TEvolOp Earthquake Forecasting

Forcasting Earthquakes

Forcatsing Earthquakes

TEvolOp Earthquake Forecasting

Time series are seen in many scientific problems and many of them are geospatial – functions of space and time and this benchmark illustrates this type. Some time series have a clear spatial structure that for example strongly relates nearby space points. The problem chosen is termed a spatial bag where there is spatial variation but it is not clearly linked to the geometric distance between spatial regions. In contrast, traffic-related time series have a strong spatial structure. We intend benchmarks that cover a broad range of problem types.

The earthquake data comes from USGS and we have chosen a 4 degrees of Latitude (32 to 36 N) and 6 degrees of Longitude (-120 to -114) region covering Southern California. The data runs from 1950 to the present day and is presented as events: magnitude, ground location, depth, and time. We have divided the data into time and space bins. The time interval is daily but in our reference models, we accumulate this into fortnightly data. Southern California is divided into a 40 by 60 grid of 0.1 by 0.1-degree “pixels” which corresponds roughly to squares with an 11 km side, The dataset also includes an assignment of pixels to known faults and a list of the largest earthquakes in that region from 1950 until today. We have chosen various samplings of the dataset to provide both input and predicted values. These include time ranges from a fortnight up to 4 years. Further, we calculate summed magnitudes and depths and counts of significant quakes (magnitude > 3.29). Other easily available quantities are powers of quake energy (using Energy ~ 101.5m where m is magnitude). Quantities are “Energy averaged” when there are multiple events in a single space-time bin except for simple event counts.

Current reference models are a basic LSTM recurrent neural network and a modification of the original science transformer. Details can be found here, and here.

TEvolOp Specific Benchmark Targets

  1. Scientific objective(s):
    • Objective: Improve the quality of Earthquake forecasting
    • Formula: Normalized Nash–Sutcliffe model efficiency coefficient (NNSE)
    • Score: The NNSE lies between 0.8 and 0.99 depending on model and predicted time series
  2. Data
    • Download: https://drive.google.com/drive/folders/1wz7K2R4gc78fXLNZMHcaSVfQvIpIhNPi?usp=sharing
    • Data Size: 5GB from USGS
    • Training samples: Data is decided spatially in an 80%-20% fashion between training and validation. The full dataset covers 6 degrees of longitude (-114 to -120) and 4 degrees of latitude (32 to 56) In Southern California. This is divided into 2400 spatial bins 0.1 degree (~11km) on a side
    • Validation samples: Most analyses use 500 most active bins of which 400 are training and 100 validation.
  3. Example implementation

Example Implementation:

The example implementation is primarily to demonstrate feasibility, show how the data is represented, help address any interpretation considerations, and potentially trigger initial ideas on how the benchmark can be improved.