of the geological bodies with featured gravity components at different depths. The results are shown as follows: (1) The low-pass filtering gravity component (BIMF4) implies the deeply buried geological units in the study region. The Cretaceous Laojunshan granitic complex and the Nanwenhe gneissic granite display negative gravity components and weak positive gravity components, respectively. The former is closely related to Sn-Zn-In mineralization, the latter is closely related to W polymetallic deposits. Therefore, the negative gravity components showing the Laojunshan granitic complex are the prospecting targets for deeply buried Sn-Zn-In deposits. The weak positive gravity components displaying the Nanwenhe gneissic granite are the prospecting targets for deeply buried W polymetallic deposits. (2) The band-pass filtering gravity component (BIMF3) implies the geological units at middle depth. The skarn-type Sn-W polymetallic deposits are observed in the transition zones from the positive to the negative gravity components, which coincide with the contact metasomatic zones between the Cretaceous granite and the Cambrian-Devonian carbonate rocks. Some small quartz vein W deposits occur within the Cretaceous Laojunshan granite with negative gravity components, and some layered W deposits occur in the Silurian gneissic granite with a weak positive gravity component. (3) High-pass filtering gravity component (BIMF2) implies geological units at the shallow crust, which shows that most of the deposits (occurrences) are located within the positive gravity component zones. These positive gravity component zones should be ore-finding targets for further prospecting for the undiscovered Sn and W polymetallic deposits. (4) The gravity components separated by BEMD coincide with the spatial distribution of the granites associated with various types of mineralization and alteration zones at different depths, which will give insights for further prospecting for concealed ore bodies in this study region. [Display omitted] • The mineralized characteristics of both the Sn-Zn-In and the W polymetallic deposits are recognized. • The gravity components implying ore-controlling factors at different depths are separated using BEMD. • The ore-finding targets of both the Sn-Zn-In and the W polymetallic deposits are identified using the gravity components. [ABSTRACT FROM AUTHOR]

• A noise evaluation method based on an envelope algorithm was proposed in this paper. • The method estimates the upper bound of the noise rather than statistical average. • Not only main frequencies, but also the harmonics can be evaluated by this method. • This noise evaluation method can be applied to any periodic signal. Controlled-source electromagnetic (CSEM) is affected by noise during exploration. Moreover, different frequencies in the data are affected differently by noise. However, past evaluation methods, such as the relative root mean square error (RRMSE) method, are not efficient or accurate, especially when there are many frequencies of interest. Therefore, a noise evaluation method based on a mixed spectrum envelope algorithm containing wavelet decomposition, Hilbert transform and peak envelope was proposed for the first time. This method estimates the upper bound of the noise effect at the frequencies of interest by the amplitude of the adjacent frequencies rather than by estimating its exact value or statistical average. Based on this method, the maximum possible value of noise effect can be obtained, which is then divided by its raw spectrum to obtain a newly defined noise evaluation parameter, "noise ratio". Using this new parameter, the degree of the noise effect at the frequencies of interest can be evaluated more accurately. Furthermore, using an appropriate threshold, a large number of candidate frequencies including harmonics can be filtered out. The performance of the method is tested on synthetic signals and real data from Shandong Province, China. The test results show that the evaluation method based on the envelope algorithm is more stable and effective than the conventional RMSE method. Based on the features of the method, it can be applied to all periodic signals in other stationary scenarios. [ABSTRACT FROM AUTHOR]