C.-C. Yin’s scientific contributions

Airborne transient electromagnetic (ATEM) data frequently shows sign reversal in the late-time channels duo to IP effect. Traditional inversion algorithms based on real resistivity cannot deliver reasonable results for such kind of data. They are usually deleted in the data processing. For in-depth understanding the characteristics of EM diffusion in a polarizable medium and acquainting the mechanism of negative responses caused by the polarizable medium, we study the IP effect on EM diffusion for an airborne EM system. We take as examples a polarizable and non-polarizable homogeneous half-space and a layered earth model. For the time-domain airborne EM system, we use direct integration to calculate the frequency-domain electric field and then obtain the frequency-domain current by using the Ohm's law. After that, we convert the frequency-domain responses to time-domain via a Hankel's transform. By introducing the time factor in the current responses, we can describe the dynamic process of EM diffusion in the underground. In the dynamic presentations (not shown in the paper but downloadable from proper website), we display the induced current, polarization current and total current diffusion in a polarizable medium as 3D animated contours. From the dynamic presentations of EM diffusion in a polarizable medium, we find that the induced current diffuses in the underground, forming the “smoking ring”. However, the polarization current has sign reversal during the diffusion process. At the early time, the polarization current is heavily connected with the induced current and flows in the same direction as the induced current, the underground medium is charged until the time when the induced current can no longer maintain the charging, the polarization current begins to reverse its sign (flow direction). The sign reversal of polarization current can result in negative EM signal in airborne EM systems. We further study the influence of chargeability and the conductivity at infinite frequency on the EM diffusion and find that the greater the chargeability, the earlier the sign reverses and the larger the influence range is. For the same chargeability, the smaller the conductivity, the earlier the sign reverses and the larger the influence range is. In a polarizable and resistive region, one can easily measure the negative EM signal. From the research of this paper, we can draw the conclusion that depending on the resistivity and IP parameters of the earth, the induced polarization can heavily influence the EM diffusion process, resulting in the possible sign reversal in AEM survey signal. Special attention needs to be paid in resistive areas with high polarizations.

For airborne EM (AEM) systems, flight altitude, transmitting waveforms, transmitting dipole moment and base frequency can strongly affect the depth of exploration. In an AEM survey, if all the magnetic field and magnetic induction components are measured, the EM dataset will be huge, resulting in costly data processing. In this paper, we investigate the exploration capability of an AEM system to different targets in the subsurface. We try to optimize parameter combinations of the airborne system (e. g. the waveform, field components, on- and off-time etc. ) to balance the survey cost and resolution of targets. For the towed-bird AEM system, we use an integral equation algorithm to calculate the frequency-domain EM field responses and convert them into the time domain via a Hankel's transform. The on- and off-time magnetic fields B and magnetic induction dB/dt are calculated for 3D targets of a plate or a prism embedded in a conductive earth. We propose a response ratio to define the largest exploration depth, based on which we calculate the exploration depth to different underground targets for different transmitting waves, field components and on- and off-time signals. We study and analyze the influence of transmitting waveforms, field components, the on- and off-time signals on the target exploration capability. We find that the off-time Bx, dBx/dt, and dBz/dt for a trapezoid transmitting wave is the best parameter combination for resolving an underground conductive prism target. For a vertical thin plate, however, the off-time Bx, dBx/dt and on-time dBz/dt for a half-sine transmitting wave perform better. The combination of off-time Bx, dBx/dt and on-time Bz, dBz/dt for a trapezoid waveform can make deeper exploration for a horizontal thick plate. From the research in this paper, we draw the conclusion that depending on the exploration target, there exists an optimal parameter combination of the AEM system that can achieve the maximum exploration capability. This will guide AEM survey design and data processing for an effective and efficient exploration. This research further lays the foundation for establishment of AEM standards and regulations.

