Detection of hydrocarbon microseepage-induced anomalies by spectral enhancements of Landsat 7 ETM+ images in part of Assam–Arakan Fold Belt, India

Garain, Santosh; Mitra, Debashis; Das, P.

doi:10.1007/s13202-019-00747-w

Cited by 14 publications

(12 citation statements)

References 24 publications

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“…For example, as we will see in Section 5, the L2 product can yield high methane concentrations on some areas, which are unlikely to be real methane sources; but a detection algorithm based on the L2 product might detect those areas as methane plumes. Lastly, we use a detailed CH4 absorption spectrum taken from the HITRAN spectral database (Gordon et al, 2017). CH4 spectrum varies depending on pressure and temperature.…”

Section: Methodsmentioning

confidence: 99%

“…Another well-established technique in remote sensing is the band ratio technique. This method is quite usual for glacier monitoring (Citterio et al, 2010) but is also used for hydrocarbon detection (Garain et al, 2019), and is highly-relevant to our goals (Varon et al, 2020, Bradley et al, 2011. Band ratios are used to enhance the spectral signature of hydrocarbons.…”

Section: Introductionmentioning

confidence: 99%

“…Band ratios are used to enhance the spectral signature of hydrocarbons. In (Garain et al, 2019), those band ratios are used jointly with a PCA. PCA of several spectral bands is computed in order to highlight the presence of hydrocarbons in the resulting principal component image.…”

Section: Introductionmentioning

confidence: 99%

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Detection of Methane Plumes in Hyperspectral Images From Sentinel-5p by Coupling Anomaly Detection and Pattern Recognition

Ouerghi

Ehret

Franchis

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Reducing methane emissions is essential to tackle climate change. Here, we address the problem of detecting large methane leaks using hyperspectral data from the Sentinel-5P satellite. For that we exploit the fine spectral sampling of Sentinel-5P data to detect methane absorption features visible in the shortwave infrared wavelength range (SWIR). Our method involves three separate steps: i) background subtraction, ii) detection of local maxima in the negative logarithmic spectrum of each pixel and iii) anomaly detection in the background-free image. In the first step, we remove the impact of the albedo using albedo maps and the impact of the atmosphere by using a principal component analysis (PCA) over a time series of past observations. In the second step, we count for each pixel the number of local maxima that correspond to a subset of local maxima in the methane absorption spectrum. This counting method allows us to set up a statistical a contrario test that controls the false alarm rate of our detections. In the last step we use an anomaly detector to isolate potential methane plumes and we intersect those potential plumes with what have been detected in the second step. This process strongly reduces the number of false alarms. We validate our method by comparing the detected plumes against a dataset of plumes manually annotated on the Sentinel-5P L2 methane product.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Detection of Methane Plumes in Hyperspectral Images From Sentinel-5p by Coupling Anomaly Detection and Pattern Recognition

Ouerghi

Ehret

Franchis

et al. 2021

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…A surface geological map of the Cachar fold belt, modified after Garain et al. (2019), is shown in Figure 1c with the major structural elements labelled after Dasgupta and Biswas (2000) and Saha et al. (2008).…”

Section: Study Area Instrumentation and Datamentioning

confidence: 99%

Structural information derived from ambient noise tomography over a hydrocarbon‐producing region in the Cachar fold belt, lower Assam, northeast India

Shekar

Gurusamy

Singh

2022

Geophysical Prospecting

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Ambient noise tomography is a powerful tool that has found increasing application in reservoir analysis and imaging. The Cachar fold belt in lower Assam, northeast India encompasses several wells under active hydrocarbon production, along with several dry wells. To overcome the lack of active seismic data over the entire fold belt, a passive seismic study was carried out to image the concealed three-dimensional sub-surface structures. The data were recorded from February to November 2011 by a network of 65 wideband seismometers spanning an area of about 40 × 60 km 2 . The data are crosscorrelated in the 2-5 s band, followed by phase-weighted stacking to estimate noise correlation functions with surface wave signatures. The traveltimes picked from the frequency-time analysis are utilized in a tomographic inversion for Rayleigh wave group velocities. The group velocity anomalies have a lateral resolution of ~3.5 × 5.5 km 2 and variations of up to ±20% for each period. The group velocities are in turn inverted for S-wave velocity distribution as a function of depth. The three-dimensional S-wave velocity tomograms reveal the tight anticlines and broad synclines, with high-and lowvelocity zones corresponding to structural highs and lows, respectively. The structural interpretation is supported for the part of the region with producing wells and covered by active seismic data, wherein the post-stack time migrated seismic section shows anticlinal and synclinal features similar to those obtained from ambient noise tomography. The structures revealed by ambient noise tomography can help identify zones of interest to be targeted by active seismic surveys in the Cachar fold belt.

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“…Another well-established technique in remote sensing is the band ratio technique. This method is quite usual for glacier monitoring (Citterio et al, 2010) but is also used for hydrocarbon detection (Garain et al, 2019), and is highly-relevant to our goals (Bradley et al, 2011, Varon et al, 2020. Band ratios are used to enhance the spectral signature of hydrocarbons.…”

Section: Introductionmentioning

confidence: 99%

Automatic Methane Plumes Detection in Time Series of Sentinel-5p L1b Images

Ouerghi

Ehret

Franchis

et al. 2022

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Reducing methane emissions is essential to tackle climate change. Here, we address the problem of detecting automatically large methane leaks using hyperspectral data from the Level 1B product of the Sentinel-5P satellite. To do this, two features of TROPOMI (TROPOspheric Monitoring Instrument), the Sentinel-5P satellite sensor, are exploited. The first one is the fine spectral sampling of the data which allows to isolate features of the methane absorption spectrum in the shortwave infrared wavelength range (SWIR). The second one is the daily coverage of the whole Earth which allows to perform time series analysis. Our method involves three main steps: i) a pixel reconstruction, ii) an angle correction and iii) a plume detection with a time series. In the first step, a simplified absorption model is inverted to recover, for each pixel, a coefficient representative of the presence of methane which we call the methane coefficient. In the second step, a correction is made to the methane coefficient to take into account the viewing angle of the satellite. In the third step, the obtained coefficient is normalized spatially and then the detection is carried out pixel by pixel, by looking for anomalies in a time series. We validate our method by comparing the detected plumes against a recently published dataset of plumes manually detected in the Sentinel-5P L2 methane product. We then show how our method can complement the Sentinel-5P L2 methane product for the detection of methane plumes.

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Detection of hydrocarbon microseepage-induced anomalies by spectral enhancements of Landsat 7 ETM+ images in part of Assam–Arakan Fold Belt, India

Cited by 14 publications

References 24 publications

Detection of Methane Plumes in Hyperspectral Images From Sentinel-5p by Coupling Anomaly Detection and Pattern Recognition

Detection of Methane Plumes in Hyperspectral Images From Sentinel-5p by Coupling Anomaly Detection and Pattern Recognition

Structural information derived from ambient noise tomography over a hydrocarbon‐producing region in the Cachar fold belt, lower Assam, northeast India

Automatic Methane Plumes Detection in Time Series of Sentinel-5p L1b Images

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