First High-Power CSEM

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First High-Power CSEM ( first-high-power-csem )

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Minerals 2022, 12, x FOR PEER REVIEW 8 of 21 Minerals 2022, 12, 1236 8 of 21 Figure 4. Example of signal display in KMS-820 acquisition program from CSEM with 1 kHz sampling Figure 4. Example of signal display in KMS-820 acquisition program from CSEM with 1 kHz sam- rate (top) and MT measurement with 40 Hz sampling rate (bottom). The magnetic field (Hx, Hy, and pling rate (top) and MT measurement with 40 Hz sampling rate (bottom). The magnetic field (Hx, Hy, and Hz) and electric field (Ex and Ey) are color-coded. Hz) and electric field (Ex and Ey) are color-coded. 3.2. Data Processing 3.2. Data Processing During data acquisition, the output (recorded) signal, as the Earth’s response, is pro- During data acquisition, the output (recorded) signal, as the Earth’s response, is pro- duced by the combination of an input (magnetic field for MT and injected current for CSEM duced by the combination of an input (magnetic field for MT and injected current for surveys) signal with the effect of the data generation process and Earth’s response [43]. CSEM surveys) signal with the effect of the data generation process and Earth’s response As in all geophysical methods and forms of data acquisition, most of an EM measure- [43]. As in all geophysical methods and forms of data acquisition, most of an EM meas- ment’s signal is “contaminated” by periodic and sporadic noise, which directly affects urement’s signal is “contaminated” by periodic and sporadic noise, which directly affects the signal-to-noise ratio (SNR). High-voltage power lines close to the study area usually the signal-to-noise ratio (SNR). High-voltage power lines close to the study area usually produce periodic noise. Sporadic noise can be seen in time series as spikes, steeps, or drifts produce periodic noise. Sporadic noise can be seen in time series as spikes, steeps, or drifts and is caused by other types of noise sources, such as fences, switching power supplies, and is caused by other types of noise sources, such as fences, switching power supplies, machinery, etc. Those noises must be eliminated or reduced through data filtering and machinery, etc. Those noises must be eliminated or reduced through data filtering and processing to increase the SNR and obtain the “true” signal from the acquired data. Since processing to increase the SNR and obtain the “true” signal from the acquired data. Since we have two different EM measurements, namely, CSEM as an active method and MT as a we have two different EM measurements, namely, CSEM as an active method and MT as passive method, they need to be separated and processed independently before interpreting a passive method, they need to be separated and processed independently before inter- the data to produce a unique model for the subsurface. preting the data to produce a unique model for the subsurface. The CSEM data, including LOTEM, must be processed using special software to The CSEM data, including LOTEM, must be processed using special software to per- perform data quality assurance, data merge/separation, and data quality control, including form data quality assurance, data merge/separation, and data quality control, including filtering. The main objective of this stage is to minimize the influence of the processing filtering. The main objective of this stage is to minimize the influence of the processing on on the acquired signal, removing the recorded noise and improving the quality of the the acquired signal, removing the recorded noise and improving the quality of the col- collected data. The data’s quality assurance incorporates time-series visualization, the lected data. The data’s quality assurance incorporates time-series visualization, the files’ files’ header check, the instrument’s calibration verification, and the configuration’s editing. header check, the instrument’s calibration verification, and the configuration’s editing. All All information available in the raw data, such as recording time, sensor types, channel information available in the raw data, such as recording time, sensor types, channel positioning, sampling frequency, file name, etc., need to be checked using the KMSProQA

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