Predict passive seismic events with a convolutional neural network
Type
Conference PaperKAUST Department
Earth Science and Engineering ProgramPhysical Science and Engineering (PSE) Division
Seismic Wave Analysis Group
Date
2020-09-30Online Publication Date
2020-09-30Print Publication Date
2020-09-30Permanent link to this record
http://hdl.handle.net/10754/665475
Metadata
Show full item recordAbstract
The ample size of time lapse data requires tremendous event detection and source locating capabilities, especially in areas like shale gas exploration regions where a large number of passive seismic events are often recorded. In many cases, the realtime monitoring and locating of these events are essential to production decisions. Conventional methods face considerable drawbacks. For example, traveltime based methods require traveltime picking; migration methods, on the other hand, require many wavefield modeling applications. These human interaction based pickings or wavefield simulations face severe issues when too many passive sources need to be located, which is common in shale gas explorations. A purely automatic method with no human interactions and less computational cost is necessary. Recently, machine learning has been utilized for this task, whether to identity passive seismic events, or to locate their sources once they are identified and picked. We propose to use an artificial neural network to directly map seismic data, without any picking, to locations of potential seismic events. A visual geometry group (VGG) neural network is trained on synthetic acoustic data, corresponding to known sources, to provide predictions of new passive source locations, and other source features such as source wavelet peak frequencies, amplitudes and the number of sources within the data segment. To reduce the size of the input-to-the-network seismic data, we correlate the traces with the central trace to allow the network to focus on the curvature of the input data. We train the network to handle both single and double events that might be included in the correlation window. An initial application of the approach on a simple V(z) model demonstrates its potential.Citation
Wang, H., Alkhalifah, T., & Hao, Q. (2020). Predict passive seismic events with a convolutional neural network. SEG Technical Program Expanded Abstracts 2020. doi:10.1190/segam2020-3425457.1Publisher
Society of Exploration GeophysicistsAdditional Links
https://library.seg.org/doi/10.1190/segam2020-3425457.1ae974a485f413a2113503eed53cd6c53
10.1190/segam2020-3425457.1