Exploration Geophysics is published by CSIRO PUBLISHING on behalf of the Australian Society of Exploration Geophysicists. Papers published report the results of significant case histories and relevant original research in geophysics, with emphasis on the Australian and similar environments.

The Lujiang–Zongyang (Luzong) ore district is well known for porphyry iron deposits that are mainly located in the Luzong volcanic basin, an important iron ore area in eastern China. Since the discovery of copper deposits in the ore district, regional tectonic evolution and geodynamic processes have become important issues for further mineral exploration. However, most prior research focused on geology and metallogeny, and little attention has been paid to the deep structure and its relationship to mineralisation. To understand this problem further, data were collected at 76 broadband magnetotelluric stations in the central Luzong basin, and then inverted in 3D using the nonlinear conjugate gradient method. Based on the inverted 3D electrical model, we consider that hot material upwelled beneath the lower crust in the centre of this region, where the crustal resistor is shallow and thin compared with surrounding resistors. We infer that one local uplift belt existed upon the upwelling material. The regional high aeromagnetic anomalies and low residual gravity anomalies hint at the presence of a shallow volcanic basin and upper mantle material. Because of the copper metallogeny in the adjacent district, we suggest that the local uplift belt in the centre basin has great potential for copper prospecting.

Induced polarization (IP) is a widely used geophysical exploration technique. Continuous random noise is one of the most prevalent interferences that can seriously contaminate the IP signal and distort the apparent electrical characteristics. We develop a noise separation algorithm based on deep learning to overcome this issue. The standard IP signals are first produced by combining the Cole-Cole model and Fourier series decomposition, and then the mathematical simulation is used to generate various types of random noise interferences, which are subsequently added to the IP signals. Then, a denoising autoencoder deep neural network structure is built and trained by using noisy signals as input samples and pure signals as output samples. The resulting optimum network is capable of automatically reconstructing a clean IP signal from the noisy input. This network is tested using synthetic data sets. The trained neural network can perform the noise reduction of thousands of survey points in a matter of seconds and reduce signal distortion from approximately 25% to less than 5%. Deep learning-based denoising provides superior computation speed and precision compared with the wavelet denoising and smoothing filtering approach. The data for high-quality signals do not vary considerably before and after noise reduction. The noise interferences are successfully suppressed for low-quality signals. Based on the findings, the denoising autoencoder deep neural network has a promising future for suppressing random noise interferences, which can aid in improving the quality of IP data with high efficiency and precision.