As an effective and efficient geophysical tool, airborne EM (AEM) is specifically suitable for the exploration in areas of high mountains, desert, swamp, and forest. With the development of national economy of China, the demand for mineral resources increases sharply, geophysical explorations in areas with favorable geological conditions have been accomplished. The exploration target is switched to areas with unfavorable and complicated geological conditions, such as in Western China. Airborne EM works efficiently due to its moving platform of helicopter or fixed-wing aircraft, no human access is needed to the survey area. This technology is especially applicable for exploration in Western China with rugged mountains and deserts. In this paper, we review AEM technology systematically with the goal to make this technology quickly applicable in the mineral exploration in China. To make a comprehensive review, we introduce the AEM technology in following sequences. We first present basic AEM theory of on/off time, apparent resistivity and depth, footprint, etc. Then, we introduce the developments of AEM technology in Western countries, including CGG/Fugro, Geotech and Aeroquest in Canada, SkyTEM survey in Denmark, and we address specifically the status of this technology in China. Since the modeling and inversions are fundamental for AEM, we present 1D/2D/3D forward theory based on semi-analytical solutions, finite element, finite difference and integral equation methods. For the inversion theory, we follow the rule from simple to complex models by presenting 1D Marquardt, LCI, and 2/3D inversions by GS, NLCG and QN. Finally, we introduce successful applications of AEM technology in mineral, oil & gas, E&E, ground water, and natural hazard forest. We give our suggestions on future development of AEM technology in China. AEM technologies have been well developed in Western countries and are playing very important role in mineral, oil & gas, E&E, ground water explorations. However, AEM has not been well developed in China to the extent of practical use due to the fact that no breakthrough has been made on key technologies. Due to the fact that time-domain and frequency-domain AEM have different features and applications, we suggest to develop AEM technology in both frequency- and time-domain in China. The frequency-domain AEM is used in E&E, ground water exploration, while time-domain AEM is used in mineral, oil & gas in deep earth. Semi-airborne EM has serious problems of volume effect and thus is not recommended. Unmanned aerial vehicle (UAV) has a small load, making it difficult to transmit high-power signal. Besides, UAV doesn't suit the complex climate and topography, so that it is not recommended, either. To explore the vast area in Western China with rugged mountains, desert, etc., we need to develop geophysical technologies based on moving platforms, like airborne EM. Considering that different AEM systems suit for different exploration targets, both frequency- and time-domain systems need to be developed. Both semi-airborne and UAV are not recommended for airborne EM due to their inherent defects, a full airborne EM technology is the best solution.

The traditional transient electromagnetic method is mainly used in mineral exploration. It is difficult to meet the demand for oil and gas exploration. The multi-transient electromagnetic (MTEM) system can solve this problem due to its grounded wire as transmitter and in-line electric field component, resulting in the underground resistive targets can be more easily detected. However, the domestic research for this method is still at the stage of theoretical derivation, lacking forward modeling and data processing. We work on full-time forward modelling and inversion for m-sequence MTEM method to provide theoretical basis for the on-going development of MTEM system as well as its data processing and interpretation. The MTEM system adopts quasi-seismic observation, measuring in-line electric field component of pseudo-random binary sequence (PRBS) waveforms. Full-wave transmitting current and multi-channel observation data are recorded, so that the resistive thin layers can be detected. We perform full-wave forward modeling for a theoretical m-sequence and actual transmitter waveforms based on superposition of square wave responses and convolution of step responses with derivative of transmitting current, respectively. Taking advantages of the correlation identification technique, the impulse response is calculated while the noise is suppressed. Finally, we invert the CMP data by the Occam's algorithm to obtain underground electrical information. Firstly, Fourier transform is used to analyze the spectral components of square waveforms, 5-order 2n-sequence and 5-order m-sequence. It shows that the m-sequence has a larger frequency bandwidth with an equal interval between spectral lines than another two waveforms. Secondly, in order to verify the full-wave forward modelling results, a homogenous half-space of 30 Ωm is set up with a suit of 4-order m-sequence transmitting current. The results obtained by superposition of square wave responses, convolution of step responses with derivate of transmitting current and convolution of impulse response with transmitting current are compared. It shows that the first two results are consistent. We further add 30 dB Gaussian noise to the responses. The relative error after correlation between modeling response and transmitting waveforms is reduced to 1/4 of the original one. The step response obtained by integrating the impulse response is very close to the theoretical value with relative error of just 0.08%. Finally, we design two three-layer models to demonstrate the capability of the MTEM method to resolve shallow and deep hydrocarbon reservoirs. In the first model, a reservoir of 500 Ωm and 50 m thickness is set at the depth of 300 m in a homogenous half-space of 50 Ωm. The survey is done respectively at offsets of 900, 1000, 1100, 1200 and 1300 m. For the second model, we assume the hydrocarbon reservoir is moved to 2000 m depth, the survey is done at 5000, 5200, 5500, 5700 and 6000 m offsets, respectively. The inversion results for the two models by the Occam's method show that the MTEM method can well resolve both shallow and deep hydrocarbon reservoirs. The CMP data have better resolution than one-offset data. The MTEM method using m-sequence as transmitting waveforms, measuring multi-channel in-line electric fields, turns out to have a good resolution to hydrocarbon reservoirs. Spectral analysis shows that the m-sequence has a wide frequency band with an equal interval between spectrum lines. The techniques based on superposition of square-wave responses and convolution of step responses with derivate of transmitting current are proved to be very effective for modeling theoretical m-sequence and actual full-wave responses. Using correlation identification technique, the influence from transmitting waveforms is removed in the calculation of impulse response while the noise is suppressed. The Occam's inversion of CMP data obtained by integration of impulse response can well resolve underground resistive targets like reservoirs.

