Machine learning (ML) in its current form is relatively new to geoscience, but during the last five years has rapidly been adopted. The number of applications in geoscience before about 2010 was modest. These applications of ML did not reach their full potential for three primary reasons: scarcity of sufficient data for training and testing, the inability to handle available data efficiently, and computers with insufficient computational speed to process large data sets. Earthquake catalogs, for example, were analyzed rather than continuous waveform data due to limitations in data storage capability as well as limitations in instrument density. For subsurface flow and transport predictions, the paucity of data was due to expensive instrumentation and measurement procedures, insufficiently resolved models etc. As we approach the exascale computational era, modeling capabilities have advanced significantly, allowing us to explore parameter and model spaces more thoroughly. We are now at the confluence of the ability to handle massive data streams or ‘big data’ : ultrafast and massive computers, significant increases in instrumentation density and quality, and advances in ML. As a result we are witnessing the beginning of the new era in geoscience where ML is applied to analysis of continuous data streams.
This Center for Nonlinear Studies conference will focus on modern applications of ML to solid earth geoscience problems, including: earthquakes (both tectonic and induced); faulting; Earth imaging with a multitude of data types including seismic, gravity, electrical methods, geodesy, state of stress, etc. We will also explore topics related to geological characterization, subsurface flow and transport, etc.
Abstracts
To submit an abstract, you must go through the registration process and enter your abstract information in the abstract submission page.
Deadline to submit an abstract is December 1st.
If you have problems with registration, abstract submission or hotel reservations, please contact Leah Gonzalez at CNLS: leahbulnes@lanl.gov
Organizing Committee:
Paul Johnson, LANL
Hari Viswanathan, LANL
Chris Johnson, LANL
Dan O'Malley, LANL