{"help": "https://data.gov.au/data/api/3/action/help_show?name=package_show", "success": true, "result": {"archived": false, "author_email": null, "contact_point": "clientservices@ga.gov.au", "creator_user_id": "c2fbbe4a-4ba0-4945-808b-67454605a4cf", "duplicate_score": 2, "geospatial_topic": [], "id": "b634814c-14f7-4606-90d6-a41dc341212c", "isopen": false, "language": "eng", "license_id": "notspecified", "license_title": "notspecified", "maintainer": null, "maintainer_email": null, "metadata_created": "2025-10-17T03:56:21.896100", "metadata_modified": "2025-10-17T03:56:21.896107", "name": "forecasting-recurrent-large-earthquakes-from-paleoearthquake-and-fault-displacement-data", "notes": "Because the recurrence interval of large earthquakes on any particular fault is typically long relative to the historical record, geological data are often utilised to constrain the frequency with which earthquakes recur, the timing of the most recent event, and to develop forecasts of the probability of future events. Geological data may constrain the timing of past earthquakes (paleoearthquake data), or simply the time period over which a certain amount of cumulative fault displacement occurred due to multiple earthquakes (slip rate data). While both types of data are typically subject to large uncertainties and may only constrain the timing of a few events, variability in the inter-event times between large earthquakes (i.e., aperiodicity) has been demonstrated for many faults, and this is particularly so in low seismicity regions. A challenge for earthquake forecasting therefore concerns how best to utilise all of the limited available data to develop forecasts, while fully considering the associated uncertainties. In this study we present a concise Bayesian model for the time-dependent probabilities of recurrent earthquakes from combined paleoearthquake and slip rate data. Using the additive property of the Brownian passage time distribution, we make inference on the model parameters jointly from single-event paleoearthquake and multiple-event cumulative fault offset data. The method incorporates data uncertainties and does not rely on a priori assumptions of quasiperiodic earthquake recurrence, allowing general application in a wide range of tectonic settings. Monte Carlo Markov Chain methods are used to sample the posterior distribution of model parameters, which are subsequently used to forecast future earthquake probabilities that account for uncertainties in the data and model parameters. The method is demonstrated using data from two reverse faults in Otago, southern Aotearoa New Zealand, a region in which aperiodic earthquake recurrence has previously been observed in the paleoearthquake record.\n\nCitation: Griffin, J. D., Wang, T., Stirling, M. W., & Gerstenberger, M. C. (2025). Forecasting recurrent large earthquakes from paleoearthquake and fault displacement data. Journal of Geophysical Research: Solid Earth, 130, e2024JB029671. https://doi.org/10.1029/2024JB029671", "num_resources": 1, "num_tags": 5, "organization": {"id": "91f054ec-d0c3-4d42-a89a-5daa2c7a6818", "name": "geoscience-australia-data", "title": "Geoscience Australia Data", "type": "organization", "description": "Harvester for Geoscience Australia Data", "image_url": "", "created": "2025-06-23T12:29:08.024111", "is_organization": true, "approval_status": "approved", "state": "active"}, "original_harvest_source": {"site_url": "https://ecat.ga.gov.au", "href": "https://ecat.ga.gov.au/geonetwork/srv/eng/csw/dataset/forecasting-recurrent-large-earthquakes-from-paleoearthquake-and-fault-displacement-data", "title": "Geoscience Australia"}, "owner_org": "91f054ec-d0c3-4d42-a89a-5daa2c7a6818", "private": false, "promotion_level": "0", "spatial": "{\"type\": \"Polygon\", \"coordinates\": [[[112.92, -54.75], [159.11, -54.75], [159.11, -9.2402], [112.92, -9.2402], [112.92, -54.75]]]}", "spatial_coverage": "{\"type\": \"Polygon\", \"coordinates\": [[[112.92, -54.75], [159.11, -54.75], [159.11, -9.2402], [112.92, -9.2402], [112.92, -54.75]]]}", "state": "active", "temporal_coverage_from": "2025-02-27 03:30:34", "title": "Forecasting recurrent large earthquakes from paleoearthquake and fault displacement data", "type": "dataset", "unpublished": false, "url": null, "version": null, "extras": [{"key": "harvest_object_id", "value": "0c2f24b6-ee28-4123-8d46-38512328bebd"}, {"key": "harvest_source_id", "value": "00080910-39e7-408f-882c-e6e1eb6baadb"}, {"key": "harvest_source_title", "value": "Geoscience Australia"}], "resources": [{"cache_last_updated": null, "cache_url": null, "created": "2025-10-17T03:56:21.900051", "datastore_active": false, "datastore_contains_all_records_of_source_file": false, "description": "Link to Journal", "format": "HTML", "hash": "", "id": "a21bf592-fc0a-4a95-aac3-ab6ccb1d6d2b", "last_modified": null, "metadata_modified": "2025-10-17T03:56:21.886609", "mimetype": null, "mimetype_inner": null, "name": "Link to Journal", "package_id": "b634814c-14f7-4606-90d6-a41dc341212c", "position": 0, "resource_locator_function": "", "resource_locator_protocol": "WWW:LINK-1.0-http--link", "resource_type": null, "size": null, "state": "active", "url": "https://doi.org/10.1029/2024JB029671", "url_type": null, "zip_extract": false}], "tags": [{"display_name": "Natural hazards", "id": "62b9f8a7-5a7d-4f9a-84d2-bd000894928c", "name": "Natural hazards", "state": "active", "vocabulary_id": null}, {"display_name": "Published_External", "id": "5178775c-8044-4b7f-881f-5428a4e2d925", "name": "Published_External", "state": "active", "vocabulary_id": null}, {"display_name": "bayesian methods", "id": "dc4cf87e-fac1-4ef0-9343-a592d5b6294e", "name": "bayesian methods", "state": "active", "vocabulary_id": null}, {"display_name": "paleoearthquake", "id": "06879a41-375b-4248-a165-0787a6f834f0", "name": "paleoearthquake", "state": "active", "vocabulary_id": null}, {"display_name": "seismic hazard", "id": "a0887df0-929c-4637-8951-02bb35c9d385", "name": "seismic hazard", "state": "active", "vocabulary_id": null}], "groups": [], "relationships_as_subject": [], "relationships_as_object": []}}