In helicopter-borne transient electromagnetic (HTEM) signal processing, removal of motion-induced noise is one of the most important steps. A special type of short-term noise, which could be classified as high-frequency motion-induced noise (HFM noise) based on its cause and time-frequency features, was observed in the field data of the Chinese Academy of Sciences-HTEM system. Because the HFM noise is an in-band noise for the HTEM response, it usually remains after the normal denoising procedure developed for the conventional motion-induced noise. To solve this problem, we have developed a three-stage workflow to remove the HFM noise using the wavelet neural network (WNN). In the first stage, the WNN training is performed, and the data segment in which the HFM noise is dominant is selected as the sample set. In the second stage, the HFM noise corresponding to the data segment in which the earth’s response coexisted with the HFM noise is predicted using the well-trained WNN. In the last stage, the predicted HFM noise is removed from the corresponding original data. As an example, we applied our workflow in the field data observed in Inner Mongolia, the HFM noise is removed effectively, and the results provide a strong data foundation for the subsequent processing procedures.

Transient electromagnetic method (TEM) is a geophysical tool to obtain resistivity distribution in the subsurface. Combining with the resistivity property of typical rocks, the TEM method can make inferences of the geological maps underground. The inversion method is the main technique to extract resistivity form the recorded TEM data. There are various inversion methods that have been applied to TEM data, each of which favors different model structures. It is essential to choose the optimal inversion algorithm for a TEM survey in a given geological setting. Thus, this paper presents a systematic summary of recent developments of inversion methods for TEM data. We first summarize the basic concept of the TEM inversion theory. Then, the recent developments TEM inverse method are divided into deterministic inversion and stochastic inversion. For the deterministic method, we present the development of constrained inversion and joint inversion. For the stochastic method, we analyze the particle swarm optimization, Bayesian inversion, and TEM pseudo-seismic imaging. Thereafter, we prospect the future research direction of the TEM method.

Existing noise removal processes for airborne electromagnetic (AEM) data generally consist of several steps, with each using a specific method to remove a specific type of noise. To improve the efficiency of AEM denoising and reduce the impact of the subjective judgment of the operators on the processing results, we have adopted a deep learning method based on a denoising autoencoder (DAE), which enables in one single processing step the removal of multisource noise. The most common noise sources in AEM data, including motion-induced noise, nearby or moderately distant sferics noise, power-line noise, and background electromagnetic noise, will be combined with a large number of simulation responses to build a training set. The data in the training set will be used to train the deep learning DAE neural network so that the neural network could fully learn the respective characteristics of the signal and noise and further effectively distinguish the AEM response signal (useful signal) from the above noise. The field data were processed using this method, and the processing results were compared with those obtained using traditional methods. The comparison test revealed that this method is helpful to reduce the influence of subjective factors on the quality of data results and compress the entire AEM data processing time.

Nickel (Ni) and Cobalt (Co) deposits are important mineral resources, and China is the largest consumer of these resources. Magmatic, hydrothermal, sedimentary, and meta-sedimentary ore-bearing rocks are the most significant Co-Ni deposit types in China, accounting for 98% of the Co production. Thus, exploring the Chinese Co-Ni resources and reserves is beneficial for promoting its economic development. We have analyzed the metallogenetic geologic model and the physical characteristics of the rocks and ores of magmatic Co-Ni sulfide and hydrothermal Cu-Co deposits and the sedimentary and meta-sedimentary rocks that host the Cu-Co ore deposits to develop an effective geophysical exploration technology system. For magmatic deposits, the small ore-forming intrusion is the main detection target; in general, gravity, magnetic, and seismic exploration and artificial-source electromagnetic methods are widely used to determine the precise location of the ore-forming intrusion. For hydrothermal deposits, ore-controlling structures and favorable positions for mineralization (e.g., alteration belts) are the main exploration targets, and the combination of hyperspectral remote sensing, gravity, magnetic-excitation, chemical prospecting, and artificial-source electromagnetic detection techniques can help locating such ore body types. For sedimentary and meta-sedimentary ore-bearing deposits, a combination of soil debris geochemical exploration measurement, induced polarization (IP) scanning, gravity method, IP sounding, and the transient electromagnetic method has been used to scan and locate the ore-controlling and ore-bearing layers. Finally, upon selecting the Xiarihamu magmatic Co-Ni deposit in the East Kunlun orogenic belt as our study area, we have conducted controlled-source electromagnetic exploration and obtained reliable results. Our study can serve as a reference for determining the most effective geophysical technique for the exploration of Cu-Ni-Co deposits in China.

The short offset transient electromagnetic method (SOTEM) has become one of the main methods for coal hydrogeology investigations and mineral explorations due to its performance advantages of high precision, large depth abilities, and high efficiency. At the present time, due to the computational complexity and memory requirements for 3D SOTEM inversion algorithms, SOTEM data inversion is mainly based on 1D, which has made it difficult to meet fine detection requirements under complex geological conditions. In this study, a particle swarm optimization-quasi-Newton hybrid algorithm (PSO-QN) was proposed, with high precision and fast 3D inversions of SOTEM data achieved. In addition, flight strategy was introduced in order to ensure the global optimization of individual particles, and a weighting strategy was adopted to avoid falling into local minimums. The 3D inversions of the SOTEM data were tested based on a variety of geoelectric models. The results showed that the dependence of the PSO-QN hybrid algorithm on the number of particle swarms had been dropped to two thirds. Furthermore, the calculation time was significantly reduced to one third. The inversion results of the field data revealed that the proposed inversion method possessed a strong anti-noise capacity, high speed, good stability, and high resolution.

The central and western regions of China have appropriate conditions for mining development, but there are many deserts, swamps and forest-covered areas in this vast land. As a result, the traditional on-ground methods are difficult to carry out efficiently in these areas. The airborne transient electromagnetic method can implement detection over large areas rapidly and efficiently, and play an important role in terms of resource and environment detection. In recent years, two Helicopter-borne Transient electromagnetic (HTEM) systems, CHTEM-I and CAS-HTEM, were developed in China. In this paper, the difficult issues in the development of HTEM hardware and software will be analyzed, firstly. By tracing the specific technical paths to solve these difficult issues, the latest research achievements in the development process of CHTEM-I and CAS-HTEM will be expounded. Finally, we would like to briefly discuss the research and development direction of HTEM technology in China to promote further development of the research and application of this technology.