As an effective and efficient geophysical tool, airborne electromagnetic (AEM) has been widely used in many fields such as geological mapping, hydrocarbon and mineral exploration, and environmental and engineering surveys. AEM data interpretation commonly uses a horizontally layered earth model. However, in rugged mountain areas, the topography relief can pose serious effects on AEM survey data, resulting in the distortion of AEM inversion results. The study of the topographic effect on AEM systems has attracted much attention worldwide, but most work has focused on frequency-domain EM systems, little for time-domain airborne EM systems. This paper presents an effort to address this issue. The finite-element (FE) method based on an unstructured grid is used to simulate 2.5-D AEM responses for a topographic earth. We adopt this method, because it can calculate the AEM responses of complex models, while the unstructured grid can very well simulate the topography. To avoid the singularity, we divide the electromagnetic field into a background field and a secondary field. We apply the Fourier transform to Maxwell's equations to transform a 2.5D problem into a 2D problem and solve it in the wavenumber domain. On the outside boundaries, we assume the field vanishes. We use the Galerkin method to discretize the Maxwell equations and solve the final FE equations by the MUMPS solver. To check the accuracy of our algorithm, we compare our results with both analytical results and those from the literature. After that, we calculate the responses of model 1) with only topography; 2) with both topography and one anomaly body; and 3) with both topography and multiple anomaly bodies both in the frequency-domain and time-domain. Finally, we calculate the relative AEM responses of abnormal bodies and topography for both the frequency and time domains to investigate the influence of topography on AEM system responses. Topography has serious effects on the responses of airborne EM systems, especially in the high-frequency range or early time-channels. Numerical experiments show that close to the node points of the topography, AEM responses are demonstrated by sharp changes. Special attention need to be paid to the topographic effect when interpreting AEM survey data over rugged topographic areas. Besides, the topographic effect mainly occurs at the high frequency end and early time-channels, the EM responses of underground conductors mainly occur at low frequencies and later time-channels. These features provide the theoretical basis to identify the responses from the targets and the topography, so that the topographic effect on the AEM system responses can be corrected.

In recent years, many experts dedicated their research to the forward modeling and inversion of airborne EM systems. In this paper, we carry out the modeling for 1D layered media and 3D earth. We calculate not only vertical component but also the in-line component of the magnetic responses. This offers the theoretical basis for airborne EM to have multi-component survey. After comparing the impulse response and step response for airborne EM systems, we find that the impulse system response has singularity at the early time, resulting in stability problems in the EM modeling, while the step system response is non-singular, so that we can develop a stable algorithm for the calculation of the full-time EM responses for time-domain airborne systems. It is proved that the algorithm is precise and maintains well the integral/derivative relationship between the magnetic field B and the magnetic induction dB/dt. Extending the algorithm to 3D models has also obtained good results.

Marine controlled-source electromagnetic method (MCSEM) is an effective technology for pre-drill reservoir appraisal.. It is an important auxiliary technology for marine seismic to distinguish between oil-and water-bearing reservoir and consequently reduces the risk of dry wells and exploration cost.. However, when surveying in shallow water using a horizontal electrical dipole (HED) as transmitting source, the airwave, propagating at the air-ocean interface, will almost totally mask the signal from the resistive reservoir.. Due to the fact that the airwave doesn't contain any information about the resistive layer, it seriously affects the MCSEM application in shallow water.. Therefore, removal of the airwave from MCSEM data to get a good inversion result becomes a research focus.. Based on Weidelt's (2007) airwave theory, we first get the leading term of airwave by derivation of spectral kernels of TE mode.. We then, discuss the reason for airwave removal before MCSEM inversion by elaborating the mechanism of its origin and effects.. Finally, we divide the current technologies dealing with airwave problem into three categories: 1)Remove airwave based on its characteristics in frequency-domain and time-domain; 2)Airwave-free survey; 3)Direct interpretation.. After analyzing these technologies, we find that all current technologies cannot completely remove the airwave effect in MCSEM data.. Each method has its own conditions and limitations for application.. A combination of these methods is more helpful and effective..