The Pandian deposit is a newly discovered contact metasomatic skarn magnetite deposit found in the Cainozoic super-thick overburden on the northwest margin of Luxi Uplift (LXU). Presently, the horizontal scale of the deposit delineated by the potential field (gravity and magnetic method) has shown giant potential for ore deposits, and mapping the ore-controlling structures in the vertical scale becomes a primary task for metallogenic prediction. In our study, the wide-field electromagnetic method (WFEM), with a strong anti-noise ability in recording electromagnetic signals on the surface at multiple frequencies, is applied to characterize the deep conductivity distribution of the Pandian area. Based on the inversion results from two parallel WFEM profiles, which consist of 105 sites and previous geological and geophysical results, the 2D geoelectric models are established. The low-resistivity regions (with a typical range of 25~32 Ω·m) in the electrical models are proven to be ore bodies of Pandian deposit, which are developed along the contact zone between Yanshanian intrusive rocks and Paleozoic Ordovician strata. The scattered bodies (typically >32 Ω·m) in Ordovician limestone strata are probably caused by intrusive diorite pluton closely related to magnetite mineralization. Due to contact metasomatism, bedded limestone near magnetite was metamorphosed into marble and accompanied by low-resistivity skarn alteration, with resistivity much different from its high-resistivity protolith. The inverted geoelectrical models visually reflect the spatial distribution features of intrusive rocks and lithologic alteration/fracture zones.

The use of neural network algorithms for recognizing vibration signal recognition requires a dataset for training. However, traditional manual dataset creation is time-consuming and labor-intensive. This paper proposes a computer-based intelligent method for constructing vibration signal datasets using the YOLOv5 target detection algorithm. The surveillance videos captured by the camera are processed using frame difference calculation, Gaussian filter, binarization, inflation, and contour recognition. The YOLOv5 algorithm is then used to detect targets and map the corresponding vibration event categories to the vibration signals captured by the geophone, resulting in the dataset. The experimental results indicate that this method produces a vibration signal dataset with distinctive features and enhances efficiency.

Seismic monitoring has been instrumental in various domains such as natural earthquake early warning, mineral mining safety assessment, and hydraulic fracturing impact evaluation. However, the monitoring data often exhibit low signal-to-noise ratio (SNR) and large volume. Therefore, developing an efficient, high-precision, and universally applicable seismic waveform automatic classification network model becomes significant and practical. We propose a physical interpretable time-frequency deep convolutional recurrent neural network (TF-DCRNN) model which consists of an integration of a time-frequency convolutional (TFconv) layer and a convolutional recurrent neural network (CRNN). Subsequently, we evaluate the classification performance by comparing five network models, including convolutional neural network (CNN) and long short-term memory (LSTM), using Ricker wavelet datasets with varying SNR levels (-15 ~ 0 dB). Our findings verify the superiority of the TF-DCRNN model in the classification of strong interference environment from both numerical and physical simulation. Moreover, integrating multiple network models or incorporating a TFconv layer can moderately enhance the classification performance, which provides the direction for network model optimization.

The Controlled Source Electromagnetic Method (CSEM) assesses electromagnetic field components across specific orientations within a field coordinate system to calculate apparent resistivity for electromagnetic detection. However, in complex terrains, capturing the x, y, or z components accurately is often impracticable, leading to discrepancies in apparent resistivity calculations and impacting detection precision. To mitigate these challenges, this paper proposes a method for observing electric and magnetic field components in any orientation, thereby enhancing wide-azimuth electromagnetic detection. Firstly, the paper analyzes the angular deviations and the limitations of zero-value bands in certain single-component observation methods. Secondly, it examines the geoelectric response of electric and magnetic fields in various orientations and the non-monotonicity in apparent resistivity calculation. These analyses validate the feasibility of the wide-azimuth electric field observation method, substantially augmenting field observation flexibility. Moreover, the study identifies the challenges in accurately calculating apparent resistivity in wide-azimuth magnetic detection. This study provides innovative insights for advancing the CSEM detection method and improving detection accuracy.