Seismic acoustic impedance inversion with multi-parameter regularization

Li, Shu; Peng, Zhenming

doi:10.1088/1742-2140/aa5e67

Cited by 28 publications

(9 citation statements)

References 30 publications

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“…where r i is the i-th element of r, l i is the i-th element of l. Fortunately, the seismic reflection coefficient is about 0.2 [8], thus Equation (5) can be employed in the seismic data. Also, Equation (5) can be rewritten as a matrix…”

Section: Forward Modelmentioning

confidence: 99%

“…Consequently, obtaining AI from the seismic data is a significant research activity in geophysics. Several methods are applied to invert AI, such as the Band-limited inversion [5], the linear inversion [6,7], and the sparse-spike inversion [8][9][10]. However, these methods employ only the seismic data and ignore the other useful prior information.…”

Section: Introductionmentioning

confidence: 99%

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Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving

Chen

Shu

et al. 2019

Energies

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The Markov chain Monte Carlo (MCMC) method based on Metropolis–Hastings (MH) sampling is a popular approach in solving seismic acoustic impedance (AI) inversion problem, as it can improve the inversion resolution by statistical prior information. However, the sampling function of the traditional MH sampling is a fixed parameter distribution. The parameter ignores the statistical information of AI that expands sampling range and reduces the inversion efficiency and resolution. To reduce the sampling range and improve the efficiency, we apply the statistical information of AI to the sampling function and build a Gaussian MH sampling with data driving (GMHDD) approach to the sampling function. Moreover, combining GMHDD and MCMC, we propose a novel Bayesian AI inversion method based on GMHDD. Finally, we use the Marmousi2 data and field data to test the proposed method based on GMHDD and other methods based on traditional MH. The results reveal that the proposed method can improve the efficiency and resolution of impedance inversion than other methods.

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Section: Forward Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving

Chen

Shu

et al. 2019

Energies

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“…In this study, the initial model was made using three horizons that were obtained in the previous stage and using a 10/ 15 Hz high-cut filter to create an initial AI model for the inversion (Figure 7). The use of hard constraints in making the initial model is useful for combining low frequency data that matches acoustic impedance so as to minimize inversion mismatches (Li and Peng, 2017).…”

Section: Initial Model Analysismentioning

confidence: 99%

Reservoir Characterizarion Using Acoustic Impedance Inversion and Multi-Attribute Analysis in Nias Waters, North Sumatra

Rifai¹,

Nainggolan

Manik³

2019

BoMG

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Seismic method is one of the most frequently applied geophysical methods in the process of oil and gas exploration. This research is conducted in Nias Waters, North Sumatra using one line 2D post-stack time migration seismic section and two wells data. Reservoir characterization is carried out to obtain physical parameters of rocks affected by fluid and rock lithology. Seismic inversion is used as a technique to create acoustic impedance distribution using seismic data as input and well data as control. As final product, multiattribute analysis is applied to integrate of inversion results with seismic data to determine the lateral distribution of other parameters contained in well data. In this research, multi-attribute analysis is used to determine the distribution of NPHI as a validation of hydrocarbon source rocks. In that area, there is a gas hydrocarbon prospect in limestone lithology in depth around 1450 ms. Based on the results of sensitivity analysis, cross-plot between acoustic impedance and NPHI are sensitive in separating rock lithology, the target rock in the form of limestone has physical characteristics in the form of acoustic impedance values ??in the range of 20,000-49,000 ((ft/s)*(g/cc)) and NPHI values in the range of 5-35 %. While the results of the cross-plot between the acoustic impedance and resistivity are able to separate fluid-containing rocks with resistivity values ??in the range about 18-30 ohmm. The result of acoustic impedance inversion using the model based method shows the potential for hydrocarbons in the well FYR-1 with acoustic impedance in the range 21,469-22,881 ((ft/s)*(gr/cc)). ABSTRAK:Metode seismik adalah salah satu metode geofisika terapan yang paling sering digunakan dalam proses ekplorasi minyak dan gas bumi. Penelitian ini dilakukan di Perairan Nias, Sumatera Utara menggunakan satu buah lintasan data seismik 2D dan dua buah data sumur. Karakterisasi reservoar dilakukan untuk mendapatkan parameter fisis batuan yang dipengaruhi oleh fluida dan litologi batuan. Seismik inversi digunakan sebagai suatu teknik untuk membuat sebaran nilai impedansi akustik menggunakan data seismik sebagai input dan data sumur sebagai kontrol. Analisis multiatribut merupakan integrasi hasil inversi dengan data seismik untuk menentukan sebaran lateral dari parameter lain yang terdapat pada data sumur. Pada penelitian ini analisis multiatribut digunakan untuk menentukan sebaran NPHI sebagai validasi batuan sumber hidrokarbon. Pada daerah tersebut terdapat prospek hidrokarbon berupa gas pada litologi batugamping dengan kedalaman pada kisaran 1450 ms. Berdasarkan hasil analisis sensitivitas, cross-plot antara impedansi akustik dan NPHI sensitif dalam memisahkan litologi batuan, batuan target berupa batugamping memiliki karakteristik fisis berupa nilai impedansi akustik pada rentang 20.000-49.000 ((ft/s)*(g/cc)) dan nilai NPHI pada rentang 5-35 %. Sedangkan hasil cross-plot antara impedansi akustik dan resistivitas mampu memisahkan batuan yang mengandung fluida dengan nilai resistivitas pada rentan...

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“…The main challenge of non-invasive blood glucose detection technology is, on the one hand, the sensitivity of the signal, which is easily affected by environmental factors; on the other hand, the relationship between the blood glucose signal and physical properties is not clear, and the detection accuracy is difficult to guarantee [8]- [10]. In particular, in recent years, most studies on blood glucose prediction have focused on processing continuous glucose monitoring(CGM) data [11], [12].…”

Section: Introductionmentioning

confidence: 99%

Evidential regression-based blood glucose detection using waveform features

Long¹,

Yang²,

Li³

et al. 2021

Preprint

Self Cite

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<div>As one of the necessary diabetes control and treatment methods, the photoacoustic blood glucose detection technology has great potential due to its deep detection depth and low interference from stray light. Previous research mainly focused on improving the detection capabilities of hardware systems and ignored the exploration of the physical meaning of the signal itself. We analyzed the characteristics of the signal amplitude decay in the photoacoustic signal and employed the forced damping vibration equation to model the signal waveform. A new waveform feature was constructed to describe the amplitude attenuation rate. Moreover, facing low accuracy of blood glucose prediction in the case of small data, we proposed a stable and effective blood glucose detection combining time-frequency feature and waveform features with evidential regression. Finally, in human tissue and glucose solution experiments, the minimum error is achieved 1.02±0.71 mg/dL and 13.28±10.33 mg/dL, respectively.</div><div><br></div>

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Seismic acoustic impedance inversion with multi-parameter regularization

Cited by 28 publications

References 30 publications

Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving

Acoustic Impedance Inversion Using Gaussian Metropolis–Hastings Sampling with Data Driving

Reservoir Characterizarion Using Acoustic Impedance Inversion and Multi-Attribute Analysis in Nias Waters, North Sumatra

Evidential regression-based blood glucose detection using waveform features

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